When Agency Fails: An Analysis of the Association Between Hospital Agency Staffing and Quality Outcomes.

Introduction: Staffing is critical to hospital quality, but recent years have seen hospitals grappling with severe shortages, forcing them to rely on contract or agency staff for urgent patient care needs. This shift in staffing mix has raised questions about its impact on quality. Consequently, this study investigated whether the increased use of agency staff has affected healthcare quality in hospitals. Given the limited recent research on this topic, practitioners remain uncertain about the effectiveness of their staffing strategies and their potential impact on quality. Methods: Drawing from agency theory, data were obtained from Definitive Healthcare which consolidates information from numerous public access databases pertaining to hospitals such as the American Hospital Association Annual Survey (hospital profile) and the Hospital Value-Based Purchasing Program (quality data). We conducted a cross-sectional study using a multivariable linear regression model (2021-2022) with appropriate organizational and market- level control variables. Quality was measured across eight variables while the independent variable of interest was agency labor cost ratio operationalized as the percentage of net patient revenue consumed by agency labor expense. Results: Our results suggested that the employment of agency staff was significantly and negatively associated with six of eight quality measures tested, including the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) star rating, Hospital Compare rating, the hospital Total Performance Score (TPS), and three of the four sub-domains that comprise the TPS: clinical domain score, person and community engagement domain score, and the efficiency and cost reduction score. Discussion: Our results indicated that the increased use of agency labor may have a significant negative influence on quality outcomes at the hospital level. Our findings support the premise that interventions that promote full-time staffing may be more supportive of the quality of care delivered as well as patients' perceptions of care.

The Association Between Facility Affiliations and Revenue Generation in Skilled Nursing Facilities - An Exploratory Study.

Background: Although hospitals have been the traditional setting for interventional and rehabilitative care, skilled nursing facilities (SNFs) can offer a high-quality and less costly alternative than hospitals. Unfortunately, the financial health of SNFs is often a matter of concern. To partially address these issues, SNF leaders have increased engagement in a number of affiliations to assist in improving quality and reducing operational costs, including Accountable Care Organizations (ACOs), Health Information Exchanges (HIEs), and participation in Bundled Payment for Care Improvement (BPCI) programs. What is not well understood is what impact these affiliations have on the financial viability of the host organizations. Given these factors, this study aims to identify what association, if any, exists between SNF affiliations and revenue generation. Methods: Data from calendar year 2022 for n=13,447 SNFs in the US were assessed using multivariate regression analysis. We evaluated two separate dependent measures of revenue generation capacity: net patient revenue per bed and net patient revenue per discharge and considered three unique facility affiliations including (1) ACOs, (2) HIEs, and (3) BPCI participants. Results: Six multivariable linear regressions revealed that ACO affiliation is negatively associated with revenue generation on both dependent measures, while HIE affiliation and BPCI participation reflected mixed results. Conclusion: A better understanding of the financial impact of SNFs' affiliations may prove insightful. By carefully considering the value of each affiliation, and how each is applicable to any given market, policymakers, funding agencies, and facility leaders may be able to better position SNFs for more sustainable financial performance in a challenging economic environment.

The Predictive Factors of Hospital Bankruptcy-An Exploratory Study.

The United States healthcare industry has witnessed a number of hospitals declare bankruptcy. This has a meaningful impact on local communities with vast implications on access, cost, and quality of care available. In our research, we seek to determine what contemporary structural and operational factors influence a bankruptcy outcome, and craft predictive models to guide healthcare leaders on how to best avoid bankruptcy in the future. In this exploratory study we performed, a single-year cross-sectional analysis of short-term acute care hospitals in the United States and subsequently developed three predictive models: logistic regression, a linear support vector machine (SVM) model with hinge function, and a perceptron neural network. Data sources include Definitive Healthcare and Becker's Hospital Review 2019 report with 3121 observations of 32 variables with 27 observed bankruptcies. The three models consistently indicate that 18 variables have a significant impact on predicting hospital bankruptcy. Currently, there is limited literature concerning financial forecasting models and knowledge detailing the factors associated with hospital bankruptcy. By having tailored knowledge of predictive factors to establish a sound financial structure, healthcare institutions at large can be empowered to take proactive steps to avoid financial distress at the organizational level and ensure long-term financial viability.

Is the Hospital Value-Based Purchasing Program Associated with Reduced Hospital Readmissions?

Introduction: Hospital readmissions have been associated with adverse outcomes and elevated financial costs to patients, families, and hospitals across the United States. Historically, nearly 20% of all Medicare discharges had a readmission within 30 days. In an effort to address this issue, the Affordable Care Act (ACA) established the Hospital Readmission Reduction Program (HRRP) in 2012 to positively influence readmissions associated with acute myocardial infarction, chronic obstructive pulmonary disease, heart failure, pneumonia, coronary artery bypass graft surgery, and total hip and/or knee arthroplasty. However, as recently as 2018, there were still 3.8 million 30-day all-cause adult hospital readmissions, with a 14% readmission rate and an average readmission cost of $15,200. The ACA also produced the Hospital Value-Based Purchasing (HVBP) program with the stated intent to (1) reduce mortality and complications, (2) reduce healthcare-associated infections, (3) increase patient safety, (4) improve the patient experience, and (5) increase efficiency and reduce costs. Given the costs and quality implications of an average readmission, it is logical to believe that HVBP eligible hospitals are simultaneously seeking to meet the goals of both programs. However, to date, no studies have examined if that is the case. Thus, in this study, we seek to determine if HVBP eligible hospitals are associated with a reduction in the core set of HRRP readmission rates better than the facilities that are not eligible for the HVBP program. Methods: Hospital-level data from calendar year 2019 for 3,276 short-term acute care hospitals in the United States were evaluated using multivariate regression analysis to examine the readmission rate performance between 2,719 HVBP eligible hospitals and 557 ineligible hospitals. Results: Our six separate multivariable linear regressions revealed a statistically significant and positive association between HVBP participation and readmission rates after controlling for numerous organizational, clinical complexity, and environmental factors. In each case, the magnitude of the positive directional association is moderate, ranging from and increase of +0.19% (pneumonia readmissions) to as high as +0.37% (all cause readmissions) for HVBP eligible hospitals. When considered in the context of the average number of discharges in our data set (x- = 9570), a third of a percent increase from the average 15.56% all cause readmissions to 16.08% in HVBP eligible hospitals equates to 50 additional readmissions annually (9570 × 15.56% = 1,489 vs 9570 × 16.08% = 1,539). At an average cost of $15,200 per readmission, which equates to an average additional cost to the average HVBP eligible hospital in excess of $760,000. Discussion: The fact that there is a positive association between HVBP participating hospitals and readmissions at all, and that the same effect appears to be persistent across all dependent measures is concerning. One would logically expect hospitals that are focused on quality-based care to thoroughly care for individuals in order for them to not be readmitted to the hospital. The results provided do not necessarily prove that either program is not working. But they also do not confirm that the HVBP and HRRP programs are working together and accomplishing what they were originally designed to do: improve patient care and lower health-care costs.

Exploring Health Outcomes for U.S. Veterans Compared to Non-Veterans from 2003 to 2019.

The physical demands on U.S. service members have increased significantly over the past several decades as the number of military operations requiring overseas deployment have expanded in frequency, duration, and intensity. These elevated demands from military operations placed upon a small subset of the population may be resulting in a group of individuals more at-risk for a variety of debilitating health conditions. To better understand how the U.S Veterans health outcomes compared to non-Veterans, this study utilized the U.S. Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) dataset to examine 10 different self-reported morbidities. Yearly age-adjusted, population estimates from 2003 to 2019 were used for Veteran vs. non-Veteran. Complex weights were used to evaluate the panel series for each morbidity overweight/obesity, heart disease, stroke, skin cancer, cancer, COPD, arthritis, mental health, kidney disease, and diabetes. General linear models (GLM's) were created using 2019 data only to investigate any possible explanatory variables associated with these morbidities. The time series analysis showed that Veterans have disproportionately higher self-reported rates of each morbidity with the exception of mental health issues and heart disease. The GLM showed that when taking into account all the variables, Veterans disproportionately self-reported a higher amount of every morbidity with the exception of mental health. These data present an overall poor state of the health of the average U.S. Veteran. Our study findings suggest that when taken as a whole, these morbidities among Veterans could prompt the U.S. Department of Veteran Affairs (VA) to help develop more effective health interventions aimed at improving the overall health of the Veterans.

Association of Electronic Health Record Vendors With Hospital Financial and Quality Performance: Retrospective Data Analysis.

BACKGROUND: Electronic health records (EHRs) are a central feature of care delivery in acute care hospitals; however, the financial and quality outcomes associated with system performance remain unclear. OBJECTIVE: In this study, we aimed to evaluate the association between the top 3 EHR vendors and measures of hospital financial and quality performance. METHODS: This study evaluated 2667 hospitals with Cerner, Epic, or Meditech as their primary EHR and considered their performance with regard to net income, Hospital Value-Based Purchasing Total Performance Score (TPS), and the unweighted subdomains of efficiency and cost reduction; clinical care; patient- and caregiver-centered experience; and patient safety. We hypothesized that there would be a difference among the 3 vendors for each measure. RESULTS: None of the EHR systems were associated with a statistically significant financial relationship in our study. Epic was positively associated with TPS outcomes (R2=23.6%; ß=.0159, SE 0.0079; P=.04) and higher patient perceptions of quality (R2=29.3%; ß=.0292, SE 0.0099; P=.003) but was negatively associated with patient safety quality scores (R2=24.3%; ß=-.0221, SE 0.0102; P=.03). Cerner and Epic were positively associated with improved efficiency (R2=31.9%; Cerner: ß=.0330, SE 0.0135, P=.01; Epic: ß=.0465, SE 0.0133, P<.001). Finally, all 3 vendors were associated with positive performance in the clinical care domain (Epic: ß=.0388, SE 0.0122, P=.002; Cerner: ß=.0283, SE 0.0124, P=.02; Meditech: ß=.0273, SE 0.0123, P=.03) but with low explanatory power (R2=4.2%). CONCLUSIONS: The results of this study provide evidence of a difference in clinical outcome performance among the top 3 EHR vendors and may serve as supportive evidence for health care leaders to target future capital investments to improve health care delivery.

Models for Heart Failure Admissions and Admission Rates, 2016 through 2018.

BACKGROUND: Approximately 6.5 to 6.9 million individuals in the United States have heart failure, and the disease costs approximately $43.6 billion in 2020. This research provides geographical incidence and cost models of this disease in the U.S. and explanatory models to account for hospitals' number of heart failure DRGs using technical, workload, financial, geographical, and time-related variables. METHODS: The number of diagnoses is forecast using regression (constrained and unconstrained) and ensemble (random forests, extra trees regressor, gradient boosting, and bagging) techniques at the hospital unit of analysis. Descriptive maps of heart failure diagnostic-related groups (DRGs) depict areas of high incidence. State- and county-level spatial and non-spatial regression models of heart failure admission rates are performed. Expenditure forecasts are estimated. RESULTS: The incidence of heart failure has increased over time with the highest intensities in the East and center of the country; however, several Northern states have seen large increases since 2016. The best predictive model for the number of diagnoses (hospital unit of analysis) was an extremely randomized tree ensemble (predictive R2 = 0.86). The important variables in this model included workload metrics and hospital type. State-level spatial lag models using first-order Queen criteria were best at estimating heart failure admission rates (R2 = 0.816). At the county level, OLS was preferred over any GIS model based on Moran's I and resultant R2; however, none of the traditional models performed well (R2 = 0.169 for the OLS). Gradient-boosted tree models predicted 36% of the total sum of squares; the most important factors were facility workload, mean cash on hand of the hospitals in the county, and mean equity of those hospitals. Online interactive maps at the state and county levels are provided. CONCLUSIONS: Heart failure and associated expenditures are increasing. Costs of DRGs in the study increased $61 billion from 2016 through 2018. The increase in the more expensive DRG 291 outpaced others with an associated increase of $92 billion. With the increase in demand and steady-state supply of cardiologists, the costs are likely to balloon over the next decade. Models such as the ones presented here are needed to inform healthcare leaders.

Thinking lean: implementing DMAIC methods to improve efficiency within a cystic fibrosis clinic.

The timely coordination of care in clinics that require frequent assessments by multiple specialists can be challenging for both patients and providers. The cornerstone of care at cystic fibrosis (CF) centers with superior clinical outcomes, as with reduced acuity of episodic disease and incidence of hospitalizations, is frequent clinical encounters coupled with aggressive therapies. However, inefficiencies in the clinical practice structure prevent optimal utilization of resources. To decrease non-value-added time, defined as time a patient spends alone in an examination room, without altering the time providers spend caring for a patient, the application of Lean methods was used to see whether reducing variation could significantly decrease lead time, considered the length of a patient visit, within a CF clinic setting. Baseline capability analyses revealed only 19.3% of patient visits were completed in 60min or less, with mean and median visit times of 84 and 81min, respectively. Final capability analyses demonstrated that 41.5% of patient visits were completed in 60min or less, 23% greater than the baseline capability. Mean and median visit times decreased by 10min per visit. Research efforts increased the available capacity by 500 patient visits per year, representing additional revenue of over US$165,000 annually with no additional administrative costs incurred.