Design and Performance of a COVID-19 Hospital Recovery Model

Objective: To determine the accuracy of a predictive model for inpatient occupancy that was implemented at a large New England hospital to aid hospital recovery planning from the COVID-19 surge. Background: During recovery from COVID surges, hospitals must plan for multiple patient populations vying for inpatient capacity, so that they maintain access for emergency department (ED) patients while enabling time-sensitive scheduled procedures to go forward. To guide pandemic recovery planning, we implemented a model to predict hospital occupancy for COVID and non-COVID patients. Methods: At a quaternary care hospital in New England, we included hospitalizations from March 10 to July 12, 2020 and subdivided them into COVID, non-COVID nonscheduled (NCNS), and non-COVID scheduled operating room (OR) hospitalizations. For the recovery period from May 25 to July 12, the model made daily hospital occupancy predictions for each population. The primary outcome was the daily mean absolute percentage error (MAPE) and mean absolute error (MAE) when comparing the predicted versus actual occupancy. Results: There were 444 COVID, 5637 NCNS, and 1218 non-COVID scheduled OR hospitalizations during the recovery period. For all populations, the MAPE and MAE for total occupancy were 2.8% or 22.3 hospitalizations per day;for general care, 2.6% or 17.8 hospitalizations per day;and for intensive care unit, 9.7% or 11.0 hospitalizations per day. Conclusions: The model was accurate in predicting hospital occupancy during the recovery period. Such models may aid hospital recovery planning so that enough capacity is maintained to care for ED hospitalizations while ensuring scheduled procedures can efficiently return. Mini-s: To guide hospital recovery planning from COVID-19 surge, we implemented a model to predict inpatient occupancy at our quaternary care hospital. We found that the model could accurately predict future occupancy. Such models can aid recovery planning to ensure capacity for urgent hospitalizations while allowing scheduled procedures to return efficiently.

Institutional Surgical Response and Associated Volume Trends Throughout the COVID-19 Pandemic and Postvaccination Recovery Period.

Importance: The COVID-19 pandemic is associated with decreased surgical procedure volumes, but existing studies have not investigated this association beyond the end of 2020, analyzed changes during the post-vaccine release period, or quantified these changes by patient acuity. Objective: To quantify changes in the volume of surgical procedures at a 1017-bed academic quaternary care center from January 6, 2019, to December 31, 2021. Design, Setting, and Participants: In this cohort study, 129 596 surgical procedure volumes were retrospectively analyzed during 4 periods: pre-COVID-19 (January 6, 2019, to January 4, 2020), COVID-19 peak (March 15, 2020, to May 2, 2020), post-COVID-19 peak (May 3, 2020, to January 2, 2021), and post-vaccine release (January 3, 2021, to December 31, 2021). Surgery volumes were analyzed by subspecialty and case class (elective, emergent, nonurgent, urgent). Statistical analysis was by autoregressive integrated moving average modeling. Main Outcomes and Measures: The primary outcome of this study was the change in weekly surgical procedure volume across the 4 COVID-19 periods. Results: A total of 129 596 records of surgical procedures were reviewed. During the COVID-19 peak, overall weekly surgical procedure volumes (mean [SD] procedures per week, 406.00 [171.45]; 95% CI, 234.56-577.46) declined 44.6% from pre-COVID-19 levels (mean [SD] procedures per week, 732.37 [12.70]; 95% CI, 719.67-745.08; P < .001). This weekly volume decrease occurred across all surgical subspecialties. During the post-COVID peak period, overall weekly surgical volumes (mean [SD] procedures per week, 624.31 [142.45]; 95% CI, 481.85-766.76) recovered to only 85.8% of pre-COVID peak volumes (P < .001). This insufficient recovery was inconsistent across subspecialties and case classes. During the post-vaccine release period, although some subspecialties experienced recovery to pre-COVID-19 volumes, others continued to experience declines. Conclusions and Relevance: This quaternary care institution effectively responded to the pressures of the COVID-19 pandemic by substantially decreasing surgical procedure volumes during the peak of the pandemic. However, overall surgical procedure volumes did not fully recover to pre-COVID-19 levels well into 2021, with inconsistent recovery rates across subspecialties and case classes. These declines suggest that delays in surgical procedures may result in potentially higher morbidity rates in the future. The differential recovery rates across subspecialties may inform institutional focus for future operational recovery.

Design and Performance of a COVID-19 Hospital Recovery Model.

To determine the accuracy of a predictive model for inpatient occupancy that was implemented at a large New England hospital to aid hospital recovery planning from the COVID-19 surge. Background: During recovery from COVID surges, hospitals must plan for multiple patient populations vying for inpatient capacity, so that they maintain access for emergency department (ED) patients while enabling time-sensitive scheduled procedures to go forward. To guide pandemic recovery planning, we implemented a model to predict hospital occupancy for COVID and non-COVID patients. Methods: At a quaternary care hospital in New England, we included hospitalizations from March 10 to July 12, 2020 and subdivided them into COVID, non-COVID nonscheduled (NCNS), and non-COVID scheduled operating room (OR) hospitalizations. For the recovery period from May 25 to July 12, the model made daily hospital occupancy predictions for each population. The primary outcome was the daily mean absolute percentage error (MAPE) and mean absolute error (MAE) when comparing the predicted versus actual occupancy. Results: There were 444 COVID, 5637 NCNS, and 1218 non-COVID scheduled OR hospitalizations during the recovery period. For all populations, the MAPE and MAE for total occupancy were 2.8% or 22.3 hospitalizations per day; for general care, 2.6% or 17.8 hospitalizations per day; and for intensive care unit, 9.7% or 11.0 hospitalizations per day. Conclusions: The model was accurate in predicting hospital occupancy during the recovery period. Such models may aid hospital recovery planning so that enough capacity is maintained to care for ED hospitalizations while ensuring scheduled procedures can efficiently return.