Efficacy of an Evidence-Based Clinical Decision Support in Primary Care Practices

Abstract Background Clinical decision support (CDS) can improve the quality and efficiency of care by increasing access to evidence-based data at the point-of-care. However, CDS adoption is limited in clinical settings. The focus on the technical design have overlooked end-users’ perceptions, social and organisational factors that may influence uptake. Using the Unified Theory of Acceptance and Use of Technology (UTAUT) model, we aimed to identify different challenges and opportunities for CDSS adoption in clinical settings. Methods A cross-sectional online survey was conducted from June-October 2019 with general practitioners, physicians and surgeons in Australia. To measure the impact of UTAUT moderators (age, gender and experience) and care settings (primary care vs. hospitals) on perceived barriers and facilitators to CDS implementation, multivariate logistic regression was carried out. Qualitative data was thematically analysed for emerging patterns and themes. Results Many respondents indicated that CDS can promote evidence-based care (79%) and improve quality and safety (52%). Overall CDS adoption is dependent on factors such as effort expectancy (ease of use), performance expectancy and a facilitating environment for system use. Clinician perceptions of barriers and facilitators to uptake are associated with type of care setting. There was higher likelihood of perceiving ease of use (OR 1.39, 95% CI 1.15-2.07), time limitations (OR 1.95, 95%CI 1.10-3.77), patient' preferences (OR 2.17, 95% CI 1.14-3.37) and threat to professional autonomy (OR 2.15, 95%CI 1.39-3.41) as factors that influence adoption in primary care. Providing relevant information in right format at right time is critical for successful CDS use. Conclusions CDS implementation requires a holistic approach addressing users' perceptions and preferences determined through stakeholder consultation. The system design must be easy to use but also facilitated through the provision of on-site training and support. Key messages CDS adoption is determined by systems’ ease of use and provision of enabling environment. Users and setting characteristics are important moderators and must be considered in CDS implementation.

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Clinical Decision Support for Worker Health: A Five-Site Qualitative Needs Assessment in Primary Care Settings

Applied Clinical Informatics ◽

10.1055/s-0040-1715895 ◽

2020 ◽

Vol 11 (04) ◽

pp. 635-643

Author(s):

Joan S. Ash ◽

Dian Chase ◽

Sherry Baron ◽

Margaret S. Filios ◽

Richard N. Shiffman ◽

...

Keyword(s):

Primary Care ◽

Decision Support ◽

Clinical Decision Support ◽

Clinical Decision ◽

Primary Care Providers ◽

Care Providers ◽

Technical Feasibility ◽

Primary Care Settings ◽

Worker Health ◽

Operational Issues

Abstract Background Although patients who work and have related health issues are usually first seen in primary care, providers in these settings do not routinely ask questions about work. Guidelines to help manage such patients are rarely used in primary care. Electronic health record (EHR) systems with worker health clinical decision support (CDS) tools have potential for assisting these practices. Objective This study aimed to identify the need for, and barriers and facilitators related to, implementation of CDS tools for the clinical management of working patients in a variety of primary care settings. Methods We used a qualitative design that included analysis of interview transcripts and observational field notes from 10 clinics in five organizations. Results We interviewed 83 providers, staff members, managers, informatics and information technology experts, and leaders and spent 35 hours observing. We identified eight themes in four categories related to CDS for worker health (operational issues, usefulness of proposed CDS, effort and time-related issues, and topic-specific issues). These categories were classified as facilitators or barriers to the use of the CDS tools. Facilitators related to operational issues include current technical feasibility and new work patterns associated with the coordinated care model. Facilitators concerning usefulness include users' need for awareness and evidence-based tools, appropriateness of the proposed CDS for their patients, and the benefits of population health data. Barriers that are effort-related include additional time this proposed CDS might take, and other pressing organizational priorities. Barriers that are topic-specific include sensitive issues related to health and work and the complexities of information about work. Conclusion We discovered several themes not previously described that can guide future CDS development: technical feasibility of the proposed CDS within commercial EHRs, the sensitive nature of some CDS content, and the need to assist the entire health care team in managing worker health.

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Computerized Clinical Decision Support Systems for Prescribing in Primary Care: Main Characteristics and Implementation Impact. Protocol of an Evidence and Gap Map.

10.21203/rs.3.rs-499725/v1 ◽

2021 ◽

Author(s):

Hector Acosta-Garcia ◽

Ingrid Ferrer-López ◽

Juan Ruano-Ruiz ◽

Bernardo Santos-Ramos ◽

Teresa Molina-López

Keyword(s):

Primary Care ◽

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Literature Search ◽

Support Systems ◽

Grey Literature ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Cochrane Library

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

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859-P: Impact of a Prediabetes Clinical Decision Support System on CV Risk Factor Control in Primary Care: Randomized Trial

Diabetes ◽

10.2337/db21-859-p ◽

2021 ◽

Vol 70 (Supplement 1) ◽

pp. 859-P

Author(s):

JAY R. DESAI ◽

A. LAUREN CRAIN ◽

DANIEL SAMAN ◽

JOANN M. SPERL-HILLEN ◽

CLAYTON ALLEN ◽

...

Keyword(s):

Primary Care ◽

Risk Factor ◽

Decision Support ◽

Decision Support System ◽

Randomized Trial ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision Support System ◽

Clinical Decision ◽

Risk Factor Control

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Clinical Decision Support Complements Evidence-Based Decision Making in Dental Practice

Journal of Evidence Based Dental Practice ◽

10.1016/j.jebdp.2006.12.016 ◽

2007 ◽

Vol 7 (1) ◽

pp. 1-5 ◽

Cited By ~ 17

Author(s):

Michael G. Newman

Keyword(s):

Decision Making ◽

Decision Support ◽

Clinical Decision Support ◽

Clinical Decision ◽

Evidence Based ◽

Dental Practice ◽

Evidence Based Decision Making

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Evaluation of a handheld clinical decision support tool for evidence-based learning and practice in medical undergraduates

Medical Education ◽

10.1111/j.1365-2929.2004.01842.x ◽

2004 ◽

Vol 38 (6) ◽

pp. 628-637 ◽

Cited By ~ 33

Author(s):

Janice M Johnston ◽

Gabriel M Leung ◽

Keith Y K Tin ◽

Lai-Ming Ho ◽

Wendy Lam ◽

...

Keyword(s):

Decision Support ◽

Clinical Decision Support ◽

Decision Support Tool ◽

Clinical Decision ◽

Evidence Based ◽

Support Tool ◽

Medical Undergraduates ◽

Clinical Decision Support Tool

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Implementation and Benefits of Computerized Physician Order Entry and Evidence-Based Clinical Decision Support Systems

Evidence Based Pathology and Laboratory Medicine ◽

10.1007/978-1-4419-1030-1_19 ◽

2011 ◽

pp. 323-335 ◽

Cited By ~ 1

Author(s):

Stacy E. F. Melanson ◽

Aileen P. Morrison ◽

David W. Bates ◽

Milenko J. Tanasijevic

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Computerized Physician Order Entry ◽

Evidence Based ◽

Order Entry ◽

Physician Order Entry

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Clinical decision support improves the appropriateness of laboratory test ordering in primary care without increasing diagnostic error: the ELMO cluster randomized trial

10.21203/rs.3.rs-64077/v2 ◽

2020 ◽

Author(s):

Nicolas Delvaux ◽

Veerle Piessens ◽

Tine De Burghgraeve ◽

Pavlos Mamouris ◽

Bert Vaes ◽

...

Keyword(s):

Primary Care ◽

Decision Support ◽

Laboratory Test ◽

Clinical Decision Support ◽

Clinical Decision ◽

Diagnostic Error ◽

Order Sets ◽

Cluster Randomized ◽

Physician Order Entry ◽

Test Ordering

Abstract Background Inappropriate laboratory test ordering poses an important burden for healthcare. Clinical decision support systems (CDSS) have been cited as promising tools to improve laboratory test ordering behavior. The objectives of this study were to evaluate the effects of an intervention that integrated a clinical decision support service into a computerized physician order entry (CPOE) on the appropriateness and volume of laboratory test ordering, and on diagnostic error in primary care.Methods This study was a pragmatic, cluster randomized, open label, controlled clinical trial. Setting 280 general practitioners (GPs) from 72 primary care practices in Belgium. Patients Patients aged ≥18 years with a laboratory test order for at least one of 17 indications; cardiovascular disease management, hypertension, check-up, chronic kidney disease (CKD), thyroid disease, type 2 diabetes mellitus, fatigue, anemia, liver disease, gout, suspicion of acute coronary syndrome (ACS), suspicion of lung embolism, rheumatoid arthritis, sexually transmitted infections (STI), acute diarrhea, chronic diarrhea, and follow-up of medication. Interventions The CDSS was integrated into a computerized physician order entry (CPOE) in the form of evidence-based order sets that suggested appropriate tests based on the indication provided by the general physician. Measurements The primary outcome of the ELMO study was the proportion of appropriate tests over the total number of ordered tests and inappropriately not-requested tests. Secondary outcomes of the ELMO study included diagnostic error, test volume and cascade activities.Results CDSS increased the proportion of appropriate tests by 0.21 (95% CI 0.16 - 0.26, p<.0001) for all tests included in the study. GPs in the CDSS arm ordered 7 (7.15 (95% CI 3.37 - 10.93, p=.0002)) tests fewer per panel. CDSS did not increase diagnostic error. The absolute difference in proportions was a decrease of 0.66% (95% CI 1.4% decrease - 0.05% increase) in possible diagnostic error.Conclusions A CDSS in the form of order sets, integrated within the CPOE improved appropriateness and decreased volume of laboratory test ordering without increasing diagnostic error. Trial Registration Clinicaltrials.gov Identifier: NCT02950142, registered on October 25, 2016Funding source This study was funded through the Belgian Health Care Knowledge Centre (KCE) Trials Programme agreement KCE16011.

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