Physician performance scores used to predict emergency department admission numbers and excessive admissions burden.

BACKGROUND: Overcrowding in hospitals is associated with a panoply of adverse events. Inappropriate decisions in the emergency department (ED) contribute to overcrowding. The performance of individual physicians as part of the admitting team is a critical factor in determining the overall rate of admissions. While previous attempts to model admission numbers have been based on a range of variables, none have included measures of individual staff performance. We construct reliable objective measures of staff performance and use these, among other factors, to predict the number of daily admissions. Such modelling will enable enhanced workforce planning and timely intervention to reduce inappropriate admissions and overcrowding. METHODS: A database was created of 232 245 ED attendances at Meir Medical Center in central Israel, spanning the years 2016-2021. We use several measures of physician performance together with historic caseload data and other variables to derive statistical models for the prediction of ED arrival and admission numbers. RESULTS: Our models predict arrival numbers with a mean absolute percentage error (MAPE) of 6.85%, and admission numbers with a MAPE of 10.6%, and provide a same-day alert for heavy admissions burden with 75% sensitivity for a false-positive rate of 20%. The inclusion of physician performance measures provides an essential boost to model performance. CONCLUSIONS: Arrival number and admission numbers can be predicted with sufficient fidelity to enable interventions to reduce excess admissions and smooth patient flow. Individual staff performance has a strong effect on admission rates and is a critical variable for the effective modelling of admission numbers.

The impact of hospitalization on inappropriate prescribing and polypharmacy in older patients: A descriptive cross-sectional study.

BACKGROUND: Polypharmacy and the use of potentially inappropriate medications (PIMs) in older individuals are widespread phenomena that are associated with an increase in morbidity and mortality. The Beers Criteria is a tool that helps to identify patients that are prescribed with PIMs, thereby reducing the risk of associated harm. Amongst other populations, the criteria identify drugs that should not be used by the majority of older patients. AIM: Determining the proportion of older inpatients who were discharged from hospitalization with polypharmacy (a prescription for more than seven drugs), or with a PIM as defined by the Beers Criteria. METHODS: A descriptive cross-sectional study based on patients aged 65 and over who were hospitalized in the years 2019-2021 in the internal medicine, orthopedic and surgical wards at a medium-size hospital. Demographic information and details about drug treatment were collected from the electronic patient records system. Patients who died during hospitalization were excluded from the study group. MAIN OUTCOME MEASURES: The proportion of inpatients with polypharmacy or a PIM as part of their regular prescription, at the time of admission and at discharge. RESULTS: 49 564 patients were included in the study cohort. At discharge, 19% of the patients were given a prescription for a PIM, with a small but significant decrease compared with the rate admission (22.1%). At discharge, 42.8% of patients had polypharmacy, representing a small but significant increase compared with the rate on admission (40.6%). CONCLUSIONS: The study demonstrated high baseline rates of PIM prescription and polypharmacy. Hospitalization was associated with a decrease in PIM prescription and an increase in polypharmacy. This highlights the importance of medication review during admission to reduce the potential risk to older adults from polypharmacy and PIM prescription.