ALBERT

Description: Background: The maturity and usage of wireless technology has influenced health services, and this has raised expectations from users that healthcare services will become more affordable due to technology growth. There is increasing evidence to justify this expectation, as telehealth is becoming more and more prevalent in many countries. Thus, health services are now offered beyond the boundaries of traditional hospitals, giving rise to many external factors dictating their quality. This has led us to investigate the factors that motivate and limit the implementation of ICT applications in the healthcare domain. Methods: We used a mixed method approach with the qualitative aspects leading the quantitative aspects. The main reason for this approach was to understand and explore the domain through the qualitative aspects as we could be part of the discussion. Then we conducted a quantitative survey to extract more responses in order to justify the claims explored in the qualitative process. Results: We found that there are a number of internal and external factors influencing ICT adoption in the healthcare environment so that services can be provided via ICT tools. These factors were grouped under factors contributing to improved outcomes, efficiency and the management of technology. We conceptualised that these three groups of factors drive ICT implementation to assure health services. Conclusions: The main lesson learned from this research was that Information Systems discipline needs to urgently consider health informatics as a serious growth area. We also found that as IS researchers, we need to 'mix' with the health environment in order to understand the environment and then develop suitable methods to answer posited research questions.

Description: Background: Patient data from general practices is already used for many types of epidemiological research and increasingly, primary care systems to facilitate randomized clinical trials. The EU funded project TRANSFoRm aims to create a ?Learning Healthcare System? at a European level that is able to support all types of research using primary care data, to recruit patients and follow patients in clinical studies and to improve diagnosis and therapy. The implementation of such a Learning Healthcare System needs an information model for clinical research (CRIM), as an informational backbone to integrate aspects of primary care with clinical trials and database searches. Methods: Workflow descriptions and corresponding data objects of two clinical use cases (Gastro-Oesophageal Reflux Disease and Type 2 Diabetes) were described in UML activity diagrams. The components of activity diagrams were mapped to information objects of PCROM (Primary Care Research Object Model) and BRIDG (Biomedical Research Integrated Domain Group) and evaluated. The class diagram of PCROM was adapted to comply with workflow descriptions. Results: The suitability of PCROM, a primary care information model already used for clinical trials, to act as an information model for TRANSFoRm was evaluated and resulted in its extension with 14 new information object types, two extensions of existing objects and the introduction of two new high-ranking concepts (CARE area and ENTRY area). No PCROM component was redundant. Our result illustrates that in primary care based research an important but underestimated portion of research activity takes place in the area of care (e.g. patient consultation, screening, recruitment and response to adverse events). The newly introduced CARE area for care-related research activities accounts for this shift and includes Episode of Care and Encounter as two new basic elements. In the ENTRY area different aspects of data collection were combined, including data semantics for observations, assessment activities, intervention activities and patient reporting to enable case report form (CRF) based data collection combined with decision support. Conclusions: Research with primary care data needs an extended information model that covers research activities at the care site which are characteristic for primary care based research and the requirements of the complicated data collection processes.

Description: Background: Management of diabetes mellitus is complex and involves controlling multiple risk factors that may lead to complications. Given that patients provide most of their own diabetes care, patient self-management training is an important strategy for improving quality of care. Web-based interventions have the potential to bridge gaps in diabetes self-care and self-management. The objective of this study was to determine the effect of a web-based patient self-management intervention on psychological (self-efficacy, quality of life, self-care) and clinical (blood pressure, cholesterol, glycemic control, weight) outcomes. Methods: For this cohort study we used repeated-measures modelling and qualitative individual interviews. We invited patients with type 2 diabetes to use a self-management website and asked them to complete questionnaires assessing self-efficacy (primary outcome) every three weeks for nine months before and nine months after they received access to the website. We collected clinical outcomes at three-month intervals over the same period. We conducted in-depth interviews at study conclusion to explore acceptability, strengths and weaknesses, and mediators of use of the website. We analyzed the data using a qualitative descriptive approach and inductive thematic analysis. Results: Eighty-one participants (mean age 57.2?years, standard deviation 12) were included in the analysis. The self-efficacy score did not improve significantly more than expected after nine months (absolute change 0.12; 95% confidence interval ?0.028, 0.263; p?=?0.11), nor did clinical outcomes. Website usage was limited (average 0.7 logins/month). Analysis of the interviews (n?=?21) revealed four themes:1) mediators of website use; 2) patterns of website use, including role of the blog in driving site traffic; 3) feedback on website; and 4) potential mechanisms for website effect. Conclusions: A self-management website for patients with type 2 diabetes did not improve self-efficacy. Website use was limited. Although its perceived reliability, availability of a blog and emailed reminders drew people to the website, participants? struggles with type 2 diabetes, competing priorities in their lives, and website accessibility were barriers to its use. Future interventions should aim to integrate the intervention seamlessly into the daily routine of end users such that it is not seen as yet another chore.

Description: Background: Meaningful Use (MU) provides financial incentives for electronic health record (EHR) implementation. EHR implementation holds promise for improving healthcare delivery, but also requires substantial changes for providers and staff. Establishing readiness for these changes may be important for realizing potential EHR benefits. Our study assesses whether provider/staff perceptions about the appropriateness of MU and their departments? ability to support MU-related changes are associated with their reported readiness for MU-related changes. Methods: We surveyed providers and staff representing 47 ambulatory practices within an integrated delivery system. We assessed whether respondent?s role and practice-setting type (primary versus specialty care) were associated with reported readiness for MU (i.e., willingness to change practice behavior and ability to document actions for MU) and hypothesized predictors of readiness (i.e., perceived appropriateness of MU and department support for MU). We then assessed associations between reported readiness and the hypothesized predictors of readiness. Results: In total, 400 providers/staff responded (response rate approximately 25%). Individuals working in specialty settings were more likely to report that MU will divert attention from other patient-care priorities (12.6% vs. 4.4%, p = 0.019), as compared to those in primary-care settings. As compared to advanced-practice providers and nursing staff, physicians were less likely to have strong confidence in their department?s ability to solve MU implementation problems (28.4% vs. 47.1% vs. 42.6%, p = 0.023) and to report strong willingness to change their work practices for MU (57.9% vs. 83.3% vs. 82.0%, p 〈 0.001). Finally, provider/staff perceptions about whether MU aligns with departmental goals (OR = 3.99, 95% confidence interval (CI) = 2.13 to 7.48); MU will divert attention from other patient-care priorities (OR = 2.26, 95% CI = 1.26 to 4.06); their department will support MU-related change efforts (OR = 3.99, 95% CI = 2.13 to 7.48); and their department will be able to solve MU implementation problems (OR = 2.26, 95% CI = 1.26 to 4.06) were associated with their willingness to change practice behavior for MU. Conclusions: Organizational leaders should gauge provider/staff perceptions about appropriateness and management support of MU-related change, as these perceptions might be related to subsequent implementation.

Description: Background: Electronic medical record systems are being implemented in many countries to support healthcare services. However, its adoption rate remains low, especially in developing countries due to technological, financial, and organizational factors. There is lack of solid evidence and empirical research regarding the pre implementation readiness of healthcare providers. The aim of this study is to assess health professionals? readiness and to identify factors that affect the acceptance and use of electronic medical recording system in the pre implementation phase at hospitals of North Gondar Zone, Ethiopia. Methods: An institution based cross-sectional quantitative study was conducted on 606 study participants from January to July 2013 at 3 hospitals in northwest Ethiopia. A pretested self-administered questionnaire was used to collect the required data. The data were entered using the Epi-Info version 3.5.1 software and analyzed using SPSS version 16 software. Descriptive statistics, bi-variate, and multi-variate logistic regression analyses were used to describe the study objectives and assess the determinants of health professionals? readiness for the system. Odds ratio at 95% CI was used to describe the association between the study and the outcome variables. Results: Out of 606 study participants only 328 (54.1%) were found ready to use the electronic medical recording system according to our criteria assessment. The majority of the study participants, 432 (71.3%) and 331(54.6%) had good knowledge and attitude for EMR system, respectively. Gender (AOR?=?1.87, 95% CI: [1.26, 2.78]), attitude (AOR?=?1.56, 95% CI: [1.03, 2.49]), knowledge (AOR?=?2.12, 95% CI: [1.32, 3.56]), and computer literacy (AOR =1.64, 95% CI: [0.99, 2.68]) were significantly associated with the readiness for EMR system. Conclusions: In this study, the overall health professionals? readiness for electronic medical record system and utilization was 54.1% and 46.5%, respectively. Gender, knowledge, attitude, and computer related skills were the determinants of the presence of a relatively low readiness and utilization of the system. Increasing awareness, knowledge, and skills of healthcare professionals on EMR system before system implementation is necessary to increase its adoption.

Description: Background: Statistical models developed using administrative databases are powerful and inexpensive tools for predicting survival. Conversely, data abstraction from chart review is time-consuming and costly. Our aim was to determine the incremental value of pathological data obtained from chart abstraction in addition to information acquired from administrative databases in predicting all-cause and prostate cancer (PC)-specific mortality. Methods: We identified a cohort of men with diabetes and PC utilizing population-based data from Ontario. We used the c-statistic and net-reclassification improvement (NRI) to compare two Cox- proportional hazard models to predict all-cause and PC-specific mortality. The first model consisted of covariates from administrative databases: age, co-morbidity, year of cohort entry, socioeconomic status and rural residence. The second model included Gleason grade and cancer volume in addition to all aforementioned variables. Results: The cohort consisted of 4001 patients. The accuracy of the admin-data only model (c-statistic) to predict 5-year all-cause mortality was 0.7 (95% CI 0.69-0.71). For the extended model (including pathology information) it was 0.74 (95% CI 0.73-0.75). This corresponded to a change in category of predicted probability of survival among 14.8% in the NRI analysis.The accuracy of the admin-data model to predict 5-year PC specific mortality was 0.76 (95%CI 0.74-0.78). The accuracy of the extended model was 0.85 (95%CI 0.83-0.87). Corresponding to a 28% change in the NRI analysis. Conclusions: Pathology chart abstraction, improved the accuracy in predicting all-cause and PC-specific mortality. The benefit is smaller for all-cause mortality, and larger for PC-specific mortality.

Description: Background: Over 100 trials show that patient decision aids effectively improve patients? information comprehension and values-based decision making. However, gaps remain in our understanding of several fundamental and applied questions, particularly related to the design of interactive, personalized decision aids. This paper describes an interdisciplinary development process for, and early field testing of, a web-based patient decision support research platform, or virtual decision lab, to address these questions. Methods: An interdisciplinary stakeholder panel designed the web-based research platform with three components: a) an introduction to shared decision making, b) a web-based patient decision aid, and c) interactive data collection items. Iterative focus groups provided feedback on paper drafts and online prototypes. A field test assessed a) feasibility for using the research platform, in terms of recruitment, usage, and acceptability; and b) feasibility of using the web-based decision aid component, compared to performance of a videobooklet decision aid in clinical care. Results: This interdisciplinary, theory-based, patient-centered design approach produced a prototype for field-testing in six months. Participants (n?=?126) reported that: the decision aid component was easy to use (98%), information was clear (90%), the length was appropriate (100%), it was appropriately detailed (90%), and it held their interest (97%). They spent a mean of 36 minutes using the decision aid and 100% preferred using their home/library computer. Participants scored a mean of 75% correct on the Decision Quality, Knowledge Subscale, and 74 out of 100 on the Preparation for Decision Making Scale. Completing the web-based decision aid reduced mean Decisional Conflict scores from 31.1 to 19.5 (p?

Description: Background: Online cancer information can support patients in making treatment decisions. However, such information may not be adequately tailored to the patient?s perspective, particularly if healthcare professionals do not sufficiently engage patient groups when developing online information. We applied qualitative user testing during the development of a patient information website on stereotactic ablative radiotherapy (SABR), a new guideline-recommended curative treatment for early-stage lung cancer. Methods: We recruited 27 participants who included patients referred for SABR and their relatives. A qualitative user test of the website was performed in 18 subjects, followed by an additional evaluation by users after website redesign (N?=?9). We primarily used the `thinking aloud? approach and semi-structured interviewing. Qualitative data analysis was performed to assess the main findings reported by the participants. Results: Study participants preferred receiving different information that had been provided initially. Problems identified with the online information related to comprehending medical terminology, understanding the scientific evidence regarding SABR, and appreciating the side-effects associated with SABR. Following redesign of the website, participants reported fewer problems with understanding content, and some additional recommendations for better online information were identified. Conclusions: Our findings indicate that input from patients and their relatives allows for a more comprehensive and usable website for providing treatment information. Such a website can facilitate improved patient participation in treatment decision-making for cancer.

Description: Background: Early recognition of severe sepsis and septic shock is challenging. The aim of this study was to determine the diagnostic accuracy of an electronic alert system in detecting severe sepsis or septic shock among emergency department (ED) patients. Methods: An electronic sepsis alert system was developed as a part of a quality-improvement project for severe sepsis and septic shock. The system screened all adult ED patients for a combination of systemic inflammatory response syndrome and organ dysfunction criteria (hypotension, hypoxemia or lactic acidosis). This study included all patients older than 14?years who presented to the ED of a tertiary care academic medical center from Oct. 1, 2012 to Jan. 31, 2013. As a comparator, emergency medicine physicians or the critical care physician identified the patients with severe sepsis or septic shock.In the ED, vital signs were manually entered into the hospital electronic heath record every hour in the critical care area and every two hours in other areas. We also calculated the time from the alert to the intensive care unit (ICU) referral. Results: Of the 49,838 patients who presented to the ED, 222 (0.4%) were identified to have severe sepsis or septic shock. The electronic sepsis alert had a sensitivity of 93.18% (95% CI, 88.78% - 96.00%), specificity of 98.44 (95% CI, 98.33% ? 98.55%), positive predictive value of 20.98% (95% CI, 18.50% ? 23.70%) and negative predictive value of 99.97% (95% CI, 99.95% ? 99.98%) for severe sepsis and septic shock. The alert preceded ICU referral by a median of 4.02?hours (Q1 - Q3: 1.25?8.55). Conclusions: Our study shows that electronic sepsis alert tool has high sensitivity and specificity in recognizing severe sepsis and septic shock, which may improve early recognition and management.

Description: Background: In a medical data set, data are commonly composed of a minority (positive or abnormal) group and a majority (negative or normal) group and the cost of misclassifying a minority sample as a majority sample is highly expensive. This is the so-called imbalanced classification problem. The traditional classification functions can be seriously affected by the skewed class distribution in the data. To deal with this problem, people often use a priori cost to adjust the learning process in the pursuit of optimal classification function. However, this priori cost is often unknown and hard to estimate in medical decision making. Methods: In this paper, we propose a new learning method, named RankCost, to classify imbalanced medical data without using a priori cost. Instead of focusing on improving the class-prediction accuracy, RankCost is to maximize the difference between the minority class and the majority class by using a scoring function, which translates the imbalanced classification problem into a partial ranking problem. The scoring function is learned via a non-parametric boosting algorithm. Results: We compare RankCost to several representative approaches on four medical data sets varying in size, imbalanced ratio, and dimension. The experimental results demonstrate that unlike the currently available methods that often perform unevenly with different priori costs, RankCost shows comparable performance in a consistent manner. Conclusions: It is a challenging task to learn an effective classification model based on imbalanced data in medical data analysis. The traditional approaches often use a priori cost to adjust the learning of the classification function. This work presents a novel approach, namely RankCost, for learning from medical imbalanced data sets without using a priori cost. The experimental results indicate that RankCost performs very well in imbalanced data classification and can be a useful method in real-world applications of medical decision making.