Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions
Authors: Morris Alan H , Stagg Brian , Lanspa Michael , Orme James , Clemmer Terry P , Weaver Lindell K , Thomas Frank , Grissom Colin K , Hirshberg Ellie , East Thomas D , Wallace Carrie Jane , Young Michael P , Sittig Dean F , Pesenti Antonio , Bombino Michela , Beck Eduardo , Sward Katherine A , Weir Charlene , Phansalkar Shobha S , Bernard Gordon R , Taylor Thompson B , Brower Roy , Truwit Jonathon D , Steingrub Jay , Duncan Hite R , Willson Douglas F , Zimmerman Jerry J , Nadkarni Vinay M , Randolph Adrienne , Curley Martha A. Q , Newth Christopher J. L , Lacroix Jacques , Agus Michael S. D , Lee Kang H , deBoisblanc Bennett P , Scott Evans R , Sorenson Dean K , Wong Anthony , Boland Michael V , Grainger David W , Dere Willard H , Crandall Alan S , Facelli Julio C , Huff Stanley M , Haug Peter J , Pielmeier Ulrike , Rees Stephen E , Karbing Dan S , Andreassen Steen , Fan Eddy , Goldring Roberta M , Berger Kenneth I , Oppenheimer Beno W , Wesley Ely E , Gajic Ognjen , Pickering Brian , Schoenfeld David A , Tocino Irena , Gonnering Russell S , Pronovost Peter J , Savitz Lucy A , Dreyfuss Didier , Slutsky Arthur S , Crapo James D , Angus Derek , Pinsky Michael R , James Brent , Berwick Donald 
Published: November 2020

AbstractClinical 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.