Determinants of implementing artificial intelligence-based clinical decision support tools in healthcare: a scoping review protocol
IntroductionArtificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical decision-making. AI-based clinical decision support (CDS) tools can improve the quality of care and appropriate use of healthcare resources, an...
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| Published in | BMJ open Vol. 13; no. 2; p. e068373 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
British Medical Journal Publishing Group
23.02.2023
BMJ Publishing Group LTD BMJ Publishing Group |
| Series | Protocol |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2044-6055 2044-6055 |
| DOI | 10.1136/bmjopen-2022-068373 |
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| Abstract | IntroductionArtificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical decision-making. AI-based clinical decision support (CDS) tools can improve the quality of care and appropriate use of healthcare resources, and decrease healthcare provider burnout. Understanding the determinants of implementing AI-based CDS tools in healthcare delivery is vital to reap the benefits of these tools. The objective of this scoping review is to map and synthesise determinants (barriers and facilitators) to implementing AI-based CDS tools in healthcare.Methods and analysisThis scoping review will follow the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews checklist. The search terms will be tailored to each database, which includes MEDLINE, Embase, CINAHL, APA PsycINFO and the Cochrane Library. Grey literature and references of included studies will also be searched. The search will include studies published from database inception until 10 May 2022. We will not limit searches by study design or language. Studies that either report determinants or describe the implementation of AI-based CDS tools in clinical practice or/and healthcare settings will be included. The identified determinants (barriers and facilitators) will be described by synthesising the themes using the Theoretical Domains Framework. The outcome variables measured will be mapped and the measures of effectiveness will be summarised using descriptive statistics.Ethics and disseminationEthics approval is not required because all data for this study have been previously published. The findings of this review will be published in a peer-reviewed journal and presented at academic conferences. Importantly, the findings of this scoping review will be widely presented to decision-makers, health system administrators, healthcare providers, and patients and family/caregivers as part of an implementation study of an AI-based CDS for the treatment of coronary artery disease. |
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| AbstractList | Artificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical decision-making. AI-based clinical decision support (CDS) tools can improve the quality of care and appropriate use of healthcare resources, and decrease healthcare provider burnout. Understanding the determinants of implementing AI-based CDS tools in healthcare delivery is vital to reap the benefits of these tools. The objective of this scoping review is to map and synthesise determinants (barriers and facilitators) to implementing AI-based CDS tools in healthcare.
This scoping review will follow the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews checklist. The search terms will be tailored to each database, which includes MEDLINE, Embase, CINAHL, APA PsycINFO and the Cochrane Library. Grey literature and references of included studies will also be searched. The search will include studies published from database inception until 10 May 2022. We will not limit searches by study design or language. Studies that either report determinants or describe the implementation of AI-based CDS tools in clinical practice or/and healthcare settings will be included. The identified determinants (barriers and facilitators) will be described by synthesising the themes using the Theoretical Domains Framework. The outcome variables measured will be mapped and the measures of effectiveness will be summarised using descriptive statistics.
Ethics approval is not required because all data for this study have been previously published. The findings of this review will be published in a peer-reviewed journal and presented at academic conferences. Importantly, the findings of this scoping review will be widely presented to decision-makers, health system administrators, healthcare providers, and patients and family/caregivers as part of an implementation study of an AI-based CDS for the treatment of coronary artery disease. Artificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical decision-making. AI-based clinical decision support (CDS) tools can improve the quality of care and appropriate use of healthcare resources, and decrease healthcare provider burnout. Understanding the determinants of implementing AI-based CDS tools in healthcare delivery is vital to reap the benefits of these tools. The objective of this scoping review is to map and synthesise determinants (barriers and facilitators) to implementing AI-based CDS tools in healthcare.INTRODUCTIONArtificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical decision-making. AI-based clinical decision support (CDS) tools can improve the quality of care and appropriate use of healthcare resources, and decrease healthcare provider burnout. Understanding the determinants of implementing AI-based CDS tools in healthcare delivery is vital to reap the benefits of these tools. The objective of this scoping review is to map and synthesise determinants (barriers and facilitators) to implementing AI-based CDS tools in healthcare.This scoping review will follow the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews checklist. The search terms will be tailored to each database, which includes MEDLINE, Embase, CINAHL, APA PsycINFO and the Cochrane Library. Grey literature and references of included studies will also be searched. The search will include studies published from database inception until 10 May 2022. We will not limit searches by study design or language. Studies that either report determinants or describe the implementation of AI-based CDS tools in clinical practice or/and healthcare settings will be included. The identified determinants (barriers and facilitators) will be described by synthesising the themes using the Theoretical Domains Framework. The outcome variables measured will be mapped and the measures of effectiveness will be summarised using descriptive statistics.METHODS AND ANALYSISThis scoping review will follow the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews checklist. The search terms will be tailored to each database, which includes MEDLINE, Embase, CINAHL, APA PsycINFO and the Cochrane Library. Grey literature and references of included studies will also be searched. The search will include studies published from database inception until 10 May 2022. We will not limit searches by study design or language. Studies that either report determinants or describe the implementation of AI-based CDS tools in clinical practice or/and healthcare settings will be included. The identified determinants (barriers and facilitators) will be described by synthesising the themes using the Theoretical Domains Framework. The outcome variables measured will be mapped and the measures of effectiveness will be summarised using descriptive statistics.Ethics approval is not required because all data for this study have been previously published. The findings of this review will be published in a peer-reviewed journal and presented at academic conferences. Importantly, the findings of this scoping review will be widely presented to decision-makers, health system administrators, healthcare providers, and patients and family/caregivers as part of an implementation study of an AI-based CDS for the treatment of coronary artery disease.ETHICS AND DISSEMINATIONEthics approval is not required because all data for this study have been previously published. The findings of this review will be published in a peer-reviewed journal and presented at academic conferences. Importantly, the findings of this scoping review will be widely presented to decision-makers, health system administrators, healthcare providers, and patients and family/caregivers as part of an implementation study of an AI-based CDS for the treatment of coronary artery disease. IntroductionArtificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical decision-making. AI-based clinical decision support (CDS) tools can improve the quality of care and appropriate use of healthcare resources, and decrease healthcare provider burnout. Understanding the determinants of implementing AI-based CDS tools in healthcare delivery is vital to reap the benefits of these tools. The objective of this scoping review is to map and synthesise determinants (barriers and facilitators) to implementing AI-based CDS tools in healthcare.Methods and analysisThis scoping review will follow the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews checklist. The search terms will be tailored to each database, which includes MEDLINE, Embase, CINAHL, APA PsycINFO and the Cochrane Library. Grey literature and references of included studies will also be searched. The search will include studies published from database inception until 10 May 2022. We will not limit searches by study design or language. Studies that either report determinants or describe the implementation of AI-based CDS tools in clinical practice or/and healthcare settings will be included. The identified determinants (barriers and facilitators) will be described by synthesising the themes using the Theoretical Domains Framework. The outcome variables measured will be mapped and the measures of effectiveness will be summarised using descriptive statistics.Ethics and disseminationEthics approval is not required because all data for this study have been previously published. The findings of this review will be published in a peer-reviewed journal and presented at academic conferences. Importantly, the findings of this scoping review will be widely presented to decision-makers, health system administrators, healthcare providers, and patients and family/caregivers as part of an implementation study of an AI-based CDS for the treatment of coronary artery disease. Introduction Artificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical decision-making. AI-based clinical decision support (CDS) tools can improve the quality of care and appropriate use of healthcare resources, and decrease healthcare provider burnout. Understanding the determinants of implementing AI-based CDS tools in healthcare delivery is vital to reap the benefits of these tools. The objective of this scoping review is to map and synthesise determinants (barriers and facilitators) to implementing AI-based CDS tools in healthcare.Methods and analysis This scoping review will follow the Joanna Briggs Institute methodology and the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews checklist. The search terms will be tailored to each database, which includes MEDLINE, Embase, CINAHL, APA PsycINFO and the Cochrane Library. Grey literature and references of included studies will also be searched. The search will include studies published from database inception until 10 May 2022. We will not limit searches by study design or language. Studies that either report determinants or describe the implementation of AI-based CDS tools in clinical practice or/and healthcare settings will be included. The identified determinants (barriers and facilitators) will be described by synthesising the themes using the Theoretical Domains Framework. The outcome variables measured will be mapped and the measures of effectiveness will be summarised using descriptive statistics.Ethics and dissemination Ethics approval is not required because all data for this study have been previously published. The findings of this review will be published in a peer-reviewed journal and presented at academic conferences. Importantly, the findings of this scoping review will be widely presented to decision-makers, health system administrators, healthcare providers, and patients and family/caregivers as part of an implementation study of an AI-based CDS for the treatment of coronary artery disease. |
| Author | Sauro, Khara Lee, Joon Bajgain, Bishnu Lorenzetti, Diane |
| AuthorAffiliation | 3 Departments of Community Health Sciences, Surgery & Oncology , University of Calgary , Calgary , Alberta , Canada 1 Department of Community Health Sciences , University of Calgary , Calgary , Alberta , Canada 2 Department of Cardiac Sciences , University of Calgary , Calgary , Alberta , Canada |
| AuthorAffiliation_xml | – name: 2 Department of Cardiac Sciences , University of Calgary , Calgary , Alberta , Canada – name: 3 Departments of Community Health Sciences, Surgery & Oncology , University of Calgary , Calgary , Alberta , Canada – name: 1 Department of Community Health Sciences , University of Calgary , Calgary , Alberta , Canada |
| Author_xml | – sequence: 1 givenname: Bishnu surname: Bajgain fullname: Bajgain, Bishnu organization: Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada – sequence: 2 givenname: Diane orcidid: 0000-0001-8423-3458 surname: Lorenzetti fullname: Lorenzetti, Diane organization: Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada – sequence: 3 givenname: Joon surname: Lee fullname: Lee, Joon organization: Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada – sequence: 4 givenname: Khara orcidid: 0000-0002-7658-4351 surname: Sauro fullname: Sauro, Khara email: kmsauro@ucalgary.ca organization: Departments of Community Health Sciences, Surgery & Oncology, University of Calgary, Calgary, Alberta, Canada |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36822813$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1080_01612840_2023_2263579 crossref_primary_10_7759_cureus_41916 crossref_primary_10_3390_iot4020009 crossref_primary_10_1186_s12912_024_02571_y crossref_primary_10_2196_50939 crossref_primary_10_1016_j_nepr_2024_104158 crossref_primary_10_1108_JHOM_06_2024_0244 crossref_primary_10_1055_a_2299_4758 crossref_primary_10_1080_14740338_2024_2338252 crossref_primary_10_1136_bmjopen_2023_078227 crossref_primary_10_7759_cureus_75518 crossref_primary_10_1016_j_jbusres_2023_114402 crossref_primary_10_2196_47353 crossref_primary_10_62486_latia202325 crossref_primary_10_1016_j_radi_2024_01_019 crossref_primary_10_1080_10408347_2023_2207652 crossref_primary_10_1038_s41746_023_00965_x crossref_primary_10_1038_s41746_024_01104_w crossref_primary_10_2147_IJGM_S449598 |
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| Snippet | IntroductionArtificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical... Artificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical... Introduction Artificial intelligence (AI), the simulation of human intelligence processes by machines, is being increasingly leveraged to facilitate clinical... |
| SourceID | doaj pubmedcentral proquest pubmed crossref bmj |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | e068373 |
| SubjectTerms | Alzheimer's disease Artificial Intelligence Chronic illnesses Clinical decision making Clinical medicine Decision Support Systems, Clinical Delivery of Health Care Health Facilities Health Informatics Health Personnel HEALTH SERVICES ADMINISTRATION & MANAGEMENT Humans Keywords Machine learning Patients Quality in health care Research Design Scoping Reviews As Topic Systematic review Technological change |
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| Title | Determinants of implementing artificial intelligence-based clinical decision support tools in healthcare: a scoping review protocol |
| URI | https://bmjopen.bmj.com/content/13/2/e068373.full https://www.ncbi.nlm.nih.gov/pubmed/36822813 https://www.proquest.com/docview/2779764032 https://www.proquest.com/docview/2780071287 https://pubmed.ncbi.nlm.nih.gov/PMC9950925 https://doaj.org/article/7e4b2da282814394bbde2900decf651c |
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