Predicting hospital admission for older emergency department patients: Insights from machine learning

Mowbray, Fabrice; Zargoush, Manaf; Jones, Aaron; Wit, Kerstin de; Costa, Andrew P.

doi:10.1016/j.ijmedinf.2020.104163

Cited by 25 publications

(15 citation statements)

References 44 publications

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“…These ndings are consistent with previous literature which analyzed admission predictors of unscheduled ED visits. (22)(23)(24)(25) A plethora of research has been dedicated to examining variables predictive of admission in the older adult cohorts, yielding results consistent with our predictors. (26-29) Receiving home care was included less as an admission predictor in the literature, representing a plausible underreporting of this variables in uence on ED visit dispositions.…”

Section: Discussionsupporting

confidence: 69%

Predicting Hospital Admission among High Acuity Triaged Patients Transported to the Emergency Department in Ontario, Canada: A Population-Based Cohort Study using Machine Learning

Strum

Mowbray

Zargoush

et al. 2022

Preprint

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Background Paramedics are mandated to transport emergently triaged patients to the closest emergency department (ED). The closest ED may not be the optimal transport destination if further distanced ED’s can provide specialized care or are less crowded. Machine learning may support paramedic decision-making to transport a specific subgroup of emergently triaged patients that are unlikely to require hospital admission or emergency care to a more appropriate ED. We examined whether prehospital patient characteristics known to paramedics were predictive of hospital admission. Methods We conducted a retrospective cohort study using machine learning algorithms to analyze ED visits of the National Ambulatory Care Reporting System from Jan 1, 2018 to Dec 31, 2019 in Ontario, Canada. We included all adult (≥ 18 years) paramedic transports to the ED who had an emergent Canadian Triage Acuity Scale score (CTAS 2). Eight prehospital characteristic classes known to paramedics were used. We applied four machine learning algorithms that were trained and assessed using 10-fold cross-validation to predict the ED visit disposition of admission to hospital or discharged from ED. Predictive model performance was determined using the area under the receiving operating characteristic curve (AUC) with 95% confidence intervals and probabilistic accuracy using the Brier Scaled score. Variable importance scores were computed to determine the top 10 predictors of hospital admission. We also reported sensitivity, specificity, and positive and negative predictive values to support performance interpretation. Results All machine learning algorithms performed similarly for the prediction of which ED patient visits would be admitted to hospital (AUC 0.77–0.78, Brier Scaled 0.22–0.24). The characteristics most predictive of admission included age 65 to 105 years, referral source from a residential care facility, presenting with a respiratory complaint, and receiving home care. Conclusions Machine learning algorithms performed well in predicting ED visit dispositions using a comprehensive list of prehospital patient characteristics. To the best of our knowledge, this study is the first to utilize machine learning to predict ED visit outcomes from patient characteristics known prior to paramedic transport. This study has potential to inform paramedic regulations regarding the distribution of emergently triaged patients.

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Section: Discussionsupporting

confidence: 69%

Predicting Hospital Admission among High Acuity Triaged Patients Transported to the Emergency Department in Ontario, Canada: A Population-Based Cohort Study using Machine Learning

Strum

Mowbray

Zargoush

et al. 2022

Preprint

Self Cite

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“…1. Admission: A hospital admission following an ED visit [37][38][39]. Each ED attendance is classified as admission or discharge according to the clinical decision made.…”

Section: Discussionmentioning

confidence: 99%

Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation

Liu¹,

Xie²,

Siddiqui³

et al. 2022

JMIR Res Protoc

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Background There is a growing demand globally for emergency department (ED) services. An increase in ED visits has resulted in overcrowding and longer waiting times. The triage process plays a crucial role in assessing and stratifying patients’ risks and ensuring that the critically ill promptly receive appropriate priority and emergency treatment. A substantial amount of research has been conducted on the use of machine learning tools to construct triage and risk prediction models; however, the black box nature of these models has limited their clinical application and interpretation. Objective In this study, we plan to develop an innovative, dynamic, and interpretable System for Emergency Risk Triage (SERT) for risk stratification in the ED by leveraging large-scale electronic health records (EHRs) and machine learning. Methods To achieve this objective, we will conduct a retrospective, single-center study based on a large, longitudinal data set obtained from the EHRs of the largest tertiary hospital in Singapore. Study outcomes include adverse events experienced by patients, such as the need for an intensive care unit and inpatient death. With preidentified candidate variables drawn from expert opinions and relevant literature, we will apply an interpretable machine learning–based AutoScore to develop 3 SERT scores. These 3 scores can be used at different times in the ED, that is, on arrival, during ED stay, and at admission. Furthermore, we will compare our novel SERT scores with established clinical scores and previously described black box machine learning models as baselines. Receiver operating characteristic analysis will be conducted on the testing cohorts for performance evaluation. Results The study is currently being conducted. The extracted data indicate approximately 1.8 million ED visits by over 810,000 unique patients. Modelling results are expected to be published in 2022. Conclusions The SERT scoring system proposed in this study will be unique and innovative because of its dynamic nature and modelling transparency. If successfully validated, our proposed solution will establish a standard for data processing and modelling by taking advantage of large-scale EHRs and interpretable machine learning tools. International Registered Report Identifier (IRRID) DERR1-10.2196/34201

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“…We reduced the dimensionality of our data by selecting a subset of variables (and discarding the rest) while retaining as much information as possible from all variables. We kept all of the medical variables, and among variables obtained from systems analysis, we calculated the pairwise Pearson correlation and removed the redundant information if the correlation was larger than 90%, as in [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach

Rastpour¹,

McGregor²

2022

JMIR Ment Health

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Background Wait times impact patient satisfaction, treatment effectiveness, and the efficiency of care that the patients receive. Wait time prediction in mental health is a complex task and is affected by the difficulty in predicting the required number of treatment sessions for outpatients, high no-show rates, and the possibility of using group treatment sessions. The task of wait time analysis becomes even more challenging if the input data has low utility, which happens when the data is highly deidentified by removing both direct and quasi identifiers. Objective The first aim of this study was to develop machine learning models to predict the wait time from referral to the first appointment for psychiatric outpatients by using real-time data. The second aim was to enhance the performance of these predictive models by utilizing the system’s knowledge while the input data were highly deidentified. The third aim was to identify the factors that drove long wait times, and the fourth aim was to build these models such that they were practical and easy-to-implement (and therefore, attractive to care providers). Methods We analyzed retrospective highly deidentified administrative data from 8 outpatient clinics at Ontario Shores Centre for Mental Health Sciences in Canada by using 6 machine learning methods to predict the first appointment wait time for new outpatients. We used the system’s knowledge to mitigate the low utility of our data. The data included 4187 patients who received care through 30,342 appointments. Results The average wait time varied widely between different types of mental health clinics. For more than half of the clinics, the average wait time was longer than 3 months. The number of scheduled appointments and the rate of no-shows varied widely among clinics. Despite these variations, the random forest method provided the minimum root mean square error values for 4 of the 8 clinics, and the second minimum root mean square error for the other 4 clinics. Utilizing the system’s knowledge increased the utility of our highly deidentified data and improved the predictive power of the models. Conclusions The random forest method, enhanced with the system’s knowledge, provided reliable wait time predictions for new outpatients, regardless of low utility of the highly deidentified input data and the high variation in wait times across different clinics and patient types. The priority system was identified as a factor that contributed to long wait times, and a fast-track system was suggested as a potential solution.

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Predicting hospital admission for older emergency department patients: Insights from machine learning

Cited by 25 publications

References 44 publications

Predicting Hospital Admission among High Acuity Triaged Patients Transported to the Emergency Department in Ontario, Canada: A Population-Based Cohort Study using Machine Learning

Predicting Hospital Admission among High Acuity Triaged Patients Transported to the Emergency Department in Ontario, Canada: A Population-Based Cohort Study using Machine Learning

Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation

Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach

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