Dataset Information

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:

Objectives

Chronic 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 criteria

Systematic 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 sources

Medline, Embase, CENTRAL, Scopus, Health Management Information Consortium and trial register clinicaltrials.gov were searched from inception to 24 June 2020.

Data extraction and synthesis

Screening, data extraction and quality assessment were performed by two reviewers independently. The Cochrane risk of bias tool was used for quality appraisal.

Results

From 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.

Conclusions

There 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 number

CRD42020218184.

SUBMITTER: El Asmar ML

PROVIDER: S-EPMC8705223 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo report the improvements achieved with clinical decision support systems and examine the heterogeneity from pooling effects across diverse clinical settings and intervention targets.DesignSystematic review and meta-analysis.Data sourcesMedline up to August 2019.Eligibility criteria for selecting studies and methodsRandomised or quasi-randomised controlled trials reporting absolute improvements in the percentage of patients receiving care recommended by clinical decision support systems. Multilevel meta-analysis accounted for within study clustering. Meta-regression was used to assess the degree to which the features of clinical decision support systems and study characteristics reduced heterogeneity in effect sizes. Where reported, clinical endpoints were also captured.ResultsIn 108 studies (94 randomised, 14 quasi-randomised), reporting 122 trials that provided analysable data from 1 203 053 patients and 10 790 providers, clinical decision support systems increased the proportion of patients receiving desired care by 5.8% (95% confidence interval 4.0% to 7.6%). This pooled effect exhibited substantial heterogeneity (I2=76%), with the top quartile of reported improvements ranging from 10% to 62%. In 30 trials reporting clinical endpoints, clinical decision support systems increased the proportion of patients achieving guideline based targets (eg, blood pressure or lipid control) by a median of 0.3% (interquartile range -0.7% to 1.9%). Two study characteristics (low baseline adherence and paediatric settings) were associated with significantly larger effects. Inclusion of these covariates in the multivariable meta-regression, however, did not reduce heterogeneity.ConclusionsMost interventions with clinical decision support systems appear to achieve small to moderate improvements in targeted processes of care, a finding confirmed by the small changes in clinical endpoints found in studies that reported them. A minority of studies achieved substantial increases in the delivery of recommended care, but predictors of these more meaningful improvements remain undefined.

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Project description:ObjectiveComputerised clinical decision support systems (CDSS) are an increasingly important part of nurse and allied health professional (AHP) roles in delivering healthcare. The impact of these technologies on these health professionals' performance and patient outcomes has not been systematically reviewed. We aimed to conduct a systematic review to investigate this.Materials and methodsThe following bibliographic databases and grey literature sources were searched by an experienced Information Professional for published and unpublished research from inception to February 2021 without language restrictions: MEDLINE (Ovid), Embase Classic+Embase (Ovid), PsycINFO (Ovid), HMIC (Ovid), AMED (Allied and Complementary Medicine) (Ovid), CINAHL (EBSCO), Cochrane Central Register of Controlled Trials (Wiley), Cochrane Database of Systematic Reviews (Wiley), Social Sciences Citation Index Expanded (Clarivate), ProQuest Dissertations & Theses Abstracts & Index, ProQuest ASSIA (Applied Social Science Index and Abstract), Clinical Trials.gov, WHO International Clinical Trials Registry (ICTRP), Health Services Research Projects in Progress (HSRProj), OpenClinical(www.OpenClinical.org), OpenGrey (www.opengrey.eu), Health.IT.gov, Agency for Healthcare Research and Quality (www.ahrq.gov). Any comparative research studies comparing CDSS with usual care were eligible for inclusion.ResultsA total of 36 106 non-duplicate records were identified. Of 35 included studies: 28 were randomised trials, three controlled-before-and-after studies, three interrupted-time-series and one non-randomised trial. There were ~1318 health professionals and ~67 595 patient participants in the studies. Most studies focused on nurse decision-makers (71%) or paramedics (5.7%). CDSS as a standalone Personal Computer/LAPTOP-technology was a feature of 88.7% of the studies; only 8.6% of the studies involved 'smart' mobile/handheld-technology.DiscussionCDSS impacted 38% of the outcome measures used positively. Care processes were better in 47% of the measures adopted; examples included, nurses' adherence to hand disinfection guidance, insulin dosing, on-time blood sampling and documenting care. Patient care outcomes in 40.7% of indicators were better; examples included, lower numbers of falls and pressure ulcers, better glycaemic control, screening of malnutrition and obesity and triaging appropriateness.ConclusionCDSS may have a positive impact on selected aspects of nurses' and AHPs' performance and care outcomes. However, comparative research is generally low quality, with a wide range of heterogeneous outcomes. After more than 13 years of synthesised research into CDSS in healthcare professions other than medicine, the need for better quality evaluative research remains as pressing.

Project description:IntroductionNowadays nearly every patient with cancer is discussed in a multidisciplinary team meeting (MDTM) to determine an optimal treatment plan. The growth in the number of patients to be discussed is unsustainable. Streamlining and use of computerised clinical decision support systems (CCDSSs) are two major ways to restructure MDTMs. Streamlining is the process of selecting the patients who need to be discussed and in which type of MDTM. Using CCDSSs, patient data is automatically loaded into the minutes and a guideline-based treatment proposal is generated. We aimed to identify the pros and cons of streamlining and CCDSSs.MethodsSemi-structured interviews were conducted with Dutch MDTM participants. With purposive sampling we maximised variation in participants' characteristics. Interview data were thematically analysed.ResultsThirty-five interviews were analysed. All interviewees agreed on the need to change the current MDTM workflow. Streamlining suggestions were thematised based on standard and complex cases and the location of the MDTM (i.e. local, regional or nationwide). Interviewees suggested easing the pressure on MDTMs by discussing standard cases briefly, not at all, or outside the MDTM with only two to three specialists. Complex cases should be discussed in tumour-type-specific regional MDTMs and highly complex cases by regional/nationwide expert teams. Categorizing patients as standard or complex was found to be the greatest challenge of streamlining. CCDSSs were recognised as promising, although none of the interviewees had made use of them. The assumed advantage was their capacity to generate protocolised treatment proposals based on automatically uploaded patient data, to unify treatment proposals and to facilitate research. However, they were thought to limit the freedom to deviate from the treatment advice.ConclusionTo make oncological MDTMs sustainable, methods of streamlining should be developed and introduced. Physicians still have doubts about the value of CCDSSs.

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