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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 study indicates medication turnaround times utilising a closed-loop EMMS are less compared to paper-based systems. This improvement may be attributable to increased accessibility of medications using automated dispensing cabinets and electronic medication administration records flagging tasks to nurses in real time.

Background Anticoagulant drugs are the leading cause of medication harm in hospitals and prescribing errors are common with traditional paper prescriptions. Electronic medicines management can reduce prescribing errors for many drugs; however, little is known about the impact of e-prescribing on anticoagulants. Our case study reports on the lessons learned during conversion from paper to e-prescribing and the ongoing optimization process. Methods The iterative implementation of an anticoagulant prescribing platform in an integrated electronic medical record (ieMR) and ongoing continuous enhancements was applied across five digital hospital sites utilizing a single domain. The collaborative management of each class of anticoagulant, optimization strategies, governance structures, and lessons learned is described. An analysis of the rate of errors and adverse events pre- and post-go live is presented. Results The transition to e-prescribing relied on a strong inter-disciplinary governance framework to promote the safe management of anticoagulants. There was no increase in overall prescribing errors, however unfamiliarity with the new system caused a transient increase in errors with unfractionated heparin (1.8/month pre-ieMR vs. 5.5/month post-ieMR). A dedicated real-time surveillance dashboard was introduced. The iterative nature of changes indicated the complexities involved with anticoagulants and the need for an interactive, optimization approach. This led to a significant decrease in anticoagulant related hospital acquired complications (12.1/month pre-ieMR vs. 7.8/month post-ieMR, p = 0.01). Conclusion Digitizing anticoagulant prescribing led to an overall reduction in errors, but a continuous iterative optimization approach was needed to achieve this outcome. The knowledge presented can help inform optimal therapeutic anticoagulation ieMR design strategies.