Implementation of an automated early warning scoring system in a surgical ward: Practical use and effects on patient outcomes

Variation in nurses' compliance with an Early Warning Score protocol: A retrospective cohort study

Introduction

Early Warning Score (EWS) protocols are based on intermittent vital sign measurements, and aim to detect clinical deterioration in a timely manner. Despite its predictive value, its effectiveness remains suboptimal. An important limitation appears to be poor compliance with the EWS protocol and its variation between general wards. The current research does not yet provide an understanding of EWS compliance and variation in different nursing wards.

Aim

To explore the variation in nurses' compliance with the EWS protocol among patients with and without complications and between different nursing wards.

Methods

In a retrospective single-center cohort study, all patient files from three nursing wards of a tertiary teaching hospital in the Netherlands were reviewed over a 1-month period. Compliance was divided into three categories:1) calculation accuracy, 2) monitoring frequency end 3) clinical response.

Results

The cohort of 210 patients contained 5864 measurements, of which 4125 (70.6 %) included EWS. Significant differences in the measured vital signs within incomplete measurements were found among nursing wards. Compliance to monitoring frequency was higher within EWSs of 0-1 (78.4 %) than within EWSs of ≥2 (26.1 %). The proportion of correct follow-up was significantly higher in patients with complications, as was the correct clinical response to an EWS of ≥3 (84.8 % vs. 55.0; p = .011).

Conclusion

Our results suggest suboptimal compliance with the EWS protocol, with large variations between patients with and without complications and between different general care wards. Nurses tended to be more compliant with the EWS protocol for patients with complications.

The Effect of Modified Early Warning Score (MEWS) and Nursing Guide Application on Postoperative Patient Outcomes: A Randomized Controlled Study

PURPOSE

The aim of this study is to determine the effect of nursing guide application (NGA) on patient outcomes in patients followed up according to the modified early warning score (MEWS) in the postoperative period.

DESIGN

A randomized controlled clinical trial.

METHODS

The sample of the study consisted of 252 patients who underwent surgical intervention under general anesthesia in a university hospital between July 29, 2022, and October 31, 2022.

FINDINGS

Results showed that the development of complications was less in the study group (SG) compared to the control group (CG) during anesthesia (P = .027), in the postanesthesia care unit (PACU) (P = .017), and in the clinic (P = .001). It was found that the duration of stay in PACU in the CG was significantly shorter than in the study group (P < .001), and as the duration of stay in PACU in CG decreased, the MEWS increased (r = -0.201, P = .024). We found that there were fewer patients transferred to the intensive care unit (ICU) after PACU (P = .007), the MEWS was lower, and the number of nursing interventions applied to patients was higher (P < .05).

CONCLUSIONS

In patients followed up according to MEWS, NGA had a positive effect on preventing the development of complications and shortening the intervention time for complications, decreasing ICU admission, decreasing MEWS and increasing the number of nursing interventions. Based on the results, it may be recommended to use MEWS+NGA in the early postoperative period as it positively affects patient outcomes.

Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial

OBJECTIVES

Machine learning algorithms can outperform older methods in predicting clinical deterioration, but rigorous prospective data on their real-world efficacy are limited. We hypothesized that real-time machine learning generated alerts sent directly to front-line providers would reduce escalations.

DESIGN

Single-center prospective pragmatic nonrandomized clustered clinical trial.

SETTING

Academic tertiary care medical center.

PATIENTS

Adult patients admitted to four medical-surgical units. Assignment to intervention or control arms was determined by initial unit admission.

INTERVENTIONS

Real-time alerts stratified according to predicted likelihood of deterioration sent either to the primary team or directly to the rapid response team (RRT). Clinical care and interventions were at the providers' discretion. For the control units, alerts were generated but not sent, and standard RRT activation criteria were used.

MEASUREMENTS AND MAIN RESULTS

The primary outcome was the rate of escalation per 1000 patient bed days. Secondary outcomes included the frequency of orders for fluids, medications, and diagnostic tests, and combined in-hospital and 30-day mortality. Propensity score modeling with stabilized inverse probability of treatment weight (IPTW) was used to account for differences between groups. Data from 2740 patients enrolled between July 2019 and March 2020 were analyzed (1488 intervention, 1252 control). Average age was 66.3 years and 1428 participants (52%) were female. The rate of escalation was 12.3 vs. 11.3 per 1000 patient bed days (difference, 1.0; 95% CI, -2.8 to 4.7) and IPTW adjusted incidence rate ratio 1.43 (95% CI, 1.16-1.78; p < 0.001). Patients in the intervention group were more likely to receive cardiovascular medication orders (16.1% vs. 11.3%; 4.7%; 95% CI, 2.1-7.4%) and IPTW adjusted relative risk (RR) (1.74; 95% CI, 1.39-2.18; p < 0.001). Combined in-hospital and 30-day-mortality was lower in the intervention group (7% vs. 9.3%; -2.4%; 95% CI, -4.5% to -0.2%) and IPTW adjusted RR (0.76; 95% CI, 0.58-0.99; p = 0.045).

CONCLUSIONS

Real-time machine learning alerts do not reduce the rate of escalation but may reduce mortality.

Safer and more efficient vital signs monitoring protocols to identify the deteriorating patients in the general hospital ward: an observational study

Background

The frequency at which patients should have their vital signs (e.g. blood pressure, pulse, oxygen saturation) measured on hospital wards is currently unknown. Current National Health Service monitoring protocols are based on expert opinion but supported by little empirical evidence. The challenge is finding the balance between insufficient monitoring (risking missing early signs of deterioration and delays in treatment) and over-observation of stable patients (wasting resources needed in other aspects of care).

Objective

Provide an evidence-based approach to creating monitoring protocols based on a patient's risk of deterioration and link these to nursing workload and economic impact.

Design

Our study consisted of two parts: (1) an observational study of nursing staff to ascertain the time to perform vital sign observations; and (2) a retrospective study of historic data on patient admissions exploring the relationships between National Early Warning Score and risk of outcome over time. These were underpinned by opinions and experiences from stakeholders.

Setting and participants

Observational study: observed nursing staff on 16 randomly selected adult general wards at four acute National Health Service hospitals. Retrospective study: extracted, linked and analysed routinely collected data from two large National Health Service acute trusts; data from over 400,000 patient admissions and 9,000,000 vital sign observations.

Results

Observational study found a variety of practices, with two hospitals having registered nurses take the majority of vital sign observations and two favouring healthcare assistants or student nurses. However, whoever took the observations spent roughly the same length of time. The average was 5:01 minutes per observation over a 'round', including time to locate and prepare the equipment and travel to the patient area. Retrospective study created survival models predicting the risk of outcomes over time since the patient was last observed. For low-risk patients, there was little difference in risk between 4 hours and 24 hours post observation.

Conclusions

We explored several different scenarios with our stakeholders (clinicians and patients), based on how 'risk' could be managed in different ways. Vital sign observations are often done more frequently than necessary from a bald assessment of the patient's risk, and we show that a maximum threshold of risk could theoretically be achieved with less resource. Existing resources could therefore be redeployed within a changed protocol to achieve better outcomes for some patients without compromising the safety of the rest. Our work supports the approach of the current monitoring protocol, whereby patients' National Early Warning Score 2 guides observation frequency. Existing practice is to observe higher-risk patients more frequently and our findings have shown that this is objectively justified. It is worth noting that important nurse-patient interactions take place during vital sign monitoring and should not be eliminated under new monitoring processes. Our study contributes to the existing evidence on how vital sign observations should be scheduled. However, ultimately, it is for the relevant professionals to decide how our work should be used.

Study registration

This study is registered as ISRCTN10863045.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 17/05/03) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 6. See the NIHR Funding and Awards website for further award information.

Understanding the role and impact of electronic health records in labor and delivery nursing practice: A scoping review protocol

Background

Electronic health records have a significant impact on nursing practice, particularly in specializations such as labor and delivery, or acute care maternity nursing practice. Although primary studies on the use of electronic health records in labor and delivery have been done, no reviews on this topic exist. Moreover, the topic of labor and delivery nurses' organizing work in the electronic health record-enabled context has not been addressed.

Objective

To (a) synthesize research on electronic health record use in labor and delivery nursing and (b) map how labor and delivery nursing organizing work is transformed by the electronic health record (as described in the reviewed studies).

Methods

The scoping review will be guided by a modified methodology based on selected recommendations from the Joanna Briggs Institute and the Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews. A comprehensive search will be conducted in the following databases: CINAHL Complete, MEDLINE, Academic Search Complete, Web of Science, Scopus and Dissertations and Theses Abstracts and Indexes. Included sources will be primary research, dissertations, or theses that address the use of electronic health records in labor and delivery nursing practice in countries with high levels of electronic health record adoption. Data extracted from included sources will be analyzed thematically. Further analysis will theorize labor and delivery nurses' organizing work in the context of electronic health record use by utilizing concepts from Davina Allen's Translational Mobilization Theory. Findings will be presented in tabular and descriptive formats.

Conclusion

The findings of this review will help understand transformations of nursing practice in the electronic health record-enabled labor and delivery context and identify areas of future research. We will propose an extension of the Translational Mobilization Theory and theorize nurses' organizing work involving the use of the electronic health record.

Can we improve healthcare with centralized management systems, supported by information technology, predictive analytics, and real-time data?: A review

This narrative review discusses the effects of implementing command centers, centralized management systems, supported by information technology, predictive analytics, and real-time data, as well as small-scale centralized operating systems, on patient outcomes, operation, care delivery, and resource utilization. Implementations of command centers and small-scale centralized operating systems have led to improvement in 3 areas: integration of both multiple services into the day-to-day operation, communication and coordination, and employment of prediction and early warning system. Additional studies are required to understand the full impact of command centers on the healthcare system.

Implementing Best Practices for Decreasing Nurse Hesitancy to Call the Rapid Response Team: An Evidence-Based Quality Improvement Initiative

BACKGROUND

Nurses, regardless of experience, sometimes wait for the Modified Early Warning Score to indicate physiological decline before initiating the hospital's Rapid Response Team (RRT). The goal of this quality improvement initiative was to reduce nurse hesitancy to call the RRT, as indicated by the monthly number of proactive calls before the Modified Early Warning Score increased and triggered an automated page.

METHOD

Education, planned handoff communication, debriefing, and good catch emails were the educational strategies used in the implementation of the quality improvement initiatives, encouraging RRT calls sooner. Increasing RRT knowledge and communication is an evidence-based practice strategy to reduce nurse reluctance to call the RRT.

RESULTS

After implementation, monthly proactive RRT calls increased (χ² [1, n = 1,964] = 14.6085, p = .000159). Bedside interventions, unscheduled transfers, and Acute Physiologic and Chronic Health Evaluation scores did not differ.

CONCLUSION

Education, structured handoff communication, and acknowledging good catches reduced nurses' reluctance to call the RRT proactively. [J Contin Educ Nurs. 2023;54(6):281-288.].

The implementation of a real time early warning system using machine learning in an Australian hospital to improve patient outcomes

BACKGROUND

Early Warning Scores (EWS) monitor inpatient deterioration predominantly using vital signs. We evaluated inpatient outcomes after implementing an Artificial Intelligence (AI) based intervention in our local EWS.

METHODS

A prior study calculated a Deterioration Index (DI) with logistic regression utilising demographics, vital signs, and laboratory results at multiple time points to predict any major adverse event (MAE-all cause mortality, ICU admission, or medical emergency team activation). The current study is a single hospital, pre-post study in Australia comparing the DI plus the existing EWS (Between the Flags-BTF) to only BTF. Data were collected on all eligible inpatients (≥ 16 years, admitted ≥ 24 hours, in general non-palliative wards). Controls were inpatients in the same hospital between January and December 2019. The DI was integrated into the electronic medical record and alerts were sent to senior ward nurse phones (July 2020 -April 2021).

RESULTS

We enrolled 28,639 patients (median age 73 years, IQR:60-83) with 52.3% female. The intervention and control groups did not show any statistically significant differences apart from reduced admissions via the emergency department in the intervention group (40.4% vs 41.6%, P=0.03). Risk for an MAE was lower in intervention than control (RR: 0.81; 95%CI: 0.74-0.89). Length of hospital stay was significantly reduced in the intervention group (3.74 days, IQR 1.84-7.26) compared to the control group (3.86 days, IQR 1.86-7.86, P=0.002) CONCLUSIONS: Implementing the DI in one hospital in Australia was associated with some improved patient outcomes. Future RCTs are needed for further validation.

Use of the Modified Early Warning Score by Medical-Surgical Nurses to Initiate the Rapid Response Team: Impact on Patient Outcomes

BACKGROUND

Cardiac arrests are often preceded by several hours of physiological deterioration that may go undetected.

LOCAL PROBLEM

Cardiac arrests frequently occurred on medical-surgical units without prior rapid response team intervention.

METHODS

A pre/postintervention design was used to evaluate a protocol to guide the use of the Modified Early Warning Score (MEWS) by medical-surgical nurses to escalate the care of deteriorating adult patients.

INTERVENTIONS

Following staff education, the MEWS protocol was implemented across 8 medical-surgical units.

RESULTS

There was a significant increase in patients experiencing a rapid response prior to a cardiac arrest after implementing the MEWS protocol ( P < .0001).

CONCLUSION

Implementing a consistent review of MEWS values allows medical-surgical nurses to initiate assistance from a rapid response team that may prevent an inpatient cardiac arrest.

A wearable patch based remote early warning score (REWS) in major abdominal cancer surgery patients

INTRODUCTION

The shift toward remote patient monitoring methods to detect clinical deterioration requires testing of wearable devices in real-life clinical settings. This study aimed to develop a remote early warning scoring (REWS) system based on continuous measurements using a wearable device, and compare its diagnostic performance for the detection of deterioration to the diagnostic performance of the conventional modified early warning score (MEWS).

MATERIALS AND METHODS

The study population of this prospective, single center trial consisted of patients who underwent major abdominal cancer surgery and were monitored using routine in-hospital spotcheck measurements of the vital parameters. Heart and respiratory rates were measured continuously using a wireless accelerometer patch (HealthDot). The prediction by MEWS of deterioration toward a complication graded Clavien-Dindo of 2 or higher was compared to the REWS derived from continuous measurements by the wearable patch.

MAIN RESULTS

A total of 103 patients and 1909 spot-check measurements were included in the analysis. Postoperative deterioration was observed in 29 patients. For both EWS systems, the sensitivity (MEWS: 0.20 95% CI: [0.13-0.29], REWS: 0.20 95% CI: [0.13-0.29]) and specificity (MEWS: 0.96 95% CI: [0.95-0.97], REWS: 0.96 95% CI: [0.95-0.97]) were assessed.

CONCLUSIONS

The diagnostic value of the REWS method, based on continuous measurements of the heart and respiratory rates, is comparable to that of the MEWS in patients following major abdominal cancer surgery. The wearable patch could detect the same amount of deteriorations, without requiring manual spot check measurements.

A scoping review of real-time automated clinical deterioration alerts and evidence of impacts on hospitalised patient outcomes

BACKGROUND

Hospital patients experiencing clinical deterioration are at greater risk of adverse events. Monitoring patients through early warning systems is widespread, despite limited published evidence that they improve patient outcomes. Current limitations including infrequent or incorrect risk calculations may be mitigated by integration into electronic medical records. Our objective was to examine the impact on patient outcomes of systems for detecting and responding to real-time, automated alerts for clinical deterioration.

METHODS

This review was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. We searched Medline, CINAHL and Embase for articles implementing real-time, automated deterioration alerts in hospitalised adults evaluating one or more patient outcomes including intensive care unit admission, length of stay, in-hospital cardiopulmonary arrest and in-hospital death.

RESULTS

Of 639 studies identified, 18 were included in this review. Most studies did not report statistically significant associations between alert implementation and better patient outcomes. Four studies reported statistically significant improvements in two or more patient outcomes, and were the only studies to directly involve the patient's clinician. However, only one of these four studies was robust to existing trends in patient outcomes. Of the six studies using robust study designs, one reported a statistically significant improvement in patient outcomes; the rest did not detect differences.

CONCLUSIONS

Most studies in this review did not detect improvements in patient outcomes following the implementation of real-time deterioration alerts. Future implementation studies should consider: directly involving the patient's physician or a dedicated surveillance nurse in structured response protocols for deteriorating patients; the workflow of alert recipients; and incorporating model features into the decision process to improve clinical utility.

The Impact of Health Information Technology for Early Detection of Patient Deterioration on Mortality and Length of Stay in the Hospital Acute Care Setting: Systematic Review and Meta-Analysis

OBJECTIVE

To evaluate the impact of health information technology (HIT) for early detection of patient deterioration on patient mortality and length of stay (LOS) in acute care hospital settings.

DATA SOURCES

We searched MEDLINE and Epub Ahead of Print, In-Process & Other Non-Indexed Citations and Daily, Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Scopus from 1990 to January 19, 2021.

STUDY SELECTION

We included studies that enrolled patients hospitalized on the floor, in the ICU, or admitted through the emergency department. Eligible studies compared HIT for early detection of patient deterioration with usual care and reported at least one end point of interest: hospital or ICU LOS or mortality at any time point.

DATA EXTRACTION

Study data were abstracted by two independent reviewers using a standardized data extraction form.

DATA SYNTHESIS

Random-effects meta-analysis was used to pool data. Among the 30 eligible studies, seven were randomized controlled trials (RCTs) and 23 were pre-post studies. Compared with usual care, HIT for early detection of patient deterioration was not associated with a reduction in hospital mortality or LOS in the meta-analyses of RCTs. In the meta-analyses of pre-post studies, HIT interventions demonstrated a significant association with improved hospital mortality for the entire study cohort (odds ratio, 0.78 [95% CI, 0.70-0.87]) and reduced hospital LOS overall.

CONCLUSIONS

HIT for early detection of patient deterioration in acute care settings was not significantly associated with improved mortality or LOS in the meta-analyses of RCTs. In the meta-analyses of pre-post studies, HIT was associated with improved hospital mortality and LOS; however, these results should be interpreted with caution. The differences in patient outcomes between the findings of the RCTs and pre-post studies may be secondary to confounding caused by unmeasured improvements in practice and workflow over time.

Algorithms for Prediction of Clinical Deterioration on the General Wards: A Scoping Review

OBJECTIVE

The primary objective of this scoping review was to identify and describe state-of-the-art models that use vital sign monitoring to predict clinical deterioration on the general ward. The secondary objective was to identify facilitators, barriers, and effects of implementing these models.

DATA SOURCES

PubMed, Embase, and CINAHL databases until November 2020.

STUDY SELECTION

We selected studies that compared vital signs-based automated real-time predictive algorithms to current track-and-trace protocols in regard to the outcome of clinical deterioration in a general ward population.

DATA EXTRACTION

Study characteristics, predictive characteristics and barriers, facilitators, and effects.

RESULTS

We identified 1741 publications, 21 of which were included in our review. Two of the these were clinical trials, 2 were prospective observational studies, and the remaining 17 were retrospective studies. All of the studies focused on hospitalized adult patients. The reported area under the receiver operating characteristic curves ranged between 0.65 and 0.95 for the outcome of clinical deterioration. Positive predictive value and sensitivity ranged between 0.223 and 0.773 and from 7.2% to 84.0%, respectively. Input variables differed widely, and predicted endpoints were inconsistently defined. We identified 57 facilitators and 48 barriers to the implementation of these models. We found 68 reported effects, 57 of which were positive.

CONCLUSION

Predictive algorithms can detect clinical deterioration on the general ward earlier and more accurately than conventional protocols, which in one recent study led to lower mortality. Consensus is needed on input variables, predictive time horizons, and definitions of endpoints to better facilitate comparative research.

Patch validation: an observational study protocol for the evaluation of a multisignal wearable sensor in patients during anaesthesia and in the postanaesthesia care unit

INTRODUCTION

Except for operating rooms, postanaesthesia care units and intensive care units, where the monitoring of vital signs is continuous, intermittent care is standard practice. However, at a time when only the patients with the most serious conditions are hospitalised and only a fraction of these patients are in intensive care units, this type of monitoring is no longer sufficient. Wireless monitoring has been proposed, but it requires rigorous validation. The aim of this observational study is to compare vital signs obtained from a precordial patch sensor to those obtained with conventional monitoring.

METHODS AND ANALYSIS

This patch validation trial will be an observational, prospective, single-centre open study of 115 anaesthetised adult patients monitored with both a wireless sensor (myAngel VitalSigns, Devinnova, Montpellier, France) and a standard bedside monitor (Carescape Monitor B850, GE Healthcare, Chicago, Illinois). Both sensors will be used to record peripheral oxygen saturation, respiratory rate, heart rate, body temperature and blood pressure (systolic and diastolic). The main objective will be to assess the degree of agreement between the two systems during the patients' stay in the postanaesthesia care unit, both at the raw signal level and at the clinical parameter level. The secondary objectives will be to assess the same performance under anaesthesia, the frequency of missing data or artefacts, the diagnostic performance of the systems, the influence of patients' characteristics on agreement between the two systems, the adverse events and the acceptability of the patch to patients. Bland-Altman plots will be used in the main analysis to detect discrepancies and estimate the limits of agreement.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the Ethical Committee (Toulouse, France) on 10 April 2020. We are not yet recruiting subjects for this study. The results will be submitted for publication in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04344093.

Adjusting Early Warning Score by clinical assessment: a study protocol for a Danish cluster-randomised, multicentre study of an Individual Early Warning Score (I-EWS)

INTRODUCTION

Track and trigger systems (TTSs) based on vital signs are implemented in hospitals worldwide to identify patients with clinical deterioration. TTSs may provide prognostic information but do not actively include clinical assessment, and their impact on severe adverse events remain uncertain. The demand for prospective, multicentre studies to demonstrate the effectiveness of TTSs has grown the last decade. Individual Early Warning Score (I-EWS) is a newly developed TTS with an aggregated score based on vital signs that can be adjusted according to the clinical assessment of the patient. The objective is to compare I-EWS with the existing National Early Warning Score (NEWS) algorithm regarding clinical outcomes and use of resources.

METHOD AND ANALYSIS

In a prospective, multicentre, cluster-randomised, crossover, non-inferiority study. Eight hospitals are randomised to use either NEWS in combination with the Capital Region of Denmark NEWS Override System (CROS) or implement I-EWS for 6.5 months, followed by a crossover. Based on their clinical assessment, the nursing staff can adjust the aggregated score with a maximum of -4 or +6 points. We expect to include 150 000 unique patients. The primary endpoint is all-cause mortality at 30 days. Coprimary endpoint is the average number of times per day a patient is NEWS/I-EWS-scored, and secondary outcomes are all-cause mortality at 48 hours and at 7 days as well as length of stay.

ETHICS AND DISSEMINATION

The study was presented for the Regional Ethics committee who decided that no formal approval was needed according to Danish law (J.no. 1701733). The I-EWS study is a large prospective, randomised multicentre study that investigates the effect of integrating a clinical assessment performed by the nursing staff in a TTS, in a head-to-head comparison with the internationally used NEWS with the opportunity to use CROS.

TRIAL REGISTRATION NUMBER

NCT03690128.