Hospital length of stay among COVID-19-positive patients.

BACKGROUND AND AIM: This study aims to determine COVID-19 patient demographics and comorbidities associated with their hospital length of stay (LOS). METHODS: Design: Single-site, retrospective study. Setting: A suburban 700-bed community hospital in Newark, Delaware, USA. Patients: Patients admitted to the hospital from March 11, 2020, to August 11, 2020, with a positive COVID-19 status. We followed a time-to-event analysis approach and used Kaplan-Meir curves and log-rank tests for bivariate analyses, and an accelerated failure time model for a multivariable model of hospital LOS. RESULTS: Six hundred and eighty-seven patients discharged alive (mean [SD] age, 60.94 [18.10] years; 339 men [49.34%]; 307 Black/African-American [44.69%]; and 267 White [38.86%]) were included in the investigation. Bivariate analysis using Kaplan-Meir curves showed that patients' age, sex, ethnicity, insurance type, comorbidity of fluid and electrolyte disorder, hypertension, renal failure, diabetes, coagulopathy, congestive heart failure, peripheral vascular disease, neurological disorder, coronary artery disease, and cardiac arrhythmias to be significantly associated with LOS (P<0.05). In the multivariable analysis, patients' age, sex, ethnicity, number of Elixhauser comorbidities, and number of weeks since onset of the pandemic was significantly associated with LOS (P<0.05). Fluid and electrolyte disorder is the only comorbidity independently associated with LOS after adjusting for patients' age, sex, race, ethnicity, number of Elixhauser comorbidities, and weeks since onset of pandemic. CONCLUSION: COVID-19 patients LOS vary based on multiple factors. Understanding these factors are crucial to improving the prediction accuracy of COVID-19 patient census in hospitals for resource planning and care delivery. RELEVANCE FOR PATIENTS: Understanding of the factors associated with LOS of the COVID-19 patients may help the care providers and the patients to better anticipate the LOS, optimize the resources and processes, and prevent protracted stays.

Prediction model for COVID-19 patient visits in the ambulatory setting.

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.

Overcoming a Pandemic:: How Engineering and Modeling Techniques Are Used to Inform a Health System From Preparation to Recovery from the COVID-19 Pandemic.

COVID-19, a novel disease that spreads across the globe, has posed multiple challenges to the healthcare systems around the world. Due to the lack of understanding of the spread and management of this disease, one major challenge is for healthcare systems to anticipate the volumes and needs of patients infected with the disease. In order to provide insights into optimal allocation of resources from preparing ChristianaCare for the pandemic to the recovery of the healthcare system, industrial engineering and predictive modeling approaches are used. This paper discusses five interrelated studies that utilize various techniques to inform multiple aspects of the healthcare system in order to be better prepared for the pandemic.