Modifying DRG-PPS to include only diagnoses present on admission: financial implications and challenges.

Agency for Healthcare Research and Quality, Department of Health and Human Services, Rockville, Maryland 20850, USA.
Medical Care (Impact Factor: 3.23). 05/2007; 45(4):288-91. DOI: 10.1097/
Source: PubMed

ABSTRACT The inability to distinguish complications acquired in hospital from comorbid conditions that are present on admission (POA) has long hampered the use of claims data in quality and safety research. Now pay-for-performance initiatives and legislation requiring Medicare to reduce payment for acquired infections add imperative for POA coding. This study used data from 2 states currently coding POA to assess the financial impact if Medicare pays based on POA conditions only and to examine the challenges in implementing POA coding.
Medicare payments were calculated based first on all diagnoses and then on POA diagnoses in the Medicare discharge abstracts from California and New York in 2003, using the Diagnosis Related Group (DRG)-based Prospective Payment System (PPS) formula. The potential savings that result from excluding non-POA diagnoses were calculated. Patterns of POA coding were explored.
Medicare could have saved $56 million in California, $51 million in New York, and $800 million nationwide in 2003 had it paid hospital claims based only on POA diagnoses. Approximately 15% of the claims had non-POA codes, but only 1.4% of the claims were reassigned to lower-cost DRGs after excluding non-POA diagnoses. Excluding non-POA diagnoses resulted in reduced payment for operating costs, but increased outlier payments because some of the claims were designated as "unusually high cost" in the lower-cost DRGs. POA coding patterns suggest some problems in current POA coding.
To be consistent with pay-for-performance principles and make claims data more useful for quality assurance, incorporating POA coding into DRG-PPS could produce sizable savings for Medicare.

1 Bookmark
  • Source
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Public reports that compare hospital mortality rates for patients with acute myocardial infarction are commonly used strategies for improving the quality of care delivered to these patients. Fair comparisons of hospital mortality rates require thorough adjustments for differences among patients in baseline mortality risk. This study examines the effect on hospital mortality rate comparisons of improved risk adjustment methods using diagnoses reported as present-at-admission. Logistic regression models and related methods originally used by California to compare hospital mortality rates for patients with acute myocardial infarction are replicated. These results are contrasted with results obtained for the same hospitals by patient-level mortality risk adjustment models using present-at-admission diagnoses, using 3 statistical methods of identifying hospitals with higher or lower than expected mortality: indirect standardization, adjusted odds ratios, and hierarchical models. Models using present-at-admission diagnoses identified substantially fewer hospitals as outliers than did California model A for each of the 3 statistical methods considered. Large improvements in statistical performance can be achieved with the use of present-at-admission diagnoses to characterize baseline mortality risk. These improvements are important because models with better statistical performance identify different hospitals as having better or worse than expected mortality.
    Circulation 01/2008; 116(25):2960-8. · 15.20 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Adverse event or complication rates are increasingly advocated as measures of hospital quality and performance. Objective of this study is to analyse patient-complexity adjusted adverse events rates to compare the performance of hospitals in Victoria, Australia. We use a unique hospital dataset that routinely records adverse events which arise during the admission. We identify hospitals with below or above average performance in comparison to their peers, and show for which types of hospitals risk adjusting makes biggest difference. We estimate adverse event rates for 87,790 elective and 43,771 emergency episodes in 34 public hospitals over the financial year 2005/06 with a complementary log-log model, using patient level administrative hospital data and controlling for patient complexity with a range of covariates. Teaching hospitals have average risk-adjusted adverse event rates of 24.3% for elective and 19.7% for emergency surgical patients. Suburban and rural hospitals have lower rates of 17.4% and 17%, and 16.1% and 15.7%, respectively. Selected non-teaching hospitals have relatively high rates, in particular hospitals in rural and socially disadvantaged areas. Risk adjustment makes a significant difference to most hospitals. We find comparably high adverse events rates for surgical patients in Australian hospitals, possibly because our data allow identification of a larger number of adverse events than data used in previous studies. There are marked variations in adverse event rates across hospitals in Victoria, even after risk adjusting. We discuss how policy makers could improve quality of care in Australian hospitals.
    Health Policy 07/2011; 104(2):146-54. · 1.51 Impact Factor