Association of the New Peer Group–Stratified Method With the Reclassification of Penalty Status in the Hospital Readmission Reduction Program

Cian P McCarthy; Muthiah Vaduganathan; Kershaw V Patel; Hussain S Lalani; Colby Ayers; Deepak L Bhatt; James L Januzzi, Jr; James A de Lemos; Clyde Yancy; Gregg C Fonarow; Ambarish Pandey

doi:10.1001/jamanetworkopen.2019.2987

. 2019 Apr 26;2(4):e192987. doi: 10.1001/jamanetworkopen.2019.2987

Association of the New Peer Group–Stratified Method With the Reclassification of Penalty Status in the Hospital Readmission Reduction Program

Cian P McCarthy ¹, Muthiah Vaduganathan ², Kershaw V Patel ³, Hussain S Lalani ³, Colby Ayers ³, Deepak L Bhatt ², James L Januzzi Jr ⁴, James A de Lemos ³, Clyde Yancy ⁵, Gregg C Fonarow ⁶, Ambarish Pandey ^3,^✉

¹Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston

²Brigham and Women’s Hospital Heart & Vascular Center, Harvard Medical School, Boston, Massachusetts

³Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas

⁴Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston

⁵Division of Cardiology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois

⁶Division of Cardiology, Ronald Reagan UCLA Medical Center, Los Angeles, California

Accepted for Publication: March 7, 2019.

Published: April 26, 2019. doi:10.1001/jamanetworkopen.2019.2987

^✉

Corresponding Author: Ambarish Pandey, MD, MSCS, Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390 (ambarish.pandey@utsouthwestern.edu).

Author Contributions: Dr Pandey had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs McCarthy and Vaduganathan contributed equally.

Concept and design: McCarthy, Vaduganathan, Yancy, Pandey.

Acquisition, analysis, or interpretation of data: McCarthy, Vaduganathan, Patel, Lalani, Ayers, Bhatt, Januzzi, de Lemos, Fonarow, Pandey.

Drafting of the manuscript: McCarthy, Vaduganathan, Januzzi, Pandey.

Critical revision of the manuscript for important intellectual content: Vaduganathan, Patel, Lalani, Ayers, Bhatt, Januzzi, de Lemos, Yancy, Fonarow, Pandey.

Statistical analysis: McCarthy, Lalani, Ayers, Pandey.

Obtained funding: Pandey.

Administrative, technical, or material support: Patel, Pandey.

Supervision: Januzzi, Yancy, Pandey.

Conflict of Interest Disclosures: Dr Vaduganathan reported support from the KL2/Catalyst Medical Research Investigator Training award from Harvard Catalyst (National Institutes of Health/National Center for Advancing Translational Sciences Award UL 1TR002541), serving on advisory boards for Amgen, AstraZeneca, Bayer, and Baxter Healthcare, and participating in clinical end point committees for studies supported by Novartis and the National Institutes of Health outside the submitted work. Dr Patel reported support from the National Heart, Lung, and Blood Institute T32 postdoctoral training grant (5T32HL125247-03) outside of the submitted work. Mr Ayers reported receiving grants and personal feels from the National Heart, Lung, and Blood Institute and consulting for the National Institutes of Health outside the submitted work. Dr Bhatt reported serving on the advisory boards of Cardax, Elsevier PracticeUpdate Cardiology, Medscape Cardiology, and Regado Biosciences; on the boards of directors for the Boston Veterans Affairs Research Institute, Society of Cardiovascular Patient Care, and TobeSoft; as chair of the American Heart Association Quality Oversight Committee; on the data monitoring committees of the Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute, for the PORTICO trial, funded by St Jude Medical, now Abbott Laboratories), Cleveland Clinic, Duke Clinical Research Institute, Mayo Clinic, Mount Sinai School of Medicine (for the ENVISAGE trial, funded by Daiichi Sankyo), and Population Health Research Institute; receiving honoraria from the American College of Cardiology (senior associate editor, Clinical Trials and News, ACC.org; vice-chair, ACC Accreditation Committee), Baim Institute for Clinical Research (formerly Harvard Clinical Research Institute; RE-DUAL PCI clinical trial steering committee funded by Boehringer Ingelheim), Belvoir Publications (editor in chief, Harvard Heart Letter), Duke Clinical Research Institute (clinical trial steering committees), HMP Global (editor in chief, Journal of Invasive Cardiology), Journal of the American College of Cardiology (guest editor and associate editor), Population Health Research Institute (for the COMPASS operations committee, publications committee, steering committee, and US national coleader, funded by Bayer), Slack Publications (chief medical editor, Cardiology Today’s Intervention), Society of Cardiovascular Patient Care (secretary/treasurer), WebMD (continuing medical education steering committees); serving as deputy editor of Clinical Cardiology; chair of the NCDR-ACTION Registry Steering Committee, and Veterans Affairs Clinical Assessment Reporting and Tracking Research and Publications Committee; receiving research funding from Abbott Laboratories, Amarin Corporation, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Chiesi Farmaceutici, Eisai, Ethicon Inc, Forest Laboratories, Idorsia, Ironwood Pharmaceuticals, Ischemix, Eli Lilly and Company, Medtronic, PhaseBio Pharmaceuticals, Pfizer, Regeneron, Roche Holding, Sanofi, Synaptic Pharmaceuticals, and The Medicines Company; receiving royalties from Elsevier (editor, Cardiovascular Intervention: A Companion to Braunwald’s Heart Disease); serving as site coinvestigator for Biotronik, Boston Scientific, St Jude Medical (now Abbott Laboratories), and Svelte; serving as trustee for the American College of Cardiology; and conducting unfunded research for FlowCo Inc, Merck, Novo Nordisk, PLx Pharma, and Takeda outside the submitted work. Dr Januzzi reported receiving grant support from Roche Diagnostics, Abbott Diagnostics, Singulex, Prevencio, and Cleveland Heart Labs; receiving consulting income from Roche Diagnostics and Critical Diagnostics; and participating in clinical end point committees and data safety monitoring boards for Siemens Diagnostics outside the submitted work. Dr de Lemos reports grant support from Abbott Diagnostics and Roche Diagnostics, and consulting fees from Abbott Diagnostics, Roche Diagnostics, Janssen Pharmaceuticals, Ortho Clinical Diagnostics, Amgen, and Regeneron outside the submitted work. Dr Yancy reports that his spouse is employed by Abbott Laboratories. Dr Fonarow reports consulting for Abbott Laboratories, Amgen, Bayer, Janssen Pharmaceuticals, Novartis, and Medtronic outside the submitted work. Dr Pandey reports funding from the Texas Health Resources Clinical Scholarship. No other disclosures were reported.

^✉

Corresponding author.

PMCID: PMC6487568 PMID: 31026033

Key Points

Question

Is the new, stratified payment adjustment method for the Hospital Readmission Reduction Program associated with an alteration in penalty distribution?

Findings

This cross-sectional study of 3173 hospitals found that the new payment adjustment method was associated with a reduction in the proportion of hospitals penalized for fiscal year 2019, which corresponds to performance from July 1, 2014, to June 30, 2017, from 79.07% of hospitals (2509 hospitals) to 75.04% (2381 hospitals) compared with the old, nonstratified method. Hospitals with the largest share of patients of low socioeconomic status had the largest reduction.

Meaning

The new payment adjustment method for the Hospital Readmission Reduction Program was associated with a more equitable distribution of penalties among hospitals, lessening the disproportionate burden carried by hospitals caring for patients of low socioeconomic status.

This cross-sectional study investigates whether the introduction of a new, stratified payment adjustment method is associated with an alteration in the distribution of penalties among hospitals included in the Hospital Readmissions Reduction Program.

Abstract

Importance

Since the introduction of the Hospital Readmission Reduction Program (HRRP), readmission penalties have been applied disproportionately to institutions that serve low-income populations. To address this concern, the US Centers for Medicare & Medicaid introduced a new, stratified payment adjustment method in fiscal year (FY; October 1 to September 30) 2019.

Objective

To determine whether the introduction of a new, stratified payment adjustment method was associated with an alteration in the distribution of penalties among hospitals included in the HRRP.

Design, Setting, and Participants

In this retrospective cross-sectional study, US hospitals included in the HRRP for FY 2018 and FY 2019 were identified. Penalty status of participating hospitals for FY 2019 was determined based on nonstratified HRRP methods and the new, stratified payment adjustment method. Hospitals caring for the highest proportion of patients enrolled in both Medicare and Medicaid based on quintile were assigned to the low–socioeconomic status (SES) group.

Exposures

Nonstratified and stratified Centers for Medicare & Medicaid payment adjustment methods.

Main Outcomes and Measures

Net reclassification of penalties among all hospitals and hospitals in the low-SES group, in states participating in Medicaid expansion, and for 4 targeted medical conditions (acute myocardial infarction, heart failure, chronic obstructive pulmonary disease, and pneumonia).

Results

Penalty status by both payment adjustment methods (nonstratified and stratified) was available for 3173 hospitals. For FY 2019, the new, stratified payment method was associated with penalties for 75.04% of hospitals (2381 of 3173), while the old, nonstratified method was associated with penalties for 79.07% (2509 hospitals), resulting in a net down-classification in penalty status for all hospitals by 4.03 percentage points (95% CI, 2.95-5.11; P < .001). For the 634 low-SES hospitals in the sample, the new method was associated with penalties for 77.60% of hospitals (492 of 634), while the old method was associated with penalties for 91.64% (581 hospitals), resulting in a net down-classification in penalty status of 14.04 percentage points (95% CI, 11.18-16.90; P < .001). Among hospitals that were not low SES (quintiles 1-4), the new payment method was associated with a small decrease in penalty status (1928 vs 1889; net down-classification, 1.54 percentage points; 95% CI, 0.38-2.69; P = .01). Among target medical conditions, the greatest reduction in penalties was observed among cardiovascular conditions (net down-classification, 6.18 percentage points; 95% CI, 4.96-7.39; P < .001).

Conclusions and Relevance

The new, stratified payment adjustment method for the HRRP was associated with a reduction in penalties across hospitals included in the program; the greatest reductions were observed among hospitals in the low-SES group, lessening but not eliminating the previously unbalanced penalty burden carried by these hospitals. Additional public policy research efforts are needed to achieve equitable payment adjustment models for all hospitals.

Introduction

Since the enactment of the Patient Protection and Affordable Care Act in 2010,¹ several value-based programs have been established by the US Centers for Medicare & Medicaid (CMS), including the Hospital Readmission Reduction Program (HRRP).² The objective of the HRRP is to lower CMS expenditures by reducing the burden of preventable repeated hospitalizations within 30 days while simultaneously improving the quality of postacute care. The program has been successful in reducing readmissions³; however, several concerns have emerged after its introduction.^4,5,6,7 Risk adjustment models used to determine hospital penalty status under HRPP do not account for socioeconomic status (SES), which may influence hospital readmission risk but is not under the direct control of the health system.^7,8,9,10 Since the establishment of the HRRP, penalties have fallen disproportionately on institutions that serve low-income populations, commonly referred to as safety-net hospitals.^11,12 To address this concern, a component of the 21st Century Cures Act in 2016 mandated an adjustment in the HRRP to account for SES.¹³ In fiscal year (FY; October 1 to September 30) 2019, a new, peer group–based payment adjustment method was introduced that appropriates penalties within hospital systems that are deemed similar with respect to the SES of the patients they care for. Dual enrollment, the proportion of fee-for-service Medicare and Medicare Advantage hospitalizations for which the patient is eligible for both Medicare and full-benefit Medicaid, was chosen as the surrogate measure of socioeconomic disadvantage. To our knowledge, the impact of the new, peer group–based vs the old nonstratified HRRP method on the penalty status of participating hospitals has not been evaluated. Accordingly, we evaluated whether the new, stratified payment adjustment method was associated with an alteration in the distribution of penalties among hospitals included in the HRRP.

Methods

Nonstratified (Old) vs Stratified (New) CMS Payment-Adjustment Methods

Hospital performance under HRRP is assessed by calculating excess readmission ratios (ERRs) for 6 target conditions: acute myocardial infarction (AMI), heart failure (HF), pneumonia, chronic obstructive pulmonary disease (COPD), coronary artery bypass graft, and elective total hip and/or knee arthroplasty. The ERR is calculated as the ratio of predicted to expected readmissions for a given condition with 25 or more eligible discharges. Between FY 2013 and FY 2018, CMS used a nonstratified method of payment adjustment whereby a condition-specific ERR greater than 1.0 for a particular hospital was considered an indicator for performance below the national average for that condition. All hospitals were assessed using the same threshold of 1.0, and a payment adjustment factor formula accounting for all conditions with ERRs greater than 1.0 was used to calculate the size of the payment reduction. A maximum of a 3% reduction in CMS payments was levied at low-performing hospitals beginning in FY 2015.

A new peer group–based assessment of hospital performance was introduced in FY 2019. All eligible hospitals are now stratified into 5 peer groups (quintiles 1-5), based on the proportion of fee-for-service Medicare and Medicare Advantage hospital stays for which the patient is dually eligible for Medicare and full-benefit Medicaid. Hospital performance is assessed by comparing the condition-specific ERRs for each hospital with the median ERR of the peer group, thereby replacing the ERR threshold of 1.0 that was used for all hospitals under the nonstratified method. Similar to the nonstratified method, conditions with 25 or more eligible discharges and an ERR greater than the median ERR within the peer group are included in the payment adjustment factor formula to calculate the readmission penalty amount. The net payment reduction based on the readmission penalty for target conditions is capped at 3% under the new method, similar to the old method, and applies to all fee-for-service Medicare base operating diagnosis-related group payments for the FY.

Setting and Participants

All hospitals that participated in the HRRP for FY 2019 were included in the analysis. We used publicly available CMS data to compare penalty status using the old and new payment adjustment methods under the HRRP, based on the ERR for 4 targeted medical conditions (AMI, HF, pneumonia, and COPD) for FY 2019 (performance period July 1, 2014, to June 30, 2017).¹⁴ Surgical conditions (coronary artery bypass graft and elective total hip and/or knee arthroplasty) were excluded from our analysis because of a lack of sufficient CMS-reported data. We compared the penalty status of both models in states stratified according to participation in Medicaid expansion. To do so, we identified states that adopted Medicaid expansion by the end of 2014.¹⁵ States with waivers were excluded from the Medicaid expansion group.

Hospital-level characteristics were obtained from American Hospital Association survey data from 2016 and linked to the publicly available data for the HRRP for FY 2019. This study was considered exempt from institutional review board or patient consent owing to use of publicly available hospital-level data. Individual patient-level data or characteristics were not accessed. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies.¹⁶

Statistical Analysis

Net reclassification analysis was used to compare penalty status by the stratified and nonstratified payment adjustment methods for FY 2019. We examined the proportion of all hospitals that were up-classified (from nonpenalty to penalty status) and down-classified (from penalty to nonpenalty status) by the new stratified payment adjustment method. Characteristics of hospitals that were penalized by both methods, neither method, the old method only, or the new method only were compared using χ² test. We then examined the net proportion of hospitals reclassified to a different penalty status by peer group quintile, by cardiovascular vs noncardiovascular condition, by targeted medical condition (AMI, HF, pneumonia, and COPD), and by state participation in Medicaid expansion during the study. Hospitals with the largest share of patients with dual-enrollment status for Medicare and Medicaid (quintile 5) were classified as the low-SES group.

Among the subgroup of hospitals that participated in the HRRP for FY 2018 (using the old payment adjustment method for performance period July 1, 2013, to June 30, 2016) and FY 2019, we performed sensitivity analyses to compare the penalty status across the 2 years based on the ERR for the 4 targeted medical conditions.^14,17,18 The difference in the net payment reduction for participating hospitals based on the readmission penalty for all target conditions was also compared between FYs 2018 and 2019. All statistical analyses were performed using SAS version 9.1 (SAS Institute). All significance tests were 2-tailed, with P < .05 considered statistically significant.

Results

Reclassification of Penalty Status for All Hospitals in FY 2019

Penalty results for FY 2019 were available for 3173 hospitals. The new method divided hospitals into 5 quintiles based on the percentage of dual-eligible stays (eFigure 1 in the Supplement): quintile 1 (<13.70% dual-eligible stays), quintile 2 (13.70%-18.40%), quintile 3 (18.41%-23.23%), quintile 4 (23.24%-30.98%), and quintile 5 (>30.98%).

Using the old method, 79.07% of hospitals (2509 of 3173) would be subject to penalties for FY 2019. However, the new method resulted in a net down-classification in penalty status for all hospitals by 4.03 percentage points (95% CI, 2.95-5.11; P < .001), to 75.04% (2381 of 3173) (Table 1). The new peer group–based method up-classified 2.80% of hospitals (89 of 3173) (95% CI, 2.20%-3.40%; P < .001) to a penalty status but down-classified 6.83% (217 of 3173) (95% CI, 5.96%-7.70%; P < .001) to a nonpenalty status (Figure 1). Hospitals that were down-classified from penalized to nonpenalized were more commonly nonteaching and physician-owned hospitals, located in rural regions, with fewer hospital beds, less fully implemented electronic medical records, and limited cardiac surgery and percutaneous coronary intervention capabilities (Table 2). Hospitals up-classified to a penalty status were more likely to be teaching hospitals, participate in bundled-payment programs, and have cardiac surgery, percutaneous coronary intervention, and cardiac rehabilitation capabilities (Table 2).

Table 1. Net Down-Classification of Penalty Status for Hospitals Across Different Groups for Target Conditions Based on Old and New Penalty Adjustment Methods for Fiscal Year 2019.

Condition	All Hospitals (N = 3173)		Hospitals That Are Not Low SES, Quintiles 1-4 (n = 2539)		Low-SES Hospitals, Quintile 5 (n = 634)		State Participation in Medicaid Expansion (n = 1317)		State Nonparticipation in Medicaid Expansion (n = 1856)
Condition	Net Down-Classification in Penalty Status, No. (%)	P Value	Net Down-Classification in Penalty Status, No. (%)	P Value	Net Down-Classification in Penalty Status, No. (%)	P Value	Net Down-Classification in Penalty Status, No. (%)	P Value	Net Down-Classification in Penalty Status, No. (%)	P Value
All conditions	128 (4.03)	<.001	39 (1.54)	.01	89 (14.04)	<.001	55 (4.18)	<.001	55 (3.07)	<.001
AMI	283 (8.92)	<.001	163 (6.42)	<.001	120 (18.93)	<.001	122 (9.26)	<.001	156 (8.72)	<.001
HF	67 (2.11)	<.001	−48 (−1.89)	.001	115 (18.14)	<.001	59 (4.48)	<.001	0	>.99
COPD	20 (0.63)	.16	−40 (−1.58)	<.001	60 (9.46)	<.001	14 (1.06)	.11	−1 (−0.06)	.93
Pneumonia	26 (0.82)	.10	−64 (−2.52)	<.001	90 (14.20)	<.001	22 (1.67)	.04	−7 (−0.39)	.54

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Abbreviations: AMI, acute myocardial infarction; COPD, chronic obstructive pulmonary disease; HF, heart failure; SES, socioeconomic status.

Figure 1. — Quintiles based on the percentage of dual-eligible stays: quintile 1, less than 13.70%; quintile 2, 13.70% to 18.40%; quintile 3, 18.41% to 23.23%; quintile 4, 23.24% to 30.98%; and quintile 5, greater than 30.98%.

^aP < .001 compared with up-classification.

Table 2. Hospital-Level Characteristics by Penalty Status Based on Old and New Penalty Adjustment Methods for Fiscal Year 2019.

Characteristic^a	No. (%)				P Value^b
Characteristic^a	Always Penalized	Down-Classified to No Penalty	Up-Classified to Penalty	Never Penalized	P Value^b
No.	2292	217	89	575	NA
Hospital beds, median (IQR), No.^c	176 (94-310)	65 (33-126)	129 (58-248)	100 (37-250)	<.001
Admissions, median (IQR), No.^c	7698 (3561-14 779)	1977 (818-4676)	5381 (2429-11 020)	3849 (1210-11 823)	<.001
Total Medicare-days, median (IQR), No.^c	18 729 (8135-35 497)	4462 (1501-11 508)	11 308 (4349-23 930)	7689 (1963-27 849)	<.001
Total Medicare discharges, median (IQR)^c	3667 (1707-6635)	873 (374-2099)	2507 (1057-5171)	1796 (575-5656)	<.001
Adopted Medicaid expansion	1020 (44.9)	93 (46.7)	38 (42.7)	166 (30.2)	<.001
Organizational structure^d
For profit	522 (23.0)	55 (27.6)	14 (15.7)	155 (28.2)	<.001
Government	308 (13.6)	52 (26.1)	11 (12.4)	77 (14.0)
Nongovernment, nonprofit	1440 (63.4)	92 (46.2)	64 (71.9)	317 (57.7)
Hospital location^d
Rural	549 (24.2)	64 (32.2)	27 (30.3)	138 (25.1)	.06
Urban	1721 (75.8)	135 (67.8)	62 (69.7)	411 (74.9)	.06
Community hospital^d	2263 (99.7)	186 (93.5)	89 (100)	539 (98.2)	<.001
Physician-owned hospital^c	67 (3.5)	16 (12.3)	5 (6.4)	82 (17.8)	<.001
Electronic health records^d
Fully implemented	1442 (85.3)	90 (76.3)	65 (91.6)	362 (86.0)	.01
Partially implemented	239 (14.1)	25 (21.2)	5 (7.0)	54 (12.8)	.01
Cardiac surgery available^d	831 (43.7)	15 (11.5)	30 (38.5)	186 (40.4)	<.001
Percutaneous coronary intervention available^d	1186 (62.4)	22 (16.9)	44 (56.4)	233 (50.7)	<.001
Participation in bundle payment program^d	464 (26.3)	14 (11.7)	20 (26.7)	112 (25.6)	.01
Cardiac rehabilitation available^d	1353 (71.2)	42 (32.3)	52 (66.7)	291 (63.3)	<.001
Accredited by The Joint Commission^d	1847 (81.4)	129 (64.8)	66 (74.2)	395 (72.0)	<.001
Teaching affiliation^d
Major	190 (8.4)	6 (3.0)	0	32 (5.8)	<.001
Minor	1005 (44.3)	66 (33.2)	37 (41.6)	217 (39.5)
Nonteaching	1075 (47.4)	127 (63.8)	52 (58.4)	300 (54.6)

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Abbreviations: IQR, interquartile range; NA, not applicable.

^{^a}

Data on hospital-level characteristics were available for 3107 of 3173 hospitals included in the analysis.

^{^b}

P value calculated for all-group comparison.

^{^c}

Continuous variables compared with Kruskal-Wallis test across groups.

^{^d}

Categorical variables compared with χ² test across groups.

Reclassification of Penalty Status by SES in FY 2019

A total of 634 hospitals in quintile 5 were classified as being low-SES hospitals in FY 2019. Applying the old method, 91.64% of low-SES hospitals (581 of 634) would be subject to penalties in FY 2019. The new method resulted in a net down-classification in penalty status, with 77.60% (492 hospitals) subject to penalties, a 14.04–percentage point decrease (95% CI, 11.18-16.90; P < .001) (Table 1). Among the 2539 hospitals that were not low SES (quintiles 1-4 of proportion of dual-coverage patients), 75.94% (1928 of 2539) would be subject to penalties using the old method. The new method led to a small net down-classification in penalty status for these hospitals by 1.54 percentage points (95% CI, 0.38-2.69; P = .01) to 74.40% (1889 of 2539) (Table 1; Figure 1).

Reclassification of Penalty Status for Targeted Medical Conditions in FY 2019

Cardiovascular conditions (AMI or HF) would result in penalization of 63.41% of hospitals (2012 of 3173; 1359 for AMI; 1481 for HF) when applying the old method to FY 2019. The new model resulted in a net down-classification for 6.18 percentage points (95% CI, 4.96-7.39; P < .001), penalizing 57.23% of hospitals (1816 of 3173) for readmissions related to cardiovascular conditions (1076 of 3173 for AMI; 1414 for HF). In the overall cohort, the net down-classification in penalty status related to cardiovascular conditions was higher for AMI (8.9 percentage points; 95% CI, 7.73-10.11; P < .001) than for HF (2.1 percentage points; 95% CI, 0.98-3.24; P < .001) (Table 1; Figure 2).

Figure 2. — Quintiles based on the percentage of dual-eligible stays: quintile 1, less than 13.70%; quintile 2, 13.70% to 18.40%; quintile 3, 18.41% to 23.23%; quintile 4, 23.24% to 30.98%; and quintile 5, greater than 30.98%. AMI indicates acute myocardial infarction; COPD, chronic obstructive pulmonary disease; HF, heart failure; and PNA, pneumonia.

^aP < .001 compared with up-classification.

Applying the old method to FY 2019, readmissions related to noncardiovascular conditions (COPD and pneumonia) would result in penalization of 63.57% of hospitals (1326 of 3173 for COPD; 1322 for pneumonia). In the overall cohort, there was no significant reclassification of hospitals’ penalty status by the new model for COPD and pneumonia for FY 2019 (COPD net down-classification: 0.63 percentage point; 95% CI, −0.27 to 1.53; P = .16; pneumonia net down-classification: 0.82 percentage point; 95% CI, −0.18 to 1.82; P = .10) (Table 1; Figure 2). Among the low-SES hospitals, there was a consistent down-classification in penalty status for all targeted medical conditions, with no up-classification in penalty status for any targeted condition (Table 1; Figure 2). In contrast, among hospitals not in the low-SES group, there was a significant net down-classification in penalty status for AMI. However, up-classification in penalty status occurred with HF, COPD, and pneumonia (Table 1; Figure 2).

Reclassification of Penalty Status According to State Participation in Medicaid Expansion in FY 2019

Overall, 1317 hospitals (41.51%) in the cohort were in states that adopted Medicaid expansion by the end of 2014, of which 84.51% (1113 of 1317) would be subject to penalties using the old method for FY 2019. The new method resulted in a significant net down-classification in penalty status of 4.18 percentage points (2.89% [38 of 1317] up-classified to penalty status, 7.06% [93 of 1317] down-classified to nonpenalty status) among hospitals in states that adopted Medicaid expansion (Table 1; eFigure 2 in the Supplement). This was largely associated with the down-classification in penalty status driven by cardiovascular conditions, particularly AMI (Table 1). Similar patterns of reclassification were also noted among hospitals in states that did not participate in Medicaid expansion during the study period (Table 1; eFigure 2 in the Supplement).

Reclassification of Penalty Status for Hospitals in FY 2019 (New Method) vs FY 2018 (Old Method)

The proportion of all hospitals subject to penalties by the HRRP for the 4 targeted medical conditions declined from 79.33% (2517 of 3173) in FY 2018 to 75.04% (2381 of 3173) in FY 2019 (net decrease, 4.3 percentage points; 95% CI, 2.95-511; P < .001). Hospitals of low SES had the largest decrease in penalties, from 92.74% (588 of 634) in FY 2018 to 77.60% (492 hospitals) in FY 2019 (net decrease, 15.1 percentage points; 95% CI, 11.90-18.39; P < .001). The proportion of hospitals not in the low-SES group that received penalties in 2018 and 2019 were numerically comparable; 74.40% (1889 of 2539) were subject to penalties in FY 2019 compared with 75.97% (1929 hospitals) in FY 2018 (eFigure 3 in the Supplement).

Examining the total amount of penalties imposed on hospitals in the low-SES group, the median (interquartile range) net payment reduction decreased from 0.46% (0.14% to 1.04%) in FY 2018 to 0.28% (0.04% to 0.69%) in FY 2019 (median change, −0.12%; interquartile range, −0.38% to 0%; P < .001) (Figure 3). Among hospitals that were not in the low-SES group, the median (interquartile range) net payment reduction increased modestly from 0.32% (0.03% to 0.85%) in FY 2018 to 0.35% (0.06% to 0.86%) in FY 2019 (median change, 0%; interquartile range, −0.12% to 0.17%; P = .01).

Figure 3. — Red boxes on the x-axis represent specific net payment reduction per hospital for FY 2018. The overlaying line graph in green shows net payment reduction in FY 2019. Median (interquartile range) payments for FY 2018 were 0.46% (0.14%-1.04%); median (interquartile range) payments for FY 2019 were 0.28% (0.04%-0.69%).

Discussion

There were several noteworthy findings in our study, which examined the new, stratified peer group–based payment adjustment method for the HRRP. First, the down-classification to nonpenalty status was more commonly observed among hospitals that provide care for a disproportionately larger population of patients with socioeconomic disadvantage. Second, stratification was associated with a net down-classification in penalty status for hospitals included in the program. However, penalty rates remain high with the new method; 75% of all hospitals were subject to penalties for FY 2019. Third, among targeted medical conditions, the new HRRP penalty model was associated with a greater net down-classification for readmissions related to cardiovascular conditions than for readmissions related to COPD or pneumonia. Fourth, no major differences were seen in the reclassification of penalty status in hospitals stratified by state participation in Medicaid expansion. Our findings were consistent when comparing old and new methodologies for FY 2019 and when comparing the actual penalties applied to hospitals by the old method in FY 2018 with the new method in FY 2019.

Hospitals that were down-classified to nonpenalty status were more often small, nonteaching, and physician owned, located in rural areas, and had the highest proportion of patients with dual-enrollment status, a strong predictor of a higher burden of socioeconomic risk factors.¹⁹ Lower-income patients tend to have worse health outcomes on many performance quality measures, and hospitals that disproportionately serve this patient population are more likely to identify as poor performing and are subject to higher penalties in traditional value-based payment programs.²⁰ However, multiple factors that are not related to the quality of care received, such as greater disease severity, lack of social support, and poor living environment, may underlie the poor clinical outcomes among patients with socioeconomic risk factors. Performance-based penalties incurred by the hospitals that predominantly care for low-income patients may take away important financial resources that are needed to care for these patients with the most complex social and medical needs. Furthermore, it may also disincentivize hospitals from taking care of such patients. Findings from our study suggest that the new payment adjustment method for the HRRP may contribute to a more socially equitable implementation of readmission performance–based penalties. We observed the largest down-classification in penalty status among hospitals in the low-SES group that were previously disproportionately penalized by the old, nonstratified adjustment method.¹¹ For these hospitals, the new system was associated with a down-classification in penalty risk status across all conditions without any up-classification. We also observed consistent patterns of reclassification in penalty status under the new method in states with and without expansion of the Medicaid program. This suggests that the down-classification in hospital penalty status may not be related to individuals who newly obtained Medicaid.

Cardiovascular conditions had consistently greater down-classification in penalty status compared with noncardiovascular conditions. The highest net down-classification across individual target conditions was observed with AMI, which has a particularly high financial burden among low-income patients.²¹ Furthermore, hospitals that were down-classified to nonpenalty status were less likely to have cardiac surgery or percutaneous coronary intervention available, reflecting the socioeconomic disparities that exist in access to cardiovascular care.

The new stratified method for assigning penalty status has several advantages. First, it recognizes the greater challenges that hospitals that care for a higher proportion of patients of low SES face in achieving high performance. Second, it may attenuate concerns that penalizing safety-net hospitals for performance could lead to worse access to care for patients of low SES. Third, failing to adjust for socioeconomic differences in populations may lead to inaccurate representation of the quality of care provided to patients by hospitals. Nevertheless, the recalibration of penalties to a peer group–based method reduces but does not eliminate the disproportionate penalty burden.

The new method accounts for some of the socioeconomic disparities across hospitals; however, the challenges related to incomplete adjustment for disease severity remain unaddressed. For instance, while small, rural, low-SES hospitals benefit from the new stratified method, large, urban safety-net hospitals remain at risk of disproportionate penalties. Taken together, the stratified peer group–based assessment of readmission-based hospital performance is a welcome initial step, but more work is needed, with further adjustments that might more capably capture social determinants of health beyond SES.

It is important to recognize that hospital performance at low-SES hospitals is influenced by factors other than just patient characteristics. Examining patients who had 2 admissions for similar diagnoses to hospitals in the best- and worst-performing quartiles for hospital readmission performance, Krumholz et al²² found that readmission rates were higher when the same patient was admitted to the worst-performing hospitals compared with the best-performing hospitals, suggesting that hospital quality is, in part, contributing to readmission rates independent of patient characteristics. Additionally, in the first 3 years of the HRRP, safety-net hospitals reduced their readmissions for AMI, HF, and pneumonia.²³ Whether such reductions were driven by improvement in hospital quality, changes in triage patterns, or upcoding of diagnoses is unknown.²⁴ It is also possible that increased resources were needed to improve quality and reduce readmissions at safety-net hospitals, which could be shifting resources from other necessary areas of clinical care. Future studies are also needed to determine if the new payment adjustment method will lead to meaningful changes in hospital behavior, fewer unintended consequences, and potentially improved health outcomes, such as mortality and readmission rates. Prospective accounting of global system costs incurred by patients, hospitals, and payers is needed to determine the potential economic impact of the new payment adjustment method.

Limitations

Our study has several limitations. First, there is no single, agreed-on method to identify hospitals that care for patients of low SES. In our study, the hospitals with the highest proportion of dual-eligible patients were considered high-socioeconomic-risk hospitals. This is consistent with findings from prior studies suggesting that the proportion of dual-eligibility patients is a robust metric in accounting for socioeconomic risk²⁵ and most closely reflects the new method of peer group comparisons. Second, given insufficient data, we did not account for surgical target conditions in the penalty status adjustment estimates based on the old and new systems. Third, some of the changes in penalty status from FY 2018 to FY 2019 may also have been related to implementation of performance improvement measures and not just changes in the HRRP method.

Conclusions

The new, stratified penalty adjustment method for the HRRP was associated with a reduction in penalties across hospitals included in the program; the greatest reductions were observed among hospitals in the low-SES group. This reduces the previously unbalanced penalty burden carried by these hospitals to some extent. Although this payment adjustment scheme appears to improve the equitable distribution of hospital penalties, 75% of hospitals were subject to penalties in FY 2019. Thus, the new peer group–based penalty model represents a step in the right direction. Sustained efforts are needed to better account for social determinants of health in hospital performance models and to allow for more socially just and equitable distribution of performance-based financial incentives.

Supplement.

eFigure 1. Distribution of Proportion of Dual Eligibility for Medicare and Full-Benefit Medicaid Across Hospitals

eFigure 2. Reclassification of Penalty Status Stratified by Medicaid State Expansion for Fiscal Year 2019

eFigure 3. Reclassification of Penalty Status From Fiscal Year 2018 (Old Methodology) to Fiscal Year 2019 (New Methodology)

Click here for additional data file.^{(272.2KB, pdf)}

References

1.Adams JE III, Bodor GS, Dávila-Román VG, et al. Cardiac troponin I: a marker with high specificity for cardiac injury. Circulation. 1993;88(1):-. doi: 10.1161/01.CIR.88.1.101 [DOI] [PubMed] [Google Scholar]
2.Katus HA, Remppis A, Neumann FJ, et al. Diagnostic efficiency of troponin T measurements in acute myocardial infarction. Circulation. 1991;83(3):902-912. doi: 10.1161/01.CIR.83.3.902 [DOI] [PubMed] [Google Scholar]
3.Dharmarajan K, Wang Y, Lin Z, et al. Association of changing hospital readmission rates with mortality rates after hospital discharge. JAMA. 2017;318(3):270-278. doi: 10.1001/jama.2017.8444 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Gupta A, Fonarow GC. The Hospital Readmissions Reduction Program: learning from failure of a healthcare policy. Eur J Heart Fail. 2018; 20(8):1169-1174. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Fonarow GC, Konstam MA, Yancy CW. The Hospital Readmission Reduction Program is associated with fewer readmissions, more deaths: time to reconsider. J Am Coll Cardiol. 2017;70(15):1931-1934. doi: 10.1016/j.jacc.2017.08.046 [DOI] [PubMed] [Google Scholar]
6.Gupta A, Allen LA, Bhatt DL, et al. Association of the Hospital Readmissions Reduction Program implementation with readmission and mortality outcomes in heart failure. JAMA Cardiol. 2018;3(1):44-53. doi: 10.1001/jamacardio.2017.4265 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Gupta A, Fonarow GC. The Hospital Readmissions Reduction Program: evidence for harm. JACC Heart Fail. 2018;6(7):607-609. doi: 10.1016/j.jchf.2018.02.012 [DOI] [PubMed] [Google Scholar]
8.Pandey A, Golwala H, Hall HM, et al. Association of US Centers for Medicare & Medicaid Services Hospital 30-Day Risk-Standardized Readmission metric with care quality and outcomes after acute myocardial infarction: findings from the National Cardiovascular Data Registry/Acute Coronary Treatment and Intervention Outcomes Network Registry-Get With the Guidelines. JAMA Cardiol. 2017;2(7):723-731. doi: 10.1001/jamacardio.2017.1143 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Roberts ET, Zaslavsky AM, Barnett ML, Landon BE, Ding L, McWilliams JM. Assessment of the effect of adjustment for patient characteristics on hospital readmission rates: implications for pay for performance. JAMA Intern Med. 2018;178(11):1498-1507. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Joynt KE, Zuckerman R, Epstein AM. Social risk factors and performance under Medicare’s value-based purchasing programs. Circ Cardiovasc Qual Outcomes. 2017;10(5):e003587. doi: 10.1161/CIRCOUTCOMES.117.003587 [DOI] [PubMed] [Google Scholar]
11.Joynt KE, Jha AK. Characteristics of hospitals receiving penalties under the Hospital Readmissions Reduction Program. JAMA. 2013;309(4):342-343. doi: 10.1001/jama.2012.94856 [DOI] [PubMed] [Google Scholar]
12.Gilman M, Hockenberry JM, Adams EK, Milstein AS, Wilson IB, Becker ER. The financial effect of value-based purchasing and the Hospital Readmissions Reduction Program on safety-net hospitals in 2014: a cohort study. Ann Intern Med. 2015;163(6):427-436. doi: 10.7326/M14-2813 [DOI] [PubMed] [Google Scholar]
13.US House of Representatives Document Repository 21st Century Cures Act. https://docs.house.gov/billsthisweek/20161128/CPRT-114-HPRT-RU00-SAHR34.pdf. Accessed August 10, 2018.
14.Centers for Medicare & Medicaid Services Hospital Readmissions Reduction Program (HRRP). https://www.cms.gov/medicare/medicare-fee-for-service-payment/acuteinpatientpps/readmissions-reduction-program.html. Accessed February 21, 2019.
15.Henry J Kaiser Family Foundation Status of state action on the Medicaid expansion decision. http://kff.org/health-reform/state-indicator/state-activity-around-expanding-medicaid-under-the-affordable-care-act/?currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D. Accessed September 18, 2018.
16.Vandenbroucke JP, von Elm E, Altman DG, et al. ; STROBE initiative . Strengthening The Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Ann Intern Med. 2007;147(8):W163-W194. doi: 10.7326/0003-4819-147-8-200710160-00010-w1 [DOI] [PubMed] [Google Scholar]
17.Centers for Medicare & Medicaid Services FY 2018 IPPS Final Rule: Hospital Readmissions Reduction Program Supplemental Data. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Archived-Supplemental-Data-Files/FY2018-IPPS-Final-Rule-HRRP-Supplemental-Data-File.html. Accessed February 22, 2019.
18.Centers for Medicare & Medicaid Services FY 2018 IPPS Proposed Rule: Hospital Readmissions Reduction Program Supplemental Data. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Archived-Supplemental-Data-Files/FY2018-IPPS-Proposed-Rule-HRRP-Supplemental-Data-File.html. Accessed February 22, 2019.
19.US Department of Health and Human Services Report to Congress: social risk factors and performance under Medicare’s value-based purchasing programs. https://aspe.hhs.gov/system/files/pdf/253971/ASPESESRTCfull.pdf. Accessed August 29, 2018.
20.National Academies of Sciences, Engineering, and Medicine Accounting for Social Risk Factors in Medicare Payment: Identifying Social Risk Factors. Washington, DC: National Academies Press; 2016. [PubMed] [Google Scholar]
21.Khera R, Valero-Elizondo J, Okunrintemi V, et al. Association of out-of-pocket annual health expenditures with financial hardship in low-income adults with atherosclerotic cardiovascular disease in the United States. JAMA Cardiol. 2018;3(8):729-738. doi: 10.1001/jamacardio.2018.1813 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Krumholz HM, Wang K, Lin Z, et al. Hospital-readmission risk: isolating hospital effects from patient effects. N Engl J Med. 2017;377(11):1055-1064. doi: 10.1056/NEJMsa1702321 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Carey K, Lin MY. Hospital Readmissions Reduction Program: safety-net hospitals show improvement, modifications to penalty formula still needed. Health Aff (Millwood). 2016;35(10):1918-1923. doi: 10.1377/hlthaff.2016.0537 [DOI] [PubMed] [Google Scholar]
24.Ibrahim AM, Dimick JB, Sinha SS, Hollingsworth JM, Nuliyalu U, Ryan AM. Association of coded severity with readmission reduction after the Hospital Readmissions Reduction Program. JAMA Intern Med. 2018;178(2):290-292. doi: 10.1001/jamainternmed.2017.6148 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Joynt KE, De Lew N, Sheingold SH, Conway PH, Goodrich K, Epstein AM. Should Medicare value-based purchasing take social risk into account? N Engl J Med. 2017;376(6):510-513. doi: 10.1056/NEJMp1616278 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eFigure 1. Distribution of Proportion of Dual Eligibility for Medicare and Full-Benefit Medicaid Across Hospitals

eFigure 2. Reclassification of Penalty Status Stratified by Medicaid State Expansion for Fiscal Year 2019

eFigure 3. Reclassification of Penalty Status From Fiscal Year 2018 (Old Methodology) to Fiscal Year 2019 (New Methodology)

Click here for additional data file.^{(272.2KB, pdf)}

[zoi190132r1] 1.Adams JE III, Bodor GS, Dávila-Román VG, et al. Cardiac troponin I: a marker with high specificity for cardiac injury. Circulation. 1993;88(1):-. doi: 10.1161/01.CIR.88.1.101 [DOI] [PubMed] [Google Scholar]

[zoi190132r2] 2.Katus HA, Remppis A, Neumann FJ, et al. Diagnostic efficiency of troponin T measurements in acute myocardial infarction. Circulation. 1991;83(3):902-912. doi: 10.1161/01.CIR.83.3.902 [DOI] [PubMed] [Google Scholar]

[zoi190132r3] 3.Dharmarajan K, Wang Y, Lin Z, et al. Association of changing hospital readmission rates with mortality rates after hospital discharge. JAMA. 2017;318(3):270-278. doi: 10.1001/jama.2017.8444 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi190132r4] 4.Gupta A, Fonarow GC. The Hospital Readmissions Reduction Program: learning from failure of a healthcare policy. Eur J Heart Fail. 2018; 20(8):1169-1174. [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi190132r5] 5.Fonarow GC, Konstam MA, Yancy CW. The Hospital Readmission Reduction Program is associated with fewer readmissions, more deaths: time to reconsider. J Am Coll Cardiol. 2017;70(15):1931-1934. doi: 10.1016/j.jacc.2017.08.046 [DOI] [PubMed] [Google Scholar]

[zoi190132r6] 6.Gupta A, Allen LA, Bhatt DL, et al. Association of the Hospital Readmissions Reduction Program implementation with readmission and mortality outcomes in heart failure. JAMA Cardiol. 2018;3(1):44-53. doi: 10.1001/jamacardio.2017.4265 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi190132r7] 7.Gupta A, Fonarow GC. The Hospital Readmissions Reduction Program: evidence for harm. JACC Heart Fail. 2018;6(7):607-609. doi: 10.1016/j.jchf.2018.02.012 [DOI] [PubMed] [Google Scholar]

[zoi190132r8] 8.Pandey A, Golwala H, Hall HM, et al. Association of US Centers for Medicare & Medicaid Services Hospital 30-Day Risk-Standardized Readmission metric with care quality and outcomes after acute myocardial infarction: findings from the National Cardiovascular Data Registry/Acute Coronary Treatment and Intervention Outcomes Network Registry-Get With the Guidelines. JAMA Cardiol. 2017;2(7):723-731. doi: 10.1001/jamacardio.2017.1143 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi190132r9] 9.Roberts ET, Zaslavsky AM, Barnett ML, Landon BE, Ding L, McWilliams JM. Assessment of the effect of adjustment for patient characteristics on hospital readmission rates: implications for pay for performance. JAMA Intern Med. 2018;178(11):1498-1507. [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi190132r10] 10.Joynt KE, Zuckerman R, Epstein AM. Social risk factors and performance under Medicare’s value-based purchasing programs. Circ Cardiovasc Qual Outcomes. 2017;10(5):e003587. doi: 10.1161/CIRCOUTCOMES.117.003587 [DOI] [PubMed] [Google Scholar]

[zoi190132r11] 11.Joynt KE, Jha AK. Characteristics of hospitals receiving penalties under the Hospital Readmissions Reduction Program. JAMA. 2013;309(4):342-343. doi: 10.1001/jama.2012.94856 [DOI] [PubMed] [Google Scholar]

[zoi190132r12] 12.Gilman M, Hockenberry JM, Adams EK, Milstein AS, Wilson IB, Becker ER. The financial effect of value-based purchasing and the Hospital Readmissions Reduction Program on safety-net hospitals in 2014: a cohort study. Ann Intern Med. 2015;163(6):427-436. doi: 10.7326/M14-2813 [DOI] [PubMed] [Google Scholar]

[zoi190132r13] 13.US House of Representatives Document Repository 21st Century Cures Act. https://docs.house.gov/billsthisweek/20161128/CPRT-114-HPRT-RU00-SAHR34.pdf. Accessed August 10, 2018.

[zoi190132r14] 14.Centers for Medicare & Medicaid Services Hospital Readmissions Reduction Program (HRRP). https://www.cms.gov/medicare/medicare-fee-for-service-payment/acuteinpatientpps/readmissions-reduction-program.html. Accessed February 21, 2019.

[zoi190132r15] 15.Henry J Kaiser Family Foundation Status of state action on the Medicaid expansion decision. http://kff.org/health-reform/state-indicator/state-activity-around-expanding-medicaid-under-the-affordable-care-act/?currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D. Accessed September 18, 2018.

[zoi190132r16] 16.Vandenbroucke JP, von Elm E, Altman DG, et al. ; STROBE initiative . Strengthening The Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Ann Intern Med. 2007;147(8):W163-W194. doi: 10.7326/0003-4819-147-8-200710160-00010-w1 [DOI] [PubMed] [Google Scholar]

[zoi190132r17] 17.Centers for Medicare & Medicaid Services FY 2018 IPPS Final Rule: Hospital Readmissions Reduction Program Supplemental Data. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Archived-Supplemental-Data-Files/FY2018-IPPS-Final-Rule-HRRP-Supplemental-Data-File.html. Accessed February 22, 2019.

[zoi190132r18] 18.Centers for Medicare & Medicaid Services FY 2018 IPPS Proposed Rule: Hospital Readmissions Reduction Program Supplemental Data. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Archived-Supplemental-Data-Files/FY2018-IPPS-Proposed-Rule-HRRP-Supplemental-Data-File.html. Accessed February 22, 2019.

[zoi190132r19] 19.US Department of Health and Human Services Report to Congress: social risk factors and performance under Medicare’s value-based purchasing programs. https://aspe.hhs.gov/system/files/pdf/253971/ASPESESRTCfull.pdf. Accessed August 29, 2018.

[zoi190132r20] 20.National Academies of Sciences, Engineering, and Medicine Accounting for Social Risk Factors in Medicare Payment: Identifying Social Risk Factors. Washington, DC: National Academies Press; 2016. [PubMed] [Google Scholar]

[zoi190132r21] 21.Khera R, Valero-Elizondo J, Okunrintemi V, et al. Association of out-of-pocket annual health expenditures with financial hardship in low-income adults with atherosclerotic cardiovascular disease in the United States. JAMA Cardiol. 2018;3(8):729-738. doi: 10.1001/jamacardio.2018.1813 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi190132r22] 22.Krumholz HM, Wang K, Lin Z, et al. Hospital-readmission risk: isolating hospital effects from patient effects. N Engl J Med. 2017;377(11):1055-1064. doi: 10.1056/NEJMsa1702321 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi190132r23] 23.Carey K, Lin MY. Hospital Readmissions Reduction Program: safety-net hospitals show improvement, modifications to penalty formula still needed. Health Aff (Millwood). 2016;35(10):1918-1923. doi: 10.1377/hlthaff.2016.0537 [DOI] [PubMed] [Google Scholar]

[zoi190132r24] 24.Ibrahim AM, Dimick JB, Sinha SS, Hollingsworth JM, Nuliyalu U, Ryan AM. Association of coded severity with readmission reduction after the Hospital Readmissions Reduction Program. JAMA Intern Med. 2018;178(2):290-292. doi: 10.1001/jamainternmed.2017.6148 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi190132r25] 25.Joynt KE, De Lew N, Sheingold SH, Conway PH, Goodrich K, Epstein AM. Should Medicare value-based purchasing take social risk into account? N Engl J Med. 2017;376(6):510-513. doi: 10.1056/NEJMp1616278 [DOI] [PubMed] [Google Scholar]

PERMALINK

Association of the New Peer Group–Stratified Method With the Reclassification of Penalty Status in the Hospital Readmission Reduction Program

Cian P McCarthy, MB, BCh, BAO

Muthiah Vaduganathan, MD, MPH

Kershaw V Patel, MD

Hussain S Lalani, MD, MPH

Colby Ayers, MS

Deepak L Bhatt, MD, MPH

James L Januzzi Jr, MD

James A de Lemos, MD

Clyde Yancy, MD

Gregg C Fonarow, MD

Ambarish Pandey, MD, MSCS

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposures

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Nonstratified (Old) vs Stratified (New) CMS Payment-Adjustment Methods

Setting and Participants

Statistical Analysis

Results

Reclassification of Penalty Status for All Hospitals in FY 2019

Table 1. Net Down-Classification of Penalty Status for Hospitals Across Different Groups for Target Conditions Based on Old and New Penalty Adjustment Methods for Fiscal Year 2019.

Figure 1. Reclassification of Penalty Status for All Hospitals According to Dual-Eligibility Quintile Based on Old and New Penalty Adjustment Methods for Fiscal Year 2019.

Table 2. Hospital-Level Characteristics by Penalty Status Based on Old and New Penalty Adjustment Methods for Fiscal Year 2019.

Reclassification of Penalty Status by SES in FY 2019

Reclassification of Penalty Status for Targeted Medical Conditions in FY 2019

Figure 2. Reclassification of Penalty Status for Targeted Conditions Based on Old and New Penalty Adjustment Methodologies for Fiscal Year 2019.

Reclassification of Penalty Status According to State Participation in Medicaid Expansion in FY 2019

Reclassification of Penalty Status for Hospitals in FY 2019 (New Method) vs FY 2018 (Old Method)

Figure 3. Net Payment Readjustment for All Target Conditions in Fiscal Year (FY) 2018 vs FY 2019 Among Hospitals in the Low–Socioeconomic Status Group.

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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