Susan S. Bjornsen

DEMS — a second generation diabetes electronic management system

Diabetes electronic management system (DEMS) is a component-based client/server application, written in Visual C++ and Visual Basic, with the database server running Sybase System 11. DEMS is built entirely with a combination of dynamic link libraries (DLLs) and ActiveX components - the only exception is the DEMS.exe. DEMS is a chronic disease management system for patients with diabetes. It is used at the point of care by all members of the diabetes team including physicians, nurses, dieticians, clinical assistants and educators. The system is designed for maximum clinical efficiency and facilitates appropriately supervised delegation of care. Dispersed clinical sites may be supervised from a central location. The system is designed for ease of navigation; immediate provision of many types of automatically generated reports; quality audits; aids to compliance with good care guidelines; and alerts, advisories, prompts, and warnings that guide the care provider. The system now contains data on over 34000 patients and is in daily use at multiple sites.

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.

Quality Performance Measurement Using the Text of Electronic Medical Records

Background. Annual foot examinations (FE) constitute a critical component of care for diabetes. Documented evidence of FE is central to quality-of-care reporting; however, manual abstraction of electronic medical records (EMR) is slow, expensive, and subject to error. The objective of this study was to test the hypothesis that text mining of the EMR results in ascertaining FE evidence with accuracy comparable to manual abstraction. Methods. The text of inpatient and outpatient clinical reports was searched with natural-language (NL) queries for evidence of neurological, vascular, and structural components of FE. A manual medical records audit was used for validation. The reference standard consisted of 3 independent sets used for development (n=200 ), validation (n=118), and reliability (n=80). Results. The reliability of manual auditing was 91% (95% confidence interval [CI]= 85—97) and was determined by comparing the results of an additional audit to the original audit using the records in the reliability set. The accuracy of the NL query requiring 1 of 3 FE components was 89% (95% CI=83—95). The accuracy of the query requiring any 2 of 3 components was 88% (95% CI=82—94). The accuracy of the query requiring all 3 components was 75% (95% CI= 68— 83). Conclusions. The free text of the EMR is a viable source of information necessary for quality of health care reporting on the evidence of FE for patients with diabetes. The low-cost methodology is scalable to monitoring large numbers of patients and can be used to streamline quality-of-care reporting.

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.

Empowering Individuals to Self-Manage Chemotherapy Side Effects

Providing concise, consistent, and individually relevant patient education is critical. At one institution, patients and families attended a chemotherapy education class consisting of an 11-minute DVD and an oncology nurse-facilitated group discussion. Postclass and eight-week follow-up surveys assessing understanding of treatment side effects, self-care management, and confidence in managing side effects were administered. Quantitative and qualitative data suggested the DVD and oncology nurse-facilitated group discussion provided consistent information, flexibility, and expert knowledge in empowering patients and families to self-manage chemotherapy side effects.

Review: Self-management training in type 2 diabetes mellitus is effective in the short term

Source Citation Norris SL, Engelgau MM, Venkat Narayan KM. Effectiveness of self-management training in type 2 diabetes. A systematic review of randomized controlled trials. Diabetes Care. 2001 Mar...