Prospective payment to encourage system wide quality improvement
ABSTRACT Casemix-based inpatient prospective payment systems allocate payments for acute care based on what is done within an episode of care without regard for the outcome. To date, they have provided little incentive to improve quality. The Centers for Medicare & Medicaid Services have recently excluded 8 avoidable complications from their payment system.
This study models an inpatient prospective payment system that comprehensively excludes not-present-on-admission and other complication diagnoses from the entire funding process, effectively adding a diagnosis-related group (DRG)-specific average complication payment across all discharges.
Complication-averaged cost weights were estimated using the same patient level cost dataset used for estimating the relative resource weights for Victorian public hospitals in 2006-07. All codes with a "C" prefix (secondary diagnoses that are coded as having arisen after admission) and codes that define a condition that prima facie represent a specific complication of care were excluded from the code string. The episodes were then regrouped to DRGs and new complication-averaged cost weights were developed.
When complication codes were excluded across 1.2 million discharges, 1.37% became ungroupable, 14.86% included at least one complication diagnosis code, and 1.56% grouped to another DRG. Modeled funding for individual metropolitan hospitals in Victoria, Australia, was redistributed by -2.5% to 1.8%.
The cost weights reflect the average cost of preventable and unpreventable complications and have the potential to drive improvements in clinical care. This study is in contrast to previous studies estimating the funding impact of preventing all complications.
- SourceAvailable from: PubMed Central
[Show abstract] [Hide abstract]
- "The concept of NSIs has far-reaching implications for informing national health policies and, in particular, policies related to an array of information system development associated with administrative activity, clinical activity, clinical management and business management including costing. It is known that data and information on performance are often tied, or inherently built into, administrative systems to support activity-based funding schemes where the data are used for hospital quality improvement initiatives (McNair et al. 2009, Duckett 2012). Yet nursing-sensitive hospital data remain, to some extent, invisible within information systems, even when policy efforts have been directed to link quality and payment (Kavanagh et al. 2012). "
ABSTRACT: AimTo report a concept analysis of nursing-sensitive indicators within the applied context of the acute care setting.Background The concept of ‘nursing sensitive indicators’ is valuable to elaborate nursing care performance. The conceptual foundation, theoretical role, meaning, use and interpretation of the concept tend to differ. The elusiveness of the concept and the ambiguity of its attributes may have hindered research efforts to advance its application in practice.DesignConcept analysis.Data sourcesUsing ‘clinical indicators’ or ‘quality of nursing care’ as subject headings and incorporating keyword combinations of ‘acute care’ and ‘nurs*’, CINAHL and MEDLINE with full text in EBSCOhost databases were searched for English language journal articles published between 2000–2012. Only primary research articles were selected.MethodsA hybrid approach was undertaken, incorporating traditional strategies as per Walker and Avant and a conceptual matrix based on Holzemer's Outcomes Model for Health Care Research.ResultsThe analysis revealed two main attributes of nursing-sensitive indicators. Structural attributes related to health service operation included: hours of nursing care per patient day, nurse staffing. Outcome attributes related to patient care included: the prevalence of pressure ulcer, falls and falls with injury, nosocomial selective infection and patient/family satisfaction with nursing care.Conclusion This concept analysis may be used as a basis to advance understandings of the theoretical structures that underpin both research and practical application of quality dimensions of nursing care performance.Journal of Advanced Nursing 08/2014; 70(11). DOI:10.1111/jan.12503 · 1.69 Impact Factor
[Show abstract] [Hide abstract]
- "There have been proposals in the health economics literature to link incentive payments to observed performance on AEs (see McNair et al (2009), and Iezzoni (2009) for a critical discussion). Our results indicate that care should be taken when interpreting fixed effects as indicators of performance, and even more so when linking payments to estimated AE rates. "
ABSTRACT: We compare adverse event rates for surgical inpatients across 36 public hospitals in the state of Victoria, Australia, conditioning on differences in patient complexity across hospitals. We estimate separate models for elective and emergency patients which stay at least one night in hospitals, using fixed effects complementary log-log models to estimate AEs as a function of patient and episode characteristics, and hospital effects. We use 4 years of patient level administrative hospital data (2002/03 to 2005/06), and estimate separate models for each year. Averaged over four years, we find that adverse event rates are 12% for elective surgical inpatients, and 12.5% for emergency surgical inpatients. Most teaching hospitals have surprisingly low adverse event rates, at least after adjusting for the higher medical complexity of their patients. Some larger regional hospitals have high adverse events rates, in particular after adjusting for the below average complexity of their patients. Also, some suburban hospitals have high rates, especially the ones located in areas of low socioeconomic profile. We speculate that high rates may be due to factors beyond the control of the hospitals, such as staff shortages. We conclude that at present, care should be taken when using adverse event rates as indicators of hospital quality
- Medical care 03/2009; 47(3):269-71. DOI:10.1097/MLR.0b013e31819c0bce · 2.94 Impact Factor