Impact of a hospital policy to redistribute admission flow across clinical services for capacity relief during COVID-19 surges.

BACKGROUND: Increased hospital admissions due to COVID-19 place a disproportionate strain on inpatient general medicine service (GMS) capacity compared to other services. OBJECTIVE: To study the impact on capacity and safety of a hospital-wide policy to redistribute admissions from GMS to non-GMS based on admitting diagnosis during surge periods. DESIGN, SETTING, AND PARTICIPANTS: Retrospective case-controlled study at a large teaching hospital. The intervention included adult patients admitted to general care wards during two surge periods (January-February 2021 and 2022) whose admission diagnosis was impacted by the policy. The control cohort included admissions during a matched number of days preceding the intervention. MAIN OUTCOMES AND MEASURES: Capacity measures included average daily admissions and hospital census occupied on GMS. Safety measures included length of stay (LOS) and adverse outcomes (death, rapid response, floor-to-intensive care unit transfer, and 30-day readmission). RESULTS: In the control cohort, there were 365 encounters with 299 (81.9%) GMS admissions and 66 (18.1%) non-GMS versus the intervention with 384 encounters, including 94 (24.5%) GMS admissions and 290 (75.5%) non-GMS (p < .001). The average GMS census decreased from 17.9 and 21.5 during control periods to 5.5 and 8.5 during intervention periods. An interrupted time series analysis confirmed a decrease in GMS daily admissions (p < .001) and average daily hospital census (p = .014; p < .001). There were no significant differences in LOS (5.9 vs. 5.9 days, p = .059) or adverse outcomes (53, 14.5% vs. 63, 16.4%; p = .482). CONCLUSION: Admission redistribution based on diagnosis is a safe lever to reduce capacity strain on GMS during COVID-19 surges.

Forecasting hospital-level COVID-19 admissions using real-time mobility data.

BACKGROUND: For each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts. METHODS: Large-scale, anonymized mobile phone data has been shown to correlate with regional case counts during the first two waves of the pandemic (spring 2020, and fall/winter 2021). Building off this success, we developed a multi-step, recursive forecasting model to predict individual hospital admissions; this model incorporates the following data: (i) hospital-level COVID-19 admissions, (ii) statewide test positivity data, and (iii) aggregate measures of large-scale human mobility, contact patterns, and commuting volume. RESULTS: Incorporating large-scale, aggregate mobility data as exogenous variables in prediction models allows us to make hospital-specific COVID-19 admission forecasts 21 days ahead. We show this through highly accurate predictions of hospital admissions for five hospitals in Massachusetts during the first year of the COVID-19 pandemic. CONCLUSIONS: The high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users' contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. Mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges.

During the COVID-19 pandemic, hospitals have needed to make challenging decisions around staffing and preparedness based on estimates of the number of admissions multiple weeks ahead. Forecasting techniques using methods from machine learning have been successfully applied to predict hospital admissions statewide, but the ability to accurately predict individual hospital admissions has proved elusive. Here, we incorporate details of the movement of people obtained from mobile phone data into a model that makes accurate predictions of the number of people who will be hospitalized 21 days ahead. This model will be useful for administrators and healthcare workers to plan staffing and discharge of patients to ensure adequate capacity to deal with forthcoming hospital admissions.