Incorporating Prescription Drug Utilization Information Into the Marketplace Risk Adjustment Model Improves Payment Accuracy and Reduces Adverse Selection Incentives

2019 ◽  
pp. 107755871987006
Author(s):  
Jianhui Xu ◽  
Erin Trish ◽  
Geoffrey Joyce

Beginning with the 2018 benefit year, the Centers for Medicare and Medicaid Services started incorporating select prescription drug utilization information into the Marketplace risk adjustment model. There has been little evidence about the impact of this change on payment accuracy and the mechanisms through which it may occur. Using commercial claims in 2017 from a large national health insurer, we find that the policy change improves payment accuracy in our sample and would help mitigate insurers’ selection incentives for some enrollees through two channels: imputing missing diagnoses and varying risk scores to better capture the heterogeneity in expenditures among patients with certain health conditions. However, while improving payment accuracy overall, there are potential perverse incentives that could distort treatment choice for marginal patients. Additionally, overcompensation and undercompensation persists for certain patient subgroups, suggesting an opportunity to further refine and improve the model.

Author(s):  
Aylin Wagner ◽  
René Schaffert ◽  
Julia Dratva

Quality indicators (QIs) based on the Resident Assessment Instrument-Home Care (RAI-HC) offer the opportunity to assess home care quality and compare home care organizations’ (HCOs) performance. For fair comparisons, providers’ QI rates must be risk-adjusted to control for different case-mix. The study’s objectives were to develop a risk adjustment model for worsening or onset of urinary incontinence (UI), measured with the RAI-HC QI bladder incontinence, using the database HomeCareData and to assess the impact of risk adjustment on quality rankings of HCOs. Risk factors of UI were identified in the scientific literature, and multivariable logistic regression was used to develop the risk adjustment model. The observed and risk-adjusted QI rates were calculated on organization level, uncertainty addressed by nonparametric bootstrapping. The differences between observed and risk-adjusted QI rates were graphically assessed with a Bland-Altman plot and the impact of risk adjustment examined by HCOs tertile ranking changes. 12,652 clients from 76 Swiss HCOs aged 18 years and older receiving home care between 1 January 2017, and 31 December 2018, were included. Eight risk factors were significantly associated with worsening or onset of UI: older age, female sex, obesity, impairment in cognition, impairment in hygiene, impairment in bathing, unsteady gait, and hospitalization. The adjustment model showed fair discrimination power and had a considerable effect on tertile ranking: 14 (20%) of 70 HCOs shifted to another tertile after risk adjustment. The study showed the importance of risk adjustment for fair comparisons of the quality of UI care between HCOs in Switzerland.


2003 ◽  
Vol 349 (23) ◽  
pp. 2224-2232 ◽  
Author(s):  
Haiden A. Huskamp ◽  
Patricia A. Deverka ◽  
Arnold M. Epstein ◽  
Robert S. Epstein ◽  
Kimberly A. McGuigan ◽  
...  

Circulation ◽  
2021 ◽  
Vol 144 (Suppl_2) ◽  
Author(s):  
Andy T Tran ◽  
Anthony Hart ◽  
John Spertus ◽  
Philip Jones ◽  
Bryan McNally ◽  
...  

Background: Given the diversity of patients resuscitated from out-of-hospital cardiac arrest (OHCA) complicated by STEMI, adequate risk adjustment is needed to account for potential differences in case-mix to reflect the quality of percutaneous coronary intervention. Objectives: We sought to build a risk-adjustment model of in-hospital mortality outcomes for patients with OHCA and STEMI requiring emergent angiography. Methods: Within the Cardiac Arrest Registry to Enhance Survival, we included adult patients with OHCA and STEMI who underwent angiography within 2 hours from January 2013 to December 2019. Using pre-hospital patient and arrest characteristics, multivariable logistic regression models were developed for in-hospital mortality. We then described model calibration, discrimination, and variability in patients’ unadjusted and adjusted mortality rates. Results: Of 2,999 hospitalized patients with OHCA and STEMI who underwent emergent angiography (mean age 61.2 ±12.0, 23.1% female, 64.6% white), 996 (33.2%) died. The final risk-adjustment model for mortality included higher age, unwitnessed arrest, non-shockable rhythms, not having sustained return of spontaneous circulation upon hospital arrival, and higher total resuscitation time on scene ( C -statistic, 0.804 with excellent calibration). The risk-adjusted proportion of patients died varied substantially and ranged from 7.8% at the 10 th percentile to 74.5% at the 90 th percentile (Figure). Conclusions: Through leveraging data from a large, multi-site registry of OHCA patients, we identified several key factors for better risk-adjustment for mortality-based quality measures. We found that STEMI patients with OHCA have highly variable mortality risk and should not be considered as a single category in public reporting. These findings can lay the foundation to build quality measures to further optimize care for the patient with OHCA and STEMI.


2009 ◽  
Vol 174 (9) ◽  
pp. 958-963 ◽  
Author(s):  
Joshua W. Devine ◽  
Shana Trice ◽  
Stacia L. Spridgen ◽  
Thomas A. Bacon

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