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

Alan H. Morris, Brian Stagg, Michael Lanspa, James Orme, Terry P. Clemmer, Lindell K. Weaver, Frank Thomas, Colin K. Grissom, Ellie Hirshberg, Thomas D. East, Carrie Jane Wallace, Michael P. Young, Dean F. Sittig, Antonio Pesenti, Michela Bombino, Eduardo Beck, Katherine A. Sward, Charlene Weir, Shobha S. Phansalkar, Gordon R. BernardB. Taylor Thompson, Roy Brower, Jonathon D. Truwit, Jay Steingrub, R. Duncan Hite, Douglas F. Willson, Jerry J. Zimmerman, Vinay M. Nadkarni, Adrienne Randolph, Martha A.Q. Curley, Christopher J.L. Newth, Jacques Lacroix, Michael S.D. Agus, Kang H. Lee, Bennett P. Deboisblanc, R. Scott Evans, Dean K. Sorenson, Anthony Wong, Michael V. Boland, David W. Grainger, Willard H. Dere, Alan S. Crandall, Julio C. Facelli, Stanley M. Huff, Peter J. Haug, Ulrike Pielmeier, Stephen E. Rees, Dan S. Karbing, Steen Andreassen, Eddy Fan, Roberta M. Goldring, Kenneth I. Berger, Beno W. Oppenheimer, E. Wesley Ely, Ognjen Gajic, Brian Pickering, David A. Schoenfeld, Irena Tocino, Russell S. Gonnering, Peter J. Pronovost, Lucy A. Savitz, Didier Dreyfuss, Arthur S. Slutsky, James D. Crapo, Derek Angus, Michael R. Pinsky, Brent James, Donald Berwick

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1330-1344
Number of pages15
JournalJournal of the American Medical Informatics Association
Volume28
Issue number6
DOIs
StatePublished - Jun 1 2021
Externally publishedYes

ASJC Scopus subject areas

  • Health Informatics

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