Integrating Technology to Support and Maintain Glycemic Control in People With Diabetes

Presented to the Faculty of the University of Alaska Anchorage in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCEType II diabetes is a chronic disease state that leads to increased morbidity and mortality and impacts the lives of millions of Americans. This quality improvement project explored the use of a free smartphone application, Glucose Buddy, in aiding people with Type II diabetes to achieve and maintain glycemic control. The project was conducted through the involvement of patients at the Creekside Family Health Clinic in Ketchikan, Alaska over a three month time period. Pre-intervention hemoglobin A1c (HA1c) was compared with post-intervention HA1c. The project, due to the small sample size and high withdraw rate, was not statistically significant. However, there was clinical significance as it showed a decrease in HA1c levels in 60% of the participants.Abstract / Introduction / Literature Review and Synthesis / Problem Statement / Research Question / Methodology / Results / Limitations / Conclusions / Outcomes / Impact on Practice / Dissemination / References / Appendix A / Appendix B / Appendix C / Appendix

Disease-Management Programs Can Improve Quality of Care for the Chronically Ill, Even in a Weak Primary Care System: A Case Study From Germany

Examines how Germany's disease management programs featuring information technology support, designated ambulatory care doctors, focus on self-management, quality assurance, and financial incentives raised quality and satisfaction. Outlines implications

Use of m-Health Technology for Preventive Interventions to Tackle Cardiometabolic Conditions and Other Non-Communicable Diseases in Latin America- Challenges and Opportunities

In Latin America, cardiovascular disease (CVD) mortality rates will increase by an estimated 145% from 1990 to 2020. Several challenges related to social strains, inadequate public health infrastructure, and underfinanced healthcare systems make cardiometabolic conditions and non-communicable diseases (NCDs) difficult to prevent and control. On the other hand, the region has high mobile phone coverage, making mobile health (mHealth) particularly attractive to complement and improve strategies toward prevention and control of these conditions in low- and middle-income countries. In this article, we describe the experiences of three Centers of Excellence for prevention and control of NCDs sponsored by the National Heart, Lung, and Blood Institute with mHealth interventions to address cardiometabolic conditions and other NCDs in Argentina, Guatemala, and Peru. The nine studies described involved the design and implementation of complex interventions targeting providers, patients and the public. The rationale, design of the interventions, and evaluation of processes and outcomes of each of these studies are described, together with barriers and enabling factors associated with their implementation.Fil: Beratarrechea, Andrea Gabriela. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Diez Canseco, Francisco. Universidad Peruana Cayetano Heredia; PerúFil: Irazola, Vilma. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Miranda, Jaime. Universidad Peruana Cayetano Heredia; PerúFil: Ramirez Zea, Manuel. Institute of Nutrition of Central America and Panama; GuatemalaFil: Rubinstein, Adolfo Luis. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

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

Protocol for the 'e-Nudge trial' : a randomised controlled trial of electronic feedback to reduce the cardiovascular risk of individuals in general practice [ISRCTN64828380]

Background: Cardiovascular disease (including coronary heart disease and stroke) is a major cause of death and disability in the United Kingdom, and is to a large extent preventable, by lifestyle modification and drug therapy. The recent standardisation of electronic codes for cardiovascular risk variables through the United Kingdom's new General Practice contract provides an opportunity for the application of risk algorithms to identify high risk individuals. This randomised controlled trial will test the benefits of an automated system of alert messages and practice searches to identify those at highest risk of cardiovascular disease in primary care databases. Design: Patients over 50 years old in practice databases will be randomised to the intervention group that will receive the alert messages and searches, and a control group who will continue to receive usual care. In addition to those at high estimated risk, potentially high risk patients will be identified who have insufficient data to allow a risk estimate to be made. Further groups identified will be those with possible undiagnosed diabetes, based either on elevated past recorded blood glucose measurements, or an absence of recent blood glucose measurement in those with established cardiovascular disease. Outcome measures: The intervention will be applied for two years, and outcome data will be collected for a further year. The primary outcome measure will be the annual rate of cardiovascular events in the intervention and control arms of the study. Secondary measures include the proportion of patients at high estimated cardiovascular risk, the proportion of patients with missing data for a risk estimate, and the proportion with undefined diabetes status at the end of the trial

Providing Underserved Patients With Medical Homes: Assessing the Readiness of Safety-Net Health Centers

Surveys safety-net health centers' potential to become patient-centered medical homes based on eight change concepts to improve care delivery, efficiency, and health outcomes. Outlines challenges, areas for improvement, and strategies for transformation

Complex Care Management Program Overview

This report includes brief updates on various forms of complex care management including: Aetna - Medicare Advantage Embedded Case Management ProgramBrigham and Women's Hospital - Care Management ProgramIndependent Health - Care PartnersIntermountain Healthcare and Oregon Health and Science University - Care Management PlusJohns Hopkins University - Hospital at HomeMount Sinai Medical Center -- New York - Mount Sinai Visiting Doctors Program/ Chelsea-Village House Calls ProgramsPartners in Care Foundation - HomeMeds ProgramPrinceton HealthCare System - Partnerships for PIECEQuality Improvement for Complex Chronic Conditions - CarePartner ProgramSenior Services - Project Enhance/EnhanceWellnessSenior Whole Health - Complex Care Management ProgramSumma Health/Ohio Department of Aging - PASSPORT Medicaid Waiver ProgramSutter Health - Sutter Care Coordination ProgramUniversity of Washington School of Medicine - TEAMcar

The impact of an EMR on the management of adult patients with type two diabetes by family physicians in ruralnewfoundland

PURPOSE This study was designed to determine whether the use of advanced features of an electronic medical record in a primary care setting could improve the process of delivering diabetes care in such a way as to produce improvements in diabetic outcome measures in adult type II diabetic patients. METHODS The study was a Retrospective Cohort Study conducted in primary care clinics that had an established electronic medical record following 307 adult patients with type II diabetes over the course of two years. The clinics had similarly trained primary care physicians, similar patient populations, and used common diabetic care guidelines. The advanced EMR features used during the diabetic study included a diabetic template, premade laboratory requisitions, appeared consultations, flow sheets, and patient alerts. The dependent variables measured included the process of the delivery of diabetic care and the measurement of diabetic outcomes. The process of care measures were: the frequency of visits specific for diabetes care, ordering of HbA1c and LDL cholesterol, the measurement of blood pressure, and the documentation of these activities. The outcome measures included glycemic, lipid and blood pressure control as measured by HbA1c, LDL and blood pressure levels. The two independent variables of interest in the study were the extent to which the advanced features EMR are use by the physician and the second any changes noted in the outcome measures. RESULTS The demographic information for the patients in this study was sex and age as well as baseline HbA1c, LDL, baseline systolic blood pressures, baseline diastolic blood pressures, and the number of visits that each patient had during the study period. The two groups were seen to be similar at baseline except for age and systolic blood pressure. The mean age of the intervention group was four years older than the control group and the comparison group had more people with systolic blood pressure at target. Age and systolic blood pressure were therefore controlled in the analysis. There was no difference in the two groups of patients in terms of measurements of HbA1c but there were differences in the frequency of measurements of LDL and blood pressures. Patients for whom the template was used during at least one clinical encounter, were 1.18 times more likely to have their LDL measured and 1.9 times more likely to have their blood pressure measured. Using logistics regression analysis there was a higher proportion of patients with an LDL at target in the intervention group. CONCLUSIONS The meaningful use of EMRs in primary care, is possible through a process of maturity by design; an individualized approach looking at the needs of a given physician(s) and their practice(s) most likely to aid EMRs in achieving their potential. The technology needs to support care by automation of clinical processes and work flow behind the computer screen in such a way as to not disrupt or significantly change the patient physician interaction and focus both of these individuals on managing meaningful clinical outcomes personalized to each patient

Can computerized clinical decision support systems improve practitioners' diagnostic test ordering behavior? A decision-maker-researcher partnership systematic review

<p>Abstract</p> <p>Background</p> <p>Underuse and overuse of diagnostic tests have important implications for health outcomes and costs. Decision support technology purports to optimize the use of diagnostic tests in clinical practice. The objective of this review was to assess whether computerized clinical decision support systems (CCDSSs) are effective at improving ordering of tests for diagnosis, monitoring of disease, or monitoring of treatment. The outcome of interest was effect on the diagnostic test-ordering behavior of practitioners.</p> <p>Methods</p> <p>We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for eligible articles published up to January 2010. We included randomized controlled trials comparing the use of CCDSSs to usual practice or non-CCDSS controls in clinical care settings. Trials were eligible if at least one component of the CCDSS gave suggestions for ordering or performing a diagnostic procedure. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of test ordering outcomes.</p> <p>Results</p> <p>Thirty-five studies were identified, with significantly higher methodological quality in those published after the year 2000 (<it>p </it>= 0.002). Thirty-three trials reported evaluable data on diagnostic test ordering, and 55% (18/33) of CCDSSs improved testing behavior overall, including 83% (5/6) for diagnosis, 63% (5/8) for treatment monitoring, 35% (6/17) for disease monitoring, and 100% (3/3) for other purposes. Four of the systems explicitly attempted to reduce test ordering rates and all succeeded. Factors of particular interest to decision makers include costs, user satisfaction, and impact on workflow but were rarely investigated or reported.</p> <p>Conclusions</p> <p>Some CCDSSs can modify practitioner test-ordering behavior. To better inform development and implementation efforts, studies should describe in more detail potentially important factors such as system design, user interface, local context, implementation strategy, and evaluate impact on user satisfaction and workflow, costs, and unintended consequences.</p

Computerized clinical decision support systems for chronic disease management: A decision-maker-researcher partnership systematic review

<p>Abstract</p> <p>Background</p> <p>The use of computerized clinical decision support systems (CCDSSs) may improve chronic disease management, which requires recurrent visits to multiple health professionals, ongoing disease and treatment monitoring, and patient behavior modification. The objective of this review was to determine if CCDSSs improve the processes of chronic care (such as diagnosis, treatment, and monitoring of disease) and associated patient outcomes (such as effects on biomarkers and clinical exacerbations).</p> <p>Methods</p> <p>We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for potentially eligible articles published up to January 2010. We included randomized controlled trials that compared the use of CCDSSs to usual practice or non-CCDSS controls. Trials were eligible if at least one component of the CCDSS was designed to support chronic disease management. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of relevant outcomes.</p> <p>Results</p> <p>Of 55 included trials, 87% (n = 48) measured system impact on the process of care and 52% (n = 25) of those demonstrated statistically significant improvements. Sixty-five percent (36/55) of trials measured impact on, typically, non-major (surrogate) patient outcomes, and 31% (n = 11) of those demonstrated benefits. Factors of interest to decision makers, such as cost, user satisfaction, system interface and feature sets, unique design and deployment characteristics, and effects on user workflow were rarely investigated or reported.</p> <p>Conclusions</p> <p>A small majority (just over half) of CCDSSs improved care processes in chronic disease management and some improved patient health. Policy makers, healthcare administrators, and practitioners should be aware that the evidence of CCDSS effectiveness is limited, especially with respect to the small number and size of studies measuring patient outcomes.</p