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Abell B, Naicker S, Rodwell D, Donovan T, Tariq A, Baysari M, Blythe R, Parsons R, McPhail SM. Identifying barriers and facilitators to successful implementation of computerized clinical decision support systems in hospitals: a NASSS framework-informed scoping review. Implement Sci 2023; 18:32. [PMID: 37495997 PMCID: PMC10373265 DOI: 10.1186/s13012-023-01287-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 07/17/2023] [Indexed: 07/28/2023] Open
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.
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Affiliation(s)
- Bridget Abell
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Sundresan Naicker
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia.
| | - David Rodwell
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Thomasina Donovan
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Amina Tariq
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Melissa Baysari
- Biomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
| | - Robin Blythe
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Rex Parsons
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Steven M McPhail
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
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Van Dort BA, Carland JE, Penm J, Ritchie A, Baysari MT. Digital interventions for antimicrobial prescribing and monitoring: a qualitative meta-synthesis of factors influencing user acceptance. J Am Med Inform Assoc 2022; 29:1786-1796. [PMID: 35897157 PMCID: PMC9471701 DOI: 10.1093/jamia/ocac125] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/16/2022] [Accepted: 07/16/2022] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVE To understand and synthesize factors influencing user acceptance of digital interventions used for antimicrobial prescribing and monitoring in hospitals. MATERIALS AND METHODS A meta-synthesis was conducted to identify qualitative studies that explored user acceptance of digital interventions for antimicrobial prescribing and/or monitoring in hospitals. Databases were searched and qualitative data were extracted and systematically classified using the unified theory of acceptance and use of technology (UTAUT) model. RESULTS Fifteen qualitative studies met the inclusion criteria. Eleven papers used interviews and four used focus groups. Most digital interventions evaluated in studies were decision support for prescribing (n = 13). Majority of perceptions were classified in the UTAUT performance expectancy domain in perceived usefulness and relative advantage constructs. Key facilitators in this domain included systems being trusted and credible sources of information, improving performance of tasks and increasing efficiency. Reported barriers were that interventions were not considered useful for all settings or patient conditions. Facilitating conditions was the second largest domain, which highlights the importance of users having infrastructure to support system use. Digital interventions were viewed positively if they were compatible with values, needs, and experiences of users. CONCLUSIONS User perceptions that drive users to accept and utilize digital interventions for antimicrobial prescribing and monitoring were predominantly related to performance expectations and facilitating conditions. To ensure digital interventions for antimicrobial prescribing are accepted and used, we recommend organizations ensure systems are evaluated and benefits are conveyed to users, that utility meets expectations, and that appropriate infrastructure is in place to support use.
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Affiliation(s)
- Bethany A Van Dort
- Biomedical Informatics and Digital Health, School of Medical Sciences, Charles Perkins Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Jane E Carland
- Department of Clinical Pharmacology and Toxicology, St Vincent's Hospital Sydney, Sydney, New South Wales, Australia.,St Vincent's Clinical School, UNSW Sydney, Sydney, New South Wales, Australia
| | - Jonathan Penm
- School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Department of Pharmacy, Prince of Wales Hospital, Randwick, New South Wales, Australia
| | - Angus Ritchie
- Health Informatics Unit, Sydney Local Health District, Camperdown, New South Wales, Australia.,Faculty of Medicine and Health, Concord Clinical School, The University of Sydney, Sydney, New South Wales, Australia
| | - Melissa T Baysari
- Biomedical Informatics and Digital Health, School of Medical Sciences, Charles Perkins Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
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Ploegmakers KJ, Medlock S, Linn AJ, Lin Y, Seppälä LJ, Petrovic M, Topinkova E, Ryg J, Mora MAC, Landi F, Thaler H, Szczerbińska K, Hartikainen S, Bahat G, Ilhan B, Morrissey Y, Masud T, van der Velde N, van Weert JCM. Barriers and facilitators in using a Clinical Decision Support System for fall risk management for older people: a European survey. Eur Geriatr Med 2022; 13:395-405. [PMID: 35032323 DOI: 10.1007/s41999-021-00599-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 11/23/2021] [Indexed: 10/19/2022]
Abstract
PURPOSE Fall-Risk Increasing Drugs (FRIDs) are an important and modifiable fall-risk factor. A Clinical Decision Support System (CDSS) could support doctors in optimal FRIDs deprescribing. Understanding barriers and facilitators is important for a successful implementation of any CDSS. We conducted a European survey to assess barriers and facilitators to CDSS use and explored differences in their perceptions. METHODS We examined and compared the relative importance and the occurrence of regional differences of a literature-based list of barriers and facilitators for CDSS usage among physicians treating older fallers from 11 European countries. RESULTS We surveyed 581 physicians (mean age 44.9 years, 64.5% female, 71.3% geriatricians). The main barriers were technical issues (66%) and indicating a reason before overriding an alert (58%). The main facilitators were a CDSS that is beneficial for patient care (68%) and easy-to-use (64%). We identified regional differences, e.g., expense and legal issues were barriers for significantly more Eastern-European physicians compared to other regions, while training was selected less often as a facilitator by West-European physicians. Some physicians believed that due to the medical complexity of their patients, their own clinical judgement is better than advice from the CDSS. CONCLUSION When designing a CDSS for Geriatric Medicine, the patient's medical complexity must be addressed whilst maintaining the doctor's decision-making autonomy. For a successful CDSS implementation in Europe, regional differences in barrier perception should be overcome. Equipping a CDSS with prediction models has the potential to provide individualized recommendations for deprescribing FRIDs in older falls patients.
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Affiliation(s)
- Kim J Ploegmakers
- Section of Geriatric Medicine, Department of Internal Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, D3-227, Meibergdreef 9, Amsterdam, 1105AZ, The Netherlands.
| | - Stephanie Medlock
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Annemiek J Linn
- Amsterdam School of Communication Research/ASCoR, University of Amsterdam, Amsterdam, The Netherlands
| | - Yumin Lin
- Amsterdam School of Communication Research/ASCoR, University of Amsterdam, Amsterdam, The Netherlands.,Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore
| | - Lotta J Seppälä
- Section of Geriatric Medicine, Department of Internal Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, D3-227, Meibergdreef 9, Amsterdam, 1105AZ, The Netherlands
| | - Mirko Petrovic
- Department of Internal Medicine and Paediatrics (Section of Geriatrics), Ghent University, Ghent, Belgium
| | - Eva Topinkova
- Department of Geriatrics and Gerontology, 1st Faculty of Medicine, Charles University, Prague, Czech Republic.,Faculty of Health and Social Sciences, South Bohemian University, Ceske Budejovice, Czech Republic
| | - Jesper Ryg
- Department of Geriatric Medicine, Odense University Hospital, Odense, Denmark.,Geriatric Research Unit, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | | | - Francesco Landi
- Department of Gerontology, Neuroscience and Orthopedics, Catholic University of the Sacred Heart, Rome, Italy
| | - Heinrich Thaler
- Trauma Center Wien-Meidling, Kundratstrasse 37, 1120, Vienna, Austria
| | - Katarzyna Szczerbińska
- Laboratory for Research on Aging Society, Department of Sociology of Medicine, Epidemiology and Preventive Medicine Chair, Faculty of Medicine, Jagiellonian University Medical College, Kraków, Poland
| | | | - Gulistan Bahat
- Division of Geriatrics, Department of Internal Medicine, Istanbul Medical School, Istanbul University, Capa, 34093, Istanbul, Turkey
| | - Birkan Ilhan
- Division of Geriatrics, Department of Internal Medicine, Şişli Hamidiye Etfal Training and Research Hospital, University of Medical Sciences, Istanbul, Turkey
| | - Yvonne Morrissey
- Health Care of Older People, East Kent Hospitals University NHS Foundation Trust, Canterbury, Kent, UK
| | - Tahir Masud
- Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Nathalie van der Velde
- Section of Geriatric Medicine, Department of Internal Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, D3-227, Meibergdreef 9, Amsterdam, 1105AZ, The Netherlands
| | - Julia C M van Weert
- Amsterdam School of Communication Research/ASCoR, University of Amsterdam, Amsterdam, The Netherlands
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Barriers and facilitators influencing medication-related CDSS acceptance according to clinicians: A systematic review. Int J Med Inform 2021; 152:104506. [PMID: 34091146 DOI: 10.1016/j.ijmedinf.2021.104506] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 04/20/2021] [Accepted: 05/18/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND A medication-related Clinical Decision Support System (CDSS) is an application that analyzes patient data to provide assistance in medication-related care processes. Despite its potential to improve the clinical decision-making process, evidence shows that clinicians do not always use CDSSs in such a way that their potential can be fully realized. This systematic literature review provides an overview of frequently-reported barriers and facilitators for acceptance of medication-related CDSS. MATERIALS AND METHODS Search terms and MeSH headings were developed in collaboration with a librarian, and database searches were conducted in Medline, Scopus, Embase and Web of Science Conference Proceedings. After screening 5404 records and 140 full papers, 63 articles were included in this review. Quality assessment was performed for all 63 included articles. The identified barriers and facilitators are categorized within the Human, Organization, Technology fit (HOT-fit) model. RESULTS A total of 327 barriers and 291 facilitators were identified. Results show that factors most often reported were related to (a lack of) usefulness and relevance of information, and ease of use and efficiency of the system. DISCUSSION This review provides a valuable insight into a broad range of barriers and facilitators for using a medication-related CDSS as perceived by clinicians. The results can be used as a stepping stone in future studies developing medication-related CDSSs.
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Chua AQ, Kwa ALH, Tan TY, Legido-Quigley H, Hsu LY. Ten-year narrative review on antimicrobial resistance in Singapore. Singapore Med J 2019; 60:387-396. [PMID: 31482178 PMCID: PMC6717780 DOI: 10.11622/smedj.2019088] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Antimicrobial resistance (AMR) results in drug-resistant infections that are harder to treat, subsequently leading to increased morbidity and mortality. In 2008, we reviewed the problem of AMR in Singapore, limiting our discussion to the human healthcare sector. Ten years later, we revisit this issue again, reviewing current efforts to contain it in order to understand the progress made as well as current and emerging challenges. Although a significant amount of work has been done to control AMR and improve antibiotic prescribing in Singapore, most of it has focused on the hospital setting, with mixed impact. The role of antibiotic use and AMR in food animals and the environment - and the link to human health - is better understood today. This issue of AMR encompasses both human health as well as animal/food safety, and efforts to control it will need to continually evolve to maintain or improve on current gains.
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Affiliation(s)
- Alvin Qijia Chua
- Department of Pharmacy, Singapore General Hospital, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Andrea Lay-Hoon Kwa
- Department of Pharmacy, Singapore General Hospital, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Thean Yen Tan
- Duke-NUS Medical School, National University of Singapore, Singapore
- Department of Laboratory Medicine, Changi General Hospital, Singapore
| | | | - Li Yang Hsu
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
- National Centre for Infectious Diseases, Singapore
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