Patient-specific evidence-based care recommendations for diabetes mellitus: development and initial clinic experience with a computerized decision support system

• Published in: International journal of medical informatics (Shannon, Ireland) Vol. 51; no. 2; pp. 127 - 135
• Main Authors: Hunt, Dereck L, Haynes, R.Brian, Hayward, R.S.A, Pim, Mary Ann, Horsman, John
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 01.08.1998
• Subjects: Automation; Computer-assisted decision support; Data collection; Database systems; Decision making; Decision support systems; Decision Support Systems, Clinical; Diabetes mellitus; Diabetes Mellitus - therapy; Diseases; Evaluation Studies as Topic; Evidence-Based Medicine; Health care; Humans; Knowledge based systems; Patient treatment; Reminder systems; Surveys and Questionnaires; User interfaces; Data collection; Evidence-based medicine; Reminder systems; Diabetes mellitus; Computer-assisted decision support
• ISSN: 1386-5056; 1872-8243
• DOI: 10.1016/S1386-5056(98)00110-5

Background: adherence with evidence-based recommendations for chronic disease management is often suboptimal. Providing patient-specific reminders at the time of clinical encounters has the potential to improve this situation. A necessary prerequisite for providing such reminders, however, is to have an efficient means of acquiring patient information that can be matched to an underlying knowledge base. The decision support system: we have developed a computer-based, self-administered questionnaire for diabetes care. The questionnaire assesses numerous diabetes-related topics. Patients complete the questionnaire using a touchscreen interface, and their responses are then matched to evidence-based guidelines so that patient-specific care suggestions can be provided for both the patients and their health care professionals. The guidelines are derived from a database of abstracts of studies of diabetes care that are screened for scientific merit and clinical relevance, supplemented by recommendations from diabetes organizations. Evaluation: initial evaluation of the system included an assessment of the agreement of responses to the automated questionnaire with responses to similar questions administered during a structured, personal interview. Overall agreement was 92.5% and the majority of disagreements were minor. More recently, patients aged 18–69 years have been completing the automated questionnaire before appointments at a diabetes clinic. The average time required has been 10.9 min and a mean of 3.0 recommendations have been provided per patient. Patient and health care practitioner satisfaction with the questionnaire and the patient-specific feedback have been high. Conclusions: evidence-based patient-specific diabetes care recommendations can be provided using a self-administered computer-based questionnaire.