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

Abstract

BackgroundThe 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).MethodsWe 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.ResultsOf 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.ConclusionsA 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.

ACC/AHA Special Report: Clinical Practice Guideline Implementation Strategies: A Summary of Systematic Reviews by the NHLBI Implementation Science Work Group

“…21 Two fair-quality reviews 23,25 reported cost-reduction findings (length of stay and lab costs) for audit and feedback interventions and the results showed mixed effectiveness. Reminders were generally effective in reducing cost in 3 reviews 18,24,25 and showed mixed effectiveness in another. 20 Reminders were also cost effective in 1 review 22 and the results showed mixed effectiveness in another.…”

“…The results were similar when only the CVD risk reduction studies were considered in 8 SRs. 9,11,[13][14][15][16][17][18] However, reminders were generally effective in improving clinical outcomes for hypertension. 9 Provider incentive interventions were included in 5 good-quality SRs and showed mixed effectiveness for both process-of-care and clinical outcomes-most of the positive outcomes were related to diabetes mellitus and asthma.…”

Medication optimisation

“…The development of CCDSSs has become an ongoing process of sophisticated generating systems that link patient characteristics with computerised knowledge bases, using algorithms (CRs) and generating patient-specific assessments or treatment recommendations [32][33][34][35][36][37][38]. CCDSSs and CRs are conceived to support a range of clinical daily tasks by integrating the electronic medical record systems and the computerised physician order entry systems (CPOEs) in such a way that reminders or warnings can be sent to guide both drug and dosage selection processes and identify deviating laboratory test results, adverse drug reactions, allergies, possible interactions and duplicates [39][40][41][42][43].…”

“…Inspectorate, VMS Veiligheidsmanagementsysteem = Safety Management System, and Dutch guideline for delirium) [35,36]. This checklist consists of 3 questions:…”

“…CCDSSs can be defined as decision-aiding tools that provide health care professionals with clinical knowledge and patient-related information, intelligently filtered or presented at appropriate times, to enhance patient care [35][36][37].…”