ALBERT

Description: Background Significant investments have been made towards the implementation of mHealth applications and eRecord systems globally. However, fragmentation of these technologies remains a big challenge, often unresolved in developing countries. In particular, evidence shows little consideration for linking mHealth applications and eRecord systems. Botswana is a typical developing country in sub-Saharan Africa that has explored mHealth applications, but the solutions are not interoperable with existing eRecord systems. This paper describes Botswana’s eRecord systems interoperability landscape and provides guidance for linking mHealth applications to eRecord systems, both for Botswana and for developing countries using Botswana as an exemplar. Methods A survey and interviews of health ICT workers and a review of the Botswana National eHealth Strategy were completed. Perceived interoperability benefits, opportunities and challenges were charted and analysed, and future guidance derived. Results Survey and interview responses showed the need for interoperable mHealth applications and eRecord systems within the health sector of Botswana and within the context of the National eHealth Strategy. However, the current Strategy does not address linking mHealth applications to eRecord systems. Across Botswana’s health sectors, global interoperability standards and Application Programming Interfaces are widely used, with some level of interoperability within, but not between, public and private facilities. Further, a mix of open source and commercial eRecord systems utilising relational database systems and similar data formats are supported. Challenges for linking mHealth applications and eRecord systems in Botswana were identified and categorised into themes which led to development of guidance to enhance the National eHealth Strategy. Conclusion Interoperability between mHealth applications and eRecord systems is needed and is feasible. Opportunities and challenges for linking mHealth applications to eRecord systems were identified, and future guidance stemming from this insight presented. Findings will aid Botswana, and other developing countries, in resolving the pervasive disconnect between mHealth applications and eRecord systems.

Description: Background To enhance teleconsultation management, demands can be classified into different patterns, and the service of each pattern demand can be improved. Methods For the effective teleconsultation classification, a novel ensemble hierarchical clustering method is proposed in this study. In the proposed method, individual clustering results are first obtained by different hierarchical clustering methods, and then ensembled by one-hot encoding, the calculation and division of cosine similarity, and network graph representation. In the built network graph about the high cosine similarity, the connected demand series can be categorized into one pattern. For verification, 43 teleconsultation demand series are used as sample data, and the efficiency and quality of teleconsultation services are respectively analyzed before and after the demand classification. Results The teleconsultation demands are classified into three categories, erratic, lumpy, and slow. Under the fixed strategies, the service analysis after demand classification reveals the deficiencies of teleconsultation services, but analysis before demand classification can’t. Conclusion The proposed ensemble hierarchical clustering method can effectively category teleconsultation demands, and the effective demand categorization can enhance teleconsultation management.

Description: Background and objectives Internet-based technologies play an increasingly important role in the management and outcome of patients with chronic kidney disease (CKD). The healthcare system is currently flooded with digital innovations and internet-based technologies as a consequence of the coronavirus disease 2019 (COVID-19) pandemic. However, information about the attitude of German CKD-patients with access to online tools towards the use of remote, internet-based interactions such as video conferencing, email, electronic medical records and apps in general and for health issues in particular, are missing. Design, setting, participants, and measurements To address the use, habits and willingness of CKD patients in handling internet-based technologies we conducted a nationwide cross-sectional questionnaire survey in adults with CKD. Results We used 380 questionnaires from adult CKD patients (47.6% on dialysis, 43.7% transplanted and 8.7% CKD before renal replacement therapy) for analysis. Of these 18.9% denied using the internet at all (nonusers). Nonusers were significantly older (74.4 years, SD 11.4) than users (54.5 years, SD 14.5, p 

Description: Background Inguinal hernia repair, gallbladder removal, and knee- and hip replacements are the most commonly performed surgical procedures, but all are subject to practice variation and variable patient-reported outcomes. Shared decision-making (SDM) has the potential to reduce surgery rates and increase patient satisfaction. This study aims to evaluate the effectiveness of an SDM strategy with online decision aids for surgical and orthopaedic practice in terms of impact on surgery rates, patient-reported outcomes, and cost-effectiveness. Methods The E-valuAID-study is designed as a multicentre, non-randomized stepped-wedge study in patients with an inguinal hernia, gallstones, knee or hip osteoarthritis in six surgical and six orthopaedic departments. The primary outcome is the surgery rate before and after implementation of the SDM strategy. Secondary outcomes are patient-reported outcomes and cost-effectiveness. Patients in the usual care cluster prior to implementation of the SDM strategy will be treated in accordance with the best available clinical evidence, physician’s knowledge and preference and the patient’s preference. The intervention consists of the implementation of the SDM strategy and provision of disease-specific online decision aids. Decision aids will be provided to the patients before the consultation in which treatment decision is made. During this consultation, treatment preferences are discussed, and the final treatment decision is confirmed. Surgery rates will be extracted from hospital files. Secondary outcomes will be evaluated using questionnaires, at baseline, 3 and 6 months. Discussion The E-valuAID-study will examine the cost-effectiveness of an SDM strategy with online decision aids in patients with an inguinal hernia, gallstones, knee or hip osteoarthritis. This study will show whether decision aids reduce operation rates while improving patient-reported outcomes. We hypothesize that the SDM strategy will lead to lower surgery rates, better patient-reported outcomes, and be cost-effective. Trial registration: The Netherlands Trial Register, Trial NL8318, registered 22 January 2020. URL: https://www.trialregister.nl/trial/8318.

Description: Background This study developed a diagnostic tool to automatically detect normal, unclear and tumor images from colonoscopy videos using artificial intelligence. Methods For the creation of training and validation sets, 47,555 images in the jpg format were extracted from colonoscopy videos for 24 patients in Korea University Anam Hospital. A gastroenterologist with the clinical experience of 15 years divided the 47,555 images into three classes of Normal (25,895), Unclear (2038) and Tumor (19,622). A single shot detector, a deep learning framework designed for object detection, was trained using the 47,255 images and validated with two sets of 300 images—each validation set included 150 images (50 normal, 50 unclear and 50 tumor cases). Half of the 47,255 images were used for building the model and the other half were used for testing the model. The learning rate of the model was 0.0001 during 250 epochs (training cycles). Results The average accuracy, precision, recall, and F1 score over the category were 0.9067, 0.9744, 0.9067 and 0.9393, respectively. These performance measures had no change with respect to the intersection-over-union threshold (0.45, 0.50, and 0.55). This finding suggests the stability of the model. Conclusion Automated detection of normal, unclear and tumor images from colonoscopy videos is possible by using a deep learning framework. This is expected to provide an invaluable decision supporting system for clinical experts.

Description: Background Diabetes is a medical and economic burden in the United States. In this study, a machine learning predictive model was developed to predict unplanned medical visits among patients with diabetes, and findings were used to design a clinical intervention in the sponsoring healthcare organization. This study presents a case study of how predictive analytics can inform clinical actions, and describes practical factors that must be incorporated in order to translate research into clinical practice. Methods Data were drawn from electronic medical records (EMRs) from a large healthcare organization in the Northern Plains region of the US, from adult (≥ 18 years old) patients with type 1 or type 2 diabetes who received care at least once during the 3-year period. A variety of machine-learning classification models were run using standard EMR variables as predictors (age, body mass index (BMI), systolic blood pressure (BP), diastolic BP, low-density lipoprotein, high-density lipoprotein (HDL), glycohemoglobin (A1C), smoking status, number of diagnoses and number of prescriptions). The best-performing model after cross-validation testing was analyzed to identify strongest predictors. Results The best-performing model was a linear-basis support vector machine, which achieved a balanced accuracy (average of sensitivity and specificity) of 65.7%. This model outperformed a conventional logistic regression by 0.4 percentage points. A sensitivity analysis identified BP and HDL as the strongest predictors, such that disrupting these variables with random noise decreased the model’s overall balanced accuracy by 1.3 and 1.4 percentage points, respectively. These recommendations, along with stakeholder engagement, behavioral economics strategies, and implementation science principles helped to inform the design of a clinical intervention targeting behavioral changes. Conclusion Our machine-learning predictive model more accurately predicted unplanned medical visits among patients with diabetes, relative to conventional models. Post-hoc analysis of the model was used for hypothesis generation, namely that HDL and BP are the strongest contributors to unplanned medical visits among patients with diabetes. These findings were translated into a clinical intervention now being piloted at the sponsoring healthcare organization. In this way, this predictive model can be used in moving from prediction to implementation and improved diabetes care management in clinical settings.

Description: Background Due to the need for informatics competencies in the field of nursing, the present study was conducted to design a psychometric instrument to determine the qualification of informatics competencies of employed nurses in educational care centers. Methods The questionnaire was made by reviewing existing scientific resources and assessment tools. Two hundred nurses were selected using simple random sampling. Structural equation modeling was used using the measurement model technique and the average variance was calculated. Linear structural relations (LISREL) software was used to test the assumptions and correlations of the model. Results Findings showed relatively good estimation in the fit of first-order measurement model. The informatics knowledge subscale with a determining rate of 0.90 had the greatest explanatory effect among the subscales and informatics skill with a determining rate of 0.67 and basic computer skill with a determining rate of 0.60 were observed. The second-order measurement model of fitness indicators showed that the three factors can well explain the multidimensional construct of informatics competency. Conclusions The designed tool can be used to develop educational strategies in relation to nursing students in the field of informatics and prepare them in the rich environment of information technology, which can be helpful in training nursing instructors.

Description: Background Questionnaires are commonly used tools in telemedicine services that can help to evaluate different aspects. Selecting the ideal questionnaire for this purpose may be challenging for researchers. This study aims to review which questionnaires are used to evaluate telemedicine services in the studies, which are most common, and what aspects of telemedicine evaluation do they capture. Methods The PubMed database was searched in August 2020 to retrieve articles. Data extracted from the final list of articles included author/year of publication, journal of publication, type of evaluation, and evaluation questionnaire. Data were analyzed using descriptive statistics. Results Fifty-three articles were included in this study. The questionnaire was used for evaluating the satisfaction (49%), usability (34%), acceptance (11.5%), and implementation (2%) of telemedicine services. Among telemedicine specific questionnaires, Telehealth Usability Questionnaire (TUQ) (19%), Telemedicine Satisfaction Questionnaire (TSQ) (13%), and Service User Technology Acceptability Questionnaire (SUTAQ) (5.5%), were respectively most frequently used in the collected articles. Other most used questionnaires generally used for evaluating the users’ satisfaction, usability, and acceptance of technology were Client Satisfaction Questionnaire (CSQ) (5.5%), Questionnaire for User Interaction Satisfaction (QUIS) (5.5%), System Usability Scale (SUS) (5.5%), Patient Satisfaction Questionnaire (PSQ) (5.5%), and Technology Acceptance Model (TAM) (3.5%) respectively. Conclusion Employing specifically designed questionnaires or designing a new questionnaire with fewer questions and more comprehensiveness in terms of the issues studied provides a better evaluation. Attention to user needs, end-user acceptance, and implementation processes, along with users' satisfaction and usability evaluation, may optimize telemedicine efforts in the future.

Description: Background Clinical Decision Support Systems (CDSSs) for Prescribing are one of the innovations designed to improve physician practice performance and patient outcomes by reducing prescription errors. This study was therefore conducted to examine the effects of various CDSSs on physician practice performance and patient outcomes. Methods This systematic review was carried out by searching PubMed, Embase, Web of Science, Scopus, and Cochrane Library from 2005 to 2019. The studies were independently reviewed by two researchers. Any discrepancies in the eligibility of the studies between the two researchers were then resolved by consulting the third researcher. In the next step, we performed a meta-analysis based on medication subgroups, CDSS-type subgroups, and outcome categories. Also, we provided the narrative style of the findings. In the meantime, we used a random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with a 95% confidence interval. Q statistics and I2 were then used to calculate heterogeneity. Results On the basis of the inclusion criteria, 45 studies were qualified for analysis in this study. CDSS for prescription drugs/COPE has been used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental diseases. In the meantime, other cases such as concurrent prescribing of multiple medications for patients and their effects on the above-mentioned results have been analyzed. The study shows that in some cases the use of CDSS has beneficial effects on patient outcomes and physician practice performance (std diff in means = 0.084, 95% CI 0.067 to 0.102). It was also statistically significant for outcome categories such as those demonstrating better results for physician practice performance and patient outcomes or both. However, there was no significant difference between some other cases and traditional approaches. We assume that this may be due to the disease type, the quantity, and the type of CDSS criteria that affected the comparison. Overall, the results of this study show positive effects on performance for all forms of CDSSs. Conclusions Our results indicate that the positive effects of the CDSS can be due to factors such as user-friendliness, compliance with clinical guidelines, patient and physician cooperation, integration of electronic health records, CDSS, and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and the real-time alerts in the prescription.

Description: Background Patients with complex health care needs may suffer adverse outcomes from fragmented and delayed care, reducing well-being and increasing health care costs. Health reform efforts, especially those in primary care, attempt to mitigate risk of adverse outcomes by better targeting resources to those most in need. However, predicting who is susceptible to adverse outcomes, such as unplanned hospitalizations, ED visits, or other potentially avoidable expenditures, can be difficult, and providing intensive levels of resources to all patients is neither wanted nor efficient. Our objective was to understand if primary care teams can predict patient risk better than standard risk scores. Methods Six primary care practices risk stratified their entire patient population over a 2-year period, and worked to mitigate risk for those at high risk through care management and coordination. Individual patient risk scores created by the practices were collected and compared to a common risk score (Hierarchical Condition Categories) in their ability to predict future expenditures, ED visits, and hospitalizations. Accuracy of predictions, sensitivity, positive predictive values (PPV), and c-statistics were calculated for each risk scoring type. Analyses were stratified by whether the practice used intuition alone, an algorithm alone, or adjudicated an algorithmic risk score. Results In all, 40,342 patients were risk stratified. Practice scores had 38.6% agreement with HCC scores on identification of high-risk patients. For the 3,381 patients with reliable outcomes data, accuracy was high (0.71–0.88) but sensitivity and PPV were low (0.16–0.40). Practice-created scores had 0.02–0.14 lower sensitivity, specificity and PPV compared to HCC in prediction of outcomes. Practices using adjudication had, on average, .16 higher sensitivity. Conclusions Practices using simple risk stratification techniques had slightly worse accuracy in predicting common outcomes than HCC, but adjudication improved prediction.

Description: Background The Ministry of Health in Saudi Arabia is expanding the country’s telemedicine services by using advanced technology in health services. In doing so, an e-health application (app), Seha, was introduced in 2018 that allows individuals to have face-to-face visual medical consultations with their doctors on their smartphones. Objective This study evaluated the effectiveness of the app in improving healthcare delivery by ensuring patient satisfaction with the care given, increasing access to care, and improving efficiency in the healthcare system. Methods A cross-sectional study design was used to assess the perceptions of users of the Seha app and non-users who continued with traditional health services. The data were collected using an online survey via Google Forms between June 2020 and September 2020. Independent t tests and chi-square (χ2) tests were conducted to answer the research questions. Results There was a significant difference between users and non-users in terms of ease of access to health services (t =  − 9.38, p 

Description: Background Poor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly’s functional balance based on Short Form Berg Balance Scale (SFBBS) score. Methods Data were collected from a waist-mounted tri-axial accelerometer while participants performed a timed up and go test. Clinically relevant variables were extracted from the segmented accelerometer signals for fitting SFBBS predictive models. Regularized regression together with random-shuffle-split cross-validation was used to facilitate the development of the predictive models for automatic balance estimation. Results Eighty-five community-dwelling older adults (72.12 ± 6.99 year) participated in our study. Our results demonstrated that combined clinical and sensor-based variables, together with regularized regression and cross-validation, achieved moderate-high predictive accuracy of SFBBS scores (mean MAE = 2.01 and mean RMSE = 2.55). Step length, gender, gait speed and linear acceleration variables describe the motor coordination were identified as significantly contributed variables of balance estimation. The predictive model also showed moderate-high discriminations in classifying the risk levels in the performance of three balance assessment motions in terms of AUC values of 0.72, 0.79 and 0.76 respectively. Conclusions The study presented a feasible option for quantitatively accurate, objectively measured, and unobtrusively collected functional balance assessment at the point-of-care or home environment. It also provided clinicians and elderly with stable and sensitive biomarkers for long-term monitoring of functional balance.

Description: Background The electronic health record (EHR) holds the prospect of providing more complete and timely access to clinical information for biomedical research, quality assessments, and quality improvement compared to other data sources, such as administrative claims. In this study, we sought to assess the completeness and timeliness of structured diagnoses in the EHR compared to computed diagnoses for hypertension (HTN), hyperlipidemia (HLD), and diabetes mellitus (DM). Methods We determined the amount of time for a structured diagnosis to be recorded in the EHR from when an equivalent diagnosis could be computed from other structured data elements, such as vital signs and laboratory results. We used EHR data for encounters from January 1, 2012 through February 10, 2019 from an academic health system. Diagnoses for HTN, HLD, and DM were computed for patients with at least two observations above threshold separated by at least 30 days, where the thresholds were outpatient blood pressure of ≥ 140/90 mmHg, any low-density lipoprotein ≥ 130 mg/dl, or any hemoglobin A1c ≥ 6.5%, respectively. The primary measure was the length of time between the computed diagnosis and the time at which a structured diagnosis could be identified within the EHR history or problem list. Results We found that 39.8% of those with HTN, 21.6% with HLD, and 5.2% with DM did not receive a corresponding structured diagnosis recorded in the EHR. For those who received a structured diagnosis, a mean of 389, 198, and 166 days elapsed before the patient had the corresponding diagnosis of HTN, HLD, or DM, respectively, recorded in the EHR. Conclusions We found a marked temporal delay between when a diagnosis can be computed or inferred and when an equivalent structured diagnosis is recorded within the EHR. These findings demonstrate the continued need for additional study of the EHR to avoid bias when using observational data and reinforce the need for computational approaches to identify clinical phenotypes.

Description: Background Data from clinical registries may be linked to gain additional insights into disease processes, risk factors and outcomes. Identifying information varies from full names, addresses and unique identification codes to statistical linkage keys to no direct identifying information at all. A number of databases in Australia contain the statistical linkage key 581 (SLK-581). Our aim was to investigate the ability to link data using SLK-581 between two national databases, and to compare this linkage to that achieved with direct identifiers or other non-identifying variables. Methods The Australian and New Zealand Society of Cardiothoracic Surgeons database (ANZSCTS-CSD) contains fully identified data. The Australian and New Zealand Intensive Care Society database (ANZICS-APD) contains non-identified data together with SLK-581. Identifying data is removed at participating hospitals prior to central collation and storage. We used the local hospital ANZICS-APD data at a large single tertiary centre prior to deidentification and linked this to ANZSCTS-CSD data. We compared linkage using SLK-581 to linkage using non-identifying variables (dates of admission and discharge, age and sex) and linkage using a complete set of unique identifiers. We compared the rate of match, rate of mismatch and clinical characteristics between unmatched patients using the different methods. Results There were 1283 patients eligible for matching in the ANZSCTS-CSD. 1242 were matched using unique identifiers. Using non-identifying variables 1151/1242 (92.6%) patients were matched. Using SLK-581, 1202/1242 (96.7%) patients were matched. The addition of non-identifying data to SLK-581 provided few additional patients (1211/1242, 97.5%). Patients who did not match were younger, had a higher mortality risk and more non-standard procedures vs matched patients. The differences between unmatched patients using different matching strategies were small. Conclusion All strategies provided an acceptable linkage. SLK-581 improved the linkage compared to non-identifying variables, but was not as successful as direct identifiers. SLK-581 may be used to improve linkage between national registries where identifying information is not available or cannot be released.

Description: Background Studies that examine the adoption of clinical decision support (CDS) by healthcare providers have generally lacked a theoretical underpinning. The Unified Theory of Acceptance and Use of Technology (UTAUT) model may provide such a theory-based explanation; however, it is unknown if the model can be applied to the CDS literature. Objective Our overall goal was to develop a taxonomy based on UTAUT constructs that could reliably characterize CDS interventions. Methods We used a two-step process: (1) identified randomized controlled trials meeting comparative effectiveness criteria, e.g., evaluating the impact of CDS interventions with and without specific features or implementation strategies; (2) iteratively developed and validated a taxonomy for characterizing differential CDS features or implementation strategies using three raters. Results Twenty-five studies with 48 comparison arms were identified. We applied three constructs from the UTAUT model and added motivational control to characterize CDS interventions. Inter-rater reliability was as follows for model constructs: performance expectancy (κ = 0.79), effort expectancy (κ = 0.85), social influence (κ = 0.71), and motivational control (κ = 0.87). Conclusion We found that constructs from the UTAUT model and motivational control can reliably characterize features and associated implementation strategies. Our next step is to examine the quantitative relationships between constructs and CDS adoption.

Description: Background We developed transformer-based deep learning models based on natural language processing for early risk assessment of Alzheimer’s disease from the picture description test. Methods The lack of large datasets poses the most important limitation for using complex models that do not require feature engineering. Transformer-based pre-trained deep language models have recently made a large leap in NLP research and application. These models are pre-trained on available large datasets to understand natural language texts appropriately, and are shown to subsequently perform well on classification tasks with small training sets. The overall classification model is a simple classifier on top of the pre-trained deep language model. Results The models are evaluated on picture description test transcripts of the Pitt corpus, which contains data of 170 AD patients with 257 interviews and 99 healthy controls with 243 interviews. The large bidirectional encoder representations from transformers (BERTLarge) embedding with logistic regression classifier achieves classification accuracy of 88.08%, which improves the state-of-the-art by 2.48%. Conclusions Using pre-trained language models can improve AD prediction. This not only solves the problem of lack of sufficiently large datasets, but also reduces the need for expert-defined features.

Description: Background Assessing the quality of healthcare data is a complex task including the selection of suitable measurement methods (MM) and adequately assessing their results. Objectives To present an interoperable data quality (DQ) assessment method that formalizes MMs based on standardized data definitions and intends to support collaborative governance of DQ-assessment knowledge, e.g. which MMs to apply and how to assess their results in different situations. Methods We describe and explain central concepts of our method using the example of its first real world application in a study on predictive biomarkers for rejection and other injuries of kidney transplants. We applied our open source tool—openCQA—that implements our method utilizing the openEHR specifications. Means to support collaborative governance of DQ-assessment knowledge are the version-control system git and openEHR clinical information models. Results Applying the method on the study’s dataset showed satisfactory practicability of the described concepts and produced useful results for DQ-assessment. Conclusions The main contribution of our work is to provide applicable concepts and a tested exemplary open source implementation for interoperable and knowledge-based DQ-assessment in healthcare that considers the need for flexible task and domain specific requirements.

Description: Background Cost control and usage regulation of medical materials (MMs) are the practical issues that the government pays close attention to. Although it is well established that there is great potential to mobilize doctors and patients in participating MMs-related clinical decisions, few interventions adopt effective measures against specific behavioral deficiencies. This study aims at developing and validating an independent consultation and feedback system (ICFS) for optimizing clinical decisions on the use of MMs for inpatients needing joint replacement surgeries. Methods Development of the research protocol is based on a problem or deficiency list derived on a trans-theoretical framework which incorporates including mainly soft systems-thinking, information asymmetry, crisis-coping, dual delegation and planned behavior. The intervention consists of two main components targeting at patients and doctors respectively. Each of the intervention ingredients is designed to tackle the doctor and patient-side problems with MMs using in joint replacement surgeries. The intervention arm receives 18 months' ICFS intervention program on the basis of the routine medical services; while the control arm, only the routine medical services. Implementation of the intervention is supported by an online platform established and maintained by the Quality Assurance Center for Medical Care in Anhui Province, a smartphone-based application program (APP) and a web-based clinical support system. Discussion The implementation of this study is expected to significantly reduce the deficiencies and moral hazards in decision-making of MMs using through the output of economic, efficient, sustainable and easy-to-promote cooperative intervention programs, thus greatly reducing medical costs and standardizing medical behaviors. Trial registration number ISRCTN10152297.

Description: Background There have been few studies describing how production EMR systems can be systematically queried to identify clinically-defined populations and limited studies utilising free-text in this process. The aim of this study is to provide a generalisable methodology for constructing clinically-defined EMR-derived patient cohorts using structured and unstructured data in EMRs. Methods Patients with possible acute coronary syndrome (ACS) were used as an exemplar. Cardiologists defined clinical criteria for patients presenting with possible ACS. These were mapped to data tables within the production EMR system creating seven inclusion criteria comprised of structured data fields (orders and investigations, procedures, scanned electrocardiogram (ECG) images, and diagnostic codes) and unstructured clinical documentation. Data were extracted from two local health districts (LHD) in Sydney, Australia. Outcome measures included examination of the relative contribution of individual inclusion criteria to the identification of eligible encounters, comparisons between inclusion criterion and evaluation of consistency of data extracts across years and LHDs. Results Among 802,742 encounters in a 5 year dataset (1/1/13–30/12/17), the presence of an ECG image (54.8% of encounters) and symptoms and keywords in clinical documentation (41.4–64.0%) were used most often to identify presentations of possible ACS. Orders and investigations (27.3%) and procedures (1.4%), were less often present for identified presentations. Relevant ICD-10/SNOMED CT codes were present for 3.7% of identified encounters. Similar trends were seen when the two LHDs were examined separately, and across years. Conclusions Clinically-defined EMR-derived cohorts combining structured and unstructured data during cohort identification is a necessary prerequisite for critical validation work required for development of real-time clinical decision support and learning health systems.

Description: Background In the intensive care unit (ICU), delirium is a common, acute, confusional state associated with high risk for short- and long-term morbidity and mortality. Machine learning (ML) has promise to address research priorities and improve delirium outcomes. However, due to clinical and billing conventions, delirium is often inconsistently or incompletely labeled in electronic health record (EHR) datasets. Here, we identify clinical actions abstracted from clinical guidelines in electronic health records (EHR) data that indicate risk of delirium among intensive care unit (ICU) patients. We develop a novel prediction model to label patients with delirium based on a large data set and assess model performance. Methods EHR data on 48,451 admissions from 2001 to 2012, available through Medical Information Mart for Intensive Care-III database (MIMIC-III), was used to identify features to develop our prediction models. Five binary ML classification models (Logistic Regression; Classification and Regression Trees; Random Forests; Naïve Bayes; and Support Vector Machines) were fit and ranked by Area Under the Curve (AUC) scores. We compared our best model with two models previously proposed in the literature for goodness of fit, precision, and through biological validation. Results Our best performing model with threshold reclassification for predicting delirium was based on a multiple logistic regression using the 31 clinical actions (AUC 0.83). Our model out performed other proposed models by biological validation on clinically meaningful, delirium-associated outcomes. Conclusions Hurdles in identifying accurate labels in large-scale datasets limit clinical applications of ML in delirium. We developed a novel labeling model for delirium in the ICU using a large, public data set. By using guideline-directed clinical actions independent from risk factors, treatments, and outcomes as model predictors, our classifier could be used as a delirium label for future clinically targeted models.

Description: Background Strabismus is a complex disease that has various treatment approaches each with its own advantages and drawbacks. In this context, shared decisions making (SDM) is a communication process with the provider sharing all the relevant treatment alternatives, all the benefits, and risks of each procedure, while the patient shares all the preferences and values regarding his/her choices. In that way, SDM is a bidirectional process that goes beyond the typical informed consent. Therefore, it is known a little of the extent to which SDM influences the satisfaction with the treatment outcome along with strabismus patients. To study this correlation, an SDM-Q-9 questionnaire was provided within surgical consultations where treatment decisions were made; the SDM-Q-9 aims to assess the relationship between the post-operative patient’s satisfaction and their SMD score. Methods The study is considered a prospective observational pilot study. Eligible patients were adult patients diagnosed with strabismus, who had multiple treatment options, were given at the right of choice without being driven into a physician’s preference. Ninety-three strabismus patients were asked to fill out the SDM-Q-9 questionnaire related to their perception of SDM during the entire period of strabismus treatment. After the treatment, patients were asked to rate their satisfaction level with the surgical outcome as excellent, good, fair, and poor. Descriptive statistics and the linear regression statistical tests (Spearman, Mann Whitney U, and Kriskal–Wallis) were used as analysis tools. Results The average age of the participants was 24, where 50.6% were women. The mean SDM-Q-9 score among patients was 32 (IQR = 3). The postoperative patient satisfaction was rated as being excellent by 16 (17.2%) patients, good by 38 (40.9%), fair by 32 (34.4%), and poor by 7 patients (7.5%). Data analysis by linear regression statistical tests showed a positive correlation between the SDM-Q-9 score and the patient satisfaction related to the surgery outcome (B = 0.005, p 

Description: Background Diabetes Mellitus (DM) has become the third chronic non-communicable disease that hits patients after tumors, cardiovascular and cerebrovascular diseases, and has become one of the major public health problems in the world. Therefore, it is of great importance to identify individuals at high risk for DM in order to establish prevention strategies for DM. Methods Aiming at the problem of high-dimensional feature space and high feature redundancy of medical data, as well as the problem of data imbalance often faced. This study explored different supervised classifiers, combined with SVM-SMOTE and two feature dimensionality reduction methods (Logistic stepwise regression and LAASO) to classify the diabetes survey sample data with unbalanced categories and complex related factors. Analysis and discussion of the classification results of 4 supervised classifiers based on 4 data processing methods. Five indicators including Accuracy, Precision, Recall, F1-Score and AUC are selected as the key indicators to evaluate the performance of the classification model. Results According to the result, Random Forest Classifier combining SVM-SMOTE resampling technology and LASSO feature screening method (Accuracy = 0.890, Precision = 0.869, Recall = 0.919, F1-Score = 0.893, AUC = 0.948) proved the best way to tell those at high risk of DM. Besides, the combined algorithm helps enhance the classification performance for prediction of high-risk people of DM. Also, age, region, heart rate, hypertension, hyperlipidemia and BMI are the top six most critical characteristic variables affecting diabetes. Conclusions The Random Forest Classifier combining with SVM-SMOTE and LASSO feature reduction method perform best in identifying high-risk people of DM from individuals. And the combined method proposed in the study would be a good tool for early screening of DM.

Description: Background A central goal among researchers and policy makers seeking to implement clinical interventions is to identify key facilitators and barriers that contribute to implementation success. Despite calls from a number of scholars, empirical insights into the complex structural and cultural predictors of why decision aids (DAs) become routinely embedded in health care settings remains limited and highly variable across implementation contexts. Methods We examined associations between “reach”, a widely used indicator (from the RE-AIM model) of implementation success, and multi-level site characteristics of nine LVAD clinics engaged over 18 months in implementation and dissemination of a decision aid for left ventricular assist device (LVAD) treatment. Based on data collected from nurse coordinators, we explored factors at the level of the organization (e.g. patient volume), patient population (e.g. health literacy; average sickness level), clinician characteristics (e.g. attitudes towards decision aid; readiness for change) and process (how the aid was administered). We generated descriptive statistics for each site and calculated zero-order correlations (Pearson’s r) between all multi-level site variables including cumulative reach at 12 months and 18 months for all sites. We used principal components analysis (PCA) to examine any latent factors governing relationships between and among all site characteristics, including reach. Results We observed strongest inclines in reach of our decision aid across the first year, with uptake fluctuating over the second year. Average reach across sites was 63% (s.d. = 19.56) at 12 months and 66% (s.d. = 19.39) at 18 months. Our PCA revealed that site characteristics positively associated with reach on two distinct dimensions, including a first dimension reflecting greater organizational infrastructure and standardization (characteristic of larger, more established clinics) and a second dimension reflecting positive attitudinal orientations, specifically, openness and capacity to give and receive decision support among coordinators and patients. Conclusions Successful implementation plans should incorporate specific efforts to promote supportive and mutually informative interactions between clinical staff members and to institute systematic and standardized protocols to enhance the availability, convenience and salience of intervention tool in routine practice. Further research is needed to understand whether “core predictors” of success vary across different intervention types.

Description: Background The coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide since December 2019. From the beginning of infection, patients will progress through different symptom stages, such as fever, dyspnea or even death. Identifying disease progression and predicting patient outcome at an early stage helps target treatment and resource allocation. However, there is no clear COVID-19 stage definition, and few studies have addressed characterizing COVID-19 progression, making the need for this study evident. Methods We proposed a temporal deep learning method, based on a time-aware long short-term memory (T-LSTM) neural network and used an online open dataset, including blood samples of 485 patients from Wuhan, China, to train the model. Our method can grasp the dynamic relations in irregularly sampled time series, which is ignored by existing works. Specifically, our method predicted the outcome of COVID-19 patients by considering both the biomarkers and the irregular time intervals. Then, we used the patient representations, extracted from T-LSTM units, to subtype the patient stages and describe the disease progression of COVID-19. Results Using our method, the accuracy of the outcome of prediction results was more than 90% at 12 days and 98, 95 and 93% at 3, 6, and 9 days, respectively. Most importantly, we found 4 stages of COVID-19 progression with different patient statuses and mortality risks. We ranked 40 biomarkers related to disease and gave the reference values of them for each stage. Top 5 is Lymph, LDH, hs-CRP, Indirect Bilirubin, Creatinine. Besides, we have found 3 complications - myocardial injury, liver function injury and renal function injury. Predicting which of the 4 stages the patient is currently in can help doctors better assess and cure the patient. Conclusions To combat the COVID-19 epidemic, this paper aims to help clinicians better assess and treat infected patients, provide relevant researchers with potential disease progression patterns, and enable more effective use of medical resources. Our method predicted patient outcomes with high accuracy and identified a four-stage disease progression. We hope that the obtained results and patterns will aid in fighting the disease.

Description: Background Rheumatoid arthritis (RA) is an autoimmune disorder with systemic inflammation and may be induced by oxidative stress that affects an inflamed joint. Our objectives were to examine isotypes of autoantibodies against 4-hydroxy-2-nonenal (HNE) modifications in RA and associate them with increased levels of autoantibodies in RA patients. Methods Serum samples from 155 female patients [60 with RA, 35 with osteoarthritis (OA), and 60 healthy controls (HCs)] were obtained. Four novel differential HNE-modified peptide adducts, complement factor H (CFAH)1211–1230, haptoglobin (HPT)78–108, immunoglobulin (Ig) kappa chain C region (IGKC)2–19, and prothrombin (THRB)328–345, were re-analyzed using tandem mass spectrometric (MS/MS) spectra (ProteomeXchange: PXD004546) from RA patients vs. HCs. Further, we determined serum protein levels of CFAH, HPT, IGKC and THRB, HNE-protein adducts, and autoantibodies against unmodified and HNE-modified peptides. Significant correlations and odds ratios (ORs) were calculated. Results Levels of HPT in RA patients were greatly higher than the levels in HCs. Levels of HNE-protein adducts and autoantibodies in RA patients were significantly greater than those of HCs. IgM anti-HPT78−108 HNE, IgM anti-IGKC2−19, and IgM anti-IGKC2−19 HNE may be considered as diagnostic biomarkers for RA. Importantly, elevated levels of IgM anti-HPT78−108 HNE, IgM anti-IGKC2−19, and IgG anti-THRB328−345 were positively correlated with the disease activity score in 28 joints for C-reactive protein (DAS28-CRP). Further, the ORs of RA development through IgM anti-HPT78−108 HNE (OR 5.235, p 

Description: Background Researchers developing prediction models are faced with numerous design choices that may impact model performance. One key decision is how to include patients who are lost to follow-up. In this paper we perform a large-scale empirical evaluation investigating the impact of this decision. In addition, we aim to provide guidelines for how to deal with loss to follow-up. Methods We generate a partially synthetic dataset with complete follow-up and simulate loss to follow-up based either on random selection or on selection based on comorbidity. In addition to our synthetic data study we investigate 21 real-world data prediction problems. We compare four simple strategies for developing models when using a cohort design that encounters loss to follow-up. Three strategies employ a binary classifier with data that: (1) include all patients (including those lost to follow-up), (2) exclude all patients lost to follow-up or (3) only exclude patients lost to follow-up who do not have the outcome before being lost to follow-up. The fourth strategy uses a survival model with data that include all patients. We empirically evaluate the discrimination and calibration performance. Results The partially synthetic data study results show that excluding patients who are lost to follow-up can introduce bias when loss to follow-up is common and does not occur at random. However, when loss to follow-up was completely at random, the choice of addressing it had negligible impact on model discrimination performance. Our empirical real-world data results showed that the four design choices investigated to deal with loss to follow-up resulted in comparable performance when the time-at-risk was 1-year but demonstrated differential bias when we looked into 3-year time-at-risk. Removing patients who are lost to follow-up before experiencing the outcome but keeping patients who are lost to follow-up after the outcome can bias a model and should be avoided. Conclusion Based on this study we therefore recommend (1) developing models using data that includes patients that are lost to follow-up and (2) evaluate the discrimination and calibration of models twice: on a test set including patients lost to follow-up and a test set excluding patients lost to follow-up.

Description: Following publication of the original article [1], it was reported that the contents of Additional file 2 were a duplicate of the files for Additional file 1.

Description: Background Information literacy competency is one of the requirements to implement Evidence-Based Practice (EBP) in nursing. It is necessary to pay attention to curricular development and use new educational methods such as virtual education to strengthen information literacy competency in nursing students. Given the scarcity of the studies on the effectiveness of virtual education in nursing, particularly in Iran, and the positive university atmosphere regarding the use of virtual education, this study investigated the effect of virtual education on the undergraduate nursing students’ information literacy competency for EBP. Methods This interventional study was performed with two groups of intervention and control and a pretest and posttest design. Seventy-nine nursing students were selected and assigned to the intervention or control groups by random sampling. Virtual education of the information literacy was uploaded on a website in the form of six modules delivered in four weeks. Questionnaires of demographic information and information literacy for EBP were used to collect data before and one month after the virtual education. Results The results showed no significant difference between the control and intervention groups in all dimensions of information literacy competency in the pre-test stage. In the post-test, the virtual education improved dimensions of information seeking skills (t = 3.14, p = 0.002) and knowledge about search operators (t = 39.84, p = 0.001) in the intervention groups compared with the control group. The virtual education did not have any significant effect on the use of different information resources and development of search strategy with assessing the frequency of selecting the most appropriate search statement in the intervention group. Conclusion Virtual education had a significant effect on information seeking skills and knowledge about search operators in nursing students. Nurse educators can benefit from our experiences in designing this method for the use of virtual education programs in nursing schools. Given the lack of effectiveness of this program in using different information resources and development of search strategy, nurse educators are recommended to train information literacy for EBP by integrating several approaches such as virtual (online and offline) and face-to-face education.

Description: Background U.S. hospitals and dialysis centers are penalized for 30-day hospital readmissions of dialysis patients, despite little infrastructure to facilitate care transitions between these settings. We are developing a third-party web-based information exchange platform, DialysisConnect, to enable clinicians to view and exchange information about dialysis patients during admission, hospitalization, and discharge. This health information technology solution could serve as a flexible and relatively affordable solution for dialysis facilities and hospitals across the nation who are seeking to serve as true partners in the improved care of dialysis patients. The purpose of this study was to evaluate the perceived coherence of DialysisConnect to key clinical stakeholders, to prepare messaging for implementation. Methods As part of a hybrid effectiveness-implementation study guided by Normalization Process Theory, we collected data on stakeholder perceptions of continuity of care for patients receiving maintenance dialysis and a DialysisConnect prototype before completing development and piloting the system. We conducted four focus groups with stakeholders from one academic hospital and associated dialysis centers [hospitalists (n = 5), hospital staff (social workers, nurses, pharmacists; n = 9), nephrologists (n = 7), and dialysis clinic staff (social workers, nurses; n = 10)]. Transcriptions were analyzed thematically within each component of the construct of coherence (differentiation, communal specification, individual specification, and internalization). Results Participants differentiated DialysisConnect from usual care variously as an information dashboard, a quick-exchange communication channel, and improved discharge information delivery; some could not differentiate it in terms of workflow. The purpose of DialysisConnect (communal specification) was viewed as fully coherent only for communicating outside of the same healthcare system. Current system workarounds were acknowledged as deterrents for practice change. All groups delegated DialysisConnect tasks (individual specification) to personnel besides themselves. Partial internalization of DialysisConnect was achieved only by dialysis clinic staff, based on experience with similar technology. Conclusions Implementing DialysisConnect for clinical users in both settings will require presenting a composite picture of current communication processes from all stakeholder groups to correct single-group misunderstandings, as well as providing data about care transitions communication beyond the local context to ease resistance to practice change.

Description: Background Researchers and policy makers have long suspected that people have differing, and potentially nefarious, motivations for participating in stated-preference studies such as discrete-choice experiments (DCE). While anecdotes and theories exist on why people participate in surveys, there is a paucity of evidence exploring variation in preferences for participating in stated-preference studies. Methods We used a DCE to estimate preferences for participating in preference research among an online survey panel sample. Preferences for the characteristics of a study to be conducted at a local hospital were assessed across five attributes (validity, relevance, bias, burden, time and payment) and described across three levels using a starring system. A D-efficient experimental design was used to construct three blocks of 12 choice tasks with two profiles each. Respondents were also asked about factors that motivated their choices. Mixed logistic regression was used to analyze the aggregate sample and latent class analysis identified segments of respondents. Results 629 respondents completed the experiment. In aggregate “study validity” was most important. Latent class results identified two segments based on underlying motivations: a quality-focused segment (76%) who focused most on validity, relevance, and bias and a convenience-focused segment (24%) who focused most on reimbursement and time. Quality-focused respondents spent more time completing the survey (p 

Description: Background This study aims to explore the information chain management model of large instrument and equipment inter-working in the operating room (OR) led by information nurses. Methods Through the chain management process of large instruments and equipment in the OR, which was based on information nurses, the management model of inter-working and integrating information chain was established, the key links were controlled, and the whole life cycle management of instruments and equipment from expected procurement to scrapping treatment was realized. Using the cluster sampling method, 1562 surgical patients were selected. Among these patients, 749 patients were assigned to the control group before the running mode, and 813 patients were assigned to the observation group after the running mode. The related indexes for large instrument and equipment management in the department before and after the running mode were compared. Results In the observation group, the average time of equipment registration was (22.05 ± 2.36), the cost was reduced by 2220 yuan/year, and the satisfaction rate of the nursing staff was 97.62%. These were significantly better, when compared to the control group (P 

Description: Background Social networking sites such as Facebook® can contribute to health promotion and behaviour change activities, but are currently underused for this purpose. In Germany, health insurance companies are relevant public health agencies that are responsible for health promotion, primary prevention, and health education. We intended to analyse the Facebook® accounts of health insurance providers to explore the range of prevention topics addressed, identify the communication formats used, and analyse user activity stimulated by prevention-related posts. Methods We performed a quantitative content analysis of text and picture data on Facebook® accounts (9 months in retrospect) in a cross-sectional study design. 64/159 German health insurance providers hosted a Facebook® page, 25/64 posted ≥ 10 posts/months. Among those 25, we selected 17 health insurance companies (12 public, 5 private) for analysis. All posts were categorized according to domains in the classification system that was developed for this study, and the number of likes and comments was counted. The data were analysed using descriptive statistics. Results We collected 3,763 Facebook® posts, 32% of which had a focus on prevention. The frequency of prevention-related posts varied among health insurance providers (1–25 per month). The behaviours addressed most frequently were healthy nutrition, physical activity, and stress/anxiety relief, often in combination with each other. All these topics yielded a moderate user engagement (30–120 likes, 2–10 comments per post). User engagement was highest when a competition or quiz were posted (11% of posts). The predominant communication pattern was health education, often supplemented by photos or links, or information about offline events (e.g. a public run). Some providers regularly engaged in two-side communication with users, inviting tips, stories or recipes, or responding to individual comments. Still, the interactive potential offered by Facebook® was only partly exploited. Conclusions Those few health insurace companies that regularly post content about prevention or healthy lifestyles on their Facebook® accounts comply with suggestions given for social media communication. Still, many health insurance providers fail to actively interact with wider audiences. Whether health communication on Facebook® can actually increase health literacy and lead to behaviour changes still needs to be evaluated.

Description: Background Systemic inflammatory response syndrome (SIRS) is defined as a non-specific inflammatory process in the absence of infection. SIRS increases susceptibility for organ dysfunction, and frequently affects the clinical outcome of affected patients. We evaluated a knowledge-based, interoperable clinical decision-support system (CDSS) for SIRS detection on a pediatric intensive care unit (PICU). Methods The CDSS developed retrieves routine data, previously transformed into an interoperable format, by using model-based queries and guideline- and knowledge-based rules. We evaluated the CDSS in a prospective diagnostic study from 08/2018–03/2019. 168 patients from a pediatric intensive care unit of a tertiary university hospital, aged 0 to 18 years, were assessed for SIRS by the CDSS and by physicians during clinical routine. Sensitivity and specificity (when compared to the reference standard) with 95% Wald confidence intervals (CI) were estimated on the level of patients and patient-days. Results Sensitivity and specificity was 91.7% (95% CI 85.5–95.4%) and 54.1% (95% CI 45.4–62.5%) on patient level, and 97.5% (95% CI 95.1–98.7%) and 91.5% (95% CI 89.3–93.3%) on the level of patient-days. Physicians’ SIRS recognition during clinical routine was considerably less accurate (sensitivity of 62.0% (95% CI 56.8–66.9%)/specificity of 83.3% (95% CI 80.4–85.9%)) when measurd on the level of patient-days. Evaluation revealed valuable insights for the general design of the CDSS as well as specific rule modifications. Despite a lower than expected specificity, diagnostic accuracy was higher than the one in daily routine ratings, thus, demonstrating high potentials of using our CDSS to help to detect SIRS in clinical routine. Conclusions We successfully evaluated an interoperable CDSS for SIRS detection in PICU. Our study demonstrated the general feasibility and potentials of the implemented algorithms but also some limitations. In the next step, the CDSS will be optimized to overcome these limitations and will be evaluated in a multi-center study. Trial registration: NCT03661450 (ClinicalTrials.gov); registered September 7, 2018.

Description: Background The Ministry of Health of Malaysia has invested significant resources to implement an electronic health record (EHR) system to ensure the full automation of hospitals for coordinated care delivery. Thus, evaluating whether the system has been effectively utilized is necessary, particularly regarding how it predicts the post-implementation primary care providers’ performance impact. Methods Convenience sampling was employed for data collection in three government hospitals for 7 months. A standardized effectiveness survey for EHR systems was administered to primary health care providers (specialists, medical officers, and nurses) as they participated in medical education programs. Empirical data were assessed by employing partial least squares-structural equation modeling for hypothesis testing. Results The results demonstrated that knowledge quality had the highest score for predicting performance and had a large effect size, whereas system compatibility was the most substantial system quality component. The findings indicated that EHR systems supported the clinical tasks and workflows of care providers, which increased system quality, whereas the increased quality of knowledge improved user performance. Conclusion Given these findings, knowledge quality and effective use should be incorporated into evaluating EHR system effectiveness in health institutions. Data mining features can be integrated into current systems for efficiently and systematically generating health populations and disease trend analysis, improving clinical knowledge of care providers, and increasing their productivity. The validated survey instrument can be further tested with empirical surveys in other public and private hospitals with different interoperable EHR systems.

Description: Background We know little about the best approaches to design training for healthcare professionals. We thus studied how user-centered and theory-based design contribute to the development of a distance learning program for professionals, to increase their shared decision-making (SDM) with older adults living with neurocognitive disorders and their caregivers. Methods In this mixed-methods study, healthcare professionals who worked in family medicine clinics and homecare services evaluated a training program in a user-centered approach with several iterative phases of quantitative and qualitative evaluation, each followed by modifications. The program comprised an e-learning activity and five evidence summaries. A subsample assessed the e-learning activity during semi-structured think-aloud sessions. A second subsample assessed the evidence summaries they received by email. All participants completed a theory-based questionnaire to assess their intention to adopt SDM. Descriptive statistical analyses and qualitative thematic analyses were integrated at each round to prioritize training improvements with regard to the determinants most likely to influence participants’ intention. Results Of 106 participants, 98 completed their evaluations of either the e-learning activity or evidence summary (93%). The professions most represented were physicians (60%) and nurses (15%). Professionals valued the e-learning component to gain knowledge on the theory and practice of SDM, and the evidence summaries to apply the knowledge gained through the e-learning activity to diverse clinical contexts. The iterative design process allowed addressing most weaknesses reported. Participants’ intentions to adopt SDM and to use the summaries were high at baseline and remained positive as the rounds progressed. Attitude and social influence significantly influenced participants' intention to use the evidence summaries (P 

Description: Background Summative eHealth evaluations frequently lack quality, which affects the generalizability of the evidence, and its use in practice and further research. To guarantee quality, a number of activities are recommended in the guidelines for evaluation planning. This study aimed to examine a case of an eHealth evaluation planning in a multi-national and interdisciplinary setting and to provide recommendations for eHealth evaluation planning guidelines. Methods An empirical eHealth evaluation process was developed through a case study. The empirical process was compared with selected guidelines for eHealth evaluation planning using a pattern-matching technique. Results Planning in the interdisciplinary and multi-national team demanded extensive negotiation and alignment to support the future use of the evidence created. The evaluation planning guidelines did not provide specific strategies for different set-ups of the evaluation teams. Further, they did not address important aspects of quality evaluation, such as feasibility analysis of the outcome measures and data collection, monitoring of data quality, and consideration of the methods and measures employed in similar evaluations. Conclusions Activities to prevent quality problems need to be incorporated in the guidelines for evaluation planning. Additionally, evaluators could benefit from guidance in evaluation planning related to the different set-ups of the evaluation teams.

Description: Background No case definition of Type 1 diabetes (T1D) for the claims data has been proposed in Japan yet. This study aimed to evaluate the performance of candidate case definitions for T1D using Electronic health care records (EHR) and claims data in a University Hospital in Japan. Methods The EHR and claims data for all the visiting patients in a University Hospital were used. As the candidate case definitions for claims data, we constructed 11 definitions by combinations of International Statistical Classification of Diseases and Related Health Problems, Tenth Revision. (ICD 10) code of T1D, the claims code of insulin needles for T1D patients, basal insulin, and syringe pump for continuous subcutaneous insulin infusion (CSII). We constructed a predictive model for T1D patients using disease names, medical practices, and medications as explanatory variables. The predictive model was applied to patients of test group (validation data), and performances of candidate case definitions were evaluated. Results As a result of performance evaluation, the sensitivity of the confirmed disease name of T1D was 32.9 (95% CI: 28.4, 37.2), and positive predictive value (PPV) was 33.3 (95% CI: 38.0, 38.4). By using the case definition of both the confirmed diagnosis of T1D and either of the claims code of the two insulin treatment methods (i.e., syringe pump for CSII and insulin needles), PPV improved to 90.2 (95% CI: 85.2, 94.4). Conclusions We have established a case definition with high PPV, and the case definition can be used for precisely detecting T1D patients from claims data in Japan.

Description: Objective To explore an effective algorithm based on artificial neural network to pick correctly the minority of pregnant women with SLE suffering fetal loss outcomes from the majority with live birth and train a well behaved model as a clinical decision assistant. Methods We integrated the thoughts of comparative and focused study into the artificial neural network and presented an effective algorithm aiming at imbalanced learning in small dataset. Results We collected 469 non-trivial pregnant patients with SLE, where 420 had live-birth outcomes and the other 49 patients ended in fetal loss. A well trained imbalanced-learning model had a high sensitivity of 19/21 ($$90.8\%$$ 90.8 % ) for the identification of patients with fetal loss outcomes. Discussion The misprediction of the two patients was explainable. Algorithm improvements in artificial neural network framework enhanced the identification in imbalanced learning problems and the external validation increased the reliability of algorithm. Conclusion The well-trained model was fully qualified to assist healthcare providers to make timely and accurate decisions.

Description: Background Semantic categorization analysis of clinical trials eligibility criteria based on natural language processing technology is crucial for the task of optimizing clinical trials design and building automated patient recruitment system. However, most of related researches focused on English eligibility criteria, and to the best of our knowledge, there are no researches studied the Chinese eligibility criteria. Thus in this study, we aimed to explore the semantic categories of Chinese eligibility criteria. Methods We downloaded the clinical trials registration files from the website of Chinese Clinical Trial Registry (ChiCTR) and extracted both the Chinese eligibility criteria and corresponding English eligibility criteria. We represented the criteria sentences based on the Unified Medical Language System semantic types and conducted the hierarchical clustering algorithm for the induction of semantic categories. Furthermore, in order to explore the classification performance of Chinese eligibility criteria with our developed semantic categories, we implemented multiple classification algorithms, include four baseline machine learning algorithms (LR, NB, kNN, SVM), three deep learning algorithms (CNN, RNN, FastText) and two pre-trained language models (BERT, ERNIE). Results We totally developed 44 types of semantic categories, summarized 8 topic groups, and investigated the average incidence and prevalence in 272 hepatocellular carcinoma related Chinese clinical trials. Compared with the previous proposed categories in English eligibility criteria, 13 novel categories are identified in Chinese eligibility criteria. The classification result shows that most of semantic categories performed quite well, the pre-trained language model ERNIE achieved best performance with macro-average F1 score of 0.7980 and micro-average F1 score of 0.8484. Conclusion As a pilot study of Chinese eligibility criteria analysis, we developed the 44 semantic categories by hierarchical clustering algorithms for the first times, and validated the classification capacity with multiple classification algorithms.

Description: Background Robust, flexible, and integrated health information (HIS) systems are essential to achieving national and international goals in health and development. Such systems are still uncommon in most low and middle income countries. This article describes a first-phase activity in Tanzania to integrate the country’s vertical health management information system with the help of an interoperability layer that enables cross-program data exchange. Methods From 2014 to 2019, the Tanzanian government and partners implemented a five-step procedure based on the “Mind the GAPS” (governance, architecture, program management, and standards) framework and using both proprietary and open-source tools. In collaboration with multiple stakeholders, the team developed the system to address major data challenges via four fully documented “use case scenarios” addressing data exchange among hospitals, between services and the supply chain, across digital data systems, and within the supply chain reporting system. This work included developing the architecture for health system data exchange, putting a middleware interoperability layer in place to facilitate the exchange, and training to support use of the system and the data it generates. Results Tanzania successfully completed the five-step procedure for all four use cases. Data exchange is currently enabled among 15 separate information systems, and has resulted in improved data availability and significant time savings. The government has adopted the health information exchange within the national strategy for health care information, and the system is being operated and managed by Tanzanian officials. Conclusion Developing an integrated HIS requires a significant time investment; but ultimately benefit both programs and patients. Tanzania’s experience may interest countries that are developing their HIS programs.

Description: Background The uptake of complex clinical decision support systems (CDSS) in daily practice remains low, despite the proven potential to reduce medical errors and to improve the quality of care. To improve successful implementation of a complex CDSS this study aims to identify the factors that hinder, or alleviate the acceptance of, clinicians toward the use of a complex CDSS for treatment allocation of patients with chronic low back pain. Methods We tested a research model in which the intention to use a CDSS by clinicians is influenced by the perceived usefulness; this usefulness, in turn is influenced by the perceived service benefits and perceived service risks. An online survey was created to test our research model and the data was analysed using Partial Least Squares Structural Equation Modelling. The study population consisted of clinicians. The online questionnaire started with demographic questions and continued with a video animation of the complex CDSS followed by the set of measurement items. The online questionnaire ended with two open questions enquiring the reasons to use and not use, a complex CDSS. Results Ninety-eight participants (46% general practitioners, 25% primary care physical therapists, and 29% clinicians at a rehabilitation centre) fully completed the questionnaire. Fifty-two percent of the respondents were male. The average age was 48 years (SD ± 12.2). The causal model suggests that perceived usefulness is the main factor contributing to the intention to use a complex CDSS. Perceived service benefits and risks are both significant antecedents of perceived usefulness and perceived service risks are affected by the perceived threat to autonomy and trusting beliefs, particularly benevolence and competence. Conclusions To improve the acceptance of complex CDSSs it is important to address the risks, but the main focus during the implementation phase should be on the expected improvements in patient outcomes and the overall gain for clinicians. Our results will help the development of complex CDSSs that fit more into the daily clinical practice of clinicians.

Description: Background This paper describes a model for estimating COVID-19 related excess deaths that are a direct consequence of insufficient hospital ward bed and intensive care unit (ICU) capacity. Methods Compartmental models were used to estimate deaths under different combinations of ICU and ward care required and received in England up to late April 2021. Model parameters were sourced from publicly available government information and organisations collating COVID-19 data. A sub-model was used to estimate the mortality scalars that represent increased mortality due to insufficient ICU or general ward bed capacity. Three illustrative scenarios for admissions numbers, ‘Optimistic’, ‘Middling’ and ‘Pessimistic’, were modelled and compared with the subsequent observations to the 3rd February. Results The key output was the demand and capacity model described. There were no excess deaths from a lack of capacity in the ‘Optimistic’ scenario. Several of the ‘Middling’ scenario applications resulted in excess deaths—up to 597 deaths (0.6% increase) with a 20% reduction compared to best estimate ICU capacity. All the ‘Pessimistic’ scenario applications resulted in excess deaths, ranging from 49,178 (17.0% increase) for a 20% increase in ward bed availability, to 103,735 (35.8% increase) for a 20% shortfall in ward bed availability. These scenarios took no account of the emergence of the new, more transmissible, variant of concern (b.1.1.7). Conclusions Mortality is increased when hospital demand exceeds available capacity. No excess deaths from breaching capacity would be expected under the ‘Optimistic’ scenario. The ‘Middling’ scenario could result in some excess deaths—up to a 0.7% increase relative to the total number of deaths. The ‘Pessimistic’ scenario would have resulted in significant excess deaths. Our sensitivity analysis indicated a range between 49,178 (17% increase) and 103,735 (35.8% increase). Given the new variant, the pessimistic scenario appeared increasingly likely and could have resulted in a substantial increase in the number of COVID-19 deaths. In the event, it would appear that capacity was not breached at any stage at a national level with no excess deaths. it will remain unclear if minor local capacity breaches resulted in any small number of excess deaths.

Description: Background Retrieving gene and disease information from a vast collection of biomedical abstracts to provide doctors with clinical decision support is one of the important research directions of Precision Medicine. Method We propose a novel article retrieval method based on expanded word and co-word analyses, also conducting Cuckoo Search to optimize parameters of the retrieval function. The main goal is to retrieve the abstracts of biomedical articles that refer to treatments. The methods mentioned in this manuscript adopt the BM25 algorithm to calculate the score of abstracts. We, however, propose an improved version of BM25 that computes the scores of expanded words and co-word leading to a composite retrieval function, which is then optimized using the Cuckoo Search. The proposed method aims to find both disease and gene information in the abstract of the same biomedical article. This is to achieve higher relevance and hence score of articles. Besides, we investigate the influence of different parameters on the retrieval algorithm and summarize how they meet various retrieval needs. Results The data used in this manuscript is sourced from medical articles presented in Text Retrieval Conference (TREC): Clinical Decision Support (CDS) Tracks of 2017, 2018, and 2019 in Precision Medicine. A total of 120 topics are tested. Three indicators are employed for the comparison of utilized methods, which are selected among the ones based only on the BM25 algorithm and its improved version to conduct comparable experiments. The results showed that the proposed algorithm achieves better results. Conclusion The proposed method, an improved version of the BM25 algorithm, utilizes both co-word implementation and Cuckoo Search, which has been verified achieving better results on a large number of experimental sets. Besides, a relatively simple query expansion method is implemented in this manuscript. Future research will focus on ontology and semantic networks to expand the query vocabulary.

Description: Background Malignant brain tumor diseases exhibit differences within molecular features depending on the patient’s age. Methods In this work, we use gene mutation data from public resources to explore age specifics about glioma. We use both an explainable clustering as well as classification approach to find and interpret age-based differences in brain tumor diseases. We estimate age clusters and correlate age specific biomarkers. Results Age group classification shows known age specifics but also points out several genes which, so far, have not been associated with glioma classification. Conclusions We highlight mutated genes to be characteristic for certain age groups and suggest novel age-based biomarkers and targets.

Description: Background Colorectal cancer (CRC) is a common malignancy worldwide. Despite being the most common cancer in Singapore, CRC screening rate remains low due to knowledge deficits, social reasons such as inconvenience and a lack of reminder or recommendation. A decision aid (DA) may facilitate an individual’s decision-making to undertake CRC screening by addressing misconceptions and barriers. We postulate that a more person-centred and culturally adapted DA will better serve the local population. The views of the target users are thus needed to develop such a DA. A CRC screening DA prototype has been adapted from an American DA to cater to the Asian users. This study aimed to explore user perspectives on an adapted CRC screening DA-prototype in terms of the design, content and perceived utility. Methods The study used in-depth interviews (IDIs) and focus group discussions (FGDs) to gather qualitative data from English-literate multi-ethnic Asian adults aged 50 years old and above. They had yet to screen for CRC before they were recruited from a public primary care clinic in Singapore. The interviews were audio-recorded, transcribed and analysed to identify emergent themes via thematic analysis. Results This study included 27 participants involved in 5 IDI and 5 FGDs. Participants found the DA easily comprehensible and of appropriate length. They appreciated information about the options and proposed having multi-lingual DAs. The design, in terms of the layout, size and font, was well-accepted but there were suggestions to digitalize the DA. Participants felt that the visuals were useful but there were concerns about modesty due to the realism of the illustration. They would use the DA for information-sharing with their family and for discussion with their doctor for decision making. They preferred the doctor’s recommendation for CRC screening and initiating the use of the DA. Conclusions Participants generally had favourable perceptions of the DA-prototype. A revised DA will be developed based on their feedback. Further input from doctors on the revised DA will be obtained before assessing its effectiveness to increase CRC screening rate in a randomized controlled trial.

Description: Over the last decades, the face of health care has changed dramatically, with big improvements in what is technically feasible. However, there are indicators that the current approach to evaluating evidence in health care is not holistic and hence in the long run, health care will not be sustainable. New conceptual and normative frameworks for the evaluation of health care need to be developed and investigated. The current paper presents a novel framework of justifiable health care and explores how the use of artificial intelligence and big data can contribute to achieving the goals of this framework.

Description: Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

Description: Background Many patients with atrial fibrillation (AF) remain undiagnosed despite availability of interventions to reduce stroke risk. Predictive models to date are limited by data requirements and theoretical usage. We aimed to develop a model for predicting the 2-year probability of AF diagnosis and implement it as proof-of-concept (POC) in a production electronic health record (EHR). Methods We used a nested case–control design using data from the Indiana Network for Patient Care. The development cohort came from 2016 to 2017 (outcome period) and 2014 to 2015 (baseline). A separate validation cohort used outcome and baseline periods shifted 2 years before respective development cohort times. Machine learning approaches were used to build predictive model. Patients ≥ 18 years, later restricted to age ≥ 40 years, with at least two encounters and no AF during baseline, were included. In the 6-week EHR prospective pilot, the model was silently implemented in the production system at a large safety-net urban hospital. Three new and two previous logistic regression models were evaluated using receiver-operating characteristics. Number, characteristics, and CHA2DS2-VASc scores of patients identified by the model in the pilot are presented. Results After restricting age to ≥ 40 years, 31,474 AF cases (mean age, 71.5 years; female 49%) and 22,078 controls (mean age, 59.5 years; female 61%) comprised the development cohort. A 10-variable model using age, acute heart disease, albumin, body mass index, chronic obstructive pulmonary disease, gender, heart failure, insurance, kidney disease, and shock yielded the best performance (C-statistic, 0.80 [95% CI 0.79–0.80]). The model performed well in the validation cohort (C-statistic, 0.81 [95% CI 0.8–0.81]). In the EHR pilot, 7916/22,272 (35.5%; mean age, 66 years; female 50%) were identified as higher risk for AF; 5582 (70%) had CHA2DS2-VASc score ≥ 2. Conclusions Using variables commonly available in the EHR, we created a predictive model to identify 2-year risk of developing AF in those previously without diagnosed AF. Successful POC implementation of the model in an EHR provided a practical strategy to identify patients who may benefit from interventions to reduce their stroke risk.

Description: Background Artificial intelligence (AI) research is highly dependent on the nature of the data available. With the steady increase of AI applications in the medical field, the demand for quality medical data is increasing significantly. We here describe the development of a platform for providing and sharing digital pathology data to AI researchers, and highlight challenges to overcome in operating a sustainable platform in conjunction with pathologists. Methods Over 3000 pathological slides from five organs (liver, colon, prostate, pancreas and biliary tract, and kidney) in histologically confirmed tumor cases by pathology departments at three hospitals were selected for the dataset. After digitalizing the slides, tumor areas were annotated and overlaid onto the images by pathologists as the ground truth for AI training. To reduce the pathologists’ workload, AI-assisted annotation was established in collaboration with university AI teams. Results A web-based data sharing platform was developed to share massive pathological image data in 2019. This platform includes 3100 images, and 5 pre-processing algorithms for AI researchers to easily load images into their learning models. Discussion Due to different regulations among countries for privacy protection, when releasing internationally shared learning platforms, it is considered to be most prudent to obtain consent from patients during data acquisition. Conclusions Despite limitations encountered during platform development and model training, the present medical image sharing platform can steadily fulfill the high demand of AI developers for quality data. This study is expected to help other researchers intending to generate similar platforms that are more effective and accessible in the future.

Description: Background Ensuring data is of appropriate quality is essential for the secondary use of electronic health records (EHRs) in research and clinical decision support. An effective method of data quality assessment (DQA) is automating data quality rules (DQRs) to replace the time-consuming, labor-intensive manual process of creating DQRs, which is difficult to guarantee standard and comparable DQA results. This paper presents a case study of automatically creating DQRs based on openEHR archetypes in a Chinese hospital to investigate the feasibility and challenges of automating DQA for EHR data. Methods The clinical data repository (CDR) of the Shanxi Dayi Hospital is an archetype-based relational database. Four steps are undertaken to automatically create DQRs in this CDR database. First, the keywords and features relevant to DQA of archetypes were identified via mapping them to a well-established DQA framework, Kahn’s DQA framework. Second, the templates of DQRs in correspondence with these identified keywords and features were created in the structured query language (SQL). Third, the quality constraints were retrieved from archetypes. Fourth, these quality constraints were automatically converted to DQRs according to the pre-designed templates and mapping relationships of archetypes and data tables. We utilized the archetypes of the CDR to automatically create DQRs to meet quality requirements of the Chinese Application-Level Ranking Standard for EHR Systems (CARSES) and evaluated their coverage by comparing with expert-created DQRs. Results We used 27 archetypes to automatically create 359 DQRs. 319 of them are in agreement with the expert-created DQRs, covering 84.97% (311/366) requirements of the CARSES. The auto-created DQRs had varying levels of coverage of the four quality domains mandated by the CARSES: 100% (45/45) of consistency, 98.11% (208/212) of completeness, 54.02% (57/87) of conformity, and 50% (11/22) of timeliness. Conclusion It’s feasible to create DQRs automatically based on openEHR archetypes. This study evaluated the coverage of the auto-created DQRs to a typical DQA task of Chinese hospitals, the CARSES. The challenges of automating DQR creation were identified, such as quality requirements based on semantic, and complex constraints of multiple elements. This research can enlighten the exploration of DQR auto-creation and contribute to the automatic DQA.

Description: Background In this work, we aimed to demonstrate how to utilize the lab test results and other clinical information to support precision medicine research and clinical decisions on complex diseases, with the support of electronic medical record facilities. We defined “clinotypes” as clinical information that could be observed and measured objectively using biomedical instruments. From well-known ‘omic’ problem definitions, we defined problems using clinotype information, including stratifying patients—identifying interested sub cohorts for future studies, mining significant associations between clinotypes and specific phenotypes-diseases, and discovering potential linkages between clinotype and genomic information. We solved these problems by integrating public omic databases and applying advanced machine learning and visual analytic techniques on two-year health exam records from a large population of healthy southern Chinese individuals (size n = 91,354). When developing the solution, we carefully addressed the missing information, imbalance and non-uniformed data annotation issues. Results We organized the techniques and solutions to address the problems and issues above into CPA framework (Clinotype Prediction and Association-finding). At the data preprocessing step, we handled the missing value issue with predicted accuracy of 0.760. We curated 12,635 clinotype-gene associations. We found 147 Associations between 147 chronic diseases-phenotype and clinotypes, which improved the disease predictive performance to AUC (average) of 0.967. We mined 182 significant clinotype-clinotype associations among 69 clinotypes. Conclusions Our results showed strong potential connectivity between the omics information and the clinical lab test information. The results further emphasized the needs to utilize and integrate the clinical information, especially the lab test results, in future PheWas and omic studies. Furthermore, it showed that the clinotype information could initiate an alternative research direction and serve as an independent field of data to support the well-known ‘phenome’ and ‘genome’ researches.

Description: Background Fatigue is a kind of non-specific symptom, which occurs widely in sub-health and various diseases. It is closely related to people's physical and mental health. Due to the lack of objective diagnostic criteria, it is often neglected in clinical diagnosis, especially in the early stage of disease. Many clinical practices and researches have shown that tongue and pulse conditions reflect the body's overall state. Establishing an objective evaluation method for diagnosing disease fatigue and non-disease fatigue by combining clinical symptom, index, and tongue and pulse data is of great significance for clinical treatment timely and effectively. Methods In this study, 2632 physical examination population were divided into healthy controls, sub-health fatigue group, and disease fatigue group. Complex network technology was used to screen out core symptoms and Western medicine indexes of sub-health fatigue and disease fatigue population. Pajek software was used to construct core symptom/index network and core symptom-index combined network. Simultaneously, canonical correlation analysis was used to analyze the objective tongue and pulse data between the two groups of fatigue population and analyze the distribution of tongue and pulse data. Results Some similarities were found in the core symptoms of sub-health fatigue and disease fatigue population, but with different node importance. The node-importance difference indicated that the diagnostic contribution rate of the same symptom to the two groups was different. The canonical correlation coefficient of tongue and pulse data in the disease fatigue group was 0.42 (P 

Description: Background Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What’s more, the misclassification cost could be very high. Methods A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

Description: Background Rare Diseases (RDs) are difficult to diagnose. Clinical Decision Support Systems (CDSS) could support the diagnosis for RDs. The Medical Informatics in Research and Medicine (MIRACUM) consortium developed a CDSS for RDs based on distributed clinical data from eight German university hospitals. To support the diagnosis for difficult patient cases, the CDSS uses data from the different hospitals to perform a patient similarity analysis to obtain an indication of a diagnosis. To optimize our CDSS, we conducted a qualitative study to investigate usability and functionality of our designed CDSS. Methods We performed a Thinking Aloud Test (TA-Test) with RDs experts working in Rare Diseases Centers (RDCs) at MIRACUM locations which are specialized in diagnosis and treatment of RDs. An instruction sheet with tasks was prepared that the participants should perform with the CDSS during the study. The TA-Test was recorded on audio and video, whereas the resulting transcripts were analysed with a qualitative content analysis, as a ruled-guided fixed procedure to analyse text-based data. Furthermore, a questionnaire was handed out at the end of the study including the System Usability Scale (SUS). Results A total of eight experts from eight MIRACUM locations with an established RDC were included in the study. Results indicate that more detailed information about patients, such as descriptive attributes or findings, can help the system perform better. The system was rated positively in terms of functionality, such as functions that enable the user to obtain an overview of similar patients or medical history of a patient. However, there is a lack of transparency in the results of the CDSS patient similarity analysis. The study participants often stated that the system should present the user with an overview of exact symptoms, diagnosis, and other characteristics that define two patients as similar. In the usability section, the CDSS received a score of 73.21 points, which is ranked as good usability. Conclusions This qualitative study investigated the usability and functionality of a CDSS of RDs. Despite positive feedback about functionality of system, the CDSS still requires some revisions and improvement in transparency of the patient similarity analysis.

Description: Background Burn is one of the most brutal harms to the human body and mind and its wide-ranging complications have many adverse effects on the patients’ quality of life. The present study was conducted to investigate the effect of rehabilitation education through social media on burn patients’ quality of life. Methods The present randomized, controlled, clinical trial was conducted on 60 patients admitted to Imam Reza Hospital Burn Center in the city of Mashhad, Iran, who were randomly assigned to either the intervention or control groups (n = 30 per group). The researcher then created a WhatsApp channel to provide educational content and a WhatsApp group for burns patients to join and get their questions answered. The intervention group patients pursued their post-discharge education through the social media for a month. The control group patients received their discharge education according to the ward’s routine procedures through pamphlets and face-to-face training by the personnel. As the study’s main variable, the Burn Specific Health Scale-Brief was completed by both groups before and 1 and 2 months after the intervention. Data were analyzed using the ANCOVA and repeated-measures ANOVA. Results There was no significant differences between the intervention and control groups in terms of the QOL score and any of the domains at baseline. The results indicated the significant effect of the intervention both 1 and 2 months post-intervention on the QOL score and all the domains (P 

Description: Background Mining massive prescriptions in Traditional Chinese Medicine (TCM) accumulated in the lengthy period of several thousand years to discover essential herbal groups for distinct efficacies is of significance for TCM modernization, thus starting to draw attentions recently. However, most existing methods for the task treat herbs with different surface forms orthogonally and determine efficacy-specific herbal groups based on the raw frequencies an herbal group occur in a collection of prescriptions. Such methods entirely overlook the fact that prescriptions in TCM are formed empirically by different people at different historical stages, and thus full of herbs with different surface forms expressing the same material, or even noisy and redundant herbs. Methods We propose a two-stage approach for efficacy-specific herbal group detection from prescriptions in TCM. For the first stage we devise a hierarchical attentive neural network model to capture essential herbs in a prescription for its efficacy, where herbs are encoded with dense real-valued vectors learned automatically to identify their differences on the semantical level. For the second stage, frequent patterns are mined to discover essential herbal groups for an efficacy from distilled prescriptions obtained in the first stage. Results We verify the effectiveness of our proposed approach from two aspects, the first one is the ability of the hierarchical attentive neural network model to distill a prescription, and the second one is the accuracy in discovering efficacy-specific herbal groups. Conclusion The experimental results demonstrate that the hierarchical attentive neural network model is capable to capture herbs in a prescription essential to its efficacy, and the distilled prescriptions significantly could improve the performance of efficacy-specific herbal group detection.

Description: Background The Thai medical application for patient triage, namely Triagist, is an mHealth application designed to support the pre-hospital process. However, since the functions of the application that are necessary for the pre-hospital process have been found not to be fully developed, the addition of a back-end system has been considered to increase its performance and usability. Objective To determine the ability of the previous version to effectively manage the pre-hospital process and analyse the current problems with the pre-hospital operation. Therefore, the new system was developed to support the connection of dispatch centres or operational centres to the Triagist mobile application and system evaluation. Method Design thinking methodology was used to analyse, design and develop a patient triage system to support the pre-hospital process in Thailand based on users’ requirements. 68 active members of the rescue teams and emergency medical staff in Chiang Mai and Lampang provinces were recruited to test the reliability of the system based on a prototype application. Results The new medical mobile application for patient triage in Thailand was validated for use due to containing the two essential functions of Initial Dispatch Code (IDC) geolocation and IDC management. When the system was tested by emergency staff who were responsible for using it, those with the least experience were found to use it better than their highly experienced colleagues. Moreover, in cases where the system had been implemented, it was found to determine the frequency of symptoms, the time period during which cases occurred, and the density of cases in each area. Conclusion This system, which has been developed based on the use of smart technology, will play an important role in supporting emergency services in Thailand by enhancing the efficiency of the pre-hospital process. Emergency centres will receive IDC information from the geolocation system so that they can determine patients’ location without undue delay. Emergency services will be able to rapidly prepare the necessary resources and administrative tasks will be supported by linking the dispatch centre to central rescue teams.

Description: Background Despite a substantial increase in the adoption of electronic medical records (EMRs) in primary health care settings, the use of advanced EMR features is limited. Several studies have identified both barriers and facilitating factors that influence primary care physicians’ (PCPs) use of advanced EMR features and the maturation of their EMR use. The purpose of this study is to explore and identify the factors that impact PCPs’ mature use of EMRs. Methods A systematic review was conducted in accordance with the Cochrane Handbook. The MEDLINE, Embase, and PsycINFO electronic databases were searched from 1946 to June 13, 2019. Two independent reviewers screened the studies for eligibility; to be included, studies had to address factors influencing PCPs’ mature use of EMRs. A narrative synthesis was conducted to collate study findings and to report on patterns identified across studies. The quality of the studies was also appraised. Results Of the 1893 studies identified, 14 were included in this study. Reported factors that influenced PCPs’ mature use of EMRs fell into one of the following 5 categories: technology, people, organization, resources, and policy. Concerns about the EMR system’s functionality, lack of physician awareness of EMR functionality, limited physician availability to learn more about EMRs, the habitual use of successfully completing clinical tasks using only basic EMR features, business-oriented organizational objectives, lack of vendor training, limited resource availability, and lack of physician readiness were reported as barriers to PCPs’ mature use of EMRs. The motivation of physicians, user satisfaction, coaching and peer mentoring, EMR experience, gender, physician perception, transition planning for changes in roles and work processes, team-based care, adequate technical support and training, sharing resources, practices affiliated with an integrated delivery system, financial incentives, and policies to increase EMR use all had a favorable impact on PCPs’ use of advanced EMR features. Conclusions By using a narrative synthesis to synthesize the evidence, we identified interrelated factors influencing the mature use of EMRs by PCPs. The findings underline the need to provide adequate training and policies that facilitate the mature use of EMRs by PCPs. Trial registration: PROSPERO CRD42019137526.

Description: Background Taking care of chronic or long-term patients at home is an arduous task. Non-professional caregivers suffer the consequences of doing so, especially in terms of their mental health. Performing some simple activities through a mobile phone app may improve their mindset and consequently increase their positivity. However, each caregiver may need support in different aspects of positive mental health. In this paper, a method is defined to calculate the utility of a set of activities for a particular caregiver in order to personalize the intervention plan proposed in the app. Methods Based on the caregivers’ answers to a questionnaire, a modular averaging method is used to calculate the personal level of competence in each positive mental health factor. A reward-penalty scoring procedure then assigns an overall impact value to each activity. Finally, the app ranks the activities using this impact value. Results The results of this new personalization method are provided based on a pilot test conducted on 111 caregivers. The results indicate that a conjunctive average is appropriate at the first stage and that reward should be greater than penalty in the second stage. Conclusions The method presented is able to personalize the intervention plan by determining the best order of carrying out the activities for each caregiver, with the aim of avoiding a high level of deterioration in any factor.

Description: Background Radiation Therapy (RT) is a common treatment after breast cancer surgery and a complex process using high energy X-rays to eradicate cancer cells, important in reducing the risk of local recurrence. The high-tech environment and unfamiliar nature of RT can affect the patient’s experience of the treatment. Misconceptions or lack of knowledge about RT processes can increase levels of anxiety and enhance feelings of being unprepared at the beginning of treatment. Moreover, the waiting time is often quite long. The primary aim of this study will be to evaluate whether a digital information tool with VR-technology and preparatory information can decrease distress as well as enhance the self-efficacy and health literacy of patients affected by breast cancer before, during, and after RT. A secondary aim will be to explore whether the digital information tool increase patient flow while maintaining or increasing the quality of care. Method The study is a prospective and longitudinal RCT study with an Action Research participatory design approach including mixed-methods data collection, i.e., standardised instruments, qualitative interviews (face-to-face and telephone) with a phenomenological hermeneutical approach, diaries, observations, and time measurements, and scheduled to take place from autumn 2020 to spring 2022. The intervention group (n = 80), will receive standard care and information (oral and written) and the digital information tool; and the control group (n = 80), will receive standard care and information (oral and written). Study recruitment and randomisation will be completed at two centres in the west of Sweden. Discussion Research in this area is scarce and, to our knowledge, only few previous studies examine VR as a tool for increasing preparedness for patients with breast cancer about to undergo RT that also includes follow-ups six months after completed treatment. The participatory approach and design will safeguard the possibilities to capture the patient perspective throughout the development process, and the RCT design supports high research quality. Digitalisation brings new possibilities to provide safe, person-centred information that also displays a realistic picture of RT treatment and its contexts. The planned study will generate generalisable knowledge of relevance in similar health care contexts. Trial registration: ClinicalTrials.gov Identifier: NCT04394325. Registered May 19, 2020. Prospectively registered.

Description: Background Currently the diagnosis of shoulder instability, particularly in children, is difficult and can take time. These diagnostic delays can lead to poorer outcome and long-term complications. A Diagnostic Decision Support System (DDSS) has the potential to reduce time to diagnosis and improve outcomes for patients. The aim of this study was to develop a concept map for a future DDSS in shoulder instability. Methods A modified nominal focus group technique, involving three clinical vignettes, was used to elicit physiotherapists decision-making processes. Results Twenty-five physiotherapists, (18F:7 M) from four separate clinical sites participated. The themes identified related to ‘Variability in diagnostic processes and lack of standardised practice’ and ‘Knowledge and attitudes towards novel technologies for facilitating assessment and clinical decision making’. Conclusion No common structured approach towards assessment and diagnosis was identified. Lack of knowledge, perceived usefulness, access and cost were identified as barriers to adoption of new technology. Based on the information elicited a conceptual design of a future DDSS has been proposed. Work to develop a systematic approach to assessment, classification and diagnosis is now proposed. Trial Registraty This was not a clinical trial and so no clinical trial registry is needed.

Description: Background Previous studies showed that transitional care reduces the complication rate and readmission rate and improves the quality of life in kidney transplant receipts, nevertheless, in fact there are no standard evaluation indexes and debatable scientific of existing indexes in kidney transplant recipients. Therefore, the aim of this study was to construct an evaluation index system to assess the effects of transitional care in kidney transplant recipients. Methods Based on Omaha system, an initial evaluation index system about the effects of transitional care in kidney transplant recipients was drafted by the literature review and semi-structured interview. Two rounds of correspondence were conducted in 19 experts and the analytic hierarchy process (AHP) was used to calculate the weights of all indexes. Results Five first-level indexes, sixteen second-level indexes, and forty-eight third-level indexes were selected in the initial evaluation index system. The authority coefficient of two-round expert consultations was 0.90 and coordination coefficients of indexes ranged from 0.24 to 0.34. Conclusion The established evaluation index system for the effectiveness of transitional care for kidney transplant recipients was scientific and reliable. Furthermore, it would be a potential method to evaluate effects of transitional care in kidney transplant recipients after further examination.

Description: Background Prediction of neonatal deaths in NICUs is important for benchmarking and evaluating healthcare services in NICUs. Application of machine learning techniques can improve physicians’ ability to predict the neonatal deaths. The aim of this study was to present a neonatal death risk prediction model using machine learning techniques. Methods This study was conducted in Tehran, Iran in two phases. Initially, important risk factors in neonatal death were identified and then several machine learning models including Artificial Neural Network (ANN), decision tree (Random Forest (RF), C5.0 and CHART tree), Support Vector Machine (SVM), Bayesian Network and Ensemble models were developed. Finally, we prospectively applied these models to predict neonatal death in a NICU and followed up the neonates to compare the outcomes of these neonates with real outcomes. Results 17 factors were considered important in neonatal mortality prediction. The highest Area Under the Curve (AUC) was achieved for the SVM and Ensemble models with 0.98. The best precision and specificity were 0.98 and 0.94, respectively for the RF model. The highest accuracy, sensitivity and F-score were achieved for the SVM model with 0.94, 0.95 and 0.96, respectively. The best performance of models in prospective evaluation was for the ANN, C5.0 and CHAID tree models. Conclusion Using the developed machine learning models can help physicians predict the neonatal deaths in NICUs.

Description: Background Screening carotid B-mode ultrasonography is a frequently used method to detect subjects with carotid atherosclerosis (CAS). Due to the asymptomatic progression of most CAS patients, early identification is challenging for clinicians, and it may trigger ischemic stroke. Recently, machine learning has shown a strong ability to classify data and a potential for prediction in the medical field. The combined use of machine learning and the electronic health records of patients could provide clinicians with a more convenient and precise method to identify asymptomatic CAS. Methods Retrospective cohort study using routine clinical data of medical check-up subjects from April 19, 2010 to November 15, 2019. Six machine learning models (logistic regression [LR], random forest [RF], decision tree [DT], eXtreme Gradient Boosting [XGB], Gaussian Naïve Bayes [GNB], and K-Nearest Neighbour [KNN]) were used to predict asymptomatic CAS and compared their predictability in terms of the area under the receiver operating characteristic curve (AUCROC), accuracy (ACC), and F1 score (F1). Results Of the 18,441 subjects, 6553 were diagnosed with asymptomatic CAS. Compared to DT (AUCROC 0.628, ACC 65.4%, and F1 52.5%), the other five models improved prediction: KNN + 7.6% (0.704, 68.8%, and 50.9%, respectively), GNB + 12.5% (0.753, 67.0%, and 46.8%, respectively), XGB + 16.0% (0.788, 73.4%, and 55.7%, respectively), RF + 16.6% (0.794, 74.5%, and 56.8%, respectively) and LR + 18.1% (0.809, 74.7%, and 59.9%, respectively). The highest achieving model, LR predicted 1045/1966 cases (sensitivity 53.2%) and 3088/3566 non-cases (specificity 86.6%). A tenfold cross-validation scheme further verified the predictive ability of the LR. Conclusions Among machine learning models, LR showed optimal performance in predicting asymptomatic CAS. Our findings set the stage for an early automatic alarming system, allowing a more precise allocation of CAS prevention measures to individuals probably to benefit most.

Description: Background Despite growing evidence that deprescribing can improve clinical outcomes, quality of life and reduce the likelihood of adverse drug events, the practice is not widespread, particularly in hospital settings. Clinical risk assessment tools, like the Drug Burden Index (DBI), can help prioritise patients for medication review and prioritise medications to deprescribe, but are not integrated within routine care. The aim of this study was to conduct formative usability testing of a computerised decision support (CDS) tool, based on DBI, to identify modifications required to the tool prior to trialling in practice. Methods Our CDS tool comprised a DBI MPage in the electronic medical record (clinical workspace) that facilitated review of a patient’s DBI and medication list, access to deprescribing resources, and the ability to deprescribe. Two rounds of scenario-based formative usability testing with think-aloud protocol were used. Seventeen end-users participated in the testing, including junior and senior doctors, and pharmacists. Results Participants expressed positive views about the DBI CDS tool but testing revealed a number of clear areas for improvement. These primarily related to terminology used (i.e. what is a DBI and how is it calculated?), and consistency of functionality and display. A key finding was that users wanted the CDS tool to look and function in a similar way to other decision support tools in the electronic medical record. Modifications were made to the CDS tool in response to user feedback. Conclusion Usability testing proved extremely useful for identifying components of our CDS tool that were confusing, difficult to locate or to understand. We recommend usability testing be adopted prior to implementation of any digital health intervention. We hope our revised CDS tool equips clinicians with the knowledge and confidence to consider discontinuation of inappropriate medications in routine care of hospitalised patients. In the next phase of our project, we plan to pilot test the tool in practice to evaluate its uptake and effectiveness in supporting deprescribing in routine hospital care.

Description: Background The large volume of medical literature makes it difficult for healthcare professionals to keep abreast of the latest studies that support Evidence-Based Medicine. Natural language processing enhances the access to relevant information, and gold standard corpora are required to improve systems. To contribute with a new dataset for this domain, we collected the Clinical Trials for Evidence-Based Medicine in Spanish (CT-EBM-SP) corpus. Methods We annotated 1200 texts about clinical trials with entities from the Unified Medical Language System semantic groups: anatomy (ANAT), pharmacological and chemical substances (CHEM), pathologies (DISO), and lab tests, diagnostic or therapeutic procedures (PROC). We doubly annotated 10% of the corpus and measured inter-annotator agreement (IAA) using F-measure. As use case, we run medical entity recognition experiments with neural network models. Results This resource contains 500 abstracts of journal articles about clinical trials and 700 announcements of trial protocols (292 173 tokens). We annotated 46 699 entities (13.98% are nested entities). Regarding IAA agreement, we obtained an average F-measure of 85.65% (±4.79, strict match) and 93.94% (±3.31, relaxed match). In the use case experiments, we achieved recognition results ranging from 80.28% (±00.99) to 86.74% (±00.19) of average F-measure. Conclusions Our results show that this resource is adequate for experiments with state-of-the-art approaches to biomedical named entity recognition. It is freely distributed at: http://www.lllf.uam.es/ESP/nlpmedterm_en.html. The methods are generalizable to other languages with similar available sources.

Description: Background Implementation of evidence-based interventions often involves strategies to engage diverse populations while also attempting to maintain external validity. When using health IT tools to deliver patient-centered health messages, systems-level requirements are often at odds with ‘on-the ground’ tailoring approaches for patient-centered care or ensuring equity among linguistically diverse populations. Methods We conducted a fidelity and acceptability-focused evaluation of the STAR MAMA Program, a 5-month bilingual (English and Spanish) intervention for reducing diabetes risk factors among 181 post-partum women with recent gestational diabetes. The study’s purpose was to explore fidelity to pre-determined ‘core’ (e.g. systems integration) and ‘modifiable’ equity components (e.g. health coaching responsiveness, and variation by language) using an adapted implementation fidelity framework. Participant-level surveys, systems-level databases of message delivery, call completion, and coaching notes were included. Results 96.6% of participants are Latina and 80.9% were born outside the US. Among those receiving the STAR MAMA intervention; 55 received the calls in Spanish (61%) and 35 English (39%). 90% (n = 81) completed ≥ one week. Initially, systems errors were common, and increased triggers for health coach call-backs. Although Spanish speakers had more triggers over the intervention period, the difference was not statistically significant. Of the calls triggering a health coach follow-up, attempts were made for 85.4% (n = 152) of the English call triggers and for 80.0% (n = 279) of the Spanish call triggers (NS). Of attempted calls, health coaching calls were complete for 55.6% (n = 85) of English-language call triggers and for 56.6% of Spanish-language call triggers (NS). Some differences in acceptability were noted by language, with Spanish-speakers reporting higher satisfaction with prevention content (p = 

Description: Background The motion capture has been used as the usual method for measuring movement parameters of human, and most of the measuring data are obtained by partial manual process based on commercial software. An automatic kinematics data process was developed by programming on MATLAB software in this paper. Methods The motion capture measurement of healthy volunteers was carried out and the MATLAB program was used for data process. Firstly, the coordinate data of markers and anatomical points on human lower limb measured by motion capture system were read and repaired through the usual and the patch program. Meantime, the local coordinate systems of human femur and tibia were established with anatomical points. Then flexion/extension, abduction/adduction and internal/external rotation of human knee tibiofemoral joint were obtained by special coordinate transformation program. Results Using the above methods, motion capture measurements and batch data processing were carried out on squatting and climbing stairs of 29 healthy volunteers. And the motion characteristics (flexion/extension, internal/external rotation and adduction/abduction) of the knee joint were obtained. For example, the maximum internal/external rotation in squatting and climbing stairs were respectively was 30.5 degrees and 14 degrees, etc. Meantime, the results of this paper also were respectively compared with the results processed by other research methods, and the results were basically consistent, thus the reliability of our research method was verified. After calibration processing, the compiled MATLAB program of this paper can directly be used for efficient batch processing and avoiding manual modeling one by one. Conclusion A novel Patch Program of this paper has been developed, which can make reasonable compensation for missing and noise signals to obtain more complete motion data. At the same time, a universal data processing program has also been developed for obtaining the relative movement of various components of the human body, and the program can be modified for detail special analysis. These motion capture technologies can be used to judge whether the human body functions are abnormal, provide a reference for rehabilitation treatment and design of rehabilitation equipment, and evaluate the effectiveness before and after surgery.

Description: An amendment to this paper has been published and can be accessed via the original article.

Description: Background Accurate, coded problem lists are valuable for data reuse, including clinical decision support and research. However, healthcare providers frequently modify coded diagnoses by including or removing common contextual properties in free-text diagnosis descriptions: uncertainty (suspected glaucoma), laterality (left glaucoma) and temporality (glaucoma 2002). These contextual properties could cause a difference in meaning between underlying diagnosis codes and modified descriptions, inhibiting data reuse. We therefore aimed to develop and evaluate an algorithm to identify these contextual properties. Methods A rule-based algorithm called UnLaTem (Uncertainty, Laterality, Temporality) was developed using a single-center dataset, including 288,935 diagnosis descriptions, of which 73,280 (25.4%) were modified by healthcare providers. Internal validation of the algorithm was conducted with an independent sample of 980 unique records. A second validation of the algorithm was conducted with 996 records from a Dutch multicenter dataset including 175,210 modified descriptions of five hospitals. Two researchers independently annotated the two validation samples. Performance of the algorithm was determined using means of the recall and precision of the validation samples. The algorithm was applied to the multicenter dataset to determine the actual prevalence of the contextual properties within the modified descriptions per specialty. Results For the single-center dataset recall (and precision) for removal of uncertainty, uncertainty, laterality and temporality respectively were 100 (60.0), 99.1 (89.9), 100 (97.3) and 97.6 (97.6). For the multicenter dataset for removal of uncertainty, uncertainty, laterality and temporality it was 57.1 (88.9), 86.3 (88.9), 99.7 (93.5) and 96.8 (90.1). Within the modified descriptions of the multicenter dataset, 1.3% contained removal of uncertainty, 9.9% uncertainty, 31.4% laterality and 9.8% temporality. Conclusions We successfully developed a rule-based algorithm named UnLaTem to identify contextual properties in Dutch modified diagnosis descriptions. UnLaTem could be extended with more trigger terms, new rules and the recognition of term order to increase the performance even further. The algorithm’s rules are available as additional file 2. Implementing UnLaTem in Dutch hospital systems can improve precision of information retrieval and extraction from diagnosis descriptions, which can be used for data reuse purposes such as decision support and research.

Description: Background Telerehabilitation has been considered a suitable alternative healthcare delivery system during the COVID-19 outbreak, and many studies have promoted its feasibility in delivering physical care to patients who live with pain and disability. Physiotherapists’ perceptions and willingness are two key factors that influence the provision of remote physiotherapy. Aim To investigate physiotherapists’ perceptions of and willingness to use telerehabilitation in Kuwait during the COVID-19 pandemic and to explore the barriers that may hinder the use of telerehabilitation in this sector. Methods The following methods were used: (1) a cross-sectional survey and (2) face-to-face semi-structured interviews. In the cross-sectional survey, an electronic questionnaire was sent to 747 physiotherapists who were working in the governmental health sector. The questionnaire included four sections: perceptions of telerehabilitation, comfort with technology, willingness to use telerehabilitation, and barriers to using telerehabilitation. Six interviews were conducted with physiotherapy managers to explore the barriers and facilitators of telerehabilitation practice. Data analysis In this study, descriptive data analysis was conducted, and a cross-tabulation technique was used to find the associations between the variables, in which chi-square tests were used to identify the significance of the results, where p 

Description: Background Passive sensor data from mobile devices can shed light on daily activities, social behavior, and maternal-child interactions to improve maternal and child health services including mental healthcare. We assessed feasibility and acceptability of the Sensing Technologies for Maternal Depression Treatment in Low Resource Settings (StandStrong) platform. The StandStrong passive data collection platform was piloted with adolescent and young mothers, including mothers experiencing postpartum depression, in Nepal. Methods Mothers (15–25 years old) with infants (

Description: Background Deep learning algorithms significantly improve the accuracy of pathological image classification, but the accuracy of breast cancer classification using only single-mode pathological images still cannot meet the needs of clinical practice. Inspired by the real scenario of pathologists reading pathological images for diagnosis, we integrate pathological images and structured data extracted from clinical electronic medical record (EMR) to further improve the accuracy of breast cancer classification. Methods In this paper, we propose a new richer fusion network for the classification of benign and malignant breast cancer based on multimodal data. To make pathological image can be integrated more sufficient with structured EMR data, we proposed a method to extract richer multilevel feature representation of the pathological image from multiple convolutional layers. Meanwhile, to minimize the information loss for each modality before data fusion, we use the denoising autoencoder as a way to increase the low-dimensional structured EMR data to high-dimensional, instead of reducing the high-dimensional image data to low-dimensional before data fusion. In addition, denoising autoencoder naturally generalizes our method to make the accurate prediction with partially missing structured EMR data. Results The experimental results show that the proposed method is superior to the most advanced method in terms of the average classification accuracy (92.9%). In addition, we have released a dataset containing structured data from 185 patients that were extracted from EMR and 3764 paired pathological images of breast cancer, which can be publicly downloaded from http://ear.ict.ac.cn/?page_id=1663. Conclusions We utilized a new richer fusion network to integrate highly heterogeneous data to leverage the structured EMR data to improve the accuracy of pathological image classification. Therefore, the application of automatic breast cancer classification algorithms in clinical practice becomes possible. Due to the generality of the proposed fusion method, it can be straightforwardly extended to the fusion of other structured data and unstructured data.

Description: Background/Introduction Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. Methods The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package. Results The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths. Conclusions The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.

Description: Background Sepsis is a highly lethal and heterogeneous disease. Utilization of an unsupervised method may identify novel clinical phenotypes that lead to targeted therapies and improved care. Methods Our objective was to derive clinically relevant sepsis phenotypes from a multivariate panel of physiological data using subgraph-augmented nonnegative matrix factorization. We utilized data from the Medical Information Mart for Intensive Care III database of patients who were admitted to the intensive care unit with sepsis. The extracted data contained patient demographics, physiological records, sequential organ failure assessment scores, and comorbidities. We applied frequent subgraph mining to extract subgraphs from physiological time series and performed nonnegative matrix factorization over the subgraphs to derive patient clusters as phenotypes. Finally, we profiled these phenotypes based on demographics, physiological patterns, disease trajectories, comorbidities and outcomes, and performed functional validation of their clinical implications. Results We analyzed a cohort of 5782 patients, derived three novel phenotypes of distinct clinical characteristics and demonstrated their prognostic implications on patient outcome. Subgroup 1 included relatively less severe/deadly patients (30-day mortality, 17%) and was the smallest-in-size group (n = 1218, 21%). It was characterized by old age (mean age, 73 years), a male majority (male-to-female ratio, 59-to-41), and complex chronic conditions. Subgroup 2 included the most severe/deadliest patients (30-day mortality, 28%) and was the second-in-size group (n = 2036, 35%). It was characterized by a male majority (male-to-female ratio, 60-to-40), severe organ dysfunction or failure compounded by a wide range of comorbidities, and uniquely high incidences of coagulopathy and liver disease. Subgroup 3 included the least severe/deadly patients (30-day mortality, 10%) and was the largest group (n = 2528, 44%). It was characterized by low age (mean age, 60 years), a balanced gender ratio (male-to-female ratio, 50-to-50), the least complicated conditions, and a uniquely high incidence of neurologic disease. These phenotypes were validated to be prognostic factors of mortality for sepsis patients. Conclusions Our results suggest that these phenotypes can be used to develop targeted therapies based on phenotypic heterogeneity and algorithms designed for monitoring, validating and intervening clinical decisions for sepsis patients.

Description: Background Blood glucose (BG) management is crucial for type-1 diabetes patients resulting in the necessity of reliable artificial pancreas or insulin infusion systems. In recent years, deep learning techniques have been utilized for a more accurate BG level prediction system. However, continuous glucose monitoring (CGM) readings are susceptible to sensor errors. As a result, inaccurate CGM readings would affect BG prediction and make it unreliable, even if the most optimal machine learning model is used. Methods In this work, we propose a novel approach to predicting blood glucose level with a stacked Long short-term memory (LSTM) based deep recurrent neural network (RNN) model considering sensor fault. We use the Kalman smoothing technique for the correction of the inaccurate CGM readings due to sensor error. Results For the OhioT1DM (2018) dataset, containing eight weeks’ data from six different patients, we achieve an average RMSE of 6.45 and 17.24 mg/dl for 30 min and 60 min of prediction horizon (PH), respectively. Conclusions To the best of our knowledge, this is the leading average prediction accuracy for the ohioT1DM dataset. Different physiological information, e.g., Kalman smoothed CGM data, carbohydrates from the meal, bolus insulin, and cumulative step counts in a fixed time interval, are crafted to represent meaningful features used as input to the model. The goal of our approach is to lower the difference between the predicted CGM values and the fingerstick blood glucose readings—the ground truth. Our results indicate that the proposed approach is feasible for more reliable BG forecasting that might improve the performance of the artificial pancreas and insulin infusion system for T1D diabetes management.

Description: Background Transitioning from an old medical coding system to a new one can be challenging, especially when the two coding systems are significantly different. The US experienced such a transition in 2015. Objective This research aims to introduce entropic measures to help users prepare for the migration to a new medical coding system by identifying and focusing preparation initiatives on clinical concepts with more likelihood of adoption challenges. Methods Two entropic measures of coding complexity are introduced. The first measure is a function of the variation in the alphabets of new codes. The second measure is based on the possible number of valid representations of an old code. Results A demonstration of how to implement the proposed techniques is carried out using the 2015 mappings between ICD-9-CM and ICD-10-CM/PCS. The significance of the resulting entropic measures is discussed in the context of clinical concepts that were likely to pose challenges regarding documentation, coding errors, and longitudinal data comparisons. Conclusion The proposed entropic techniques are suitable to assess the complexity between any two medical coding systems where mappings or crosswalks exist. The more the entropy, the more likelihood of adoption challenges. Users can utilize the suggested techniques as a guide to prioritize training efforts to improve documentation and increase the chances of accurate coding, code validity, and longitudinal data comparisons.

Description: Background Since decision making about treatment with disease-modifying drugs (DMDs) for multiple sclerosis (MS) is preference sensitive, shared decision making between patient and healthcare professional should take place. Patient decision aids could support this shared decision making process by providing information about the disease and the treatment options, to elicit the patient’s preference and to support patients and healthcare professionals in discussing these preferences and matching them with a treatment. Therefore, a prototype of a patient decision aid for MS patients in the Netherlands—based on the principles of multi-criteria decision analysis (MCDA) —was developed, following the recommendations of the International Patient Decision Aid Standards. MCDA was chosen as it might reduce cognitive burden of considering treatment options and matching patient preferences with the treatment options. Results After determining the scope to include DMDs labelled for relapsing-remitting MS and clinically isolated syndrome, users’ informational needs were assessed using focus groups (N = 19 patients) and best-worst scaling surveys with patients (N = 185), neurologists and nurses (N = 60) to determine which information about DMDs should be included in the patient decision aid. Next, an online format and computer-based delivery of the patient decision aid was chosen to enable embedding of MCDA. A literature review was conducting to collect evidence on the effectiveness and burden of use of the DMDs. A prototype was developed next, and alpha testing to evaluate its comprehensibility and usability with in total thirteen patients and four healthcare professionals identified several issues regarding content and framing, methods for weighting importance of criteria in the MCDA structure, and the presentation of the conclusions of the patient decision aid ranking the treatment options according to the patient’s preferences. Adaptations were made accordingly, but verification of the rankings provided, validation of the patient decision aid, evaluation of the feasibility of implementation and assessing its value for supporting shared decision making should be addressed in further development of the patient decision aid. Conclusion This paper aimed to provide more transparency regarding the developmental process of an MCDA-based patient decision aid for treatment decisions for MS and the challenges faced during this process. Issues identified in the prototype were resolved as much as possible, though some issues remain. Further development is needed to overcome these issues before beta pilot testing with patients and healthcare professionals at the point of clinical decision-making can take place to ultimately enable making conclusions about the value of the MCDA-based patient decision aid for MS patients, healthcare professionals and the quality of care.

Description: Background Little data exists regarding decision-making preferences for parents and surgeons in pediatric surgery. Here we investigate whether parents and surgeons have similar decision-making preferences as well as which factors influence those preferences. Specifically, we compare parents’ and surgeons’ assessments of the urgency and complexity of pediatric surgical scenarios and the impact of their assessments on decision-making preferences. Methods A survey was emailed to parents of patients evaluated in a university-based pediatric surgery clinic and surgeons belonging to the American Pediatric Surgical Association. The survey asked respondents to rate 6 clinical vignettes for urgency, complexity, and desired level of surgeon guidance using the Controlled Preferences Scale (CPS). Results Regarding urgency, parents were more likely than surgeons to rate scenarios as emergent when cancer was involved (parents: 68.8% cancer vs. 29.5% non-cancer, p 

Description: Background The provision of unnecessary Emergency Medical Services care remains a challenge throughout the US and contributes to Emergency Department overcrowding, delayed services and lower quality of care. New EMS models of care have shown promise in improving access to health services for patients who do not need urgent care. The goals of this study were (1) to identify factors associated with EMS utilization (911) and (2) their effects on total EMS calls and transports in an MIH program. Methods The study sample included 110 MIH patients referred to the program or considered high-users of EMS services between November 2016 and September 2018. The study employed descriptive statistics and Poisson regressions to estimate the effects of covariates on total EMS calls and transports. Results The typical enrollee is a 60-year-old single Black male living with two other individuals. He has a PCP, takes 12 medications and is compliant with his treatment. The likelihood of calling and/or being transported by EMS was higher for males, patients at high risk for falls, patients with asthma/COPD, psychiatric or behavioral illnesses, and longer travel times to a PCP. Each prescribed medication increased the risk for EMS calls or transports by 4%. The program achieved clear reductions in 911 calls and transports and savings of more than 140,000 USD in the first month. Conclusions This study shows that age, marital status, high fall risk scores, the number of medications, psychiatric/behavioral illness, asthma/COPD, CHF, CVA/stroke and medication compliance may be good predictors of EMS use in an MIH setting. MIH programs can help control utilization of EMS care and reduce both EMS calls and transports.

Description: Background Currently, the Internet seems to be a helpful tool for obtaining information about everything that we think about, including diseases, their prevention and treatment approaches. However, doubts exist regarding the quality and readability of such information. This study sought to assess the quality and readability of web-based Arabic information on periodontal disease. Methods In this infodemiological study, the Google, Yahoo!, and Bing search engines were searched using specific Arabic terms on periodontal disease. The first 100 consecutive websites from each engine were obtained. The eligible websites were categorized as commercial, health/professional, journalism, and other. The following tools were applied to assess the quality of the information on the included websites: the Health on the Net Foundation Code of Conduct (HONcode), the Journal of the American Medical Association (JAMA) benchmarks, and the DISCERN tool. The readability was assessed using an online readability tool. Results Of the 300 websites, 89 were eligible for quality and readability analyses. Only two websites (2.3%) were HONcode certified. Based on the DISCERN tool, 43 (48.3%) websites had low scores. The mean score of the JAMA benchmarks was 1.6 ± 1.0, but only 3 (3.4%) websites achieved “yes” responses for all four JAMA criteria. Based on the DISCERN tool, health/professional websites revealed the highest quality of information compared to other website categories. Most of the health/professional websites revealed moderate-quality information, while 55% of the commercial websites, 66% of journalism websites, and 43% of other websites showed poor quality information. Regarding readability, most of the analyzed websites presented simple and readable written content. Conclusions Aside from readable content, Arabic health information on the analyzed websites on periodontal disease is below the required level of quality.

Description: Background Adverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug data. Method Based on the Word2Vec model in Nature Language Processing, we propose a new knowledge graph embedding method that embeds drugs and ADRs into their respective vectors and builds a logistic regression classification model to predict whether a given drug will have ADRs. Result First, a new knowledge graph embedding method was proposed, and comparison with similar studies showed that our model not only had high prediction accuracy but also was simpler in model structure. In our experiments, the AUC of the classification model reached a maximum of 0.87, and the mean AUC was 0.863. Conclusion In this paper, we introduce a new method to embed knowledge graph to vectorize drugs and ADRs, then use a logistic regression classification model to predict whether there is a causal relationship between them. The experiment showed that the use of knowledge graph embedding can effectively encode drugs and ADRs. And the proposed ADRs prediction system is also very effective.

Description: Background Helping Babies Breathe (HBB) is a life-saving program that has helped reduce neonatal morbidity and mortality, but knowledge and skills retention after training remains a significant challenge for sustainability of impact. User-centred design (UCD) can be used to develop solutions to target knowledge and skills maintenance. Methods We applied a process of UCD beginning with understanding the facilitators of, and barriers to, learning and retaining HBB knowledge and skills. HBB Master Trainers and frontline HBB providers participated in a series of focus group discussions (FGDs) to uncover the processes of skills acquisition and maintenance to develop a mobile application called “HBB Prompt”. Themes derived from each FGD were identified and implications for development of the HBB Prompt app were explored, including feasibility of incorporating strategies into the format of an app. Data analysis took place after each iteration in Phase 1 to incorporate feedback and improve subsequent versions of HBB Prompt. Results Six HBB trainers and seven frontline HBB providers participated in a series of FGDs in Phase 1 of this study. Common themes included lack of motivation to practise, improving confidence in ventilation skills, ability to achieve the Golden Minute, fear of forgetting knowledge or skills, importance of feedback, and peer-to-peer learning. Themes identified that were not feasible to address pertained to health system challenges. Feedback about HBB Prompt was generally positive. Based on initial and iterative feedback, HBB Prompt was created with four primary functions: Training Mode, Simulation Mode, Quizzes, and Dashboard/Scoreboard. Conclusions Developing HBB Prompt with UCD to help improve knowledge and skills retention was feasible and revealed key concepts, including drivers for successes and challenges faced for learning and maintaining HBB skills. HBB Prompt will be piloted in Phase 2 of this study, where knowledge and skills retention after HBB training will be compared between an intervention group with HBB Prompt and a control group without the app. Trial registration Clinicaltrials.gov (NCT03577054). Retrospectively registered July 5, 2018, https://clinicaltrials.gov/ct2/show/study/NCT03577054.

Description: Background This article investigates the research problem of digital solutions to overcome the pandemic, more closely examining the limited effectiveness and scope of the governmental COVID-19 tracing apps, using the German COVID-19 tracing app (Corona-Warn-App) as an example. A well-designed and effective instrument in the technological toolbox is of utmost importance to overcome the pandemic. Method A multi-methodological design science research approach was applied. In three development and evaluation cycles, we presented, prototyped, and tested user-centered ideas of functional and design improvement. The applied procedure contains (1) a survey featuring 1993 participants from Germany for evaluating the current app, (2) a gathering of recommendations from epidemiologists and from a focus group discussion with IT and health experts identifying relevant functional requirements, and (3) an online survey combined with testing our prototype with 53 participants to evaluate the enhanced tracing app. Results This contribution presents 14 identified issues of the German COVID-19 tracing app, six meta-requirements, and three design principles for COVID-19 tracing apps and future pandemic apps (e.g., more user involvement and transparency). Using an interactive prototype, this study presents an extended pandemic app, containing 13 potential front-end (i.e., information on the regional infection situation, education and health literacy, crowd and event notification) and six potential back-end functional requirements (i.e., ongoing modification of risk score calculation, indoor versus outdoor). In addition, a user story approach for the COVID-19 tracing app was derived from the findings, supporting a holistic development approach. Conclusion Throughout this study, practical relevant findings can be directly transferred to the German and other international COVID-19 tracing applications. Moreover, we apply our findings to crisis management theory—particularly pandemic-related apps—and derive interdisciplinary learnings. It might be recommendable for the involved decision-makers and stakeholders to forego classic application management and switch to using an agile setup, which allows for a more flexible reaction to upcoming changes. It is even more important for governments to have a well-established, flexible, design-oriented process for creating and adapting technology to handle a crisis, as this pandemic will not be the last one.

Description: Background Data sparsity is a major limitation to estimating national and global dementia burden. Surveys with full diagnostic evaluations of dementia prevalence are prohibitively resource-intensive in many settings. However, validation samples from nationally representative surveys allow for the development of algorithms for the prediction of dementia prevalence nationally. Methods Using cognitive testing data and data on functional limitations from Wave A (2001–2003) of the ADAMS study (n = 744) and the 2000 wave of the HRS study (n = 6358) we estimated a two-dimensional item response theory model to calculate cognition and function scores for all individuals over 70. Based on diagnostic information from the formal clinical adjudication in ADAMS, we fit a logistic regression model for the classification of dementia status using cognition and function scores and applied this algorithm to the full HRS sample to calculate dementia prevalence by age and sex. Results Our algorithm had a cross-validated predictive accuracy of 88% (86–90), and an area under the curve of 0.97 (0.97–0.98) in ADAMS. Prevalence was higher in females than males and increased over age, with a prevalence of 4% (3–4) in individuals 70–79, 11% (9–12) in individuals 80–89 years old, and 28% (22–35) in those 90 and older. Conclusions Our model had similar or better accuracy as compared to previously reviewed algorithms for the prediction of dementia prevalence in HRS, while utilizing more flexible methods. These methods could be more easily generalized and utilized to estimate dementia prevalence in other national surveys.

Description: Background Advanced analytics, such as artificial intelligence (AI), increasingly gain relevance in medicine. However, patients’ responses to the involvement of AI in the care process remains largely unclear. The study aims to explore whether individuals were more likely to follow a recommendation when a physician used AI in the diagnostic process considering a highly (vs. less) severe disease compared to when the physician did not use AI or when AI fully replaced the physician. Methods Participants from the USA (n = 452) were randomly assigned to a hypothetical scenario where they imagined that they received a treatment recommendation after a skin cancer diagnosis (high vs. low severity) from a physician, a physician using AI, or an automated AI tool. They then indicated their intention to follow the recommendation. Regression analyses were used to test hypotheses. Beta coefficients (ß) describe the nature and strength of relationships between predictors and outcome variables; confidence intervals [CI] excluding zero indicate significant mediation effects. Results The total effects reveal the inferiority of automated AI (ß = .47, p = .001 vs. physician; ß = .49, p = .001 vs. physician using AI). Two pathways increase intention to follow the recommendation. When a physician performs the assessment (vs. automated AI), the perception that the physician is real and present (a concept called social presence) is high, which increases intention to follow the recommendation (ß = .22, 95% CI [.09; 0.39]). When AI performs the assessment (vs. physician only), perceived innovativeness of the method is high, which increases intention to follow the recommendation (ß = .15, 95% CI [− .28; − .04]). When physicians use AI, social presence does not decrease and perceived innovativeness increases. Conclusion Pairing AI with a physician in medical diagnosis and treatment in a hypothetical scenario using topical therapy and oral medication as treatment recommendations leads to a higher intention to follow the recommendation than AI on its own. The findings might help develop practice guidelines for cases where AI involvement benefits outweigh risks, such as using AI in pathology and radiology, to enable augmented human intelligence and inform physicians about diagnoses and treatments.

Description: Background Rhinosinusitis is an inflammation of the sinonasal cavity which affects roughly one in seven people per year. Acute rhinosinusitis (ARS) is mostly, apart from allergic etiology, caused by a viral infection and, in some cases (30–50%), by a bacterial superinfection. Antibiotics, indicated only in rare cases according to EPOS guidelines, are nevertheless prescribed in more than 80% of ARS cases, which increases the resistant bacterial strains in the population. Methods We have designed a clinical decision support system (CDSS), RHINA, based on a web application created in HTML 5, using JavaScript, jQuery, CCS3 and PHP scripting language. The presented CDSS RHINA helps general physicians to decide whether or not to prescribe antibiotics in patients with rhinosinusitis. Results In a retrospective study of a total of 1465 patients with rhinosinusitis, the CDSS RHINA presented a 90.2% consistency with the diagnosis and treatment made by the ENT specialist. Conclusion Patients assessed with the assistance of our CDSS RHINA would decrease the over-prescription of antibiotics, which in turn would help to reduce the bacterial resistance to the most commonly prescribed antibiotics.

Description: Background Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches. Methods The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes. Results Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility. Conclusions There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.

Description: Background It is encouraging to see a substantial increase in individuals surviving cancer. Even more so since most of them will have a positive effect on society by returning to work. However, many cancer survivors have unmet needs, especially when it comes to improving their quality of life (QoL). Only few survivors are able to meet all of the recommendations regarding well-being and there is a body of evidence that cancer survivors’ needs often remain neglected from health policy and national cancer control plans. This increases the impact of inequalities in cancer care and adds a dangerous component to it. The inequalities affect the individual survivor, their career, along with their relatives and society as a whole. The current study will evaluate the impact of the use of big data analytics and artificial intelligence on the self-efficacy of participants following intervention supported by digital tools. The secondary endpoints include evaluation of the impact of patient trajectories (from retrospective data) and patient gathered health data on prediction and improved intervention against possible secondary disease or negative outcomes (e.g. late toxicities, fatal events). Methods/design The study is designed as a single-case experimental prospective study where each individual serves as its own control group with basal measurements obtained at the recruitment and subsequent measurements performed every 6 months during follow ups. The measurement will involve CASE-cancer, Patient Activation Measure and System Usability Scale. The study will involve 160 survivors (80 survivors of Breast Cancer and 80 survivors of Colorectal Cancer) from four countries, Belgium, Latvia, Slovenia, and Spain. The intervention will be implemented via a digital tool (mHealthApplication), collecting objective biomarkers (vital signs) and subjective biomarkers (PROs) with the support of a (embodied) conversational agent. Additionally, the Clinical Decision Support system (CDSS), including visualization of cohorts and trajectories will enable oncologists to personalize treatment for an efficient care plan and follow-up management. Discussion We expect that cancer survivors will significantly increase their self-efficacy following the personalized intervention supported by the m-HealthApplication compared to control measurements at recruitment. We expect to observe improvement in healthy habits, disease self-management and self-perceived QoL. Trial registration ISRCTN97617326. https://doi.org/10.1186/ISRCTN97617326. Original Registration Date: 26/03/2021.

Description: Background A clinical librarian is a member of the medical team in many countries. To strengthen this new job, librarians need to acquire professional skills in order to provide information services to medical staff. In this study, we aimed to explor the skills required for the presence of a clinical librarian in the treatment team. Methods In this study, we sonducted a qualitative study in which 15 experienced librarians were interviewed in connection with information services. Also, a treatment team was involved in this study using purposive-convenience and snowball sampling methods. The data collection tool was a semi-structured interview that continued until the data was saturated; finally the data analysis was performed using thematic analysis. Results Out of the total interviews, 158 primary codes and, 107 main codes were extracted in 25 subclasses. After careful evaluation and integration of subclasses and classes, they were finally classified into 13 categories and four main themes, namely clinical librarian’s role, professional and specialized skills, communication skills, and training programs. Conclusion The results showed that specialized skills and training programs for the clinical librarian are defined based on his/her duties in the treatment team. We also defined the most important key skills for the clinical librarian in two categories of professional and communication skills such as specialized information search, content production, resource management, familiarity with various sources related to evidence-based medicine, teamwork, and effective communication. To acquire these skills, officials and policy-makers should develop and implement related educational programs at medical universities and colleges.

Description: Background A range of factors can reduce the effectiveness of treatment prescribed for the long-term management of chronic health conditions, such as growth disorders. In particular, prescription medications may not achieve the positive outcomes expected because approximately half of patients adhere poorly to the prescribed treatment regimen. Methods Adherence to treatment has previously been assessed using relatively unreliable subjective methods, such as patient self-reporting during clinical follow-up, or counting prescriptions filled or vials returned by patients. Here, we report on a new approach, the use of electronically recorded objective evidence of date, time, and dose taken which was obtained through a comprehensive eHealth ecosystem, based around the easypod™ electromechanical auto-injection device and web-based connect software. The benefits of this eHealth approach are also illustrated here by two case studies, selected from the Finnish cohort of the easypod™ Connect Observational Study (ECOS), a 5-year, open-label, observational study that enrolled children from 24 countries who were being treated with growth hormone (GH) via the auto-injection device. Results Analyses of data from 9314 records from the easypod™ connect database showed that, at each time point studied, a significantly greater proportion of female patients had high adherence (≥ 85%) than male patients (2849/3867 [74%] vs 3879/5447 [71%]; P 

Description: Background In the recent decades, the use of computerized decision support software (CDSS)-integrated telephone triage (TT) has become an important tool for managing rising healthcare demands and overcrowding in the emergency department. Though these services have generally been shown to be effective, large gaps in the literature exist with regards to the overall quality of these systems. In the current systematic review, we aim to document the consistency of decisions that are generated in CDSS-integrated TT. Furthermore, we also seek to map those factors in the literature that have been identified to have an impact on the consistency of generated triage decisions. Methods As part of the TRANS-SENIOR international training and research network, a systematic review of the literature was conducted in November 2019. PubMed, Web of Science, CENTRAL, and the CINAHL database were searched. Quantitative articles including a CDSS component and addressing consistency of triage decisions and/or factors associated with triage decisions were eligible for inclusion in the current review. Studies exploring the use of other types of digital support systems for triage (i.e. web chat, video conferencing) were excluded. Quality appraisal of included studies were performed independently by two authors using the Methodological Index for Non-Randomized Studies. Results From a total of 1551 records that were identified, 39 full-texts were assessed for eligibility and seven studies were included in the review. All of the studies (n = 7) identified as part of our search were observational and were based on nurse-led telephone triage. Scientific efforts investigating our first aim was very limited. In total, two articles were found to investigate the consistency of decisions that are generated in CDSS-integrated TT. Research efforts were targeted largely towards the second aim of our study—all of the included articles reported factors related to the operator- (n = 6), patient- (n = 1), and/or CDSS-integrated (n = 2) characteristics to have an influence on the consistency of CDSS-integrated TT decisions. Conclusion To date, some efforts have been made to better understand how the use of CDSS-integrated TT systems may vary across settings. In general, however, the evidence-base surrounding this field of literature is largely inconclusive. Further evaluations must be prompted to better understand this area of research. Protocol registration The protocol for this study is registered in the PROSPERO database (registration number: CRD42020146323).

Description: Background Biomedical language translation requires multi-lingual fluency as well as relevant domain knowledge. Such requirements make it challenging to train qualified translators and costly to generate high-quality translations. Machine translation represents an effective alternative, but accurate machine translation requires large amounts of in-domain data. While such datasets are abundant in general domains, they are less accessible in the biomedical domain. Chinese and English are two of the most widely spoken languages, yet to our knowledge, a parallel corpus does not exist for this language pair in the biomedical domain. Description We developed an effective pipeline to acquire and process an English-Chinese parallel corpus from the New England Journal of Medicine (NEJM). This corpus consists of about 100,000 sentence pairs and 3,000,000 tokens on each side. We showed that training on out-of-domain data and fine-tuning with as few as 4000 NEJM sentence pairs improve translation quality by 25.3 (13.4) BLEU for en$$ ightarrow$$ → zh (zh$$ ightarrow$$ → en) directions. Translation quality continues to improve at a slower pace on larger in-domain data subsets, with a total increase of 33.0 (24.3) BLEU for en$$ ightarrow$$ → zh (zh$$ ightarrow$$ → en) directions on the full dataset. Conclusions The code and data are available at https://github.com/boxiangliu/ParaMed.

Description: Background The International Consortium for Health Outcomes Measurement (ICHOM) develops condition-specific Standard Sets of outcomes to be measured in clinical practice for value-based healthcare evaluation. Standard Sets are developed by different working groups, which is inefficient and may lead to inconsistencies in selected PROs and PROMs. We aimed to identify common PROs across ICHOM Standard Sets and examined to what extend these PROs can be measured with a generic set of PROMs: the Patient-Reported Outcomes Measurement Information System (PROMIS®). Methods We extracted all PROs and recommended PROMs from 39 ICHOM Standard Sets. Similar PROs were categorized into unique PRO concepts. We examined which of these PRO concepts can be measured with PROMIS. Results A total of 307 PROs were identified in 39 ICHOM Standard Sets and 114 unique PROMs are recommended for measuring these PROs. The 307 PROs could be categorized into 22 unique PRO concepts. More than half (17/22) of these PRO concepts (covering about 75% of the PROs and 75% of the PROMs) can be measured with a PROMIS measure. Conclusion Considerable overlap was found in PROs across ICHOM Standard Sets, and large differences in terminology used and PROMs recommended, even for the same PROs. We recommend a more universal and standardized approach to the selection of PROs and PROMs. Such an approach, focusing on a set of core PROs for all patients, measured with a system like PROMIS, may provide more opportunities for patient-centered care and facilitate the uptake of Standard Sets in clinical practice.

Description: Background Although middle-aged and elderly users are the main group targeted by health maintenance-oriented WeChat official accounts (HM-WOAs), few studies have explored the relationship of these accounts and their users. Exploring the factors that influence the continuous adoption of WOAs is helpful to strengthen the health education of middle-aged and elderly individuals. Objective We developed a new theoretical model and explored the factors that influence middle-aged and elderly individuals' continuous usage intention for HM-WOA. Performance expectancy mediated the effects of the model in explaining continuous usage intention and introduced health literacy into the model. Methods We established a hybrid theoretical model on the basis of the unified theory of acceptance and use of technology 2 model (UTAUT2), the health belief model (BHM), protection motivation theory (PMT), and health literacy. We collected valid responses from 396 middle-aged and elderly users aged ≥ 45 years in China. To verify our hypotheses, we analyzed the data using structural equation modeling. Results Performance expectancy (β = 0.383, P 

Description: Background Understanding prognostic information can help patients know what may happen to their health over time and make informed decisions. However, communicating prognostic information well can be challenging. Purpose To conduct a systematic review to identify and synthesize research that has evaluated visual presentations that communicate quantitative prognostic information to patients or the public. Data sources MEDLINE, EMBASE, CINAHL, PsycINFO, ERIC and the Cochrane Central Register of Controlled Trials (CENTRAL) (from inception to December 2020), and forward and backward citation search. Study selection Two authors independently screened search results and assessed eligibility. To be eligible, studies required a quantitative design and comparison of at least one visual presentation with another presentation of quantitative prognostic information. The primary outcome was comprehension of the presented information. Secondary outcomes were preferences for or satisfaction with the presentations viewed, and behavioral intentions. Data extraction Two authors independently assessed risk of bias and extracted data. Data synthesis Eleven studies (all randomized trials) were identified. We grouped studies according to the presentation type evaluated. Bar graph versus pictograph (3 studies): no difference in comprehension between the groups. Survival vs mortality curves (2 studies): no difference in one study; higher comprehension in survival curve group in another study. Tabular format versus pictograph (4 studies): 2 studies reported similar comprehension between groups; 2 found higher comprehension in pictograph groups. Tabular versus free text (3 studies): 2 studies found no difference between groups; 1 found higher comprehension in a tabular group. Limitations Heterogeneity in the visual presentations and outcome measures, precluding meta-analysis. Conclusions No visual presentation appears to be consistently superior to communicate quantitative prognostic information.

Description: Background Digital health technologies enable patients to make a personal contribution to the improvement of their health by enabling them to manage their health. In order to exploit the potential of digital health technologies, Internet-based networking between patients and health care providers is required. However, this networking and access to digital health technologies are less prevalent in sociodemographically deprived cohorts. The paper explores how the use of digital health technologies, which connect patients with health care providers and health insurers has changed during the COVID-19 pandemic. Methods The data from a German-based cross-sectional online study conducted between April 29 and May 8, 2020, were used for this purpose. A total of 1.570 participants were included in the study. Accordingly, the influence of sociodemographic determinants, subjective perceptions, and personal competencies will affect the use of online booking of medical appointments and medications, video consultations with providers, and the data transmission to health insurers via an app. Results The highest level of education (OR 1.806) and the presence of a chronic illness (OR 1.706) particularly increased the likelihood of using online booking. With regard to data transmission via an app to a health insurance company, the strongest increase in the probability of use was shown by belonging to the highest subjective social status (OR 1.757) and generation Y (OR 2.303). Furthermore, the results show that the higher the subjectively perceived restriction of the subjects' life situation was due to the COVID-19 pandemic, the higher the relative probability of using online booking (OR 1.103) as well as data transmission via an app to a health insurance company (OR 1.113). In addition, higher digital literacy contributes to the use of online booking (OR 1.033) and data transmission via an app to the health insurer (OR 1.034). Conclusions Socially determined differences can be identified for the likelihood of using digital technologies in health care, which persist even under restrictive conditions during the COVID-19 pandemic. Thus, the results indicate a digital divide with regard to the technologies investigated in this study.