Are Teaching Hospitals Treated Fairly in the Hospital-Acquired Condition Reduction Program?

Teaching and Safety-Net Hospital Penalization in the Hospital-Acquired Condition Reduction Program

Importance

The Hospital-Acquired Condition Reduction Program (HACRP) evaluates acute care hospitals on the occurrence of patient safety events and health care-associated infections. Since its implementation, several studies have raised concerns about the overpenalization of teaching and safety-net hospitals, and although several changes in the program's methodology have been applied in the last few years, whether these changes reversed the overpenalization of teaching and safety-net hospitals is unknown.

Objective

To determine hospital characteristics associated with HACRP penalization and penalization reversal.

Design, Setting, and Participants

This retrospective cross-sectional study assessed data from 3117 acute care hospitals participating in the HACRP. The HACRP penalization and hospital characteristics were obtained from Hospital Compare (2020 and 2021), the Inpatient Prospective Payment System impact file (2020), and the American Hospital Association annual survey (2018).

Exposures

Hospital characteristics, including safety-net status and teaching intensity (no teaching and very minor, minor, major, and very major teaching levels).

Main Outcomes and Measures

The primary outcome was HACRP penalization (ie, hospitals that fell within the worst quartile of the program's performance). Multivariable models initially included all covariates, and then backward stepwise variable selection was used.

Results

Of 3117 hospitals that participated in HACRP in 2020, 779 (25.0%) were safety-net hospitals and 1090 (35.0%) were teaching institutions. In total, 771 hospitals (24.7%) were penalized. The HACRP penalization was associated with safety-net status (odds ratio [OR], 1.41 [95% CI, 1.16-1.71]) and very major teaching intensity (OR, 1.94 [95% CI, 1.15-3.28]). In addition, non-federal government hospitals were more likely to be penalized than for-profit hospitals (OR, 1.62 [95% CI, 1.23-2.14]), as were level I trauma centers (OR, 2.05 [95% CI, 1.43-2.96]) and hospitals located in the New England region (OR, 1.65 [95% CI, 1.12-2.43]). Safety-net hospitals with major teaching levels were twice as likely to be penalized as non-safety-net nonteaching hospitals (OR, 2.15 [95% CI, 1.14-4.03]). Furthermore, safety-net hospitals penalized in 2020 were less likely (OR, 0.64 [95% CI, 0.43-0.96]) to revert their HACRP penalization status in 2021.

Conclusions and Relevance

Findings from this cross-sectional study indicated that teaching and safety-net hospital status continued to be associated with overpenalization in the HACRP despite recent changes in its methodology. Most of these hospitals were also less likely to revert their penalization status. A reevaluation of the program methodology is needed to avoid depleting resources of hospitals caring for underserved populations.

Hospital value-based payment programs and disparity in the United States: A review of current evidence and future perspectives

Beginning in the early 2010s, an array of Value-Based Purchasing (VBP) programs has been developed in the United States (U.S.) to contain costs and improve health care quality. Despite documented successes in these efforts in some instances, there have been growing concerns about the programs' unintended consequences for health care disparities due to their built-in biases against health care organizations that serve a disproportionate share of disadvantaged patient populations. We explore the effects of three Medicare hospital VBP programs on health and health care disparities in the U.S. by reviewing their designs, implementation history, and evidence on health care disparities. The available empirical evidence thus far suggests varied impacts of hospital VBP programs on health care disparities. Most of the reviewed studies in this paper demonstrate that hospital VBP programs have the tendency to exacerbate health care disparities, while a few others found evidence of little or no worsening impacts on disparities. We discuss several policy options and recommendations which include various reform approaches and specific programs ranging from those addressing upstream structural barriers to health care access, to health care delivery strategies that target service utilization and health outcomes of vulnerable populations under the VBP programs. Future studies are needed to produce more explicit, conclusive, and consistent evidence on the impacts of hospital VBP programs on disparities.

Unintended Consequences of Health Care Reform: Impact of Changes in Payor Mix on Patient Safety Indicators

OBJECTIVE

To evaluate the impact of the Affordable Care Act's Medicaid expansion on patient safety metrics at the hospital level by expansion status, across varying levels of safety-net burden, and over time.

SUMMARY BACKGROUND DATA

Medicaid expansion has raised concerns over the influx of additional medically and socially complex populations on hospital systems. Whether increases in Medicaid and uninsured payor mix impact hospital performance metrics remains largely unknown. We sought to evaluate the effects of expansion on Centers for Medicare and Medicaid Services-endorsed Patient Safety Indicators (PSI-90).

METHODS

Three hundred fifty-eight hospitals were identified using State Inpatient Databases (2012-2015) from 3 expansions (KY, MD, NJ) and 2 nonexpansion (FL, NC) states. PSI-90 scores were calculated using Agency for Healthcare Research and Quality modules. Hospital Medicaid and uninsured patients were categorized into safety-net burden (SNB) quartiles. Hospital-level, multivariate linear regression was performed to measure the effects of expansion and change in SNB on PSI-90.

RESULTS

PSI-90 decreased (safety improved) over time across all hospitals (-5.2%), with comparable reductions in expansion versus nonexpansion states (-5.9% vs -4.7%, respectively; P = 0.441) and across high SNB hospitals within expansion versus nonexpansion states (-3.9% vs -5.2%, P = 0.639). Pre-ACA SNB quartile did not predict changes in PSI-90 post-ACA. However, when hospitals increased their SNB by 5%, they incurred significantly more safety events in expansion relative to nonexpansion states (+1.87% vs -14.0%, P = 0.013).

CONCLUSIONS

Despite overall improvement in patient safety, increased SNB was associated with increased safety events in expansion states. Accordingly, Centers for Medicare and Medicaid Services measures may unintentionally penalize hospitals with increased SNB following Medicaid expansion.

Medicare's Hospital Acquired Condition Reduction Program Disproportionately Affects Minority-serving Hospitals: Variation by Race, Socioeconomic Status, and Disproportionate Share Hospital Payment Receipt

OBJECTIVE

To assess whether a hospital's percentage of Black patients associates with variations in FY2017 overall/domain-specific Hospital Acquired-Condition Reduction Program (HACRP) scores and penalty receipt. Differences in socioeconomic status and receipt of disproportionate share hospital payments (a marker of safety-net status) were also assessed.

SUMMARY OF BACKGROUND DATA

In FY2015, Medicare began reducing payments to hospitals with high adverse event rates. Concern has been expressed that HACRP penalties could adversely affect minority-serving hospitals, leading to reductions in resources and exasperation of disparities among hospitals with the greatest need.

METHODS

100% Medicare FFS claims from 2013 to 2014 identified older adult inpatients, aged ≥65 years, presenting for 8 common surgical conditions. Multilevel mixed-effects regression determined differences in FY2017 HACRP scores/penalties among hospitals managing the highest decile of minority patients.

RESULTS

A total of 695,775 patients from 2923 hospitals were included. As a hospital's percentage of Black patients increased, climbing from 0.6% to 32.5% (lowest vs highest decile), average HACRP scores also increased, rising from 5.33 to 6.36 (higher values indicate worse scores). Increases in HACRP penalties did not follow the same stepwise increase, instead exhibiting a marked jump within the highest decile of racial minority-serving extent (45.7% vs 36.7%; OR [95% CI]: 1.45[1.42-1.47]). Similar patterns were observed for high disproportionate share hospital (OR [95% CI]: 1.44 [1.42-1.47]; absolute difference: +7.4 percentage-points) and low socioeconomic status-serving (1.38[1.35-1.40]; +7.3% percentage-points) hospitals. Restricted analyses accounting for the influence of teaching status and severity of patient case-mix both accentuated disparities in HACRP penalties when limiting hospitals to those at the highest known penalty-risk (more residents-to-beds, more severe), absolute differences +13.9, +20.5 percentage-points. Restriction to high operative volume, in contrast, reduced the penalty difference, +6.6 percentage-points.

CONCLUSIONS

Minority-serving hospitals are being disproportionately penalized by the HACRP. As the program continues to develop, efforts are needed to identify and protect patients in vulnerable institutions to ensure that disparities do not increase.

Evaluation of the National Healthcare Safety Network standardized infection ratio risk adjustment for healthcare-facility-onset Clostridioides difficile infection in intensive care, oncology, and hematopoietic cell transplant units in general acute-care hospitals

OBJECTIVE

To evaluate the National Health Safety Network (NHSN) hospital-onset Clostridioides difficile infection (HO-CDI) standardized infection ratio (SIR) risk adjustment for general acute-care hospitals with large numbers of intensive care unit (ICU), oncology unit, and hematopoietic cell transplant (HCT) patients.

DESIGN

Retrospective cohort study.

SETTING

Eight tertiary-care referral general hospitals in California.

METHODS

We used FY 2016 data and the published 2015 rebaseline NHSN HO-CDI SIR. We compared facility-wide inpatient HO-CDI events and SIRs, with and without ICU data, oncology and/or HCT unit data, and ICU bed adjustment.

RESULTS

For these hospitals, the median unmodified HO-CDI SIR was 1.24 (interquartile range [IQR], 1.15-1.34); 7 hospitals qualified for the highest ICU bed adjustment; 1 hospital received the second highest ICU bed adjustment; and all had oncology-HCT units with no additional adjustment per the NHSN. Removal of ICU data and the ICU bed adjustment decreased HO-CDI events (median, -25%; IQR, -20% to -29%) but increased the SIR at all hospitals (median, 104%; IQR, 90%-105%). Removal of oncology-HCT unit data decreased HO-CDI events (median, -15%; IQR, -14% to -21%) and decreased the SIR at all hospitals (median, -8%; IQR, -4% to -11%).

CONCLUSIONS

For tertiary-care referral hospitals with specialized ICUs and a large number of ICU beds, the ICU bed adjustor functions as a global adjustment in the SIR calculation, accounting for the increased complexity of patients in ICUs and non-ICUs at these facilities. However, the SIR decrease with removal of oncology and HCT unit data, even with the ICU bed adjustment, suggests that an additional adjustment should be considered for oncology and HCT units within general hospitals, perhaps similar to what is done for ICU beds in the current SIR.