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.

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.

Potential impact of hospital at home on postoperative readmissions.

OBJECTIVES: Hospital at home (HAH) is a health care delivery model that substitutes hospital-level services in the home for inpatient hospitalizations. HAH has been shown to be safe and effective for medical patients but has not been investigated in surgical readmissions. We estimated the potential impact of an HAH program for patients readmitted within 60 days postoperatively and described the characteristics of eligible patients to aid in the design of future programs. STUDY DESIGN: This was a cross-sectional study of 60-day postoperative readmissions at a tertiary care center in 2018. METHODS: We identified the number of readmissions that may have been eligible for HAH, collected descriptive information, and estimated the financial margin that could have been generated had eligible readmissions been diverted to HAH. RESULTS: There were 2366 readmissions within 60 days of surgery in 2018. A total of 731 readmissions met inclusion criteria for HAH (30.1%), accounting for 4152 bed days. Of these readmissions, the most common diagnoses were infection, gastrointestinal complications, and cardiac complications. Patients' home addresses were within 16 miles of the hospital in 447 cases (61.1%). Avoidance of these readmissions and use of the beds for new admissions represented a potential backfill margin of $8.8 million, not incorporating the cost of HAH. CONCLUSIONS: Many 60-day postoperative readmissions may be amenable to HAH enrollment, representing a significant opportunity to improve patient experience and generate hospital revenue. This is of particular interest in the post-COVID-19 era. To maximize their impact, HAH programs should tailor clinical and operational services to this population.

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.

COVID-19 Level-Loading: Transferring Emergency Department Patients to a Partner Academic Medical Center Within a Healthcare System.

COVID-19 continues to challenge bed capacity and the ability of hospitals to provide quality care for patients around the country. However, the COVID-19 pandemic at a given point in time does not impact all hospitals equally-even within a single healthcare system, one hospital may be caring for patients in the hallways, while another has available inpatient beds. Here, we demonstrate a program to level-load COVID-19 patients between 2 academic medical centers in a healthcare system by transferring patients at the time of admission from the emergency department of one institution directly to an inpatient bed of the other institution. Over 42 days, 50 patients were transferred which saved 432 bed-days at the home academic medical center without any adverse events during transfer or upgrades to the ICU within the first 24 hours of admission. Programs like this can expand a healthcare system's ability to allocate personnel and resources efficiently for patients and maximize the quality of care delivered even during a pandemic.

The Power of Modeling in Emergency Preparedness for COVID-19: A Moonshot Moment for Hospitals.

Before coronavirus disease 2019 (COVID-19), few hospitals had fully tested emergency surge plans. Uncertainty in the timing and degree of surge complicates planning efforts, putting hospitals at risk of being overwhelmed. Many lack access to hospital-specific, data-driven projections of future patient demand to guide operational planning. Our hospital experienced one of the largest surges in New England. We developed statistical models to project hospitalizations during the first wave of the pandemic. We describe how we used these models to meet key planning objectives. To build the models successfully, we emphasize the criticality of having a team that combines data scientists with frontline operational and clinical leadership. While modeling was a cornerstone of our response, models currently available to most hospitals are built outside of their institution and are difficult to translate to their environment for operational planning. Creating data-driven, hospital-specific, and operationally relevant surge targets and activation triggers should be a major objective of all health systems.