Exploring Autonomy Support in Shared Decision Making and Patient Activation of Diabetes Self-Care Behaviors

Introduction: Chronic disease places a different set of demands on an individual and family. Self-care behaviors and daily decision making is an integral part of diabetes management. According to the CDC (2014), the prevalence of Diabetes is estimated at 29.1 million and an alarming 86 million Americans have Pre-diabetes. Despite a plethora of evidence on the importance of diabetes self-care behaviors on clinical outcomes, studies have highlighted the current disconnect of patients not able to follow self-care behavior recommendations and not asking for help from their health care providers. There is no literature on the impact of an autonomy supported healthcare environment in shared decision making and patient activation levels of diabetes self-care behaviors. Methods: This non-experimental, observational study investigated the relationship between the patient’s perceived autonomy support in a shared decision making encounter and their patient activation levels of diabetes self-care behaviors. The study design included both quantitative and qualitative methodology for exploratory, descriptive, and correlational research. Patients at Geisinger Health System Endocrinology clinic and Community Practice Service Line Clinics (CPSL) who met the eligibility criteria were sent the electronic survey per protocol email distribution requirements. A sample of 101 patients participated in this study. Results: Mean duration of diabetes was 13 years with a range of 1-40 years living with diabetes. Gender was reported as 40% male and 60% female for those completing the survey. Only 22% of responders did not receive and previous diabetes education sessions. Perceived autonomy support explained about 23% of the shared variance with Patient activation. There was no relationship between the number of diabetes education session and patient activation levels. The relationship between gender and patient activation levels and duration of diabetes and patient activation levels was weak. In the linear multiple regression model including four predictor variables on patient activation, the amount of variance explained increased to 27%. The only two variables of significance in the model were duration of diabetes and perceived autonomy support. Qualitative findings revealed responses analogous with perceive autonomy support and feeling comfortable in the healthcare encounter. This included 33% of the patient responding in themes related to “feeling valued, understood and respected with caring professionals”. In comparison, another 33% of patients described their visits around the time limitations of the visit. The second question which queried the patient on the most important factors to them in their diabetes healthcare visit, three factors aligned (71%) with the importance of autonomy supported environment. Conclusion: This study increases our understanding of perceived autonomy support in shared decision making and patient activation levels for diabetes self-care behaviors. Helping patients to initiate and maintain these self-care behaviors must remain a priority now and in the future. Greater than 25 % of patients suggest that perceived autonomy support in shard decision making does enhance patient activation levels. Multiple themes including feeling valued, supported, and encouraged in the healthcare interaction were dominant areas of importance based on qualitative analysis of survey responders. These themes are analogous with an autonomy supported environment. In healthcare practices, we can increase patients perceived autonomy support and thus increase patient activation levels in patients with diabetes

Exploring Autonomy Support in Shared Decision Making and Patient Activation of Diabetes Self-Care Behaviors

Introduction: Chronic disease places a different set of demands on an individual and family. Self-care behaviors and daily decision making is an integral part of diabetes management. According to the CDC (2014), the prevalence of Diabetes is estimated at 29.1 million and an alarming 86 million Americans have Pre-diabetes. Despite a plethora of evidence on the importance of diabetes self-care behaviors on clinical outcomes, studies have highlighted the current disconnect of patients not able to follow self-care behavior recommendations and not asking for help from their health care providers. There is no literature on the impact of an autonomy supported healthcare environment in shared decision making and patient activation levels of diabetes self-care behaviors. Methods: This non-experimental, observational study investigated the relationship between the patient’s perceived autonomy support in a shared decision making encounter and their patient activation levels of diabetes self-care behaviors. The study design included both quantitative and qualitative methodology for exploratory, descriptive, and correlational research. Patients at Geisinger Health System Endocrinology clinic and Community Practice Service Line Clinics (CPSL) who met the eligibility criteria were sent the electronic survey per protocol email distribution requirements. A sample of 101 patients participated in this study. Results: Mean duration of diabetes was 13 years with a range of 1-40 years living with diabetes. Gender was reported as 40% male and 60% female for those completing the survey. Only 22% of responders did not receive and previous diabetes education sessions. Perceived autonomy support explained about 23% of the shared variance with Patient activation. There was no relationship between the number of diabetes education session and patient activation levels. The relationship between gender and patient activation levels and duration of diabetes and patient activation levels was weak. In the linear multiple regression model including four predictor variables on patient activation, the amount of variance explained increased to 27%. The only two variables of significance in the model were duration of diabetes and perceived autonomy support. Qualitative findings revealed responses analogous with perceive autonomy support and feeling comfortable in the healthcare encounter. This included 33% of the patient responding in themes related to “feeling valued, understood and respected with caring professionals”. In comparison, another 33% of patients described their visits around the time limitations of the visit. The second question which queried the patient on the most important factors to them in their diabetes healthcare visit, three factors aligned (71%) with the importance of autonomy supported environment. Conclusion: This study increases our understanding of perceived autonomy support in shared decision making and patient activation levels for diabetes self-care behaviors. Helping patients to initiate and maintain these self-care behaviors must remain a priority now and in the future. Greater than 25 % of patients suggest that perceived autonomy support in shard decision making does enhance patient activation levels. Multiple themes including feeling valued, supported, and encouraged in the healthcare interaction were dominant areas of importance based on qualitative analysis of survey responders. These themes are analogous with an autonomy supported environment. In healthcare practices, we can increase patients perceived autonomy support and thus increase patient activation levels in patients with diabetes

From design to implementation - The Joint Asia Diabetes Evaluation (JADE) program: A descriptive report of an electronic web-based diabetes management program

<p>Abstract</p> <p>Background</p> <p>The Joint Asia Diabetes Evaluation (JADE) Program is a web-based program incorporating a comprehensive risk engine, care protocols, and clinical decision support to improve ambulatory diabetes care.</p> <p>Methods</p> <p>The JADE Program uses information technology to facilitate healthcare professionals to create a diabetes registry and to deliver an evidence-based care and education protocol tailored to patients' risk profiles. With written informed consent from participating patients and care providers, all data are anonymized and stored in a databank to establish an Asian Diabetes Database for research and publication purpose.</p> <p>Results</p> <p>The JADE electronic portal (e-portal: <url>http://www.jade-adf.org</url>) is implemented as a Java application using the Apache web server, the mySQL database and the Cocoon framework. The JADE e-portal comprises a risk engine which predicts 5-year probability of major clinical events based on parameters collected during an annual comprehensive assessment. Based on this risk stratification, the JADE e-portal recommends a care protocol tailored to these risk levels with decision support triggered by various risk factors. Apart from establishing a registry for quality assurance and data tracking, the JADE e-portal also displays trends of risk factor control at each visit to promote doctor-patient dialogues and to empower both parties to make informed decisions.</p> <p>Conclusions</p> <p>The JADE Program is a prototype using information technology to facilitate implementation of a comprehensive care model, as recommended by the International Diabetes Federation. It also enables health care teams to record, manage, track and analyze the clinical course and outcomes of people with diabetes.</p

Preventive Care Now or Pay Later? A Personalized Medicine Approach for Healthcare Management

Preventive care, including routine check-ups and screenings, aims to avert severe illnesses and champion health equity. However, existing recommendations often neglect the need for personalization and patient convenience, resulting in significant underutilization. This study proposes a multi-objective reinforcement learning framework tailored for optimizing patient-centric diabetes-related preventive care, balancing patient convenience and treatment cost. Based on the electronic health records from over 500,000 patients, we show that the optimal preventive care rate could be fourfold the current rate. Our framework could cut annual patient costs by 1.1%, with more pronounced savings for groups such as young adults, the elderly, males, and diabetic patients. We further validate this method with the Michigan Model for Diabetes, a well-established diabetes progression simulation software. Our study contributes to the design of healthcare decision support systems, spotlighting the significance of personalization and the pressing need for value-based incentives to enhance preventive care adoption among targeted patient groups

A dashboard-based system for supporting diabetes care

[EN] Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. Results The use of the decision support component in clinical activities produced a reduction in visit duration (P¿¿¿.01) and an increase in the number of screening exams for complications (P¿<¿.01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system¿s capability of identifying and understanding the characteristics of patient subgroups treated at the center. Conclusion Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.This work was supported by the European Union in the Seventh Framework Programme, grant number 600914.Dagliati, A.; Sacchi, L.; Tibollo, V.; Cogni, G.; Teliti, M.; Martinez-Millana, A.; Traver Salcedo, V.... (2018). A dashboard-based system for supporting diabetes care. Journal of the American Medical Informatics Association. 25(5):538-547. https://doi.org/10.1093/jamia/ocx159S538547255Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., & Tang, P. C. (2001). Clinical Decision Support Systems for the Practice of Evidence-based Medicine. Journal of the American Medical Informatics Association, 8(6), 527-534. doi:10.1136/jamia.2001.0080527Palmer, A. J., Roze, S., Valentine, W. J., Minshall, M. E., Foos, V., Lurati, F. M., … Spinas, G. A. (2004). The CORE Diabetes Model: Projecting Long-term Clinical Outcomes, Costs and Costeffectiveness of Interventions in Diabetes Mellitus (Types 1 and 2) to Support Clinical and Reimbursement Decision-making. Current Medical Research and Opinion, 20(sup1), S5-S26. doi:10.1185/030079904x1980O’Connor, P. J., Bodkin, N. L., Fradkin, J., Glasgow, R. E., Greenfield, S., Gregg, E., … Wysham, C. H. (2011). Diabetes Performance Measures: Current Status and Future Directions. Diabetes Care, 34(7), 1651-1659. doi:10.2337/dc11-0735Donsa, K., Beck, P., Höll, B., Mader, J. K., Schaupp, L., Plank, J., … Pieber, T. R. (2016). Impact of errors in paper-based and computerized diabetes management with decision support for hospitalized patients with type 2 diabetes. A post-hoc analysis of a before and after study. International Journal of Medical Informatics, 90, 58-67. doi:10.1016/j.ijmedinf.2016.03.007Sáenz, A., Brito, M., Morón, I., Torralba, A., García-Sanz, E., & Redondo, J. (2012). Development and Validation of a Computer Application to Aid the Physician’s Decision-Making Process at the Start of and during Treatment with Insulin in Type 2 Diabetes: A Randomized and Controlled Trial. Journal of Diabetes Science and Technology, 6(3), 581-588. doi:10.1177/193229681200600313Ampudia-Blasco, F. J., Benhamou, P. Y., Charpentier, G., Consoli, A., Diamant, M., Gallwitz, B., … Stoevelaar, H. (2015). A Decision Support Tool for Appropriate Glucose-Lowering Therapy in Patients with Type 2 Diabetes. Diabetes Technology & Therapeutics, 17(3), 194-202. doi:10.1089/dia.2014.0260Lim, S., Kang, S. M., Shin, H., Lee, H. J., Won Yoon, J., Yu, S. H., … Jang, H. C. (2011). Improved Glycemic Control Without Hypoglycemia in Elderly Diabetic Patients Using the Ubiquitous Healthcare Service, a New Medical Information System. Diabetes Care, 34(2), 308-313. doi:10.2337/dc10-1447Lipton, J. A., Barendse, R. J., Akkerhuis, K. M., Schinkel, A. F. L., & Simoons, M. L. (2010). Evaluation of a Clinical Decision Support System for Glucose Control. Critical Pathways in Cardiology: A Journal of Evidence-Based Medicine, 9(3), 140-147. doi:10.1097/hpc.0b013e3181e7d7caNeubauer, K. M., Mader, J. K., Höll, B., Aberer, F., Donsa, K., Augustin, T., … Pieber, T. R. (2015). Standardized Glycemic Management with a Computerized Workflow and Decision Support System for Hospitalized Patients with Type 2 Diabetes on Different Wards. Diabetes Technology & Therapeutics, 17(10), 685-692. doi:10.1089/dia.2015.0027Rodbard, D., & Vigersky, R. A. (2011). Design of a Decision Support System to Help Clinicians Manage Glycemia in Patients with Type 2 Diabetes Mellitus. Journal of Diabetes Science and Technology, 5(2), 402-411. doi:10.1177/193229681100500230Augstein, P., Vogt, L., Kohnert, K.-D., Heinke, P., & Salzsieder, E. (2010). Translation of Personalized Decision Support into Routine Diabetes Care. Journal of Diabetes Science and Technology, 4(6), 1532-1539. doi:10.1177/193229681000400631Reza, A. W., & Eswaran, C. (2009). A Decision Support System for Automatic Screening of Non-proliferative Diabetic Retinopathy. Journal of Medical Systems, 35(1), 17-24. doi:10.1007/s10916-009-9337-yKumar, S. J. J., & Madheswaran, M. (2012). An Improved Medical Decision Support System to Identify the Diabetic Retinopathy Using Fundus Images. Journal of Medical Systems, 36(6), 3573-3581. doi:10.1007/s10916-012-9833-3Cho, B. H., Yu, H., Kim, K.-W., Kim, T. H., Kim, I. Y., & Kim, S. I. (2008). Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods. Artificial Intelligence in Medicine, 42(1), 37-53. doi:10.1016/j.artmed.2007.09.005Cleveringa, F. G. W., Gorter, K. J., van den Donk, M., & Rutten, G. E. H. M. (2008). Combined Task Delegation, Computerized Decision Support, and Feedback Improve Cardiovascular Risk for Type 2 Diabetic Patients: A cluster randomized trial in primary care. Diabetes Care, 31(12), 2273-2275. doi:10.2337/dc08-0312Haussler, B., Fischer, G. C., Meyer, S., & Sturm, D. (2007). Risk assessment in diabetes management: how do general practitioners estimate risks due to diabetes? Quality and Safety in Health Care, 16(3), 208-212. doi:10.1136/qshc.2006.019539Heselmans, A., Van de Velde, S., Ramaekers, D., Vander Stichele, R., & Aertgeerts, B. (2013). Feasibility and impact of an evidence-based electronic decision support system for diabetes care in family medicine: protocol for a cluster randomized controlled trial. Implementation Science, 8(1). doi:10.1186/1748-5908-8-83Koopman, R. J., Kochendorfer, K. M., Moore, J. L., Mehr, D. R., Wakefield, D. S., Yadamsuren, B., … Belden, J. L. (2011). A Diabetes Dashboard and Physician Efficiency and Accuracy in Accessing Data Needed for High-Quality Diabetes Care. The Annals of Family Medicine, 9(5), 398-405. doi:10.1370/afm.1286Den Ouden, H., Vos, R. C., Reidsma, C., & Rutten, G. E. (2015). Shared decision making in type 2 diabetes with a support decision tool that takes into account clinical factors, the intensity of treatment and patient preferences: design of a cluster randomised (OPTIMAL) trial. BMC Family Practice, 16(1). doi:10.1186/s12875-015-0230-0Holbrook, A., Thabane, L., Keshavjee, K., Dolovich, L., Bernstein, B., … Chan, D. (2009). Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. Canadian Medical Association Journal, 181(1-2), 37-44. doi:10.1503/cmaj.081272O’Reilly, D., Holbrook, A., Blackhouse, G., Troyan, S., & Goeree, R. (2012). Cost-effectiveness of a shared computerized decision support system for diabetes linked to electronic medical records. Journal of the American Medical Informatics Association, 19(3), 341-345. doi:10.1136/amiajnl-2011-000371Parker, R. F., Mohamed, A. Z., Hassoun, S. A., Miles, S., & Fernando, D. J. S. (2014). The Effect of Using a Shared Electronic Health Record on Quality of Care in People With Type 2 Diabetes. Journal of Diabetes Science and Technology, 8(5), 1064-1065. doi:10.1177/1932296814536880Caban, J. J., & Gotz, D. (2015). Visual analytics in healthcare - opportunities and research challenges. Journal of the American Medical Informatics Association, 22(2), 260-262. doi:10.1093/jamia/ocv006Mick, J. (2011). Data-Driven Decision Making. JONA: The Journal of Nursing Administration, 41(10), 391-393. doi:10.1097/nna.0b013e31822edb8cBatley, N. J., Osman, H. O., Kazzi, A. A., & Musallam, K. M. (2011). Implementation of an Emergency Department Computer System: Design Features That Users Value. The Journal of Emergency Medicine, 41(6), 693-700. doi:10.1016/j.jemermed.2010.05.014Sprague, A. E., Dunn, S. I., Fell, D. B., Harrold, J., Walker, M. C., Kelly, S., & Smith, G. N. (2013). Measuring Quality in Maternal-Newborn Care: Developing a Clinical Dashboard. Journal of Obstetrics and Gynaecology Canada, 35(1), 29-38. doi:10.1016/s1701-2163(15)31045-8WILBANKS, B. A., & LANGFORD, P. A. (2014). A Review of Dashboards for Data Analytics in Nursing. CIN: Computers, Informatics, Nursing, 32(11), 545-549. doi:10.1097/cin.0000000000000106Hartzler, A. L., Izard, J. P., Dalkin, B. L., Mikles, S. P., & Gore, J. L. (2015). Design and feasibility of integrating personalized PRO dashboards into prostate cancer care. Journal of the American Medical Informatics Association, 23(1), 38-47. doi:10.1093/jamia/ocv101Dixon, B. E., Jabour, A. M., Phillips, E. O., & Marrero, D. G. (2014). An informatics approach to medication adherence assessment and improvement using clinical, billing, and patient-entered data. Journal of the American Medical Informatics Association, 21(3), 517-521. doi:10.1136/amiajnl-2013-001959Murphy, S. N., Weber, G., Mendis, M., Gainer, V., Chueh, H. C., Churchill, S., & Kohane, I. (2010). Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association, 17(2), 124-130. doi:10.1136/jamia.2009.000893Shahar, Y., & Musen, M. A. (1996). Knowledge-based temporal abstraction in clinical domains. Artificial Intelligence in Medicine, 8(3), 267-298. doi:10.1016/0933-3657(95)00036-4Sacchi, L., Capozzi, D., Bellazzi, R., & Larizza, C. (2015). JTSA: An open source framework for time series abstractions. Computer Methods and Programs in Biomedicine, 121(3), 175-188. doi:10.1016/j.cmpb.2015.05.006Dagliati, A., Sacchi, L., Zambelli, A., Tibollo, V., Pavesi, L., Holmes, J. H., & Bellazzi, R. (2017). Temporal electronic phenotyping by mining careflows of breast cancer patients. Journal of Biomedical Informatics, 66, 136-147. doi:10.1016/j.jbi.2016.12.012Hripcsak, G., & Albers, D. J. (2013). Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association, 20(1), 117-121. doi:10.1136/amiajnl-2012-001145Bijlsma, M. J., Janssen, F., & Hak, E. (2015). Estimating time-varying drug adherence using electronic records: extending the proportion of days covered (PDC) method. Pharmacoepidemiology and Drug Safety, 25(3), 325-332. doi:10.1002/pds.3935Robusto, F., Lepore, V., D’Ettorre, A., Lucisano, G., De Berardis, G., Bisceglia, L., … Nicolucci, A. (2016). The Drug Derived Complexity Index (DDCI) Predicts Mortality, Unplanned Hospitalization and Hospital Readmissions at the Population Level. PLOS ONE, 11(2), e0149203. doi:10.1371/journal.pone.0149203De Berardis, G., D’Ettorre, A., Graziano, G., Lucisano, G., Pellegrini, F., Cammarota, S., … Nicolucci, A. (2012). The burden of hospitalization related to diabetes mellitus: A population-based study. Nutrition, Metabolism and Cardiovascular Diseases, 22(7), 605-612. doi:10.1016/j.numecd.2010.10.016Van Gemert-Pijnen, J. E., Nijland, N., van Limburg, M., Ossebaard, H. C., Kelders, S. M., Eysenbach, G., & Seydel, E. R. (2011). A Holistic Framework to Improve the Uptake and Impact of eHealth Technologies. Journal of Medical Internet Research, 13(4), e111. doi:10.2196/jmir.1672Shahar, Y. (1997). A framework for knowledge-based temporal abstraction. Artificial Intelligence, 90(1-2), 79-133. doi:10.1016/s0004-3702(96)00025-2Tenenbaum, J. D., Avillach, P., Benham-Hutchins, M., Breitenstein, M. K., Crowgey, E. L., Hoffman, M. A., … Freimuth, R. R. (2016). An informatics research agenda to support precision medicine: seven key areas. Journal of the American Medical Informatics Association, 23(4), 791-795. doi:10.1093/jamia/ocv213Bottomly, D., McWeeney, S. K., & Wilmot, B. (2015). HitWalker2: visual analytics for precision medicine and beyond. Bioinformatics, 32(8), 1253-1255. doi:10.1093/bioinformatics/btv739Fabris, C., Facchinetti, A., Fico, G., Sambo, F., Arredondo, M. T., & Cobelli, C. (2015). Parsimonious Description of Glucose Variability in Type 2 Diabetes by Sparse Principal Component Analysis. Journal of Diabetes Science and Technology, 10(1), 119-124. doi:10.1177/1932296815596173Hassenzahl, M., Wiklund-Engblom, A., Bengs, A., Hägglund, S., & Diefenbach, S. (2015). Experience-Oriented and Product-Oriented Evaluation: Psychological Need Fulfillment, Positive Affect, and Product Perception. International Journal of Human-Computer Interaction, 31(8), 530-544. doi:10.1080/10447318.2015.106466

Supporting Diabetes Self-Management with Ubiquitous Computing Technologies: A User-Centered Inquiry

Ubiquitous computing technologies offer opportunities to improve treatments for chronic health conditions. Type 1 diabetes is a compelling use-case for such approaches, given its severity, and need for individuals to make frequent care decisions, informed by complex data. However, current apps, typically based on effortful reflection on collected data, generally show poor adoption, lack vital cognitive and emotional support, and are poorly tailored to users’ actual diabetes decision making processes. This thesis investigates how diabetes apps can be improved from a user-centered perspective. An initial questionnaire-based study investigated how well existing diabetes apps meet user needs. Perceived benefits, limitations, and reasons for low adoption rates were identified. A talk-aloud study of detailed user interactions with diabetes logging apps was conducted to characterize the benefits and limitations of diverse UI elements for T1 diabetes management, and to more precisely identify wider problems with current interaction designs. This led to positing a refined version of Mamykina et al.’s model for diabetes self-management, to account for observed practices, whereby the previously accepted habitual and sensemaking cognitive states are augmented by a posited ‘fluid contextual reasoning’ (FCR) mode, which allows multiple contextual factors to be balanced for dynamic course correction when navigating complex situations, using previously learned knowledge. To investigate user perceptions of the levels and kinds of monitoring anticipated in next generation diabetes decision support systems, a 4-week technology probe, in which participants used multiple networked devices and external data aggregation, was used to frame requirements for user-centered development of such future systems. Integrating all of the above work, an iterative design process was undertaken to create DUETS, a card-based system to facilitate reflection by designers, users, and other stakeholders on diabetes support management systems. The resulting tool and method were then implemented and evaluated through structured sessions with stakeholder focus groups

A cluster randomised controlled trial of the clinical and cost-effectiveness of a 'whole systems' model of self-management support for the management of long- term conditions in primary care: trial protocol

BackgroundPatients with long-term conditions are increasingly the focus of quality improvement activities in health services to reduce the impact of these conditions on quality of life and to reduce the burden on care utilisation. There is significant interest in the potential for self-management support to improve health and reduce utilisation in these patient populations, but little consensus concerning the optimal model that would best provide such support. We describe the implementation and evaluation of self-management support through an evidence-based 'whole systems' model involving patient support, training for primary care teams, and service re-organisation, all integrated into routine delivery within primary care.MethodsThe evaluation involves a large-scale, multi-site study of the implementation, effectiveness, and cost-effectiveness of this model of self-management support using a cluster randomised controlled trial in patients with three long-term conditions of diabetes, chronic obstructive pulmonary disease (COPD), and irritable bowel syndrome (IBS). The outcome measures include healthcare utilisation and quality of life. We describe the methods of the cluster randomised trial.DiscussionIf the 'whole systems' model proves effective and cost-effective, it will provide decision-makers with a model for the delivery of self-management support for populations with long-term conditions that can be implemented widely to maximise 'reach' across the wider patient population.Trial registration numberISRCTN: ISRCTN9094004

Development and evaluation of an intelligent handheld insulin dose advisor for patients with Type-1 diabetes

Diabetes mellitus is an increasingly common, chronic, incurable disease requiring careful monitoring and treatment so as to minimise the risk of serious long-term complications. It has been suggested that computers used by healthcare professionals and/or patients themselves may playa useful role in the diabetes care process. Seven key systems (AIDA, ADICOL, DIABETES, DIAS, IIumaLink, T-IDDM, POIRO) in the area of diabetes decision support, and their underlying techniques and approaches are summarised and compared. The development of the Patient-Oriented Insulin Regimen Optimiser (POIRO) for insulindependent (Type-I) diabetes, and its hybrid statistical and rule-based expert system is then taken forward. The re-implementation and updating of the system for the Palm OS family of modern Personal Digital Assistants (PDAs) is described. The evaluation of this new version in a seven week, randomised, open, cross-over clinical pilot study involving eight patients on short-acting plus long-acting insulin basalbolus regimens showed it to be easy-to-operate, reliable, not time consuming and well liked by patients. Following this, the characteristics and use of all currently available insulin formulations, and the corresponding insulin regimens are summarised. Algorithms to provide dose advice and decision support for patients taking the new rapid-acting, intermediate-acting and premixed insulin formulations are then developed. The user interface is improved and extended, amongst others through the development and use of a model describing individual user's meal time habits. Implementation-related issues encountered are discussed, and further work and future directions are identified and outlined. Motivated by the complex and safety-critical nature of systems such as POIRO, we also report on the use of the B abstract machine notation for the formal specification of the original POIRO system, and focusing on projects and published case studies. review the use of formal methods in the development of medical computer systems

A systematic review of chronic disease management interventions in primary care

Background: Primary and community care are key settings for the effective management of long term conditions. We aimed to evaluate the pattern of health outcomes in chronic disease management interventions for adults with physical health problems implemented in primary or community care settings. Methods: The methods were based on our previous review published in 2006. We performed database searches for articles published from 2006 to 2014 and conducted a systematic review with narrative synthesis using the Cochrane Effective Practice and Organisation of Care taxonomy to classify interventions and outcomes. The interventions were mapped to Chronic Care Model elements. The pattern of outcomes related to interventions was summarized by frequency of statistically significant improvements in health care provision and patient outcomes. Results: A total of 9589 journal articles were retrieved from database searches and snowballing. After screening and verification, 165 articles that detailed 157 studies were included. There were few studies with Health Care Organization (1.9% of studies) or Community Resources (0.6% of studies) as the primary intervention element. Self-Management Support interventions (45.8% of studies) most frequently resulted in improvements in patient-level outcomes. Delivery System Design interventions (22.6% of studies) showed benefits in both professional and patient-level outcomes for a narrow range of conditions. Decision Support interventions (21.3% of studies) had impact limited to professional-level outcomes, in particular use of medications. The small number of studies of Clinical Information System interventions (8.9%) showed benefits for both professional- and patient-level outcomes. Conclusions: The published literature has expanded substantially since 2006. This review confirms that Self-Management Support is the most frequent Chronic Care Model intervention that is associated with statistically significant improvements, predominately for diabetes and hypertension

The Value of Information Technology-Enabled Diabetes Management

Reviews different technologies used in diabetes disease management, as well as the costs, benefits, and quality implications of technology-enabled diabetes management programs in the United States