Volatility and Persistence of Value-Based Purchasing Adjustments
A Challenge to Integrating Population Health and Community Benefit Into Business Operations
Bibliographic Data
| ID | 22088693 |
|---|---|
| Authors | Jason S Turner (0000-0002-5511-027X, Rush University, corresponding author), Kevin D Broom (0000-0001-7310-0908, University of Pittsburgh), Kenton J Johnston (0000-0002-7760-203X, Saint Louis University), Steven Howard (0000-0003-0056-6680, Saint Louis University), Steven W Howard, S Freeman (Rush University), Susan L Freeman, Travis Englund (Bryan Health) |
| Year | 2020 |
| Volume | 8 |
| Pages | 165-165 |
| Publication date | 2020-06-09 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2020.00165 |
| PMID | 32582599 |
| OpenAlex | W3035655352 |
| Language | EN |
| References cited | 29 |
With the passage of the Deficit Reduction Act of 2005 and the Patient Protection and Affordable Care Act in 2010, Medicare's Inpatient Prospective Payment System (IPPS) began a transition to value-based purchasing (VBP) that rewards or penalizes hospitals based on patient satisfaction, clinical processes of care, outcomes, and efficiency metrics. However, hospital-level volatility vs. persistence in value-based payments year-over-year could result in unpredictable cash flows that negatively influence investment behavior, drive underinvestment in community benefit/population health management initiatives, and make management of the factors that drive the VBP adjustment more challenging. To evaluate the volatility and persistence of hospital VBP adjustments, the sample includes VBP adjustments and the associated domain scores for the 2,547 hospitals that participated in the program from 2013 to 2016. The sample includes urban (74%), teaching (29.1%), system affiliated (46.5%), and not-for-profit (63.6%) facilities. Volatility was measured using basic descriptive statistics, relative risk ratios, and a fixed effect, autoregressive, dynamic panel model that robust-clustered the standard errors. There is substantial change in a given facility's total VBP score with an average standard deviation of 10.74 (on a 100-point scale) that is driven by significant volatility in all metrics but particularly by efficiency and outcomes metrics. Relative risk ratios have dropped substantially over the life of the program, and there is low persistence of VBP scores from one period to the next. Findings indicate that if hospitals receive a positive adjustment in 1 year, they are almost as likely to receive a negative adjustment as a positive adjustment the following year. Furthermore, using a fixed-effect dynamic panel model that controls for autocorrelation, we find that only 13.5% of a facility's prior year IPPS adjustment (positive or negative) carries forward to the next year. The low persistence makes investment in population health management and community benefit more challenging
Actuarial science · Business · Econometrics · Economics · Environmental health · Population · Purchasing · Value-Based Purchasing · Computer Science · Engineering · Healthcare Policy and Management · Medicine · Patient Satisfaction in Healthcare · Primary Care and Health Outcomes · Marketing
| Citation velocity | historical |
|---|---|
| Highly cited | No |