Cost Evaluation of the Ontario Virtual Urgent Care Pilot Program: Population-Based, Matched Cohort Study.

BACKGROUND: In 2020, the Ministry of Health (MoH) in Ontario, Canada, introduced a virtual urgent care (VUC) pilot program to provide alternative access to urgent care services and reduce the need for in-person emergency department (ED) visits for patients with low acuity health concerns. OBJECTIVE: This study aims to compare the 30-day costs associated with VUC and in-person ED encounters from an MoH perspective. METHODS: Using administrative data from Ontario (the most populous province of Canada), a population-based, matched cohort study of Ontarians who used VUC services from December 2020 to September 2021 was conducted. As it was expected that VUC and in-person ED users would be different, two cohorts of VUC users were defined: (1) those who were promptly referred to an ED by a VUC provider and subsequently presented to an ED within 72 hours (these patients were matched to in-person ED users with any discharge disposition) and (2) those seen by a VUC provider with no referral to an in-person ED (these patients were matched to patients who presented in-person to the ED and were discharged home by the ED physician). Bootstrap techniques were used to compare the 30-day mean costs of VUC (operational costs to set up the VUC program plus health care expenditures) versus in-person ED care (health care expenditures) from an MoH perspective. All costs are expressed in Canadian dollars (a currency exchange rate of CAD $1=US $0.76 is applicable). RESULTS: We matched 2129 patients who presented to an ED within 72 hours of VUC referral and 14,179 patients seen by a VUC provider without a referral to an ED. Our matched populations represented 99% (2129/2150) of eligible VUC patients referred to the ED by their VUC provider and 98% (14,179/14,498) of eligible VUC patients not referred to the ED by their VUC provider. Compared to matched in-person ED patients, 30-day costs per patient were significantly higher for the cohort of VUC patients who presented to an ED within 72 hours of VUC referral ($2805 vs $2299; difference of $506, 95% CI $139-$885) and significantly lower for the VUC cohort of patients who did not require ED referral ($907 vs $1270; difference of $362, 95% CI 284-$446). Overall, the absolute 30-day costs associated with the 2 VUC cohorts were $18.9 million (ie, $6.0 million + $12.9 million) versus $22.9 million ($4.9 million + $18.0 million) for the 2 in-person ED cohorts. CONCLUSIONS: This costing evaluation supports the use of VUC as most complaints were addressed without referral to ED. Future research should evaluate targeted applications of VUC (eg, VUC models led by nurse practitioners or physician assistants with support from ED physicians) to inform future resource allocation and policy decisions.

A qualitative study on the Virtual Emergency Department care experiences of equity-deserving populations.

Patients from equity-deserving populations, such as those who are from racialized communities, the 2SLGBTQI+ community, who are refugees or immigrants, and/or who have a disability, may experience a unique set of challenges accessing virtual models of care. The objective of this qualitative study was to describe the experiences of patients from equity-deserving communities and their family members who received care from a Virtual Emergency Department (ED) in Toronto, Canada. Forty-three participants (36 patients and 7 family caregivers) with different and intersecting identities who used the Virtual ED participated in the study. Semi-structured interviews were conducted to explore reasons for accessing the Virtual ED, barriers to access, and how the Virtual ED met their care needs and expectations, including ways their experience could have been improved. Thematic analysis was used to identify themes from the data. Patients from equity-deserving populations described negative past experiences with ED in-person care, which included recounts of discrimination or culturally insensitive care while waiting to see the ED physician or nurse. Conversely, participants found the Virtual ED to be a socially and culturally safe space since they could now by-pass the waiting room experience. However, virtual care could not replace in-person care for certain issues (e.g., physical exam), and there was a need for greater promotion of the service to specific communities that might benefit from having access to the Virtual ED. Targeted outreach to help raise awareness of the service to equity-deserving communities is an important future direction.

Building bridges to outpatient treatment services for post-overdose care via paramedic buprenorphine field initiation.

INTRODUCTION: Despite sustained efforts to reduce opioid-related overdose fatalities, rates have continued to rise. In many areas, overdose response involves emergency medical service (EMS) personnel administering naloxone and transporting patients to the emergency department (ED). However, a substantial number of patients decline transport, and many EDs do not provide medication for opioid use disorder (MOUD). One approach to filling this gap involves delivering MOUD to overdose patients in the field with trained post-overdose EMS teams who can initiate buprenorphine. In this MOUD field initiation pilot program, a trained EMS Community Paramedicine team initiates buprenorphine in the field and links patients to care. The program includes three pathways to treatment with the first designed for EMS to initiate buprenorphine after overdose reversal when the patient is in withdrawal from naloxone; a second pathway initiates buprenorphine after overdose when the patient is not in withdrawal; and a third enables self-referral via a connection to the community EMS team not necessarily related to a recent overdose. METHODS: We conducted a retrospective cohort study of the MOUD field initiation pilot program. Data are from 28 patients who entered care immediately post-overdose initiation of buprenorphine, 21 patients who initiated on buprenorphine while not in naloxone withdrawal, and 37 patients who self-referred to treatment following outreach efforts by paramedicine and peer support professionals. RESULTS: A total of 118 patients initiated buprenorphine during the 12-month study period and 104 (83 %) visited the clinic for their first appointment. Over two thirds (68 %, n = 80) remained engaged in care after 30 days. Retained patients tended to be male, white, uninsured, food insecure, have unstable housing, lack reliable transportation, and report prior involvement with the criminal legal system. CONCLUSION: The initial 12-month period of the pilot program demonstrated the feasibility of initiating buprenorphine at the site of overdose without requiring transport to the ED and offer self-referral pathways for people experiencing barriers to treatment. Specialized EMS can play a critical role in expanding access to MOUD treatment by bridging the gap between overdose and comprehensive community-based care.

Evaluating the Reliability of a Remote Acuity Prediction Tool in a Canadian Academic Emergency Department.

STUDY OBJECTIVE: There is increasing interest in harnessing artificial intelligence to virtually triage patients seeking care. The objective was to examine the reliability of a virtual machine learning algorithm to remotely predict acuity scores for patients seeking emergency department (ED) care by applying the algorithm to retrospective ED data. METHODS: This was a retrospective review of adult patients conducted at an academic tertiary care ED (annual census 65,000) from January 2021 to August 2022. Data including ED visit date and time, patient age, sex, reason for visit, presenting complaint and patient-reported pain score were used by the machine learning algorithm to predict acuity scores. The algorithm was designed to up-triage high-risk complaints to promote safety for remote use. The predicted scores were then compared to nurse-led triage scores previously derived in real time using the electronic Canadian Triage and Acuity Scale (eCTAS), an electronic triage decision-support tool used in the ED. Interrater reliability was estimated using kappa statistics with 95% confidence intervals (CIs). RESULTS: In total, 21,469 unique ED patient encounters were included. Exact modal agreement was achieved for 10,396 (48.4%) patient encounters. Interrater reliability ranged from poor to fair, as estimated using unweighted kappa (0.18, 95% CI 0.17 to 0.19), linear-weighted kappa (0.25, 95% CI 0.24 to 0.26), and quadratic-weighted kappa (0.36, 95% CI 0.35 to 0.37) statistics. Using the nurse-led eCTAS score as the reference, the machine learning algorithm overtriaged 9,897 (46.1%) and undertriaged 1,176 (5.5%) cases. Some of the presenting complaints under-triaged were conditions generally requiring further probing to delineate their nature, including abnormal lab/imaging results, visual disturbance, and fever. CONCLUSION: This machine learning algorithm needs further refinement before being safely implemented for patient use.