Enhancing patient flow in an acute care hospital: successful strategies at the Juravinski Hospital.

This article shares the experiences of one acute care hospital as it made a concerted effort through a corporate initiative to improve patient throughput and quality of care despite competing demands and constrained resources. Organizational structures and specialized resources were employed to collect, analyze and use data in order to improve flow and access to care. The successes and lessons learned from this corporate initiative are summarized and may provide useful insights.

Province-Wide Prevalence Testing for SARS-CoV-2 of In-Center Hemodialysis Patients and Staff in Ontario, Canada: A Cross-Sectional Study.

BACKGROUND: People receiving in-center hemodialysis face a high risk for contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and experience poor outcomes. During the first wave of the coronavirus disease 2019 (COVID-19) pandemic in Ontario (between March and June 2020), it was unclear whether asymptomatic or presymptomatic cases were common and whether widespread testing of all dialysis patients and staff would identify cases earlier and prevent transmission. Ontario has a population of about 14.5 million. Approximately 8900 people receive dialysis across 102 in-center dialysis units. OBJECTIVE: The objective of this study was to determine participation rates for patients and staff in point prevalence testing in dialysis units across the province and to determine the prevalence of asymptomatic or presymptomatic infection. DESIGN: Cross-sectional study design. SETTING: In-center hemodialysis units at 27 renal programs across Ontario. PARTICIPANTS: Patients and staff in in-center dialysis units in Ontario. MEASUREMENTS: Participation rates, demographic data, SARS-CoV-2 positivity rates, and COVID-19-related symptom data. METHODS: From June 8 to 30, 2020, all in-center dialysis patients and staff in the Province of Ontario were requested to undergo a symptom screening assessment and nasopharyngeal swab. Testing was done using polymerase chain reaction to detect SARS-CoV-2. A standardized questionnaire of atypical and typical COVID-19-related symptoms was administered to patients, to assess for new or worsening COVID-19-related symptoms. RESULTS: Patient participation was 83% (7155 of 8612) of which 15 tests were positive: less than 5 (<0.07%) were new positive cases, 7 were false positive, and the remaining were recovered positives. Half of the new positive cases had symptoms. Common symptoms reported included fatigue (4%), falls (4%), runny nose (3%), dyspnea (3%), and cough (3%). Staff participation was 49% (2109 of 4325), and less than 5 (<0.24%) were asymptomatic positive. LIMITATIONS: As point prevalence testing was voluntary, not all patients and staff participated. Lower participation rate may be due to decreasing new cases in Ontario, and testing or pandemic fatigue, among other factors. This study did not use serology to identify prior infections because it was not widely available in Ontario. With respect to the standardized symptom questionnaire, it was only available in English and French and could not be tested due to the urgency of the initiative. CONCLUSIONS: Participation among patients in point prevalence testing was good, but participation among staff was relatively low. Asymptomatic positivity in the dialysis patient and staff population was rare during the first wave of the COVID-19 pandemic in Ontario.

Forecasting Ontario Oncology Drug Expenditures: A Hybrid Approach to Improving Accuracy.

BACKGROUND: The Provincial Drug Reimbursement Program (PDRP) at Cancer Care Ontario (CCO) is responsible for monitoring actual and projected outpatient intravenous cancer drug spending in the province. We developed a hybrid forecasting approach combining automated time-series forecasting with expert-customizable input. OBJECTIVE: Our objectives were to provide a flexible tool in which to incorporate multiple forecasts and to improve the accuracy of the resulting forecast. METHODS: The approach employed linear and non-linear time-series techniques and a combined hybrid model incorporating both approaches. We developed an interactive tool that incorporated the statistical models and identified the best performing forecast according to standard goodness-of-fit measures. Model selection procedures considered both the amount of historical expenditure data available per drug policy and the individual policy contributions to the overall budget. The user was allowed to customize forecasts based on knowledge of external factors related to policy or price changes and new drugs that come to market RESULTS: A comparison of 2016/17 fiscal year expenditures showed that all policies with a significant contribution to the overall budget were forecast with < 4% error. Forecasting error was reduced by at least $Can5 million for the nine most expensive policies compared with expert opinion. This approach to drug budget forecasting was implemented in Ontario for the first time in the 2017/18 fiscal year, where 1% error was observed for the overall budget, corresponding to an overestimate of expenditures by $Can3.0 million. CONCLUSION: We introduced a pragmatic approach for regular forecasting by budget holders in Ontario. Our approach to isolating 'big budget' from 'small budget' drugs using an 80-20 rule and providing multiple forecasts depending on the length of the drug expenditure histories is transferable to other jurisdictions.