1
|
Pozniak A, Lammers E, Mukhopadhyay P, Cogan C, Ding Z, Goyat R, Hanslits K, Ji N, Jin Y, Repeck K, Schrager J, Young E, Turenne M. Association of the Home Health Value-Based Purchasing Model With Quality, Utilization, and Medicare Payments After the First 5 Years. JAMA Health Forum 2022; 3:e222723. [PMID: 36218946 PMCID: PMC9508657 DOI: 10.1001/jamahealthforum.2022.2723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Question How did quality, utilization, and Medicare payments differ after the 5 years of the Home Health Value-Based Purchasing (HHVBP) model? Findings In this cohort study of US patients who received care at a home health agency between 2013 and 2020 in 9 original HHVBP states compared with those in comparison states, a difference-in-differences analysis found the HHVBP model was associated with lower Medicare payments that were associated with lower utilization of inpatient and skilled nursing facility services. Quality was better or similar. Meaning The study results suggest that financial incentives for home health agency quality performance were associated with reduced Medicare payments and utilization while improving or maintaining quality. Importance The original Home Health Value-Based Purchasing (HHVBP) model provided financial incentives to home health agencies for quality improvement in 9 randomly selected US states. Objective To evaluate quality, utilization, and Medicare payments for home health patients in HHVBP states compared with those in comparison states. Design, Setting, and Participants This cohort study was conducted in 2021 with secondary data from January 2013 to December 2020. A difference-in-differences design and multivariate linear regression were used to compare outcomes for Medicare and Medicaid beneficiaries who received home health care in HHVBP states with those in 41 comparison states during 3 years of preintervention (2013-2015) and the subsequent 5 years (2016-2020). Exposures Home health care provided by a home health agency in HHVBP states and comparison states. Main Outcomes and Measures Utilization (unplanned hospitalizations, emergency department visits, skilled nursing facility [SNF] visits) for Medicare beneficiaries within 60 days of beginning home health, Medicare payments during and 37 days after home health episodes, and quality of care (functional status, patient experience) during home health episodes. Results Among 34 058 796 home health episodes (16 584 870 beneficiaries; mean [SD] age of 76.6 [11.7] years; 60.5% female; 11.2% Black non-Hispanic; 79.5% White non-Hispanic) from January 2016 to December 2020, 22.6% were in HHVBP states and 77.4% were in non-HHVBP states. For the HHVBP and non-HHVBP groups, 60.4% and 61.0% of episodes were provided to female patients; 10.0% and 13.6% were provided to Black non-Hispanic patients, and 82.4% and 75.2% were provided to White non-Hispanic patients, respectively. Unplanned hospitalizations decreased by 0.15 percentage points (95% CI, –0.30 to –0.01) more in HHVBP states, a 1.0% decline compared with 15.7% at baseline. The use of SNFs decreased by 0.34 percentage points (95% CI, –0.40 to –0.27) more in HHVBP states, a 6.9% decline compared with the 4.9% baseline average. There was an association between HHVBP and a reduction in average Medicare payments per day of $2.17 (95% CI, –$3.67 to –$0.68) in HHVBP states, primarily associated with reduced inpatient and SNF services, which corresponded to an average annual Medicare savings of $190 million. There was greater functional improvement in HHVBP states than comparison states and no statistically significant change in emergency department use or most measures of patient experience. Conclusions and Relevance In this cohort study, the HHVBP model was associated with lower Medicare payments that were associated with lower utilization of inpatient and SNF services, with better or similar quality of care.
Collapse
Affiliation(s)
- Alyssa Pozniak
- Arbor Research Collaborative for Health, Ann Arbor, Michigan
| | - Eric Lammers
- Arbor Research Collaborative for Health, Ann Arbor, Michigan
| | | | - Chad Cogan
- Arbor Research Collaborative for Health, Ann Arbor, Michigan
| | - Zhechen Ding
- Arbor Research Collaborative for Health, Ann Arbor, Michigan
| | - Rashmi Goyat
- Arbor Research Collaborative for Health, Ann Arbor, Michigan
| | | | - Nan Ji
- Arbor Research Collaborative for Health, Ann Arbor, Michigan
| | - Yan Jin
- Arbor Research Collaborative for Health, Ann Arbor, Michigan
| | - Kaitlyn Repeck
- Arbor Research Collaborative for Health, Ann Arbor, Michigan
| | | | - Eric Young
- Arbor Research Collaborative for Health, Ann Arbor, Michigan
| | - Marc Turenne
- Arbor Research Collaborative for Health, Ann Arbor, Michigan
| |
Collapse
|
2
|
Pozniak A, Lammers E, Mukhopadhyay P, Cogan C, Ding Z, Hanslits K, Ji N, Jin Y, Repeck K, Schrager J, Turenne M. Impacts of the Home Health
Value‐Based
Purchasing (
HHVBP
) Model After the First Payment Adjustment Year. Health Serv Res 2021. [DOI: 10.1111/1475-6773.13799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Affiliation(s)
- Alyssa Pozniak
- Arbor Research Collaborative for Health Ann Arbor Michigan USA
| | - Eric Lammers
- Arbor Research Collaborative for Health Ann Arbor Michigan USA
| | | | - Chad Cogan
- Arbor Research Collaborative for Health Ann Arbor Michigan USA
| | - Zhechen Ding
- Arbor Research Collaborative for Health Ann Arbor Michigan USA
| | | | - Nan Ji
- Arbor Research Collaborative for Health Ann Arbor Michigan USA
| | - Yan Jin
- Arbor Research Collaborative for Health Ann Arbor Michigan USA
| | - Kaitlyn Repeck
- Arbor Research Collaborative for Health Ann Arbor Michigan USA
| | | | - Marc Turenne
- Arbor Research Collaborative for Health Ann Arbor Michigan USA
| |
Collapse
|
3
|
Mukhopadhyay P, Woodside KJ, Schaubel DE, Repeck K, McCullough K, Shahinian VB, Pisoni RL, Saran R. Survival Among Incident Peritoneal Dialysis Versus Hemodialysis Patients Who Initiate With an Arteriovenous Fistula. Kidney Med 2020; 2:732-741.e1. [PMID: 33319197 PMCID: PMC7729241 DOI: 10.1016/j.xkme.2020.09.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Rationale & Objective Comparisons of outcomes between in-center hemodialysis (HD) and peritoneal dialysis (PD) are confounded by selection bias because PD patients are typically younger and healthier and may have received longer predialysis care. We compared first-year survival between what we hypothesized were clinically equivalent groups; namely, patients who initiate maintenance HD using an arteriovenous fistula (AVF) and those selecting PD as their initial modality. Study Design Observational, registry-based, retrospective cohort study. Setting & Participants US Renal Data System data for 5 annual cohorts (2010-2014; n = 130,324) of incident HD with an AVF and incident PD patients. Exposures and Predictors Exposure was more than 1 day receiving PD or more than 1 day receiving HD with an AVF. Time at risk for both cohorts was determined for 12 consecutive 30-day segments, censoring for transplantation, loss to follow-up, or end of time. Predictors included patient-level characteristics obtained from Centers for Medicare & Medicaid Services 2728 Form and other data sources. Outcomes Patient survival. Analytical Approach Unadjusted and multivariable risk-adjusted HRs for death of HD versus PD patients, averaged over 2010 to 2014, were calculated. Results The HD cohort's average unadjusted mortality rate was consistently higher than for the PD cohort. The HR of HD versus PD was 1.25 (95% CI, 1.20-1.30) in the unadjusted model and 0.84 (95% CI, 0.80-0.87) in the adjusted model. However, multivariable risk-adjusted analyses showed the HR of HD versus PD for the first 90 days was 1.06 (95% CI, 0.98-1.14), decreasing to 0.74 (95% CI, 0.68-0.80) in the 270- to 360-day period. Limitations Residual confounding due to selection bias inherent in dialysis modality choice and the observational study design. Form 2728 provides baseline data at dialysis incidence alone, but not over time. Conclusions US patients receiving HD with an AVF appear to have a survival advantage over PD patients after 90 days of dialysis initiation after accounting for patient characteristics. These findings have implications in the choice of initial dialysis modality and vascular access for patients.
Collapse
Affiliation(s)
| | | | - Douglas E Schaubel
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA
| | | | | | - Vahakn B Shahinian
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | | | - Rajiv Saran
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI.,Kidney Epidemiology and Cost Center, University of Michigan, Ann Arbor, MI.,Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI
| |
Collapse
|
4
|
Saran R, Robinson B, Abbott KC, Bragg-Gresham J, Chen X, Gipson D, Gu H, Hirth RA, Hutton D, Jin Y, Kapke A, Kurtz V, Li Y, McCullough K, Modi Z, Morgenstern H, Mukhopadhyay P, Pearson J, Pisoni R, Repeck K, Schaubel DE, Shamraj R, Steffick D, Turf M, Woodside KJ, Xiang J, Yin M, Zhang X, Shahinian V. US Renal Data System 2019 Annual Data Report: Epidemiology of Kidney Disease in the United States. Am J Kidney Dis 2019; 75:A6-A7. [PMID: 31704083 DOI: 10.1053/j.ajkd.2019.09.003] [Citation(s) in RCA: 466] [Impact Index Per Article: 93.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
5
|
Saran R, Robinson B, Abbott KC, Agodoa LYC, Bhave N, Bragg-Gresham J, Balkrishnan R, Dietrich X, Eckard A, Eggers PW, Gaipov A, Gillen D, Gipson D, Hailpern SM, Hall YN, Han Y, He K, Herman W, Heung M, Hirth RA, Hutton D, Jacobsen SJ, Jin Y, Kalantar-Zadeh K, Kapke A, Kovesdy CP, Lavallee D, Leslie J, McCullough K, Modi Z, Molnar MZ, Montez-Rath M, Moradi H, Morgenstern H, Mukhopadhyay P, Nallamothu B, Nguyen DV, Norris KC, O'Hare AM, Obi Y, Park C, Pearson J, Pisoni R, Potukuchi PK, Rao P, Repeck K, Rhee CM, Schrager J, Schaubel DE, Selewski DT, Shaw SF, Shi JM, Shieu M, Sim JJ, Soohoo M, Steffick D, Streja E, Sumida K, Tamura MK, Tilea A, Tong L, Wang D, Wang M, Woodside KJ, Xin X, Yin M, You AS, Zhou H, Shahinian V. US Renal Data System 2017 Annual Data Report: Epidemiology of Kidney Disease in the United States. Am J Kidney Dis 2019; 71:A7. [PMID: 29477157 DOI: 10.1053/j.ajkd.2018.01.002] [Citation(s) in RCA: 483] [Impact Index Per Article: 96.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
6
|
Saran R, Robinson B, Abbott KC, Agodoa LYC, Bragg-Gresham J, Balkrishnan R, Bhave N, Dietrich X, Ding Z, Eggers PW, Gaipov A, Gillen D, Gipson D, Gu H, Guro P, Haggerty D, Han Y, He K, Herman W, Heung M, Hirth RA, Hsiung JT, Hutton D, Inoue A, Jacobsen SJ, Jin Y, Kalantar-Zadeh K, Kapke A, Kleine CE, Kovesdy CP, Krueter W, Kurtz V, Li Y, Liu S, Marroquin MV, McCullough K, Molnar MZ, Modi Z, Montez-Rath M, Moradi H, Morgenstern H, Mukhopadhyay P, Nallamothu B, Nguyen DV, Norris KC, O'Hare AM, Obi Y, Park C, Pearson J, Pisoni R, Potukuchi PK, Repeck K, Rhee CM, Schaubel DE, Schrager J, Selewski DT, Shamraj R, Shaw SF, Shi JM, Shieu M, Sim JJ, Soohoo M, Steffick D, Streja E, Sumida K, Kurella Tamura M, Tilea A, Turf M, Wang D, Weng W, Woodside KJ, Wyncott A, Xiang J, Xin X, Yin M, You AS, Zhang X, Zhou H, Shahinian V. US Renal Data System 2018 Annual Data Report: Epidemiology of Kidney Disease in the United States. Am J Kidney Dis 2019; 73:A7-A8. [PMID: 30798791 DOI: 10.1053/j.ajkd.2019.01.001] [Citation(s) in RCA: 597] [Impact Index Per Article: 119.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
|