» Articles » PMID: 37495997

Identifying Barriers and Facilitators to Successful Implementation of Computerized Clinical Decision Support Systems in Hospitals: a NASSS Framework-informed Scoping Review

Overview
Journal Implement Sci
Publisher Biomed Central
Specialty Health Services
Date 2023 Jul 26
PMID 37495997
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Successful implementation and utilization of Computerized Clinical Decision Support Systems (CDSS) in hospitals is complex and challenging. Implementation science, and in particular the Nonadoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework, may offer a systematic approach for identifying and addressing these challenges. This review aimed to identify, categorize, and describe barriers and facilitators to CDSS implementation in hospital settings and map them to the NASSS framework. Exploring the applicability of the NASSS framework to CDSS implementation was a secondary aim.

Methods: Electronic database searches were conducted (21 July 2020; updated 5 April 2022) in Ovid MEDLINE, Embase, Scopus, PyscInfo, and CINAHL. Original research studies reporting on measured or perceived barriers and/or facilitators to implementation and adoption of CDSS in hospital settings, or attitudes of healthcare professionals towards CDSS were included. Articles with a primary focus on CDSS development were excluded. No language or date restrictions were applied. We used qualitative content analysis to identify determinants and organize them into higher-order themes, which were then reflexively mapped to the NASSS framework.

Results: Forty-four publications were included. These comprised a range of study designs, geographic locations, participants, technology types, CDSS functions, and clinical contexts of implementation. A total of 227 individual barriers and 130 individual facilitators were identified across the included studies. The most commonly reported influences on implementation were fit of CDSS with workflows (19 studies), the usefulness of the CDSS output in practice (17 studies), CDSS technical dependencies and design (16 studies), trust of users in the CDSS input data and evidence base (15 studies), and the contextual fit of the CDSS with the user's role or clinical setting (14 studies). Most determinants could be appropriately categorized into domains of the NASSS framework with barriers and facilitators in the "Technology," "Organization," and "Adopters" domains most frequently reported. No determinants were assigned to the "Embedding and Adaptation Over Time" domain.

Conclusions: This review identified the most common determinants which could be targeted for modification to either remove barriers or facilitate the adoption and use of CDSS within hospitals. Greater adoption of implementation theory should be encouraged to support CDSS implementation.

Citing Articles

Health IT Implementation and the Impact of the COVID-19 Pandemic on Clinician-IT Dynamics: Qualitative Study.

Bamgboje-Ayodele A, Boscolo A, Burger M, Hutchings O, Shaw M, Shaw T J Med Internet Res. 2025; 27:e57847.

PMID: 39933168 PMC: 11862760. DOI: 10.2196/57847.


Using routinely available electronic health record data elements to develop and validate a digital divide risk score.

Faro J, Obermiller E, Obermiller C, Trinkley K, Wright G, Sadasivam R JAMIA Open. 2025; 8(1):ooaf004.

PMID: 39906363 PMC: 11792649. DOI: 10.1093/jamiaopen/ooaf004.


The complexity of home-based rehabilitation technology implementation for post-stroke motor rehabilitation in the Netherlands.

Te Boekhorst K, Kuipers S, Ribbers G, Cramm J BMC Health Serv Res. 2025; 25(1):21.

PMID: 39755612 PMC: 11699700. DOI: 10.1186/s12913-024-12044-2.


An Electronic Medical Record-Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation.

Parsons R, Blythe R, Cramb S, Abdel-Hafez A, McPhail S J Med Internet Res. 2024; 26:e59634.

PMID: 39536309 PMC: 11602763. DOI: 10.2196/59634.


Decision Support Intervention and Anticoagulation for Emergency Department Atrial Fibrillation: The O'CAFÉ Stepped-Wedge Cluster Randomized Clinical Trial.

Vinson D, Warton E, Durant E, Mark D, Ballard D, Hofmann E JAMA Netw Open. 2024; 7(11):e2443097.

PMID: 39504024 PMC: 11541643. DOI: 10.1001/jamanetworkopen.2024.43097.


References
1.
Grau L, Weiss J, OLeary T, Camenga D, Bernstein S . Electronic decision support for treatment of hospitalized smokers: A qualitative analysis of physicians' knowledge, attitudes, and practices. Drug Alcohol Depend. 2018; 194:296-301. PMC: 7720717. DOI: 10.1016/j.drugalcdep.2018.10.006. View

2.
Jacobs J, Weir C, Evans R, Staes C . Assessment of readiness for clinical decision support to aid laboratory monitoring of immunosuppressive care at U.S. liver transplant centers. Appl Clin Inform. 2015; 5(4):988-1004. PMC: 4287676. DOI: 10.4338/ACI-2014-08-RA-0060. View

3.
Perski O, Short C . Acceptability of digital health interventions: embracing the complexity. Transl Behav Med. 2021; 11(7):1473-1480. PMC: 8320880. DOI: 10.1093/tbm/ibab048. View

4.
Westafer L, Kunz A, Bugajska P, Hughes A, Mazor K, Schoenfeld E . Provider Perspectives on the Use of Evidence-based Risk Stratification Tools in the Evaluation of Pulmonary Embolism: A Qualitative Study. Acad Emerg Med. 2020; 27(6):447-456. PMC: 7418048. DOI: 10.1111/acem.13908. View

5.
Kadesjo Banck J, Bernhardsson S . Experiences from implementation of internet-delivered cognitive behaviour therapy for insomnia in psychiatric health care: a qualitative study applying the NASSS framework. BMC Health Serv Res. 2020; 20(1):729. PMC: 7414663. DOI: 10.1186/s12913-020-05596-6. View