Early detection of critical illness outside the intensive care unit: Clarifying treatment plans and honoring goals of care using a supportive care team

Granich, Russ; Sutton, Zoe; Ys, Kim; Anderson, Megan; Wood, Helen; Scharf, John; Ragins, Arona I.; Escobar, Gabriel J.

doi:10.1002/jhm.2660

Cited by 13 publications

(8 citation statements)

References 20 publications

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“…Escobar et al 24 describe the quantitative as well as the electronic architecture of an early warning system (EWS) pilot at 2 hospitals that are part of an integrated healthcare delivery system. Dummett et al 25 then show how a clinical rescue component was developed to take advantage of the EWS, whereas Granich et al 26 describe the complementary component (integration of supportive care and ensuring that patient preferences are respected). The paper by Liu et al 27 concludes by placing all of this work in a much broader context, that of the learning healthcare system.…”

Section: Articles In This Issuementioning

confidence: 99%

Early detection, prevention, and mitigation of critical illness outside intensive care settings

Escobar

Dellinger

2016

Journal of Hospital Medicine

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Patients who deteriorate outside the intensive care unit (ICU) are known to have elevated mortality and morbidity. Rapid response teams (RRTs) were developed to address such deterioration. It has not been possible to establish that RRTs employing manual detection methods have definitively improved hospital outcomes. Because of this, automated early detection systems based on data from modern electronic medical records have been developed. This article attempts to establish a conceptual framework for early detection, prevention, and mitigation of critical illness in hospitalized patients outside the ICU. Taking a step back from the now voluminous clinical and statistical literature on early warning systems, this article focuses on definitional issues (What is early detection? What does it aim to detect, and how?). Particular attention is given to how early detection systems interface with patients who are near the end of life. Also addressed are problems of how one quantifies benefit, which includes consideration of downsides such as potential harms of early detection. Journal of Hospital Medicine 2016;11:S5–S10. © 2016 Society of Hospital Medicine

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Section: Articles In This Issuementioning

confidence: 99%

Early detection, prevention, and mitigation of critical illness outside intensive care settings

Escobar

Dellinger

2016

Journal of Hospital Medicine

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“…Other authors have described many aspects of digital, prognostic modelling for the deteriorating patient 31 and methods for incorporating treatment limitations when evaluating prognostic risk scores like HAVEN and note the importance of capturing these data electronically. 32 …”

Section: Discussionmentioning

confidence: 99%

Evaluation of a digital system to predict unplanned admissions to the intensive care unit: A mixed-methods approach

et al. 2022

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Background We have developed the Hospital Alerting Via Electronic Noticeboard (HAVEN) which aims to identify hospitalised patients most at risk of reversible deterioration. HAVEN combines patients’ vital-sign measurements with laboratory results, demographics and comorbidities using a machine learnt algorithm. Objectives The aim of this study was to identify variables or concepts that could improve HAVEN predictive performance. Methods This was an embedded, mixed methods study. Eligible patients with the five highest HAVEN scores in the hospital (i.e., ‘HAVEN Top 5′) had their medical identification details recorded. We conducted a structured medical note review on these patients 48 hours post their identifiers being recorded. Methods of constant comparison were used during data collection and to analyse patient data. Results The 129 patients not admitted to ICU then underwent constant comparison review, which produced three main groups. Group 1 were patients referred to specialist services (n = 37). Group 2 responded to ward-based treatment, (n = 38). Group 3 were frail and had documented treatment limitations (n = 47). Conclusions Digital-only validation methods code the cohort not admitted to ICU as ‘falsely positive’ in sensitivity analyses however this approach limits the evaluation of model performance. Our study suggested that coding for patients referred to other specialist teams, those with treatment limitations in place, along with those who are deteriorating but then respond to ward-based therapies, would give a more accurate measure of the value of the scores, especially in relation to cost-effectiveness of resource utilisation.

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“…In a recently described pilot, [25][26][27][28][29] we implemented an RRS for general medical/surgical ward patients at 2 KPNC hospitals. The pilot sites, which went live in 2013 and 2014, had 120 and 333 beds and 7,000 and 14,000 annual discharges, respectively.…”

Section: Initial Approachmentioning

confidence: 99%

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

Paulson

Dummett

Green

et al. 2020

The Joint Commission Journal on Quality and Patient Safety

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Background: Adults who deteriorate outside the ICU have high mortality. Most rapid response systems (RRSs) have employed manual detection processes that rapid response teams (RRTs) use to identify patients at risk. This project piloted the use of an automated early warning system (EWS), based on a very large database, that provides RRTs with 12 hours lead time to mount a response. Results from a 2-hospital pilot were encouraging, so leadership decided to deploy the Advance Alert Monitor (AAM) program in 19 more hospitals. Challenge: How can one deploy an RRS using an automated EWS at scale? Solution: EWS displays were removed from frontline clinicians' hospital electronic dashboards, and a Virtual Quality Team (VQT) RN was interposed between the EWS and the RRT. VQT RNs monitor the EWS remotely-when alerts are issued, they conduct a preliminary chart review and contact hospital RRT RNs. VQT and RRT RNs review the cases jointly. The RRT RNs then consult with hospitalists regarding clinical rescue and/or palliative care workflows. Subsequently, VQT RNs monitor patient charts, ensuring adherence to RRS practice standards. To enable this process, the project team developed a governance structure, clinical workflows, palliative care workflows, and documentation standards. Results: The AAM Program now functions in 21 Kaiser Permanente Northern California hospitals. VQT RNs monitor EWS alerts 24 hours a day, 7 days a week. The AAM Program handles ∼16,000 alerts per year. Its implementation has resulted in standardization of RRT staffing, clinical rescue workflows, and in-hospital palliative care.

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Early detection of critical illness outside the intensive care unit: Clarifying treatment plans and honoring goals of care using a supportive care team

Cited by 13 publications

References 20 publications

Early detection, prevention, and mitigation of critical illness outside intensive care settings

Early detection, prevention, and mitigation of critical illness outside intensive care settings

Evaluation of a digital system to predict unplanned admissions to the intensive care unit: A mixed-methods approach

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

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