Impact of a Repatriation Program Between Quaternary and Community Hospitals.

BACKGROUND: Capacity challenges at quaternary hospitals cause delays or denials in patient transfers from community hospitals that can compromise quality and safety. Repatriation is an innovative approach to increase capacity at the quaternary hospital by transferring a patient back to their originating community hospital after the quaternary portion of their care is completed. METHODS: A repatriation program was implemented at a large quaternary care teaching hospital over a one-year period (2020 to 2021). The authors characterized the rate of successful repatriation and associated patient characteristics, determined the impact on quaternary hospital capacity in terms of bed days saved, and estimated the resultant number of backfilled admissions that could be accommodated. The research team also monitored the rate of readmissions for repatriations back to the quaternary hospital. RESULTS: Overall, 215 repatriations were attempted, and 103 (47.5%) were successful. The most common diagnoses were sepsis (13, 12.6%), stroke (12, 11.7%), intracranial bleed (10, 9.7%), gastrointestinal perforation/obstruction (9, 8.7%), and trauma (9, 8.7%). The median length of stay at the quaternary hospital was 13 days (interquartile range [IQR] 7-20) and 12 days (IQR 4-26) at the community hospital. There were 2,842 bed days saved at the quaternary hospital, with a backfill opportunity of 431 admissions. The readmission rate to the quaternary hospital was 1.9%. CONCLUSION: By dynamically matching patient need with hospital capability at different phases of the patient's care, Repatriation can save bed days at the quaternary hospital, creating capacity to improve access for patients needing timely transfer. The low observed readmission rate suggests that repatriation is safe.

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.

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.

Hazardous substances releases associated with Hurricanes Katrina and Rita in industrial settings, Louisiana and Texas.

The scientific literature concerning the public health response to the unprecedented hurricanes striking the Gulf Coast in August and September 2005 has focused mainly on assessing health-related needs and surveillance of injuries, infectious diseases, and other illnesses. However, the hurricanes also resulted in unintended hazardous substances releases in the affected states. Data from two states (Louisiana and Texas) participating in the Hazardous Substances Emergency Events Surveillance (HSEES) system were analyzed to describe the characteristics of hazardous substances releases in industrial settings associated with Hurricanes Katrina and Rita. HSEES is an active multi-state Web-based surveillance system maintained by the Agency for Toxic Substances and Disease Registry (ATSDR). In 2005, 166 hurricane-related hazardous substances events in industrial settings in Louisiana and Texas were reported. Most (72.3%) releases were due to emergency shut downs in preparation for the hurricanes and start-ups after the hurricanes. Emphasis is given to the contributing causal factors, hazardous substances released, and event scenarios. Recommendations are made to prevent or minimize acute releases of hazardous substances during future hurricanes, including installing backup power generation, securing equipment and piping to withstand high winds, establishing procedures to shutdown process operations safely, following established and up-to-date start-up procedures and checklists, and carefully performing pre-start-up safety reviews.

Role of liver function tests in detecting methotrexate-induced liver damage in sarcoidosis.

BACKGROUND: Methotrexate has become a standard second-line agent for the treatment of sarcoidosis. Because sarcoidosis has a high frequency of liver involvement, we routinely perform a liver biopsy after each cumulative gram of methotrexate therapy in patients with sarcoidosis in whom we plan to continue therapy. METHODS: Following a previously described protocol for methotrexate therapy, we have performed 100 liver biopsies on 68 patients with chronic sarcoidosis at our institution. On the basis of the liver biopsy results, we identified the following 4 groups: sarcoidosis (47 cases), toxic effects of methotrexate (14 cases), hepatitis C (2 cases), and normal liver tissue (37 cases). RESULTS: We found no difference among the groups in terms of age, weight at time of biopsy, the number of patients receiving corticosteroids at the time of biopsy, cumulative dose of methotrexate, race, or sex. The 14 cases of toxic reactions to methotrexate included 5 patients who had undergone 1 or more previous liver biopsies in which the results did not show toxic effects. We found a significant difference between groups for levels of alkaline phosphatase and asparate aminotransferase at the time of starting (or restarting) methotrexate therapy (analysis of variance, P<.05). This finding was also true for the liver function tests performed at the time of the biopsy (analysis of variance, P<.05). The highest values were for those whose biopsy findings showed sarcoidosis. CONCLUSIONS: Toxic reactions to methotrexate eventually occurred in more than 10% of patients with sarcoidosis treated for more than 2 years with methotrexate. Because of hepatic involvement owing to sarcoidosis, results of serial liver function tests were not useful in determining which patients would have this reaction to methotrexate.