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Pfaff H, Schmitt J. Shifting from Theoretical Best Evidence to Practical Best Evidence: an Approach to Overcome Structural Conservatism of Evidence-Based Medicine and Health Policy. DAS GESUNDHEITSWESEN 2024; 86:S239-S250. [PMID: 39146964 DOI: 10.1055/a-2350-6435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
There is disparity in the healthcare sector between the extent of innovation in medical products (e. g., drugs) and healthcare structures. The reason is not a lack of ideas, concepts, or (quasi-) experimental studies on structural innovations. Instead, we argue that the slow implementation of structural innovations has created this disparity partly because evidence-based medicine (EBM) instruments are well suited to evaluate product innovations but less suited to evaluate structural innovations. This article argues that the unintentional interplay between EBM, which has changed significantly over time to become primarily theoretical, on the one hand, and caution and inertia in health policy, on the other, has resulted in structural conservatism. Structural conservatism is present when healthcare structures persistently and essentially resist innovation. We interpret this phenomenon as an unintended consequence of deliberate EBM action. Therefore, we propose a new assessment framework to respond to structural innovations in healthcare, centered on the differentiation between the theoretical best (possible) evidence, the practical best (possible) evidence, and the best available evidence.
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Affiliation(s)
- Holger Pfaff
- University of Cologne, Faculty of Human Sciences & Faculty of Medicine and University Hospital Cologne, Institute of Medical Sociology, Health Services Research and Rehabilitation Science, Chair of Quality Development and Evaluation in Rehabilitation; Cologne, Germany
| | - Jochen Schmitt
- Zentrum für Evidenzbasierte Gesundheitsversorgung, Med. Fakultät der TU Dresden, Universitätsklinikum Carl Gustav Carus Dresden, Dresden, Germany
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Hogg HDJ, Martindale APL, Liu X, Denniston AK. Clinical Evaluation of Artificial Intelligence-Enabled Interventions. Invest Ophthalmol Vis Sci 2024; 65:10. [PMID: 39106058 PMCID: PMC11309043 DOI: 10.1167/iovs.65.10.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 07/02/2024] [Indexed: 08/07/2024] Open
Abstract
Artificial intelligence (AI) health technologies are increasingly available for use in real-world care. This emerging opportunity is accompanied by a need for decision makers and practitioners across healthcare systems to evaluate the safety and effectiveness of these interventions against the needs of their own setting. To meet this need, high-quality evidence regarding AI-enabled interventions must be made available, and decision makers in varying roles and settings must be empowered to evaluate that evidence within the context in which they work. This article summarizes good practices across four stages of evidence generation for AI health technologies: study design, study conduct, study reporting, and study appraisal.
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Affiliation(s)
- H. D. Jeffry Hogg
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- NIHR-Supported Incubator in AI & Digital Healthcare, Birmingham, United Kingdom
| | | | - Xiaoxuan Liu
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- NIHR-Supported Incubator in AI & Digital Healthcare, Birmingham, United Kingdom
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, United Kingdom
| | - Alastair K. Denniston
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- NIHR-Supported Incubator in AI & Digital Healthcare, Birmingham, United Kingdom
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, United Kingdom
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Younas A, Reynolds SS. Leveraging Artificial Intelligence for Expediting Implementation Efforts. Creat Nurs 2024; 30:111-117. [PMID: 38509712 DOI: 10.1177/10784535241239059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
Expedited implementation of evidence into practice and policymaking is critical to ensure the delivery of effective care and improve health-care outcomes. Implementation science deals with the designing of methods and strategies for increasing and facilitating the uptake of evidence into practice and policymaking. Nevertheless, the process of designing and selecting methods and strategies for implementing evidence is complicated because of the complexity of health-care settings where implementation is desired. Artificial intelligence (AI) has revolutionized a range of fields, including genomics, education, drug trials, research, and health care. This commentary discusses how AI can be leveraged to expedite implementation science efforts for transforming health-care practice. Four key aspects of AI use in implementation science are highlighted: (a) AI for implementation planning (e.g., needs assessment, predictive analytics, and data management), (b) AI for developing implementation tools and guidelines, (c) AI for designing and applying implementation strategies, and (d) AI for monitoring and evaluating implementation outcomes. Use of AI along the implementation continuum from planning to delivery and evaluation can enable more precise and accurate implementation of evidence into practice.
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Trinkley KE, An R, Maw AM, Glasgow RE, Brownson RC. Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions. Implement Sci 2024; 19:17. [PMID: 38383393 PMCID: PMC10880216 DOI: 10.1186/s13012-024-01346-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/25/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods. MAIN TEXT This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly. CONCLUSIONS Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.
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Affiliation(s)
- Katy E Trinkley
- Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Adult and Child Center for Outcomes Research and Delivery Science Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Colorado Center for Personalized Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Ruopeng An
- Brown School and Division of Computational and Data Sciences at Washington University in St. Louis, St. Louis, MO, USA
| | - Anna M Maw
- Adult and Child Center for Outcomes Research and Delivery Science Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- School of Medicine, Division of Hospital Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Russell E Glasgow
- Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Adult and Child Center for Outcomes Research and Delivery Science Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Ross C Brownson
- Prevention Research Center, Brown School at Washington University in St. Louis, St. Louis, MO, USA
- Department of Surgery, Division of Public Health Sciences, and Alvin J. Siteman Cancer Center, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
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