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Prediction model for COVID-19 patient visits in the ambulatory setting
Riza C Li1, Cecelia K Harrison1, Claudine T Jurkovitz1
1Christiana Care Health System.
Insights
Forecasting weekly patient visits, including COVID-19 cases, is crucial for healthcare planning. Time series models incorporating COVID-19 data improve prediction accuracy over simple averages.
Area of Science:
- Healthcare Management
- Epidemiology
- Health Informatics
Background:
- The COVID-19 pandemic significantly disrupted healthcare systems globally.
- Pandemic-related policies led to reduced patient volumes in ambulatory care settings.
- The long-term impact of COVID-19 on ambulatory care remains uncertain.
Conclusions:
- Accurate prediction of ambulatory patient volumes is vital for resource allocation and safety protocols.
- Time series models accounting for COVID-19 patient numbers offer enhanced accuracy for predicting weekly ambulatory visits compared to moving averages.
Objective:
Healthcare systems globally were shocked by coronavirus disease 2019 (COVID-19). Policies put in place to curb the tide of the pandemic resulted in a decrease of patient volumes throughout the ambulatory system. The future implications of COVID-19 in healthcare are still unknown, specifically the continued impact on the ambulatory landscape. The primary objective of this study is to accurately forecast the number of COVID-19 and non-COVID-19 weekly visits in primary care practices.
Materials And Methods:
This retrospective study was conducted in a single health system in Delaware. All patients' records were abstracted from our electronic health records system (EHR) from January 1, 2019 to July 25, 2020. Patient demographics and comorbidities were compared using t-tests, Chi square, and Mann Whitney U analyses as appropriate. ARIMA time series models were developed to provide an 8-week future forecast for two ambulatory practices (AmbP) and compare it to a naïve moving average approach.
Results:
Among the 271,530 patients considered during this study period, 4,195 patients (1.5%) were identified as COVID-19 patients. The best fitting ARIMA models for the two AmbP are as follows: AmbP1 COVID-19+ ARIMAX(4,0,1), AmbP1 nonCOVID-19 ARIMA(2,0,1), AmbP2 COVID-19+ ARIMAX(1,1,1), and AmbP2 nonCOVID-19 ARIMA(1,0,0).
Discussion And Conclusion:
Accurately predicting future patient volumes in the ambulatory setting is essential for resource planning and developing safety guidelines. Our findings show that a time series model that accounts for the number of positive COVID-19 patients delivers better performance than a moving average approach for predicting weekly ambulatory patient volumes in a short-term period.
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