Validity and effectiveness of paediatric early warning systems and track and trigger tools for identifying and reducing clinical deterioration in hospitalised children: A systematic review

Objective: To assess (1) how well validated existing paediatric track and trigger tools (PTTT) are for predicting adverse outcomes in hospitalised children, and (2) how effective broader paediatric early warning systems are at reducing adverse outcomes in hospitalised children.Design: Systematic review.Data sources: British Nursing Index, Cumulative Index of Nursing and Allied Health Literature, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effectiveness, EMBASE, Health Management Information Centre, Medline, Medline in Process, Scopus and Web of Knowledge searched through May 2018. Eligibility criteria: We included (1) papers reporting on the development or validation of a PTTT or (2) the implementation of a broader early warning system in paediatric units (age 0–18 years), where adverse outcome metrics were reported. Several study designs were considered.Data extraction and synthesis: Data extraction was conducted by two independent reviewers using template forms. Studies were quality assessed using a modified Downs and Black rating scale. Results: 36 validation studies and 30 effectiveness studies were included, with 27 unique PTTT identified. Validation studies were largely retrospective case-control studies or chart reviews, while effectiveness studies were predominantly uncontrolled before-after studies. Metrics of adverse outcomes varied considerably. Some PTTT demonstrated good diagnostic accuracy in retrospective case-control studies (primarily for predicting paediatric intensive care unit transfers), but positive predictive value was consistently low, suggesting potential for alarm fatigue. A small number of effectiveness studies reported significant decreases in mortality, arrests or code calls, but were limited by methodological concerns. Overall, there was limited evidence of paediatric early warning system interventions leading to reductions in deterioration. Conclusion: There are several fundamental methodological limitations in the PTTT literature, and the predominance of single-site studies carried out in specialist centres greatly limits generalisability. With limited evidence of effectiveness, calls to make PTTT mandatory across all paediatric units are not supported by the evidence base

Implementation of the National Early Warning Score Tool in the Acute Care Setting

Practice Problem: The organization arbitrarily called a rapid response or code blue call based on abnormal vital signs or intuition and needed a new process to identify early recognition of patient deterioration. PICOT: For adult inpatients in an acute care setting in a large healthcare system (P), will implementation of the National Early Warning Score (NEWS) across the organization (I) compared to data from the last fiscal year where NEWS was not used (C) decrease code blue calls (O) within 8-weeks (T)? Evidence: Eleven high quality studies met the inclusion criteria and found that the NEWS is a validated track and trigger tool, which promotes early detection of patients’ clinical deterioration and more accurate rapid response calls. Intervention: Staff fulfilled virtual training on completion of the NEWS tool within the computerized patient record system. Chart audits were conducted to measure compliance with the number of times the NEWS tool was used to trigger an event, and the number of times the trigger was missed. Outcome: The result of the two-tailed paired samples t-test was not statistically significant for rapid response calls. However, the clinical significance of NEWS implementation was that there was an increase in rapid responses and a decrease in code blue responses post NEWS implementation. Conclusion: The NEWS tool provided accurate identification of clinical deterioration to improve patient outcomes

Modified Early Warning Scoring (MEWS) versus Epic Deterioration Index (EDI): Battle royale for which has the best patient outcomes in the inpatient setting

Abstract Background: The increased workload bedside nurses face today requires new tools to assist with the identification of deteriorating patients during hospitalization. The Modified Early Warning Score (MEWS) tool has formed the background of early warning tools. Newer, more complex tools, like Epic’s Deterioration Index (EDI), have been developed to identify patient deterioration earlier. There is lack of evidence in the literature comparing different early warning tools, implementation, and patient outcomes. Objective: The purpose of the study was to examine models for EWS notification for RRT and patient outcomes between the use of the MEWS and EDI in an adult, acute care in-patient setting. Methods: This study was a retrospective analysis of admitted adult patients hospitalized during two different 3-month intervals. This study compared the 3-tier alert trigger (RN: 45, Provider: 55. Rapid Response Team: 65) for the EDI to the MEWS’ one alert trigger (MEWS \u3e6). The study endpoints examined were Rapid Response notifications, in-hospital mortality rate, hospital length of stay (LOS), code blue activations, unexpected transfers to the intensive care unit (ICU), mechanical ventilation after a rapid response activation, and the use of supplemental oxygen after rapid response activation. Data analysis was performed using descriptive and correlational statistics. Results: A total of 12,210 patients were examined (n = 6,602 in MEWS cohort and n = 5,608 patients in the EDI cohort). Significant differences were found in Rapid Response notifications (MEWS: 370, EDI: 251, p=0.005), LOS (median: MEWS 1.99, EDI 1.79, p=0.012), unintended ICU transfers (MEWS: 243, EDI 145, p = Conclusions: The EDI in tandem with a proactive model of monitoring for deteriorations demonstrated to have better patient outcomes as compared to the MEWS’ reactive model

Differences in the Rothman Index Score in Evolving Emergent Events in Medical-Surgical Patients

Background: The Rothman Index (RI), an early warning system using software integrated with the electronic medical record provides scores monitoring patient conditions. Minimal findings exist regarding RI scores in medical-surgical patients. Objectives: Explore differences in the RI scores in medical-surgical patients who suffered rapid response, cardiopulmonary resuscitation or death events. Methods: A retrospective comparative design of 75 subjects with a rapid response or cardiopulmonary resuscitation event on medical-surgical units over 12-months at an academic medical center using RI scores at admission, 48- and 24-hours before and at time of event. Deaths were identified immediately following the emergent events. Results: The RI scores were significantly higher on admission compared to RI scores at time of rapid response or cardiopulmonary resuscitation event (p\u3c0.001). The RI scores at 48 hours prior to event were significantly higher compared to the scores at event time (p\u3c0.001). RI scores at 24 hours before the event were significantly higher compared to the RI scores at event time (p\u3c0.001). No differences were found between the RI change scores in patients who died and those who remained alive (p=0.83). Conclusions: Differences existed in RI scores from admission, 48 and 24 hours prior to the time of emergent events. Earlier identification of patient condition changes through the nursing process, combined with an integrated early warning system in the electronic medical record, may reduce emergent events in medical-surgical patients. A collaborative dialogue between nursing and medical staff is crucial to timely recognize and treat conditions to minimize opportunities for emergent events

Development and Use of a Tablet-Based Resuscitation Sheet for Improving Outcomes During Intensive Patient Care

Data documentation from resuscitation events in hospitals, termed code blue events, utilizes a paper form, which is institution-specific. Problems with data capture and transcription exists, due to the challenges of dynamic documentation of patient, event and outcome variables as the code blue event unfolds. We hypothesize that an electronic version of code blue real-time data capture would lead to improved resuscitation data transcription, and enable clinicians to address deficiencies in quality of care. To this effect, we present the design of a tablet-based application and its use by 20 nurses at the Mayo Clinic hospital. The results showed that the nurses preferred the tablet application over the paper based form. Furthermore, a qualitative survey showed the clinicians perceived the electronic version to be more accurate and efficient than paper-based documentation, both of which are essential for an emergency code blue resuscitation procedure

Nurses’ judgments of patient risk of deterioration at change-of-shift handoff : agreement between nurses and comparison with early warning scores

Background Nurses begin forming judgments regarding patients’ clinical stability during change-of-shift handoffs. Objectives To examine the agreement between incoming and outgoing nurses’ judgments of deterioration risk following handoff and compare these judgments to commonly used early warning scores (MEWS, NEWS, ViEWS). Methods Following handoffs on three medical/surgical units, nurses completed the Patient Acuity Rating. Nurse ratings were compared with computed early warning scores based on clinical data. In follow-up interviews, nurses were invited to describe their experiences of using the rating scale. Results Sixty-two nurses carried out 444 handoffs for 158 patients. While the agreement between incoming and outgoing nurses was fair, correlations with early warning scores were low. Nurses struggled with predicting risk and used their impressions of differential risk across all the patients to whom they had been assigned to arrive at their ratings. Conclusion Nurses shared information that influenced their clinical judgments at handoff; not all of these cues may necessarily be captured in early warning scores

The Quality Application of Deep Learning in Clinical Outcome Predictions Using Electronic Health Record Data: A Systematic Review

Introduction: Electronic Health Record (EHR) is a significant source of medical data that can be used to develop predictive modelling with therapeutically useful outcomes. Predictive modelling using EHR data has been increasingly utilized in healthcare, achieving outstanding performance and improving healthcare outcomes. Objectives: The main goal of this review study is to examine different deep learning approaches and techniques used to EHR data processing. Methods: To find possibly pertinent articles that have used deep learning on EHR data, the PubMed database was searched. Using EHR data, we assessed and summarized deep learning performance in a number of clinical applications that focus on making specific predictions about clinical outcomes, and we compared the outcomes with those of conventional machine learning models. Results: For this study, a total of 57 papers were chosen. There have been five identified clinical outcome predictions: illness (n=33), intervention (n=6), mortality (n=5), Hospital readmission (n=7), and duration of stay (n=1). The majority of research (39 out of 57) used structured EHR data. RNNs were used as deep learning models the most frequently (LSTM: 17 studies, GRU: 6 research). The analysis shows that deep learning models have excelled when applied to a variety of clinical outcome predictions. While deep learning's application to EHR data has advanced rapidly, it's crucial that these models remain reliable, offering critical insights to assist clinicians in making informed decision. Conclusions: The findings demonstrate that deep learning can outperform classic machine learning techniques since it has the advantage of utilizing extensive and sophisticated datasets, such as longitudinal data seen in EHR. We think that deep learning will keep expanding because it has been quite successful in enhancing healthcare outcomes utilizing EHR data

Advance Alert Monitor

Problem: The acute deterioration of patients outside the Intensive Care Unit (ICU) are safety and quality concerns. Studies have shown that these deteriorations are associated with increased morbidity and mortality. This study aims to standardize the Rapid Response Team (RRT) nurse documentation in response to an Advanced Alert Monitor (AAM) alert, as at baseline no such alert nor standardized response and documentation exist. Context: Hospitals are continually challenged to innovate and create systems that can track multiple parameters and identify at-risk patients earlier on. An Early Warning System (EWS) in combination with a RRT significantly reduces patients’ potential for clinical decline. Predictive analytic systems such as an EWS are being introduced in response to this challenge and are anticipated to become the standard of care. The healthcare system/organization examined in this study aims to provide high quality, affordable health care services; and to improve the health of its members and the communities it serves. The Advance Alert Monitor (AAM) program enables this healthcare system/organization to better deliver on that mission by closing the quality gap of failure to recognize clinical decline in patients’ conditions. Interventions: The health system’s EWS is the AAM program. Its goal is to address safety and quality concerns associated with failing to identify a decline in patients’ conditions in a timely manner. The electronic health record and other sources are scanned constantly to generate an AAM score hour. If the score is eight percent or greater risk of deteriorating within 12 hours, E-Hospital staff review the patient’s chart and notify the RRT nurse. The RRT nurse collaborates with the primary nurse to assess the patient and communicate findings to the attending hospitalist. A standardized RRT nursing note is utilized to document the response for all initial AAM alerts. Measures: A family of measures was developed for the project. The outcome measure focused on the percentage of RRT nursing notes present for all initial AAM alerts. This measure recorded both a response and documentation of that response to the alert. Process measures included RRT proactive rounding documentation, and training of 100% RRT nurses on the AAM workflow. Tracking of code blue events outside the ICU was used as a balancing measure. Results: From January 1through June 30, 2018, there were 527 initial AAM alerts. Of those, 504 (95.6%) initial AAM alerts had the RRT nursing note present which indicates an intervention was made. Conclusions: The aim of this project was to integrate AAM predictive analytics with RRT practices that include a newly implemented standardized RRT nursing note; with AAM enabling early intervention to prevent a decline in patients’ conditions, and the RRT nursing note the documentation of such. The project was successfully implemented at the medical center with 95.6% RRT nursing note completion - and thus an intervention made - for all initial AAM alerts

Implementation of Artificial Intelligence Initiated Rapid Responses to Reduce In-Hospital Cardiac Arrest

Hospitals with strong and consistently activated rapid response teams (RRTs) have significantly fewer cardiac arrests. Early recognition of clinical deterioration supports the timely activation of RRTs, which increases earlier assessment and intervention. Current early warning tools are not sufficient and reliable for recognizing patient deterioration, and they are evolving, incorporating artificial intelligence (AI) to identify clinical decline much earlier. The project organization had previously implemented the medical early warning score tool into the RRT nurses’ practice to prioritize patient assessments, but this was not sustained due to its unreliability in identifying patients at risk. Aiming to reduce the number of in-hospital cardiac arrests by implementing AI to recognize and notify the RRT of patient deterioration, the primary key performance indicator was the number of in-hospital cardiac arrests outside the intensive care setting. Outcomes data also included the number of rapid responses pre- and post-implementation. Qualitative data were collected from the project team and RRT nurses during the implementation and self-assessment. Outcomes showed decreased cardiac arrests from 13 to 9, but the pre- and post-intervention cardiac arrest rate remained the same at 7.2%. The number and rate of rapid responses increased as expected based on previous evidence from 1.04 to 1.25 per day, indicating that the addition of AI technology stimulated recognition of patient deterioration. With more time and data as we continue to improve AI implementation, we can better understand the true effect. Future utilization of AI technology to support faster, more reliable clinical warnings should be considered