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

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 hea lth 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. De cision-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 interpr etation 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 therapeu tic decision-support tools based on credible clinical outcome evidence:computer protocols leading to replicable clinician actions (eActions). eActions enable diff...

Source: Journal of the American Medical Informatics Association - February 16, 2021 Category: Information Technology Source Type: research