Synergistic Patient Factors are Driving Recent Increased Pediatric Urgent Care Demand

Emily Lehan, Peyton Briand, Eileen O’Brien, Aleena Amjad Hafeez, Daniel J. Mulder

Abstract

We aimed to use the high fidelity urgent care patient data to model the factors that have led to the increased demand at our local pediatric urgent care centre.

Introduction

Emergency department (ED) volumes have recently increased across the developed world, including for children [1–3]. Increased ED wait times and overcrowding are known to increase mortality and threaten patient safety [4–5]. Although the issue of increased emergency department use is multifaceted, two important recent shifts in patient characteristics are notable: lack of access to primary care services and the COVID-19 pandemic [2,6]. The rising burden of care may be secondary not just to increased absolute number of emergency visits, but also from increased length of stay [7]. It is unclear what demographic and/or patient-related factors are driving these changes. How these factors have altered the pediatric emergency care demands is poorly understood.

Methods

The dataset for this retrospective cohort study was obtained from our local healthcare centre’s NACRS reporting data. The data was collected from the electronic health record database (EHR) for our hospital centre that is used for mandatory reporting to the Ministry of Health. This data is reviewed by our Decision Support teams prior to reporting, so it is of relatively high quality. Thus, we extracted only data fields from the EHR that are cleaned and reported to the Ministry. Applications to access the raw data used for this project can be made via the Canadian Institute for Health Information (cihi.ca, help@cihi.ca). The urgent care is a walk-in service open weekdays that cares for any patient under 18 years of age, regardless of healthcare coverage, where they can be seen by a pediatrician without an appointment.

Results

A total of 164,660 visits were included in the analysis. The median visit length was 85 minutes with a range of 1 to 667 minutes. Age was consistent across the study periods with a linear regression p-value of 0.14 for age (in years) compared to year of visit. Sex distribution was similarly consistent over the study period with a Pearson’s chi-squared p-value of 0.06. The relative disposition of patients between discharge home, admission, and “left without being seen” shifted significantly, with a Pearson’s chi-squared p-value of <2.2*10−16 due to an increased proportion of admissions since 2020, with a commensurate decrease in discharges home.

Discussion

This study used over 17 years of data from this single centre pediatric urgent care to identify patient factors that have shifted over time driving the recent increased pediatric urgent care service needs. We found that declining access to primary care, increased circulating infectious diagnoses, and shifts in chief complaints are driving increased frequency and duration of visits. Machine learning models demonstrated the relative importance of patient factors responsible for a portion of these shifts.

Previous work provided a foundations for using statistical analysis combining trends over time and machine learning to predict length of stay for hospital inpatients [7,12]. A previously identified limitation in this type of study was the inherent complexity of patient-based data, although high levels of structure have enabled high model fidelity [13–15].

Citation: Lehan E, Briand P, O’Brien E, Hafeez AA, Mulder DJ (2024) Synergistic patient factors are driving recent increased pediatric urgent care demand. PLOS Digit Health 3(8): e0000572. https://doi.org/10.1371/journal.pdig.0000572

Editor: Cleva Villanueva, Instituto Politécnico Nacional Escuela Superior de Medicina: Instituto Politecnico Nacional Escuela Superior de Medicina, MEXICO

Received: April 25, 2024; Accepted: July 4, 2024; Published: August 22, 2024

Copyright: © 2024 Lehan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data cannot be shared publicly because it contains personal health information. Data are available from the Canadian Institutes for Health Information (CIHI) for researchers who meet the criteria for access to confidential data. Applications to access the raw data used for this project can be made via the Canadian Institute for Health Information (cihi.ca, help@cihi.ca).
Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

 

 

Source: https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000572#sec010