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Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions.

ABSTRACT: Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

SUBMITTER: Morris AH

PROVIDER: S-EPMC8661391 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions.

Morris Alan H AH Stagg Brian B Lanspa Michael M Orme James J Clemmer Terry P TP Weaver Lindell K LK Thomas Frank F Grissom Colin K CK Hirshberg Ellie E East Thomas D TD Wallace Carrie Jane CJ Young Michael P MP Sittig Dean F DF Pesenti Antonio A Bombino Michela M Beck Eduardo E Sward Katherine A KA Weir Charlene C Phansalkar Shobha S SS Bernard Gordon R GR Taylor Thompson B B Brower Roy R Truwit Jonathon D JD Steingrub Jay J Duncan Hite R R Willson Douglas F DF Zimmerman Jerry J JJ Nadkarni Vinay M VM Randolph Adrienne A Curley Martha A Q MAQ Newth Christopher J L CJL Lacroix Jacques J Agus Michael S D MSD Lee Kang H KH deBoisblanc Bennett P BP Scott Evans R R Sorenson Dean K DK Wong Anthony A Boland Michael V MV Grainger David W DW Dere Willard H WH Crandall Alan S AS Facelli Julio C JC Huff Stanley M SM Haug Peter J PJ Pielmeier Ulrike U Rees Stephen E SE Karbing Dan S DS Andreassen Steen S Fan Eddy E Goldring Roberta M RM Berger Kenneth I KI Oppenheimer Beno W BW Wesley Ely E E Gajic Ognjen O Pickering Brian B Schoenfeld David A DA Tocino Irena I Gonnering Russell S RS Pronovost Peter J PJ Savitz Lucy A LA Dreyfuss Didier D Slutsky Arthur S AS Crapo James D JD Angus Derek D Pinsky Michael R MR James Brent B Berwick Donald D

Journal of the American Medical Informatics Association : JAMIA 20210601 6

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools th ...[more]

PMID: 33594410

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Project description:Clinician educators-who work at the intersection of patient care and resident education-are well positioned to respond to calls for better, safer patient care and resident education. Explicit lessons that address implementing health care improvement and associated residency training came out of the Academic Chronic Care Collaboratives and include the importance of: (1) redesigning the clinical practice as a core component of the residency curriculum; (2) exploiting the efficiencies of the practice team; (3) replacing "faculty development" with "everyone's a learner;" (4) linking faculty across learning communities to build expertise; and (5) using rigorous methodology to design and evaluate interventions for practice redesign. There has been progress in addressing three thorny academic faculty issues-professional satisfaction, promotion and publication. For example, consensus criteria have been proposed for both faculty promotion as well as the institutional settings that nurture academic health care improvement careers, and the SQUIRE Publication Guidelines have been developed as a general framework for scholarly improvement publications. Extensive curricular resources exist for developing the expert faculty cadre. Curricula from representative training programs include quantitative and qualitative research methods, statistical methodologies appropriate for measuring systems change, organizational culture, management, leadership and scholarly writing for the improvement literature. Clinician educators-particularly those in general internal medicine-bear the principal responsibility for both patient care and resident training in academic departments of internal medicine. The intersection of these activities presents a unique opportunity for their playing a central role in implementing health care improvement and associated residency training. However, this role in academic settings will require an unambiguous development strategy both for faculty and their institutions.

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Dataset Information

Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions.

Publications

Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions.

Similar Datasets

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