Comparison of Computer-based Clinical Decision Support Systems and Content for Diabetes Mellitus

Abstract

KeywordsComputer assisted decision making, hospital information system, clinical decision support systems, computerized medical record system, clinical decision support, medicine clinical information system, clinical care, disease management, specific conditions, diabetes mellitus Summary Background: Computer-based clinical decision support (CDS) systems have been shown to improve quality of care and workflow efficiency, and health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known. Objective: We aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care. Methods: We compared CDS systems in six collaborating sites of the Clinical Decision Support Consortium. We gathered CDS content on care for patients with diabetes mellitus and surveyed institutions on characteristics of their site, the infrastructure of CDS at these sites, and the capabilities of CDS at these sites. Results: The approach to CDS and the characteristics of CDS content varied among sites. Some commonalities included providing customizability by role or user, applying sophisticated exclusion criteria, and using CDS automatically at the time of decision-making. Many messages were actionable recommendations. Most sites had monitoring rules (e.g. assessing hemoglobin A1c), but few had rules to diagnose diabetes or suggest specific treatments. All sites had numerous prevention rules including reminders for providing eye examinations, influenza vaccines, lipid screenings, nephropathy screenings, and pneumococcal vaccines. Conclusion: Computer-based CDS systems vary widely across sites in content and scope, but both institution-created and purchased systems had many similar features and functionality, such as integration of alerts and reminders into the decision-making workflow of the provider and providing messages that are actionable recommendations.

Development of a clinical decision support system for diabetes care: A pilot study

“…The rapid rise in diabetes burden coupled with limited healthcare resources in an austere environment has made CDSS an attractive tool to improve delivery of care in a scalable manner [ 13 – 17 ]. CDSS designs can differ significantly in content and scope [ 18 ]. They have been used to automate test and treatment recommendations [ 19 , 20 ], assist in risk stratification for diabetic foot screening [ 21 ], promote health communication with patients [ 22 ], predict blood glucose [ 23 ], interpret self-monitoring of blood glucose data [ 24 , 25 ], monitor guideline adherence [ 26 ], correct/ prevent medication error [ 27 ], and detect potential adverse drug interactions [ 28 ].…”

A Framework for Usable and Effective Clinical Decision Support: Experience from the iCPR Randomized Clinical Trial

“…The success of actionable decision support can be measured by changes in behavior and/or outcome. The term “actionable CDS” is not new, 32 – 37 and implies at the very least that advice is being provided that the user can then take action on and thus influence behavior and outcome. For example, the alert might state “this patient has diabetes and we recommend starting the medication metformin.” In this example, the user then needs to stop whatever he or she was doing at the moment of alert and go to a separate section of the EHR to place an order for metformin.…”