Mind the Gap: Putting Evidence into Practice in the Era of Learning Health Systems

Guíse, Jeanne Marie; Savitz, Lucy A.; Friedman, Charles P.

doi:10.1007/s11606-018-4633-1

Cited by 77 publications

(81 citation statements)

References 8 publications

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“…Although this barrier was not frequently stated in interviews with IDx experts, it is implicit in the historical difficulty in bringing attention to and improving diagnoses . As our LHS interviewees commented, we are in desperate need of system‐based solutions that catalyze K2P, institutionalizing the pipeline—curation, management, and dissemination—that translates evidence‐based medicine and ML algorithms into the clinical exam room …”

Section: Discussionmentioning

confidence: 99%

Understanding the roles of three academic communities in a prospective learning health ecosystem for diagnostic excellence

Satterfield

Rubin

Yang

et al. 2019

Learning Health Systems

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Inaccurate, untimely, and miscommunicated medical diagnoses represent a wicked problem requiring comprehensive and coordinated approaches, such as those demonstrated in the characteristics of learning health systems (LHSs). To appreciate a vision for how LHS methods can optimize processes and outcomes in medical diagnosis (diagnostic excellence), we interviewed 32 individuals with relevant expertise: 18 who have studied diagnostic processes using traditional behavioral science and health services research methods, six focused on machine learning (ML) and artificial intelligence (AI) approaches, and eight multidisciplinary researchers experienced in advocating for and incorporating LHS methods, ie, scalable continuous learning in health care. We report on barriers and facilitators, identified by these subjects, to applying their methods toward optimizing medical diagnosis. We then employ their insights to envision the emergence of a learning ecosystem that leverages the tools of each of the three research groups to advance diagnostic excellence. We found that these communities represent a natural fit forward, in which together, they can better measure diagnostic processes and close the loop of putting insights into practice. Members of the three academic communities will need to network and bring in additional stakeholders before they can design and implement the necessary infrastructure that would support ongoing learning of diagnostic processes at an economy of scale and scope. KEYWORDS diagnostic error, learning community, learning health systems, machine learning, medical diagnosis, multidisciplinary

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Section: Discussionmentioning

confidence: 99%

Understanding the roles of three academic communities in a prospective learning health ecosystem for diagnostic excellence

Satterfield

Rubin

Yang

et al. 2019

Learning Health Systems

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“…In the commentary by Guise et al, the authors describe a learning cycle for learning health systems in which evidence is rapidly generated, integrated into practice, and further evidence can be generated for further medical and clinical insights. 1 The novel aspect suggested is the archiving of data, whether from clinical trials, systematic reviews and metaanalyses, and other study types, in such a manner that enables rapid reproducibility and continuous updating of medical evidence. In the clinical world, this is indeed a novel approach: the realities of healthcare delivery-the fragmented systems of care, misaligned values and incentives, slow adoption of new effective technologies, or the recognizable black box in which clinicians enter burdensome amounts of data without the feedback of intelligent insights-could all significantly benefit from the findability, accessibility, interoperability, and reusability of data in all of its forms.…”

Section: T O the Editorsmentioning

confidence: 99%

Putting FAIR Evidence into Practice

Leung

Dumontier

2019

J GEN INTERN MED

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“…The positive feedback regarding these products suggests that they may be a useful step toward bridging the gap between evidence production and use in health systems. 12 Overall, based on these pilot projects, we learned the following:…”

Section: Recommendationsmentioning

confidence: 99%

AHRQ EPC Pilot Projects Summary: Improving Health Systems' Access to High Quality Evidence

Fiordalisi¹,

Borsky²,

Chang³

et al. 2019

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Purpose of report This report summarizes nine Evidence-based Practice Center (EPC) pilot projects that developed companion products intended to accelerate the uptake and implementation of evidence from EPC reviews in health systems. Key messages • EPCs developed nine companion products, which included dissemination products (e.g., short report summaries), interactive data visualization products (e.g., interactive maps), and implementation products (e.g., electronic health tools and decision aids) to help health systems use the findings from EPC reviews. • Developing companion products to EPC evidence reviews required additional time, resources and information, and expertise. • Before starting an evidence review, working with health systems to understand the needs and decisional dilemma they are facing will allow systematic reviewers to incorporate additional context specific information to improve usefulness for health systems. • Companion products may help health systems use findings from AHRQ evidence reviews. Products should prioritize clear writing, meaningful tables and graphs, and tailor the evidence to the needs of a partner. iii This report is based on research conducted by the Agency for Healthcare Research and Quality (AHRQ) Scientific Resource Center, funded through the following contract: Scientific Resource Center III (290-2017-00003C). The findings and conclusions in this document are those of the authors, who are responsible for its contents; the findings and conclusions do not necessarily represent the views of AHRQ. Therefore, no statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services. None of the investigators have any affiliations or financial involvement that conflicts with the material presented in this report. The information in this report is intended to help EPCs and AHRQ understand how EPC reports can be improved to benefit health-system decision making. This report is not intended to be a substitute for the application of clinical judgment. Anyone who makes decisions concerning the provision of clinical care should consider this report in the same way as any medical reference and in conjunction with all other pertinent information, i.e., in the context of available resources and circumstances presented by individual patients. This report is made available to the public under the terms of a licensing agreement between the authors and the Agency for Healthcare Research and Quality. This report may be used and reprinted without permission except those copyrighted materials that are clearly noted in the report. Further reproduction of those copyrighted materials is prohibited without the express permission of copyright holders. AHRQ or U.S. Department of Health and Human Services endorsement of any derivative products that may be developed from this report, such as clinical practice guidelines, other quality enhancement tools, or reimbursement or coverage policies, may not be stated or implied.

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Mind the Gap: Putting Evidence into Practice in the Era of Learning Health Systems

Cited by 77 publications

References 8 publications

Understanding the roles of three academic communities in a prospective learning health ecosystem for diagnostic excellence

Understanding the roles of three academic communities in a prospective learning health ecosystem for diagnostic excellence

Putting FAIR Evidence into Practice

AHRQ EPC Pilot Projects Summary: Improving Health Systems' Access to High Quality Evidence

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