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.

The Role of Electronic Medical Records in Reducing Unwarranted Clinical Variation in Acute Health Care: Systematic Review.

BACKGROUND: The use of electronic medical records (EMRs)/electronic health records (EHRs) provides potential to reduce unwarranted clinical variation and thereby improve patient health care outcomes. Minimization of unwarranted clinical variation may raise and refine the standard of patient care provided and satisfy the quadruple aim of health care. OBJECTIVE: A systematic review of the impact of EMRs and specific subcomponents (PowerPlans/SmartSets) on variation in clinical care processes in hospital settings was undertaken to summarize the existing literature on the effects of EMRs on clinical variation and patient outcomes. METHODS: Articles from January 2000 to November 2020 were identified through a comprehensive search that examined EMRs/EHRs and clinical variation or PowerPlans/SmartSets. Thirty-six articles met the inclusion criteria. Articles were examined for evidence for EMR-induced changes in variation and effects on health care outcomes and mapped to the quadruple aim of health care. RESULTS: Most of the studies reported positive effects of EMR-related interventions (30/36, 83%). All of the 36 included studies discussed clinical variation, but only half measured it (18/36, 50%). Those studies that measured variation generally examined how changes to variation affected individual patient care (11/36, 31%) or costs (9/36, 25%), while other outcomes (population health and clinician experience) were seldom studied. High-quality study designs were rare. CONCLUSIONS: The literature provides some evidence that EMRs can help reduce unwarranted clinical variation and thereby improve health care outcomes. However, the evidence is surprisingly thin because of insufficient attention to the measurement of clinical variation, and to the chain of evidence from EMRs to variation in clinical practices to health care outcomes.

Evaluating the usability of speech recognition to create clinical documentation using a commercial electronic health record.

OBJECTIVE: To conduct a usability study exploring the value of using speech recognition (SR) for clinical documentation tasks within an electronic health record (EHR) system. METHODS: Thirty-five emergency department clinicians completed a system usability scale (SUS) questionnaire. The study was undertaken after participants undertook randomly allocated clinical documentation tasks using keyboard and mouse (KBM) or SR. SUS scores were analyzed and the results with KBM were compared to SR results. RESULTS: Significant difference in SUS scores between EHR system use with and without SR were observed (KBM 67, SR 61; P = 0.045; CI, 0.1 to 12.0). Nineteen of 35 participants scored higher for EHR with KBM, 11 higher for EHR with SR and 5 gave the same score for both. Factor analysis showed no significant difference in scores for the sub-element of usability (EHR with KBM 65, EHR with SR 62; P = 0.255; CI, -2.6 to 9.5). Scores for the sub-element of learnability were significantly different (KBM 72, SR 55; P < 0.001; CI, 9.8 to 23.5). A significant correlation was found between the perceived usability of the two system configurations (EHR with KBM or SR) and the efficiency of documentation (time to document) (P = 0.002; CI, 10.5 to -0.1) but not with safety (number of errors) (P = 0.90; CI, -2.3 to 2.6). DISCUSSION: SR was associated with significantly reduced overall usability scores, even though it is often positioned as ease of use technology. SR was perceived to impose larger costs in terms of learnability via training and support requirements for EHR based documentation when compared to using KBM. Lower usability scores were significantly associated with longer documentation times. CONCLUSION: The usability of EHR systems with any input modality is an area that requires continued development. The addition of an SR component to an EHR system may cause a significant reduction in terms of perceived usability by clinicians.

Evaluating the Efficiency and Safety of Speech Recognition within a Commercial Electronic Health Record System: A Replication Study.

OBJECTIVE: To conduct a replication study to validate previously identified significant risks and inefficiencies associated with the use of speech recognition (SR) for documentation within an electronic health record (EHR) system. METHODS: Thirty-five emergency department clinicians undertook randomly allocated clinical documentation tasks using keyboard and mouse (KBM) or SR using a commercial EHR system. The experiment design, setting, and tasks (E2) replicated an earlier study (E1), while technical integration issues that may have led to poorer SR performance were addressed. RESULTS: Complex tasks were significantly slower to complete using SR (16.94%) than KBM (KBM: 191.9 s, SR: 224.4 s; p = 0.009; CI, 11.9-48.3), replicating task completion times observed in the earlier experiment. Errors (non-typographical) were significantly higher with SR compared with KBM for both simple (KBM: 3, SR: 84; p < 0.001; CI, 1.5-2.5) and complex tasks (KBM: 23, SR: 53; p = 0.001; CI, 0.5-1.0), again replicating earlier results (E1: 170, E2: 163; p = 0.660; CI, 0.0-0.0). Typographical errors were reduced significantly in the new study (E1: 465, E2: 150; p < 0.001; CI, 2.0-3.0). DISCUSSION: The results of this study replicate those reported earlier. The use of SR for clinical documentation within an EHR system appears to be consistently associated with decreased time efficiencies and increased errors. Modifications implemented to optimize SR integration in the EHR seem to have resulted in minor improvements that did not fundamentally change overall results. CONCLUSION: This replication study adds further evidence for the poor performance of SR-assisted clinical documentation within an EHR. Replication studies remain rare in informatics literature, especially where study results are unexpected or have significant implication; such studies are clearly needed to avoid overdependence on the results of a single study.

Efficiency and safety of speech recognition for documentation in the electronic health record.

OBJECTIVE: To compare the efficiency and safety of using speech recognition (SR) assisted clinical documentation within an electronic health record (EHR) system with use of keyboard and mouse (KBM). METHODS: Thirty-five emergency department clinicians undertook randomly allocated clinical documentation tasks using KBM or SR on a commercial EHR system. Tasks were simple or complex, and with or without interruption. Outcome measures included task completion times and observed errors. Errors were classed by their potential for patient harm. Error causes were classified as due to IT system/system integration, user interaction, comprehension, or as typographical. User-related errors could be by either omission or commission. RESULTS: Mean task completion times were 18.11% slower overall when using SR compared to KBM (P = .001), 16.95% slower for simple tasks (P = .050), and 18.40% slower for complex tasks (P = .009). Increased errors were observed with use of SR (KBM 32, SR 138) for both simple (KBM 9, SR 75; P < 0.001) and complex (KBM 23, SR 63; P < 0.001) tasks. Interruptions did not significantly affect task completion times or error rates for either modality. DISCUSSION: For clinical documentation, SR was slower and increased the risk of documentation errors, including errors with the potential to cause clinical harm compared to KBM. Some of the observed increase in errors may be due to suboptimal SR to EHR integration and workflow. CONCLUSION: Use of SR to drive interactive clinical documentation in the EHR requires careful evaluation. Current generation implementations may require significant development before they are safe and effective. Improving system integration and workflow, as well as SR accuracy and user-focused error correction strategies, may improve SR performance.

Risks and benefits of speech recognition for clinical documentation: a systematic review.

OBJECTIVE: To review literature assessing the impact of speech recognition (SR) on clinical documentation. METHODS: Studies published prior to December 2014 reporting clinical documentation using SR were identified by searching Scopus, Compendex and Inspect, PubMed, and Google Scholar. Outcome variables analyzed included dictation and editing time, document turnaround time (TAT), SR accuracy, error rates per document, and economic benefit. Twenty-three articles met inclusion criteria from a pool of 441. RESULTS: Most studies compared SR to dictation and transcription (DT) in radiology, and heterogeneity across studies was high. Document editing time increased using SR compared to DT in four of six studies (+1876.47% to -16.50%). Dictation time similarly increased in three of five studies (+91.60% to -25.00%). TAT consistently improved using SR compared to DT (16.41% to 82.34%); across all studies the improvement was 0.90% per year. SR accuracy was reported in ten studies (88.90% to 96.00%) and appears to improve 0.03% per year as the technology matured. Mean number of errors per report increased using SR (0.05 to 6.66) compared to DT (0.02 to 0.40). Economic benefits were poorly reported. CONCLUSIONS: SR is steadily maturing and offers some advantages for clinical documentation. However, evidence supporting the use of SR is weak, and further investigation is required to assess the impact of SR on documentation error types, rates, and clinical outcomes.