Improving the Hospital-Acquired Condition Reduction Program through Rulemaking

Hospital-acquired conditions (HACs) are pervasive, expensive, and cause unnecessary morbidity and mortality. As of 2017, 9 HACs still occur for every 100 discharges.¹ The Hospital-Acquired Conditions Reduction Program (HACRP) was created to reduce HACs.

Despite critical need to improve safety, research indicates the HACRP has not been effective. While improvement on claims-data-based measures accelerated post-HACRP implementation, the program did not improve patient outcomes². Risk adjustment is inadequate, leading to disproportionate penalties for teaching hospitals and hospitals caring for more disadvantaged patients.³ Penalization has not improved safety.⁴ And the HACRP was not associated with improvement on included non-claims-data-based measures⁵ or high-quality registry data.⁶

These dismal results highlight two HACRP problems: (1) inaccurate, unreliable HAC assessment⁷ and (2) penalties disproportionately, and unfairly, affecting certain hospitals. However, crucial modifications could be made through rulemaking. Rulemaking entails the executive branch specifying Congressional policy and regulation details. After public comment and revision of Federal Register-published Proposed Rules, a Final Rule is published creating binding regulations. For Medicare policies, the Department of Health and Human Services (DHHS) plays a leading role.

The HACRP legislation (Section 3008, Affordable Care Act) requires that hospitals in the worst-performing HAC quartile receive a 1% Medicare inpatient payment penalty. However, specific measures, performance determination, auditing, and other details are not described. Given lack of specificity, DHHS has considerable degrees of freedom to improve the HACRP through rulemaking.

1. Modify measures. A leading strategy to reduce central line bloodstream infections and catheter-associated urinary tract infections (CAUTI) is reducing central line or catheter exposure. However, the CAUTI measure, for instance, considers only the ratio of infections to catheter days. Since hospitals have experience acquiring catheterization-duration data, rewarding decreased catheter exposure would reduce HACs without requiring many documentation changes.

   Moreover, some measures (e.g. pneumothorax) are especially preventable and uncommon. It is impossible to reliably distinguish hospitals on them. They should be eliminated.

2. Improve risk adjustment. Risk adjustment variables are used to calculate hospitals’ predicted number of HACs (to compare with the observed number), affecting hospital performance comparisons. Yet the current methodology is insufficient to address the large heterogeneity across patients and hospitals. Compared with other programs, HACRP measures incorporate wide-ranging diagnoses, increasing heterogeneity in patient characteristics across hospitals. For instance, hospitals performing more surgery face increased risk for additional HACs, as surgical patients are eligible for more HACRP measures.

   Additionally, certain measures omit key patient characteristics that increase risk of the HAC. For example, surgical site infection risk adjustment neglects (1) preoperative diagnosis, (2) whether the case is elective or emergent, and (3) patient immunosuppression – despite the impact of these variables on risk and their variation across hospitals.

   Although risk adjustment can never fully address such differences, the current methodology can be improved. One approach is enhancing risk adjustment variables to better account for patient characteristics and medical complexity that increase HAC risk. Second– following Hospital Readmissions Reduction Program and Bundled Payment for Care Improvement Advanced – penalty thresholds could be set differently for hospital peer groups to further address systematic differences. Despite statutory language that a “hospital is in the top quartile of all subsection (d) hospitals, relative to the national average,” we believe the provision that “the Secretary shall establish and apply an appropriate risk adjustment methodology” allows a penalty threshold based on the national average of similar hospitals.

   By implementing such changes, hospitals disproportionately at-risk for HACs, given patient compositions and procedures, will be less disadvantaged in performance comparisons. Once established, such changes require little effort.

3. Address the “small n problem”. To receive a given measure score, a hospital needs 3+ eligible discharges or 1+ predicted HAC (depending on the measure), leading some hospitals to receive scores with small sample sizes. This has an outsized effect on performance comparisons if those hospitals lack other scores. Despite minimum sample sizes for “moderate” reliability for measures, hospitals frequently receive scores based on smaller caseloads.

   In response, more data years could increase sample size, more hospitals could be excluded from receiving measure scores due to unreliable data, or Bayesian shrinkage strategies could be employed. There is already precedent for this (e.g. transplant center outcomes, HRRP).

4. Establish clear guidelines; enhance auditing. Wide variability exists in surveillance, testing, and potentially reporting practices for known HACs. Hospitals currently rely on their own resources to determine these procedures. Providers can also reach different diagnoses on clinical basis alone, underscoring the importance of conclusive testing. And the Government Accountability Office found the Centers for Medicare & Medicaid Services’ hospital selection practices for quality measure reporting validation decreased capacity to identify gaming strategies. Moreover, hospitals with aberrant data patterns were not used for additional scrutiny. Insufficiently stringent and incomprehensive auditing, and limited consequences for failing, undermine incentives to accurately document HACs and negatively impact hospitals fairly surveying for, testing for, and reporting HACs. Additionally, hospitals with more resources can hire consultants to determine optimal procedures to identify and report HACs, which may exacerbate penalty disparities.

   Together, this necessitates clearer guidelines, initial technical assistance, enhanced auditing, and larger consequences for failing validation. While enhanced auditing could initially require more time and resources than other changes, strengthening this and consequences for failing would reduce data concerns in future years and improve HAC tracking and reduction.

The premise underlying the HACRP - that hospitals accurately collect data on patient safety events that will then be used to penalize them – creates an inherent conflict that is hard to overcome. Nonetheless, it is imperative to improve the HACRP through rulemaking. Although such changes require DHHS investments to develop, finalize, and implement, they require less effort in future years and enhance the program. While not a panacea, these changes make penalties fairer and strengthen reliability and validity of safety measurement. The COVID-19 pandemic further highlights measurement limitations and the dire need to prevent infections. Instead of discouraging preventive actions – or encouraging efforts to reduce reportable HACs without truly improving quality – these changes better incentivize actions to reduce actual HACs. This in turn could make the HACRP a program that saves lives and money.