Machine learning to identify attributes that predict patients who leave without being seen in a pediatric emergency department.

Purpose: To characterize patients who left without being seen (LWBS) from a Canadian pediatric Emergency Department (ED) and create predictive models using machine learning to identify key attributes associated with LWBS.

Methods: We analyzed administrative ED data from April 1, 2017, to March 31, 2020, from IWK Health ED in Halifax, NS. Variables included: visit disposition; Canadian Triage Acuity Scale (CTAS); triage month, week, day, hour, minute, and day of the week; sex; age; postal code; access to primary care provider; visit payor; referral source; arrival by ambulance; main problem (ICD10); length of stay in minutes; driving distance in minutes; and ED patient load.

A framework for comparing N95 and elastomeric facepiece respirators on cost and function for healthcare use during a pandemic- A literature review.

SARS-CoV-2 has posed implications for personal protective equipment supply. In this literature review we examine if elastomeric facepiece respirators (EFRs) are effective substitutes for N95 respirators through comparing their functionality and cost. We reviewed 30 articles which researched the advantages and disadvantages of each respirator.

ORchestra: an online reference database of OR/MS literature in health care.

We introduce the categorized reference database ORchestra, which is available online at http://www.utwente.nl/choir/orchestra/.

Accounting for inpatient wards when developing master surgical schedules.

Background: As the demand for health care services increases, the need to improve patient flow between departments has likewise increased. Understanding how the master surgical schedule (MSS) affects the inpatient wards and exploiting this relationship can lead to a decrease in surgery cancellations, a more balanced workload, and an improvement in resource utilization. We modeled this relationship and used the model to evaluate and select a new MSS for a hospital.

A comprehensive simulation for wait time reduction and capacity planning applied in general surgery.

This paper describes the use of operational research techniques to analyze the wait list for the Division of General Surgery at the Capital District Health Authority in Halifax, Nova Scotia, Canada. A discrete event simulation model was developed to aid capacity planning decisions and to analyze the performance of the division. The analysis examined the consequences of redistributing beds between sites, and achieving standard patient lengths of stay, while contrasting them to current and additional resource options.