A framework of evidence-based decision-making in health system management: a best-fit framework synthesis.

Best-fit framework synthesis Evidence-based decision-making Health system Management

Journal

Archives of public health = Archives belges de sante publique
ISSN: 0778-7367
Titre abrégé: Arch Public Health
Pays: England
ID NLM: 9208826

Informations de publication

Date de publication:
29 Mar 2022
Historique:
received: 30 10 2021
accepted: 08 03 2022
entrez: 30 3 2022
pubmed: 31 3 2022
medline: 31 3 2022
Statut: epublish

Résumé

Scientific evidence is the basis for improving public health; decision-making without sufficient attention to evidence may lead to unpleasant consequences. Despite efforts to create comprehensive guidelines and models for evidence-based decision-making (EBDM), there isn`t any to make the best decisions concerning scarce resources and unlimited needs. The present study aimed to develop a comprehensive applied framework for EBDM. This was a Best-Fit Framework (BFF) synthesis conducted in 2020. A comprehensive systematic review was done via six main databases including PUBMED, Scopus, Web of Science, Science Direct, EMBASE, and ProQuest using related keywords. After the evidence quality appraisal, data were extracted and analyzed via thematic analysis. Results of the thematic analysis and the concepts generated by the research team were then synthesized to achieve the best-fit framework applying Carroll et al. (2013) approach. Four thousand six hundred thirteen studies were retrieved, and due to the full-text screening of the studies, 17 final articles were selected for extracting the components and steps of EBDM in Health System Management (HSM). After collecting, synthesizing, and categorizing key information, the framework of EBDM in HSM was developed in the form of four general scopes. These comprised inquiring, inspecting, implementing, and integrating, which included 10 main steps and 47 sub-steps. The present framework provided a comprehensive guideline that can be well adapted for implementing EBDM in health systems and related organizations especially in underdeveloped and developing countries where there is usually a lag in updating and applying evidence in their decision-making process. In addition, this framework by providing a complete, well-detailed, and the sequential process can be tested in the organizational decision-making process by developed countries to improve their EBDM cycle.

Sections du résumé

BACKGROUND BACKGROUND
Scientific evidence is the basis for improving public health; decision-making without sufficient attention to evidence may lead to unpleasant consequences. Despite efforts to create comprehensive guidelines and models for evidence-based decision-making (EBDM), there isn`t any to make the best decisions concerning scarce resources and unlimited needs. The present study aimed to develop a comprehensive applied framework for EBDM.
METHODS METHODS
This was a Best-Fit Framework (BFF) synthesis conducted in 2020. A comprehensive systematic review was done via six main databases including PUBMED, Scopus, Web of Science, Science Direct, EMBASE, and ProQuest using related keywords. After the evidence quality appraisal, data were extracted and analyzed via thematic analysis. Results of the thematic analysis and the concepts generated by the research team were then synthesized to achieve the best-fit framework applying Carroll et al. (2013) approach.
RESULTS RESULTS
Four thousand six hundred thirteen studies were retrieved, and due to the full-text screening of the studies, 17 final articles were selected for extracting the components and steps of EBDM in Health System Management (HSM). After collecting, synthesizing, and categorizing key information, the framework of EBDM in HSM was developed in the form of four general scopes. These comprised inquiring, inspecting, implementing, and integrating, which included 10 main steps and 47 sub-steps.
CONCLUSIONS CONCLUSIONS
The present framework provided a comprehensive guideline that can be well adapted for implementing EBDM in health systems and related organizations especially in underdeveloped and developing countries where there is usually a lag in updating and applying evidence in their decision-making process. In addition, this framework by providing a complete, well-detailed, and the sequential process can be tested in the organizational decision-making process by developed countries to improve their EBDM cycle.

Identifiants

pubmed: 35351210
doi: 10.1186/s13690-022-00843-0
pii: 10.1186/s13690-022-00843-0
pmc: PMC8961960
doi:

Types de publication

Journal Article

Langues

eng

Pagination

96

Informations de copyright

© 2022. The Author(s).

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Auteurs

Tahereh Shafaghat (T)

School of Management and Medical Informatics, Health Human Recourses Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Department of Health Care Management, School of Public Health, Health Policy and Management Research Center, Shahid Saoughi University of Medical Sciences, Yazd, Iran.

Peivand Bastani (P)

School of Management and Medical Informatics, Health Human Recourses Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. bastanip@sums.ac.ir.
Faculty of Health and Behavioral Sciences, School of Dentistry, University of Queensland, QLD, 4072, Brisbane, Australia. bastanip@sums.ac.ir.

Mohammad Hasan Imani Nasab (MHI)

Social Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran.

Mohammad Amin Bahrami (MA)

School of Management and Medical Informatics, Health Human Recourses Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.

Mahsa Roozrokh Arshadi Montazer (MRA)

Student Research Committee, School of Management and Medical Informatics, Shiraz University of Medical Sciences, Shiraz, Iran.

Mohammad Kazem Rahimi Zarchi (MKR)

Department of Health Care Management, School of Public Health, Health Policy and Management Research Center, Shahid Saoughi University of Medical Sciences, Yazd, Iran.

Sisira Edirippulige (S)

Faculty of Medicine, Center for Health Services Research, The University of Queensland, Brisbane, Australia.

Classifications MeSH