Association of the New Peer Group-Stratified Method With the Reclassification of Penalty Status in the Hospital Readmission Reduction Program.

Importance: Since the introduction of the Hospital Readmission Reduction Program (HRRP), readmission penalties have been applied disproportionately to institutions that serve low-income populations. To address this concern, the US Centers for Medicare & Medicaid introduced a new, stratified payment adjustment method in fiscal year (FY; October 1 to September 30) 2019. Objective: To determine whether the introduction of a new, stratified payment adjustment method was associated with an alteration in the distribution of penalties among hospitals included in the HRRP. Design, Setting, and Participants: In this retrospective cross-sectional study, US hospitals included in the HRRP for FY 2018 and FY 2019 were identified. Penalty status of participating hospitals for FY 2019 was determined based on nonstratified HRRP methods and the new, stratified payment adjustment method. Hospitals caring for the highest proportion of patients enrolled in both Medicare and Medicaid based on quintile were assigned to the low-socioeconomic status (SES) group. Exposures: Nonstratified and stratified Centers for Medicare & Medicaid payment adjustment methods. Main Outcomes and Measures: Net reclassification of penalties among all hospitals and hospitals in the low-SES group, in states participating in Medicaid expansion, and for 4 targeted medical conditions (acute myocardial infarction, heart failure, chronic obstructive pulmonary disease, and pneumonia). Results: Penalty status by both payment adjustment methods (nonstratified and stratified) was available for 3173 hospitals. For FY 2019, the new, stratified payment method was associated with penalties for 75.04% of hospitals (2381 of 3173), while the old, nonstratified method was associated with penalties for 79.07% (2509 hospitals), resulting in a net down-classification in penalty status for all hospitals by 4.03 percentage points (95% CI, 2.95-5.11; P < .001). For the 634 low-SES hospitals in the sample, the new method was associated with penalties for 77.60% of hospitals (492 of 634), while the old method was associated with penalties for 91.64% (581 hospitals), resulting in a net down-classification in penalty status of 14.04 percentage points (95% CI, 11.18-16.90; P < .001). Among hospitals that were not low SES (quintiles 1-4), the new payment method was associated with a small decrease in penalty status (1928 vs 1889; net down-classification, 1.54 percentage points; 95% CI, 0.38-2.69; P = .01). Among target medical conditions, the greatest reduction in penalties was observed among cardiovascular conditions (net down-classification, 6.18 percentage points; 95% CI, 4.96-7.39; P < .001). Conclusions and Relevance: The new, stratified payment adjustment method for the HRRP was associated with a reduction in penalties across hospitals included in the program; the greatest reductions were observed among hospitals in the low-SES group, lessening but not eliminating the previously unbalanced penalty burden carried by these hospitals. Additional public policy research efforts are needed to achieve equitable payment adjustment models for all hospitals.

The determinants of hospital cost: a cost-volume-profit analysis of health services in the occupied territories: Palestine.

OBJECTIVE: The purpose of this study is to examine the unit costs of a multi-service hospital in Palestine for the period 2005-2007. We investigate the cost structure of the Rafidya Hospital located in Nablus city, for both inpatient and outpatient departments. METHODS: This study uses cost-volume-profit (CVP) analysis, also known as breakeven analysis. CVP analysis requires examining total costs, along with fixed and variable costs. CVP analysis illuminates how changes in assumptions about cost behaviour and the relevant range in which those assumptions are valid affect the relationships among revenues, variable costs and fixed costs at various production levels. KEY FINDINGS: For the hospital of interest, we find that fixed costs account for 70% of total costs, and variable costs were 30% of total costs. Inpatient departments accounted for 86% of total costs, and outpatient departments were 14% of total costs. Results of the breakeven analysis illustrate that several departments charge sufficient fees to cover all unit costs. CONCLUSIONS: Results provide useful information about unit cost based on four categories: (1) unit cost per admission of each department, (2) unit cost per patient day of each department, (3) unit cost per admission with annual capital cost of each department and (4) unit cost per patient day with annual capital cost. Our results provide hospital cost information that can be used by decision-makers to provide and expand healthcare services, in an effort to increase sustainability and profitability. The use of cost analysis by administrators and regulators will improve the quality of financial information, as well as enhance the efficient use of scarce resources.

Stochastic frontier analysis of hospital inefficiency: a review of empirical issues and an assessment of robustness.

Twenty stochastic frontier analysis (SFA) studies of hospital inefficiency in the United States were analyzed. Results from best-practice methods were compared against previously used methods in hospital studies to ascertain the robustness of SFA in estimating cost inefficiency. To compare past studies and analyze new data, SFA methods were varied by (a) the assumptions of the structure of costs and distribution of the error term, (b) inclusion of quality and product descriptor measures, and (c) use of simultaneous and two-stage estimation techniques. SFA results were relatively insensitive to several model variations.

Comparing apples to apples: the relative financial performance of Manitoba's acute care hospitals.

This paper presents comparative financial ratios that can be adopted by health system administrators and policy analysts to begin to evaluate the performance of acute care hospitals. We combined financial, statistical and clinical information for 73 acute care hospitals in Manitoba for fiscal 1997/98 to calculate 15 indicators of financial performance. Our findings suggest that there is variability between hospital types in their average costs per weighted case, cost structure and financial performance.

The relative cost of inpatient care in Manitoba's largest hospitals.

Comparative information from 1991/92 to 1997/98 regarding an indicator of the relative financial performance of teaching, urban community and major rural hospitals in Manitoba is presented. Results suggest that the acuity and complexity of patients in teaching and urban community hospitals was similar in 1997/98, and that teaching facilities had higher inpatient care costs in all time periods, even after accounting for differences in case-mix, physician remuneration, capital expenditures and the direct costs of teaching programs.

Reconsideration of discharge data to measure competition in the hospital industry.

Economists have long used state-collected discharge data to construct Hirschman-Herfindahl (HH) indices measuring hospital competition. Since data are collected to determine the facility providing the service rather than ownership, the difference between the number of reporting facilities and the number of competitors has grown over time due to mergers and networking activities. Consequently, the validity of the discharge HH methodology, as currently employed, is in doubt. Comparing the annual census of New York state acute-care hospitals by the State and the American Hospital Association (AHA), we find that it is increasingly important to account for changes in ownership when constructing such indices.

Revision of the CPI hospital services component.

Upcoming modifications are designed to capture current service delivery patterns, reimbursement methods, and payment sources for hospital visits, rather than what the hospital charges for individual treatment inputs; the result will be an index that better reflects price changes in the dynamic health care field.