Understanding the Digital Disruption of Health Care: An Ethnographic Study of Real-Time Multidisciplinary Clinical Behavior in a New Digital Hospital.

BACKGROUND: Understanding electronic medical record (EMR) implementation in digital hospitals has focused on retrospective "work as imagined" experiences of multidisciplinary clinicians, rather than "work as done" behaviors. Our research question was "what is the behavior of multidisciplinary clinicians during the transition to a new digital hospital?" OBJECTIVES: The aim of the study is to: (1) Observe clinical behavior of multidisciplinary clinicians in a new digital hospital using ethnography. (2) Develop a thematic framework of clinical behavior in a new digital hospital. METHODS: The setting was the go-live of a greenfield 182-bed digital specialist public hospital in Queensland, Australia. Participants were multidisciplinary clinicians (allied health, nursing, medical, and pharmacy). Clinical ethnographic observations were conducted between March and April 2021 (approximately 1 month post-EMR implementation). Observers shadowed clinicians in real-time performing a diverse range of routine clinical activities and recorded any clinical behavior related to interaction with the digital hospital. Data were analyzed in two phases: (1) content analysis using machine learning (Leximancer v4.5); (2) researcher-led interpretation of the text analytics to generate contextual meaning and finalize themes. RESULTS: A total of 55 multidisciplinary clinicians (41.8% allied health, 23.6% nursing, 20% medical, 14.6% pharmacy) were observed across 58 hours and 99 individual patient encounters. Five themes were derived: (1) Workflows for clinical documentation; (2) Navigating a digital hospital; (3) Digital efficiencies; (4) Digital challenges; (5) Patient experience. There was no observed harm attributable to the digital transition. Clinicians primarily used blended digital and paper workflows to achieve clinical goals. The EMR was generally used seamlessly. New digital workflows affected clinical productivity and caused frustration. Digitization enabled multitasking, clinical opportunism, and benefits to patient safety; however, clinicians were hesitant to trust digital information. CONCLUSION: This study improves our real-time understanding of the digital disruption of health care and can guide clinicians, managers, and health services toward digital transformation strategies based upon "work as done."

Toward a Learning Health Care System: A Systematic Review and Evidence-Based Conceptual Framework for Implementation of Clinical Analytics in a Digital Hospital.

OBJECTIVE: A learning health care system (LHS) uses routinely collected data to continuously monitor and improve health care outcomes. Little is reported on the challenges and methods used to implement the analytics underpinning an LHS. Our aim was to systematically review the literature for reports of real-time clinical analytics implementation in digital hospitals and to use these findings to synthesize a conceptual framework for LHS implementation. METHODS: Embase, PubMed, and Web of Science databases were searched for clinical analytics derived from electronic health records in adult inpatient and emergency department settings between 2015 and 2021. Evidence was coded from the final study selection that related to (1) dashboard implementation challenges, (2) methods to overcome implementation challenges, and (3) dashboard assessment and impact. The evidences obtained, together with evidence extracted from relevant prior reviews, were mapped to an existing digital health transformation model to derive a conceptual framework for LHS analytics implementation. RESULTS: A total of 238 candidate articles were reviewed and 14 met inclusion criteria. From the selected studies, we extracted 37 implementation challenges and 64 methods employed to overcome such challenges. We identified common approaches for evaluating the implementation of clinical dashboards. Six studies assessed clinical process outcomes and only four studies evaluated patient health outcomes. A conceptual framework for implementing the analytics of an LHS was developed. CONCLUSION: Health care organizations face diverse challenges when trying to implement real-time data analytics. These challenges have shifted over the past decade. While prior reviews identified fundamental information problems, such as data size and complexity, our review uncovered more postpilot challenges, such as supporting diverse users, workflows, and user-interface screens. Our review identified practical methods to overcome these challenges which have been incorporated into a conceptual framework. It is hoped this framework will support health care organizations deploying near-real-time clinical dashboards and progress toward an LHS.