Chronic care model and shared care in diabetes: randomized trial of an electronic decision support system.

OBJECTIVE: To assess the effect of a specialist telemedicine intervention for improving diabetes care using the chronic care model (CCM). PARTICIPANTS AND METHODS: As part of the CCM, 97 primary care physicians at 6 primary care practices in Rochester, MN, referred 639 patients to an on-site diabetes educator between July 1, 2001, and December 31, 2003. On first referral, physicians were centrally randomized to receive a telemedicine intervention (specialty advice and evidence-based messages regarding medication management for cardiovascular risk) or no intervention, keeping outcome assessors and data analysts blinded to group assignment. After each subsequent clinical encounter, endocrinologists reviewed an abstract from the patient's electronic medical record and provided management recommendations and supporting evidence to intervention physicians via e-mail. Control physicians received e-mail with periodic generic information about cardiovascular risk reduction in diabetes. Outcome measures included diabetes care processes (diabetes test completion), outcomes (metabolic and cardiovascular risk factors, estimated coronary artery disease risk), and patient costs (payer perspective). RESULTS: During the intervention, 951 (70%) of the 1361 endocrinology reviews detected performance gaps and resulted in a message; primary care physicians reported using 49% of messages in patient care. With a mean of 21 months' follow-up, the intervention, compared with control, did not significantly enhance metabolic outcomes or reduce estimated risk of coronary artery disease (adjusted mean difference, -1%; 95% confidence interval, -19% to 17%). The intervention group incurred lower costs (P=.02) but not in diabetes-related costs. CONCLUSION: Specialty telemedicine did not significantly enhance the value of CCM in primary care.

Automatic classification of foot examination findings using clinical notes and machine learning.

We examine the feasibility of a machine learning approach to identification of foot examination (FE) findings from the unstructured text of clinical reports. A Support Vector Machine (SVM) based system was constructed to process the text of physical examination sections of in- and out-patient clinical notes to identify if the findings of structural, neurological, and vascular components of a FE revealed normal or abnormal findings or were not assessed. The system was tested on 145 randomly selected patients for each FE component using 10-fold cross validation. The accuracy was 80%, 87% and 88% for structural, neurological, and vascular component classifiers, respectively. Our results indicate that using machine learning to identify FE findings from clinical reports is a viable alternative to manual review and warrants further investigation. This application may improve quality and safety by providing inexpensive and scalable methodology for quality and risk factor assessments at the point of care.

Cardiovascular risk reduction and diabetes education: what are we telling our patients?

PURPOSE: The purpose of this study was to determine the extent to which diabetes education encounters include evidence-based content aimed at reducing the risk of cardiovascular disease. METHODS: During a 2-week period in November 2001, 3 certified diabetes educators (CDEs) listed the statements they made while teaching patients. These statements/comments were then assigned to the 7 outcome areas identified by the Diabetes Self-Management Assessment Report Tool (D-SMART). All educational encounters completed during that same month by 21 educators were reviewed for content areas or modules consistent with the American Diabetes Association National Standards. RESULTS: Of all statements made by the 3 CDEs, 63% were about glycemic control while only 5% were directly relevant to cardiovascular risk reduction. There were 1043 educational encounters in November 2001, of which only 10% targeted cardiovascular risk. Educators focused most of their educational efforts (62%) on glycemic control. CONCLUSIONS: Despite its potential impact and strong evidence base, diabetes education gives little attention to the reduction of cardiovascular risk. Diabetes educators should emphasize interventions that are most likely to be effective in reducing cardiovascular morbidity and mortality in patients with diabetes.

The impact of planned care and a diabetes electronic management system on community-based diabetes care: the Mayo Health System Diabetes Translation Project.

OBJECTIVE: The Mayo Health System Diabetes Translation Project sought to assess models of community-based diabetes care and use of a diabetes electronic management system (DEMS). Planned care is a redesigned model of chronic disease care that involves guideline implementation, support of self-management, and use of clinical information systems. RESEARCH DESIGN AND METHODS: We studied adult diabetic patients attending three primary care practice sites in Wisconsin and Minnesota. We implemented planned care at all sites and DEMS in the practice of 16 primary care providers. We assessed quality of diabetes care using standard indicators for 200 patients randomly selected from each site at baseline and at 24 months of implementation. We used multivariable analyses to estimate the association between planned care and DEMS and each quality indicator. RESULTS: Planned care was associated with improvements in measurement of HbA(1c) (odds ratio 7.0 [95% CI 4.2-11.6]), HDL cholesterol (5.6 [4.1-7.5]), and microalbuminuria (5.3 [3.5-8.0]), as well as the provision of tobacco advice (6.9 [4.7-10.1]), among other performance measures. DEMS use was associated with improvements in all indicators, including microalbuminuria (3.2 [1.9-5.2]), retinal examination (2.4 [1.5-3.9]), foot examinations (2.3 [1.2-4.4]), and self-management support (2.6 [1.7-3.8]). Although planned care was associated with improvements in metabolic control, we observed no additional metabolic benefit when providers used DEMS. CONCLUSIONS: Planned care was associated with improved performance and metabolic outcomes in primary care. DEMS use augmented the impact of planned care on performance outcomes but not on metabolic outcomes. Optimal identification of the best translation of evidence to diabetes practice will require longer follow-up or new care-delivery models.

Performance of the provider recognition program's survey to assess patient satisfaction with the provision of diabetes care in primary care.

OBJECTIVE: To measure, for the first time, the performance of the American Diabetes Association-National Committee for Quality Assurance Provider Recognition Program (PRP) survey in assessing patient satisfaction with the provision of diabetes care. STUDY DESIGN: Postal survey. PATIENTS AND METHODS: The PRP survey satisfaction questions and the Diabetes Treatment Satisfaction Questionnaire were mailed to a random sample of 607 patients with diabetes attending 3 medical practices (63 primary care providers), with an additional mailing to nonresponders. RESULTS: On face validity, the PRP survey incompletely addressed satisfaction with the provision of diabetes care. The response rate was 67%. The items in the PRP survey were correlated internally (Cronbach alpha coefficient = .89) and with the Overall Satisfaction scale (r = 0.40-0.56; P < .001 for all) of the Diabetes Treatment Satisfaction Questionnaire (concurrent validity). The instrument was skewed toward satisfaction. After recoding all less-than-totally satisfied responses as expressing dissatisfaction, 25% of the surveys indicated dissatisfaction with diabetes care. CONCLUSIONS: The PRP patient satisfaction survey is an internally consistent and valid measure of patient satisfaction. However, it is incomplete and skewed toward satisfaction. Development of a patient dissatisfaction metric might play a more significant role in orienting quality improvement efforts and benchmarking.