scholarly journals 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

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e054659
Author(s):  
Marie line El Asmar ◽  
Kanika I Dharmayat ◽  
Antonio J Vallejo-Vaz ◽  
Ryan Irwin ◽  
Nikolaos Mastellos

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.

Cancers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1032 ◽  
Author(s):  
Sosse E. Klarenbeek ◽  
Harm H.A. Weekenstroo ◽  
J.P. Michiel Sedelaar ◽  
Jurgen J. Fütterer ◽  
Mathias Prokop ◽  
...  

Background: To deal with complexity in cancer care, computerized clinical decision support systems (CDSSs) are developed to support quality of care and improve decision-making. We performed a systematic review to explore the value of CDSSs using automated clinical guidelines, Artificial Intelligence, datamining or statistical methods (higher level CDSSs) on the quality of care in oncology. Materials and Methods: The search strategy combined synonyms for ‘CDSS’ and ‘cancer.’ Pubmed, Embase, The Cochrane Library, Institute of Electrical and Electronics Engineers, Association of Computing Machinery digital library and Web of Science were systematically searched from January 2000 to December 2019. Included studies evaluated the impact of higher level CDSSs on process outcomes, guideline adherence and clinical outcomes. Results: 11,397 studies were selected for screening, after which 61 full-text articles were assessed for eligibility. Finally, nine studies were included in the final analysis with a total population size of 7985 patients. Types of cancer included breast cancer (63.1%), lung cancer (27.8%), prostate cancer (4.1%), colorectal cancer (3.1%) and other cancer types (1.9%). The included studies demonstrated significant improvements of higher level CDSSs on process outcomes and guideline adherence across diverse settings in oncology. No significant differences were reported for clinical outcomes. Conclusion: Higher level CDSSs seem to improve process outcomes and guidelines adherence but not clinical outcomes. It should be noticed that the included studies primarily focused on breast and lung cancer. To further explore the impact of higher level CDSSs on quality of care, high-quality research is required.


2021 ◽  
Author(s):  
Hector Acosta-Garcia ◽  
Ingrid Ferrer-López ◽  
Juan Ruano-Ruiz ◽  
Bernardo Santos-Ramos ◽  
Teresa Molina-López

Abstract Background Computerized clinical decision support systems are used by clinicians at the point-of-care to improve quality of healthcare processes (prescribing error prevention, adherence to clinical guidelines...) and clinical outcomes (preventive, therapeutic, and diagnostics). Attempts to summarize results of computerized clinical decision support systems to support prescription in primary care have been challenging, and most systematic reviews and meta-analyses failed due to an extremely high degree of heterogeneity present among the included primary studies. The aim of our study will be to synthesize the evidence, considering all methodological factors that could explain these differences, and to build an evidence and gap map to identify important remaining research questions. Methods A literature search will be conducted from January 2010 onwards in Medline, Embase, The Cochrane Library and Web of Science databases. Two reviewers will independently screen all citations, full-text and abstract data. The study methodological quality and risk of bias will be appraised using appropriate tools if applicable. A flow diagram with the screened studies will be presented, and all included studies will be displayed using interactive evidence and gap maps. Results will be reported in accordance with recommendations from The Campbell Collaboration on the development of evidence and gap maps. Discussion Evidence behind computerized clinical decision support systems to support prescription use in primary care, has so far been difficult to be synthesized. Evidence and gap maps represent an innovative approach that has emerged and is increasingly being used to address a broader research question, where multiple types of intervention and outcomes reported may be evaluated. Broad inclusion criteria have been chosen with regards to study designs, in order to collect all available information. Regarding the limitations we will only include English and Spanish language studies from the last 10 years, we will not perform a grey literature search, and we will not carry out a meta-analysis due to the predictable heterogeneity of available studies. Systematic Review registration: This study is registered in Open Science Framework https://bit.ly/2RqKrWp


2019 ◽  
Vol 41 (3) ◽  
pp. 552-581 ◽  
Author(s):  
Eduardo Carracedo-Martinez ◽  
Christian Gonzalez-Gonzalez ◽  
Antonio Teixeira-Rodrigues ◽  
Jesus Prego-Dominguez ◽  
Bahi Takkouche ◽  
...  

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