Effect of computerised, knowledge-based, clinical decision support systems on patient-reported and clinical outcomes of patients with chronic disease managed in primary care settings: a systematic review

Abstract

ObjectivesChronic diseases are the leading cause of disability globally. Most chronic disease management occurs in primary care with outcomes varying across primary care providers. Computerised clinical decision support systems (CDSS) have been shown to positively affect clinician behaviour by improving adherence to clinical guidelines. This study provides a summary of the available evidence on the effect of CDSS embedded in electronic health records on patient-reported and clinical outcomes of adult patients with chronic disease managed in primary care.Design and eligibility criteriaSystematic review, including randomised controlled trials (RCTs), cluster RCTs, quasi-RCTs, interrupted time series and controlled before-and-after studies, assessing the effect of CDSS (vs usual care) on patient-reported or clinical outcomes of adult patients with selected common chronic diseases (asthma, chronic obstructive pulmonary disease, heart failure, myocardial ischaemia, hypertension, diabetes mellitus, hyperlipidaemia, arthritis and osteoporosis) managed in primary care.Data sourcesMedline, Embase, CENTRAL, Scopus, Health Management Information Consortium and trial register clinicaltrials.gov were searched from inception to 24 June 2020.Data extraction and synthesisScreening, data extraction and quality assessment were performed by two reviewers independently. The Cochrane risk of bias tool was used for quality appraisal.ResultsFrom 5430 articles, 8 studies met the inclusion criteria. Studies were heterogeneous in population characteristics, intervention components and outcome measurements and focused on diabetes, asthma, hyperlipidaemia and hypertension. Most outcomes were clinical with one study reporting on patient-reported outcomes. Quality of the evidence was impacted by methodological biases of studies.ConclusionsThere is inconclusive evidence in support of CDSS. A firm inference on the intervention effect was not possible due to methodological biases and study heterogeneity. Further research is needed to provide evidence on the intervention effect and the interplay between healthcare setting features, CDSS characteristics and implementation processes.PROSPERO registration numberCRD42020218184.

Use of an Electronic Medication Management Support System in Patients with Polypharmacy in General Practice: A Quantitative Process Evaluation of the AdAM Trial

“…The inclusion criteria for our study were broad and the patient group correspondingly heterogeneous. Unlike other similar studies, the group was not preselected according to medication, disease, or age group [23,24]. As a result, the focus of GP training was on dealing with polypharmacy in general, and did not attempt to provide in-depth instruction on how to optimize specific cases, as is the case in polypharmacy trials with narrower inclusion criteria [25,26].…”

A Fuzzy-Based Clinical Decision Support System for Coeliac Disease

“…The interest of the scientific community in the development of DSS systems, also thanks to new performing machine learning techniques, is certainly growing [13] [14]. However, the problem of developing CD diagnosis support systems is still poorly explored, perhaps due to the difficulty of the problem but undoubtedly also due to the lack of public databases.…”

Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis

“… 18–27 Other examples of recent CDS systematic reviews with a specific scope include: reviews that focus on a single disease (eg, diabetes); 28–30 reviews of CDS with a focus on specific end-users (eg, nursing staff) 31 ; and reviews that target a specific CDS type—such as laboratory test-related CDS, 20 , 32 , 33 or diagnostic image-related CDS. 34 , 35 In contrast to these systematic reviews, few recent reviews have had a broad chronic disease 36–38 or all-disease focus 39–42 —significantly, of these recent reviews, only 2 have explicitly excluded outdated computerized CDS systems that are not EHR-integrated. 36 , 39 Thus our review sought to investigate the effectiveness of contemporary, EHR-based CDS interventions, with a broad chronic disease focus.…”

“… 34 , 35 In contrast to these systematic reviews, few recent reviews have had a broad chronic disease 36–38 or all-disease focus 39–42 —significantly, of these recent reviews, only 2 have explicitly excluded outdated computerized CDS systems that are not EHR-integrated. 36 , 39 Thus our review sought to investigate the effectiveness of contemporary, EHR-based CDS interventions, with a broad chronic disease focus. Our review scope is most similar to El Asmar et al’s 2021 review with a focus on EHR-based CDS for chronic diseases—however, the authors of this review had a limited number of outcomes of interest, and only included a small number of studies ( n = 8).…”

“…Our review scope is most similar to El Asmar et al’s 2021 review with a focus on EHR-based CDS for chronic diseases—however, the authors of this review had a limited number of outcomes of interest, and only included a small number of studies ( n = 8). 36 , 43 …”