What Do We Do After the Pilot Is Done? Implementation of a Hospital Early Warning System at Scale

Paulson, Shirley; Dummett, B. Alex; Green, Julia; Scruth, Elizabeth; Reyes, Vivian; Escobar, Gabriel J.

doi:10.1016/j.jcjq.2020.01.003

Cited by 13 publications

(24 citation statements)

References 29 publications

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“…With respect to dashboard positioning, eight studies described the implementation of real-time analytics tools in an existing EHR. 21 22 23 24 25 26 27 28 The remaining six studies 29 30 31 32 33 34 implemented the dashboard outside an existing EHR, including displaying the dashboard in a separate monitor 29 33 34 and hosted on an independent web application. 32 …”

Section: Resultsmentioning

confidence: 99%

“…Forty-three per cent of challenges (16/37) were related to technology problems, indicating that health organizations are still grappling with the technology required to implement dashboards. A further 30% (11/37) were people related, including clinician resistance, 25 26 lack of clinician time to use the dashboard, 31 and concern over IT resources. 32 Most of the remaining challenges (22%, 8/37) were process related including the wide and diverse array of implementation environments 21 29 and disagreement over clinical ownership of dashboard elements.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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“…Escobar et al [ 35 ] reported the development of the early detection of impending physiological deterioration algorithm, which was further developed and subsequently named the Advanced Alert Monitor [ 44 ] and the early detection of impending physiological deterioration version 2 algorithm [ 40 ]. These models were developed to predict the risk of unplanned ICU transfer or mortality, using historical data of hundreds of thousands of patients retrieved from the Epic system, have also been retrospectively tested in a simulation study for feasibility [ 40 ], piloted in 2 hospitals [ 62 ], and more recently implemented as the Advanced Alert Monitor program and evaluated across the remaining Kaiser Permanente Hospitals [ 63 , 69 , 70 ].…”

Section: Resultsmentioning

confidence: 99%

Predicting Patient Deterioration: A Review of Tools in the Digital Hospital Setting

Mann¹,

Good²,

Fatehi³

et al. 2021

J Med Internet Res

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Background Early warning tools identify patients at risk of deterioration in hospitals. Electronic medical records in hospitals offer real-time data and the opportunity to automate early warning tools and provide real-time, dynamic risk estimates. Objective This review describes published studies on the development, validation, and implementation of tools for predicting patient deterioration in general wards in hospitals. Methods An electronic database search of peer reviewed journal papers from 2008-2020 identified studies reporting the use of tools and algorithms for predicting patient deterioration, defined by unplanned transfer to the intensive care unit, cardiac arrest, or death. Studies conducted solely in intensive care units, emergency departments, or single diagnosis patient groups were excluded. Results A total of 46 publications were eligible for inclusion. These publications were heterogeneous in design, setting, and outcome measures. Most studies were retrospective studies using cohort data to develop, validate, or statistically evaluate prediction tools. The tools consisted of early warning, screening, or scoring systems based on physiologic data, as well as more complex algorithms developed to better represent real-time data, deal with complexities of longitudinal data, and warn of deterioration risk earlier. Only a few studies detailed the results of the implementation of deterioration warning tools. Conclusions Despite relative progress in the development of algorithms to predict patient deterioration, the literature has not shown that the deployment or implementation of such algorithms is reproducibly associated with improvements in patient outcomes. Further work is needed to realize the potential of automated predictions and update dynamic risk estimates as part of an operational early warning system for inpatient deterioration.

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“…Escobar and colleagues used an iterative approach to the implementation of their EWS programme, which was ultimately found to decrease inpatient mortality 11. Their nuanced implementation included standardised guidelines of roles, responsibilities and next steps that were created after piloting the programme on a smaller scale 28. Like our hospital’s implementation, they used virtual centralised monitoring and allowed for deliberate non-reporting of alerts based on the virtual nurse’s assessment.…”

Section: Discussionmentioning

confidence: 99%

Nursing implications of an early warning system implemented to reduce adverse events: a qualitative study

et al. 2022

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BackgroundUnrecognised changes in a hospitalised patient’s clinical course may lead to a preventable adverse event. Early warning systems (EWS) use patient data, such as vital signs, nursing assessments and laboratory values, to aid in the detection of early clinical deterioration. In 2018, an EWS programme was deployed at an academic hospital that consisted of a commercially available EWS algorithm and a centralised virtual nurse team to monitor alerts. Our objective was to understand the nursing perspective on the use of an EWS programme with centralised monitoring.MethodsWe conducted and audio-recorded semistructured focus groups during nurse staff meetings on six inpatient units, stratified by alert frequency (high: >100 alerts/month; medium: 50–100 alerts/month; low: <50 alerts/month). Discussion topics included EWS programme experiences, perception of EWS programme utility and EWS programme implementation. Investigators analysed the focus group transcripts using a grounded theory approach.ResultsWe conducted 28 focus groups with 227 bedside nurses across all shifts. We identified six principal themes: (1) Alert timeliness, nurses reported being aware of the patient’s deterioration before the EWS alert, (2) Lack of accuracy, nurses perceived most alerts as false positives, (3) Workflow interruptions caused by EWS alerts, (4) Questions of actionability of alerts, nurses were often uncertain about next steps, (5) Concerns around an underappreciation of core nursing skills via reliance on the EWS programme and (6) The opportunity cost of deploying the EWS programme.ConclusionThis qualitative study of nurses demonstrates the importance of earning user trust, ensuring timeliness and outlining actionable next steps when implementing an EWS. Careful attention to user workflow is required to maximise EWS impact on improving hospital quality and patient safety.

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What Do We Do After the Pilot Is Done? Implementation of a Hospital Early Warning System at Scale

Cited by 13 publications

References 29 publications

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

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

Predicting Patient Deterioration: A Review of Tools in the Digital Hospital Setting

Nursing implications of an early warning system implemented to reduce adverse events: a qualitative study

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