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

Abstract

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.

The Effect of Digitization on the Safe Management of Anticoagulants

“…To date, the literature has identified a distinct lack of research surrounding both digital anticoagulant management and clinical analytics products and their impact on patient care outcomes despite these drugs being the leading cause of medication-induced avoidable harm. 18 34 The inclusion of four metrics allowed a broad overview of patient care outcomes that may be impacted and key opportunities to maximize safety and quality.…”

The Texas Health Resources Clinical Scholars Program: Learning healthcare system workforce development through embedded translational research

“…With the broad adoption of EHRs in the United States, 2 the health data availability prerequisite for learning healthcare systems has been fulfilled. 3 However, many organizations still face substantive LHS implementation challenges, with a key issue being the paucity of a trained workforce that can extract and analyze data, synthesize findings into actionable improvement opportunities, and present information to care team and consumer stakeholders in a relatable fashion. 4 , 5 , 6 , 7…”

“…With the broad adoption of EHRs in the United States, 2 the health data availability prerequisite for learning healthcare systems has been fulfilled. 3 However, many organizations still face substantive LHS implementation challenges, with a key issue being the paucity of a trained workforce that can extract and analyze data, synthesize findings into actionable improvement opportunities, and present information to care team and consumer stakeholders in a relatable fashion. [4][5][6][7] Similar to how homegrown EHR systems were mainly evaluated by a small group of major academic institutions, 8 Lim et al demonstrated that in general, analytics approaches supporting the LHS model are evolving and being assessed in major academic and tertiary medical centers.…”

“…[4][5][6][7] Similar to how homegrown EHR systems were mainly evaluated by a small group of major academic institutions, 8 Lim et al demonstrated that in general, analytics approaches supporting the LHS model are evolving and being assessed in major academic and tertiary medical centers. 3 Operationalizing as an LHS and training an LHS workforce are time consuming and expensive activities, creating a barrier for smaller health systems and hospitals to fully engage in LHS activities. 9 As 84% of the 6093 hospitals in the United States are categorized as community hospitals, 10…”

“…Prior to the widespread use of Electronic Health Records (EHR) and its associated extensive creation of health data as a by‐product of care, the cost and personnel requirement for the data collection and data management underpinning the LHS was a major impediment to deployment of the model. With the broad adoption of EHRs in the United States, 2 the health data availability prerequisite for learning healthcare systems has been fulfilled 3 . However, many organizations still face substantive LHS implementation challenges, with a key issue being the paucity of a trained workforce that can extract and analyze data, synthesize findings into actionable improvement opportunities, and present information to care team and consumer stakeholders in a relatable fashion 4‐7 …”