Because the snow reveals no signal of stopping, we’d as properly keep cozy inside and browse up on the newest well being coverage analysis from January! This month we examine affected person care after non-public fairness acquisition of hospitals, and the way to enhance risk-adjustment accuracy in Medicare Benefit.
Modifications in Affected person Care Expertise After Non-public Fairness Acquisition of US Hospitals
Anjali Bhatla, Victoria L. Bartlett, Michael Liu, ZhaoNian Zheng, Rishi Wadhera. JAMA. January 2025. Out there right here.
Researchers funded by the American Coronary heart Affiliation used knowledge from Irving Levin Associates and Pitchbook, the American Hospital Affiliation, and CMS Impression Information to judge whether or not the acquisition of hospitals by non-public fairness (PE) corporations was related to modifications within the high quality of affected person care, when put next with non-PE hospitals chosen as controls.
What it Finds
- Following acquisition of a hospital there was a lower in patient-reported workers responsiveness at PE hospitals in contrast with management hospitals.
- International measures of affected person care expertise worsened after a hospitals’ acquisition by non-public fairness, and the distinction in total affected person care expertise measures between PE hospitals and non-PE hospitals grew every year following acquisition reaching round 5 share factors by yr three post-acquisition.
- These modifications exceeded the nationwide 3.6% decline in affected person care expertise scores noticed in the course of the COVID-19 pandemic.
Why it Issues
Enhancing patient-centered care is a nationwide precedence, and these findings spotlight how affected person care expertise might decline with non-public fairness possession, elevating questions in regards to the high quality of medical care, staffing ranges, and affected person outcomes. This evaluation discovered that the lower in affected person expertise scores at PE hospitals in contrast with management hospitals grew every year following acquisition, suggesting that the consequences of organizational modifications carried out by PE might compound over time. The findings counsel that personal fairness methods might prioritize monetary returns over affected person care, which might have long-term penalties on well being outcomes and medical high quality. Policymakers want to think about the implications of personal fairness possession on affected person care and discover choices for oversight to safeguard affected person pursuits.
Combining Affected person Survey Information With Prognosis Codes Improved Medicare Benefit Threat-Adjustment Accuracy
Meghan Bellerose, Hannah O. James, Jay Shroff, Andrew M. Ryan, David J. Meyers. Well being Affairs. January 2025. Out there right here.
A analysis crew at Brown College linked 2016-2019 medical and pharmaceutical claims to Client Evaluation of Healthcare Suppliers and Methods (CAHPS) survey responses of Medicare Benefit (MA) enrollees to check the predictive accuracy of various risk-adjustment methods versus the usual Hierarchical Situation Classes (HCC) method.
What it Finds
- Survey-based fashions, notably when mixed with HCC scores (which CMS estimates from beneficiaries’ prognosis codes and demographic traits), had been discovered to enhance prediction of MA utilization, particularly for beneficiaries with the best and lowest predicted healthcare use.
- The exclusion of prognosis codes from well being danger assessments (HRAs) and chart opinions resulted in barely much less predictive accuracy in comparison with customary HCC scores, although including survey knowledge enhanced mannequin efficiency.
Why it Issues
Enhancing risk-adjustment fashions is essential to making sure that Medicare Benefit plans are reimbursed pretty for managing their enrollees’ care whereas decreasing incentives for discretionary prognosis coding or upcoding. Higher danger adjustment might additionally enhance the monetary sustainability of the Medicare program. Integrating well being survey responses into danger adjustment might assist counteract risk-score inflation and be certain that increased funds are directed to plans protecting sicker populations. If CMS excludes diagnoses most susceptible to upcoding, surveys might fill in gaps of vital well being data and enhance equity in funds to plans serving high-need beneficiaries.
