Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984-2022.

This study reviews scholarly publications on data envelopment analysis (DEA) studies on acute care hospital (ACH) efficiency published between 1984 and 2022 in scholarly peer-reviewed journals. We employ systematic literature review (SLR) method to identify and analyze pertinent past research using predetermined steps. The SLR offers a comprehensive resource that meticulously analyzes DEA methodology for practitioners and researchers focusing on ACH efficiency measurement. The articles reviewed in the SLR are analyzed and synthesized based on the nature of the DEA modelling process and the key findings from the DEA models. The key findings from the DEA models are presented under the following sections: effects of different ownership structures; impacts of specific healthcare reforms or other policy interventions; international and multi-state comparisons; effects of changes in competitive environment; impacts of new technology implementations; effects of hospital location; impacts of quality management interventions; impact of COVID-19 on hospital performance; impact of teaching status, and impact of merger. Furthermore, the nature of DEA modelling process focuses on use of sensitivity analysis; choice of inputs and outputs; comparison with Stochastic Frontier Analysis; use of congestion analysis; use of bootstrapping; imposition of weight restrictions; use of DEA window analysis; and exogenous factors. The findings demonstrate that, despite several innovative DEA extensions and hospital applications, over half of the research used the conventional DEA models. The findings also show that the most often used inputs in the DEA models were labor-oriented inputs and hospital beds, whereas the most frequently used outputs were outpatient visits, followed by surgeries, admissions, and inpatient days. Further research on the impact of healthcare reforms and health information technology (HIT) on hospital performance is required, given the number of reforms being implemented in many countries and the role HIT plays in enhancing care quality and lowering costs. We conclude by offering several new research directions for future studies.

Emergency departments: The gatekeepers of admissions in Pennsylvania's rural hospitals.

STUDY OBJECTIVE: To analyze trends in admission rates and the proportion of admissions via the ED at rural hospitals in Pennsylvania and to identify factors that may impact admission rates. METHODS: We use retrospective, longitudinal data on rural acute care hospitals in Pennsylvania for 2000-19 to investigate temporal patterns in admission rates and the proportion of admissions via the ED. Regression analysis is then used to identify factors that may impact admission rates. RESULTS: In general admission rates, which averaged 14.5%, experienced a gradual decline (Change: -16.9%; from 15.7% to 13%) between 2000 and 2019. The proportion of hospital admissions via the ED, which averaged 64.9%, increased steadily (21%; from 57% to 69%). Critical access hospitals experienced a sharp decline in admissions via the ED (-49.1%) and admission rates (-55.3%). The fixed-effects regression model revealed several hospital- and ED-level characteristics were significantly associated with admission rate. CONCLUSIONS: Emergency departments are the gatekeepers of admissions at rural acute care hospitals in Pennsylvania. Many hospitals in rural Pennsylvania, including CAHs, are admitting most of their patients through the ED, concomitant with a significant decline in admissions and admission rates. This highlights the need to strengthen primary care practices serving rural Pennsylvania as well as the need to improve rural emergency and trauma systems. In the short to medium term, policy makers should explore innovative ways to fund smaller hospitals, especially CAHs, to develop level IV trauma center capabilities.

Complexities of Simultaneously Improving Quality and Lowering Costs in Hospitals Comment on "Hospitals Bending the Cost Curve With Increased Quality: A Scoping Review Into Integrated Hospital Strategies".

As health systems transition to value-based care delivery models, reducing costs and improving quality of care without sacrificing either remains a challenge for many healthcare organizations. There is extensive research on hospital costs, however, works addressing the complex relationship between hospital costs and the quality of care have been limited. In this commentary, I expound on the scoping review on integrated hospital strategies by Wackers et al that aim to improve quality while lowering costs. Specifically, I reiterate the complexity of the relationship between cost and quality and delve into major interdependent themes identified by the authors as relevant for the implementation of hospitals' integrated strategy.

A comparison of critical access hospitals and other rural acute care hospitals in Pennsylvania.

PURPOSE: As the Flex Program celebrates its 25th anniversary, we examined changes in critical access hospital (CAH) financial performance, investigated whether CAH status has reduced hospitals' financial vulnerability, and identified factors influencing financial performance. METHODS: We collected data on acute care hospitals in Pennsylvania's rural counties for 2000-20. Our sample contained 1,444 hospital-year observations. We used trend analysis to compare the financial performance of CAHs and rural prospective payment system (PPS) hospitals (non-CAHs). We investigated the effect of CAH status on financial performance and identified the time-variant factors impacting financial performance using fixed-effects regression analysis. RESULTS: The median total margin of CAHs lagged behind that of non-CAHs. When compared to non-CAH costs over the same period, the median cost per patient day incurred by CAHs has increased, with the rate of increase being significantly higher in the most recent decade. Our findings show that while CAH status does not appear to have a direct impact on the total margin, it is significantly associated with a higher cost per patient day. CONCLUSIONS: CAHs in Pennsylvania appear to be facing a double whammy of declining margins and rising costs compared to non-CAHs. Our findings demonstrate how crucial the Flex program has been in sustaining CAHs in Pennsylvania ever since its inception. Our findings have implications for rural health care delivery as well. While providing financial support and operational flexibility to CAHs should be a continuing policy priority, a long-term policy goal should be to envision an economic development strategy that capitalizes on the unique strengths of each of the rural archetypes.

Predicting hospital admission from emergency department triage data for patients presenting with fall-related fractures.

PURPOSE: Predict in advance the need for hospitalization of adult patients for fall-related fractures based on information available at the time of triage to help decision-making at the emergency department (ED). METHODS: We developed machine learning models using routinely collected triage data at a regional hospital chain in Pennsylvania to predict admission to an inpatient unit. We considered all patients presenting to the ED for fall-related fractures. Patients who were 18 years or younger, who left the ED against medical advice, left the ED waiting room without being seen by a provider, and left the ED after initial diagnostics were excluded from the analysis. We compared models obtained using triage data (pre-model) with models developed using additional data obtained after physicians' diagnoses (post-model). RESULTS: Our results show good discriminatory power on predicting hospital admissions. Neural network models performed the best (AUC: pre-model = 0.938 [CI 0.920-0.956], post-model = 0.983 [0.974-0.992]). The logistic regression analysis provides additional insights into the data and the relationships between the variables. CONCLUSIONS: Using limited data available at the time of triage, we developed four machine learning models aimed at predicting hospitalization for patients presenting to the ED for fall-related fractures. All the four models were robust and performed well. Neural network method, however, performed the best for both pre- and post-models. Simple, parsimonious machine learning models can provide high accuracy for predicting hospital admission.

Determining the relative risk of hospitalisation and surgery of fall injury patients.

Falls are one of the most common cause of nonfatal and fatal injuries in the U.S. costing over an estimated $54 billion annually. A significant percentage of patients presenting to hospital emergency departments (ED) for falls are hospitalised. This paper analyzes a regional hospital data pertaining to adults presenting to the ED because of falls. We use patient demographics and medical conditions to help identify patients at risk for immediate undesirable outcomes after a fall. Furthermore, we determine the relative risk of patient hospitalisation and surgery and their characteristics. Our results indicate that older patient's, patients arriving by ambulance, patients with higher severity levels and patients with pre-existing comorbidities were at a higher relative risk of hospitalisation and surgery. Furthermore, patients with medical conditions pertaining to femur and tibia fractures, pelvis, renal failure, ambulatory dysfunction, and cellulitis, among others, and non-Hispanic whites were at a much higher relative risk of hospitalisation and surgery.

Does efficiency and quality of care affect hospital closures?

In recent decades, a large number of hospitals in Pennsylvania and across the United States have been forced to close entirely, or to transform their beds for alternative uses including outpatient care. Hospital closures have severe repercussions for the stakeholders. A better understanding of hospital closures could help take corrective measures to alleviate the adverse impact closures have on communities. Using Pennsylvania Department of Health data compiled from various sources, we address the following questions: Are less efficient hospitals less likely to survive in the long run? What are the effects of quality of care on hospital closures? Does teaching status and location (urban or rural) have any impact on the probability of hospital closure? The result demonstrates several factors of varying significance affect hospital closures/survivals. Hospitals with higher ratio of registered nurses per bed, higher operating margin, lower percentage of revenues from Medicare and Medicaid, and lower competition were less likely to close. Efficiency measures such as DEA efficiency, cost per patient day, and cost per discharge were not found to have a significant impact on hospital closures. The results suggest that hospital administrators may focus more on quality of care and less on cost reduction and efficiency.

COPD: Does Inpatient Education Impact Hospital Costs and Length of Stay?

Background: Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of death in the United States and costs approximately $50 billion in annual healthcare costs. Certain interventions such as COPD inpatient education programs have demonstrated effectiveness in reducing healthcare utilization and reducing healthcare associated costs. Purpose: To assess the effectiveness of chronic obstructive pulmonary disease (COPD) inpatient education using existing respiratory therapy staff in an academic health system. Methodology/Approach: This retrospective observational study employed a matched case-control design. Inpatients admitted with a COPD related condition in this study received self-management interventions from Registered Respiratory Therapists (RTs). The sample includes retrospective administrative and medical record data on 84 inpatients with a diagnosis of COPD admitted in 2016 through 2017. Patients received self-management interventions at the bedside by trained RTs while admitted to acute care areas, progressive care units and intermediate care units. Effectiveness of inpatient education was compared before and after the interventions. Hospitalization costs and length of stay (LOS) are the primary outcome measures. Results: Statistical analyses revealed that inpatient COPD education appears to reduce hospital length of stay and associated costs. Post hoc regression analyses revealed that age, gender, marital status, and number of visits were significantly associated with LOS; whereas, smoking, LOS, and number of visits were significantly associated with hospitalization costs. Practice Implications: COPD patient education may be an effective strategy at reducing hospital costs and healthcare utilization overall. Empowering patients to take responsibility for their own health outcomes by improving self-efficacy has proven to demonstrate value.