User interface approaches implemented with automated patient deterioration surveillance tools: protocol for a scoping review

IntroductionEarly identification of patients who may suffer from unexpected adverse events (eg, sepsis, sudden cardiac arrest) gives bedside staff valuable lead time to care for these patients appropriately. Consequently, many machine learning algorithms have been developed to predict adverse events. However, little research focuses on how these systems are implemented and how system design impacts clinicians’ decisions or patient outcomes. This protocol outlines the steps to review the designs of these tools.Methods and analysisWe will use scoping review methods to explore how tools that leverage machine learning algorithms in predicting adverse events are designed to integrate into clinical practice. We will explore the types of user interfaces deployed, what information is displayed, and how clinical workflows are supported. Electronic sources include Medline, Embase, CINAHL Complete, Cochrane Library (including CENTRAL), and IEEE Xplore from 1 January 2009 to present. We will only review primary research articles that report findings from the implementation of patient deterioration surveillance tools for hospital clinicians. The articles must also include a description of the tool’s user interface. Since our primary focus is on how the user interacts with automated tools driven by machine learning algorithms, electronic tools that do not extract data from clinical data documentation or recording systems such as an EHR or patient monitor, or otherwise require manual entry, will be excluded. Similarly, tools that do not synthesise information from more than one data variable will also be excluded. This review will be limited to English-language articles. Two reviewers will review the articles and extract the data. Findings from both researchers will be compared with minimise bias. The results will be quantified, synthesised and presented using appropriate formats.Ethics and disseminationEthics review is not required for this scoping review. Findings will be disseminated through peer-reviewed publications.

Implementation of a structured emergency nursing framework results in significant cost benefit

Background Patients are at risk of deterioration on discharge from an emergency department (ED) to a ward, particularly in the first 72 h. The implementation of a structured emergency nursing framework (HIRAID) in regional New South Wales (NSW), Australia, resulted in a 50% reduction of clinical deterioration related to emergency nursing care. To date the cost implications of this are unknown. The aim of this study was to determine any net financial benefits arising from the implementation of the HIRAID emergency nursing framework. Methods This retrospective cohort study was conducted between March 2018 and February 2019 across two hospitals in regional NSW, Australia. Costs associated with the implementation of HIRAID at the study sites were calculated using an estimate of initial HIRAID implementation costs (AUD) ($492,917) and ongoing HIRAID implementation costs ($134,077). Equivalent savings per annum (i.e. in less patient deterioration) were calculated using projected estimates of ED admission and patient deterioration episodes via OLS regression with confidence intervals for incremental additional deterioration costs per episode used as the basis for scenario analysis. Results The HIRAID-equivalent savings per annum exceed the costs of implementation under all scenarios (Conservative, Expected and Optimistic). The estimated preliminary savings to the study sites per annum was $1,914,252 with a payback period of 75 days. Conservative projections estimated a net benefit of $1,813,760 per annum by 2022–23. The state-wide projected equivalent savings benefits of HIRAID equalled $227,585,008 per annum, by 2022–23. Conclusions The implementation of HIRAID reduced costs associated with resources consumed from patient deterioration episodes. The HIRAID-equivalent savings per annum to the hospital exceed the costs of implementation across a range of scenarios, and upscaling would result in significant patient and cost benefit.

Physiological Observations and Nursing Interventions in the Pre-Arrest Patient: A Descriptive Study

<p>Aim: To identify how nurses respond to abnormal physiological observations in the 12 hours prior to a patient having a cardiac arrest. Methods: A descriptive observational design was used to retrospectively review the observation charts and nursing notes of 28 patients who had an in-hospital cardiac arrest, during a 20 month period. This study was performed in a large, tertiary teaching hospital in New Zealand. Key Findings Only one patient met the hospitals minimum standard of four hourly observations and a full set of vital signs were performed on only three patients. The nursing responses were limited to increasing the frequency of observations or informing the doctor. There were few other interventions to treat the abnormality. Eight (32%) patients who had either no response or a partial response to their abnormal physiology did not survive. The nursing documentation demonstrated that abnormal neurological observations were tolerated for significant periods of time and were not acted upon in 62% of these patients. The nursing documentation revealed that the delivery of oxygen was often insufficient to meet the patient's requirements and the medical staff were aware of less than half the patients with abnormal physiology. Discussion removed statement re pt survival: This research identified major deficiencies with recording patient vital signs. If these are not recorded regularly, patient deterioration will be missed and treatment cannot be initiated. Nurses need to respond to abnormal physiology beyond repeating vital signs and informing the medical staff. They are accountable for initiating interventions to prevent further deterioration. Conclusion: The early recognition of patient deterioration and treatment are essential to prevent cardiac arrest. Education strategies are required to improve compliance with recording patient vital signs, communication between nursing and medical staff and how to respond to patient deterioration. The barriers to these must be addressed and solutions sought if patient mortality is to be improved.</p>

Physiological Observations and Nursing Interventions in the Pre-Arrest Patient: A Descriptive Study

<p>Aim: To identify how nurses respond to abnormal physiological observations in the 12 hours prior to a patient having a cardiac arrest. Methods: A descriptive observational design was used to retrospectively review the observation charts and nursing notes of 28 patients who had an in-hospital cardiac arrest, during a 20 month period. This study was performed in a large, tertiary teaching hospital in New Zealand. Key Findings Only one patient met the hospitals minimum standard of four hourly observations and a full set of vital signs were performed on only three patients. The nursing responses were limited to increasing the frequency of observations or informing the doctor. There were few other interventions to treat the abnormality. Eight (32%) patients who had either no response or a partial response to their abnormal physiology did not survive. The nursing documentation demonstrated that abnormal neurological observations were tolerated for significant periods of time and were not acted upon in 62% of these patients. The nursing documentation revealed that the delivery of oxygen was often insufficient to meet the patient's requirements and the medical staff were aware of less than half the patients with abnormal physiology. Discussion removed statement re pt survival: This research identified major deficiencies with recording patient vital signs. If these are not recorded regularly, patient deterioration will be missed and treatment cannot be initiated. Nurses need to respond to abnormal physiology beyond repeating vital signs and informing the medical staff. They are accountable for initiating interventions to prevent further deterioration. Conclusion: The early recognition of patient deterioration and treatment are essential to prevent cardiac arrest. Education strategies are required to improve compliance with recording patient vital signs, communication between nursing and medical staff and how to respond to patient deterioration. The barriers to these must be addressed and solutions sought if patient mortality is to be improved.</p>

A LIME-Based Explainable Machine Learning Model for Predicting the Severity Level of COVID-19 Diagnosed Patients

The fast and seemingly uncontrollable spread of the novel coronavirus disease (COVID-19) poses great challenges to an already overloaded health system worldwide. It thus exemplifies an urgent need for fast and effective triage. Such triage can help in the implementation of the necessary measures to prevent patient deterioration and conserve strained hospital resources. We examine two types of machine learning models, a multilayer perceptron artificial neural networks and decision trees, to predict the severity level of illness for patients diagnosed with COVID-19, based on their medical history and laboratory test results. In addition, we combine the machine learning models with a LIME-based explainable model to provide explainability of the model prediction. Our experimental results indicate that the model can achieve up to 80% prediction accuracy for the dataset we used. Finally, we integrate the explainable machine learning models into a mobile application to enable the usage of the proposed models by medical staff worldwide.