Evaluation of an Electronic Consultation Service for COVID-19 Care.

PURPOSE: COVID-19 has increased the need for innovative virtual care solutions. Electronic consultation (eConsult) services allow primary care practitioners to pose clinical questions to specialists using a secure remote application. We examined eConsult cases submitted to a COVID-19 specialist group in order to assess usage patterns, impact on response times and referrals, and the content of clinical questions being asked. METHODS: This was a mixed-methods analysis of eConsult cases submitted between March and September 2020 in Ontario, Canada to 2 services. We performed a descriptive analysis of the average response time and the total time spent by the specialist for eConsults. Primary care practitioners completed a post-eConsult questionnaire that asked about the outcome of the eConsult. We performed an inductive and deductive content analysis of a subset of cases to identify common themes among the clinical questions asked. RESULTS: A total of 208 primary care practitioners submitted 289 eConsult cases. The median specialist response time was 0.6 days (range = 3 minutes to 15 days); the average time spent by specialists per case was 16 minutes (range = 5 to 59 minutes). In 69 cases (24%), the eConsult enabled avoidance of a face-to-face referral. Content analysis of 51 cases identified 5 major themes: precautions for high-risk and special populations, diagnostic clarification and/or need for COVID-19 testing, guidance on self-isolation and return to work, guidance on personal protective equipment, and management of chronic symptoms. CONCLUSIONS: This study demonstrates the considerable potential of eConsults during a pandemic as our service was quickly implemented across Ontario and resulted in primary care practitioners' rapid and low-barrier access to specialist input.

Virtual learning collaboratives to improve urine culturing and antibiotic prescribing in long-term care: controlled before-and-after study.

BACKGROUND: Urine culturing practices are highly variable in long-term care and contribute to overprescribing of antibiotics for presumed urinary tract infections. The purpose of this study was to evaluate the use of virtual learning collaboratives to support long-term care homes in implementing a quality improvement programme focused on reducing unnecessary urine culturing and antibiotic overprescribing. METHODS: Over a 4-month period (May 2018-August 2018), 45 long-term care homes were self-selected from five regions to participate in virtual learning collaborative sessions, which provided an orientation to a quality improvement programme and guidance for implementation. A process evaluation complemented the use of a controlled before-and-after study with a propensity score matched control group (n=127) and a difference-in-difference analysis. Primary outcomes included rates of urine cultures performed and urinary antibiotic prescriptions. Secondary outcomes included rates of emergency department visits, hospital admission and mortality. An 18-month baseline period was compared with a 16-month postimplementation period with the use of administrative data sources. RESULTS: Rates of urine culturing and urinary antibiotic prescriptions per 1000 resident days decreased significantly more among long-term care homes that participated in learning collaboratives compared with matched controls (differential reductions of 19% and 13%, respectively, p<0.0001). There was no statistically significant changes to rates of emergency department visits, hospital admissions or mortality. These outcomes were observed with moderate adherence to the programme model. CONCLUSIONS: Rates of urine culturing and urinary antibiotic prescriptions declined among long-term care homes that participated in a virtual learning collaborative to support implementation of a quality improvement programme. The results of this study have refined a model to scale this programme in long-term care.

Association Between Nursing Home Crowding and COVID-19 Infection and Mortality in Ontario, Canada.

Importance: Nursing home residents have been disproportionately affected by coronavirus disease 2019 (COVID-19). Prevention recommendations emphasize frequent testing of health care personnel and residents, but additional strategies are needed. Objective: To develop a reproducible index of nursing home crowding and determine whether crowding was associated with COVID-19 cases and mortality in the first months of the COVID-19 epidemic. Design, Setting, and Participants: This population-based retrospective cohort study included more than 78 000 residents across more than 600 nursing homes in Ontario, Canada, and was conducted from March 29 to May 20, 2020. Exposures: The nursing home crowding index equaled the mean number of residents per bedroom and bathroom. Main Outcomes and Measures: The cumulative incidence of COVID-19 cases confirmed by a validated nucleic acid amplification assay and mortality per 100 residents; the introduction of COVID-19 into a home (≥1 resident case) was a negative tracer. Results: Of 623 homes in Ontario, we obtained complete information on 618 homes (99%) housing 78 607 residents (women, 54 160 [68.9%]; age ≥85 years, 42 919 [54.6%]). A total of 5218 residents (6.6%) developed COVID-19 infection, and 1452 (1.8%) died of COVID-19 infection as of May 20, 2020. COVID-19 infection was distributed unevenly across nursing homes; 4496 infections (86%) occurred in 63 homes (10%). The crowding index ranged across homes from 1.3 (mainly single-occupancy rooms) to 4.0 (exclusively quadruple occupancy rooms); 308 homes (50%) had a high crowding index (≥2). Incidence in high crowding index homes was 9.7% vs 4.5% in low crowding index homes (P < .001), while COVID-19 mortality was 2.7% vs 1.3%, respectively (P < .001). The likelihood of COVID-19 introduction did not differ (high = 31.3% vs low = 30.2%; P = .79). After adjustment for regional, nursing home, and resident covariates, the crowding index remained associated with an increased incidence of infection (relative risk [RR] = 1.73, 95% CI, 1.10-2.72) and mortality (RR, 1.69; 95% CI, 0.99-2.87). A propensity score analysis yielded similar conclusions for infection (RR, 2.09; 95% CI, 1.30-3.38) and mortality (RR, 1.83; 95% CI, 1.09-3.08). Simulations suggested that converting all 4-bed rooms to 2-bed rooms would have averted 998 COVID-19 cases (19.1%) and 263 deaths (18.1%). Conclusions and Relevance: In this cohort of Canadian nursing homes, crowding was common and crowded homes were more likely to experience larger and deadlier COVID-19 outbreaks.