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Hospital Quality: The Devil is in the Details

In the debate over health care reform, one of the few issues everyone agrees on is the need for quality improvement (QI). Many believe we should reward the best performers and penalize those that fail to meet quality standards. It seems like a no-brainer, but of course the devil is in the details: What metrics should be used to accurately measure quality?

Source: A Tale of a Halo (Public Domain)

Source: A Tale of a Halo (Public Domain)

QI is one of the core pillars of the (ACA) and is being implemented through a number of programs. The Hospital Acquired Condition (HAC) Reduction Program, an effort to reduce preventable infections that occur during hospitalizations, began penalizing the worst performers on October 1, 2014.

A recent study in the Journal of the American Medical Association examined the hospitals penalized in the HAC program and found that although these hospitals landed at the bottom quartile based on the HAC metrics, they were more likely to perform better on other traditional quality measures, have higher patient volumes, more accreditations, and offer more advanced services. These hospitals were also more likely to be large, major teaching institutions, safety net providers and/or level I trauma centers, and serve the most complex patient mix. Based on these findings, the authors suggest that penalization in the HAC program may not accurately reflect poor quality care, but rather that the current metrics are not accurately measuring quality.

Two possible explanations are: 1) surveillance bias (i.e., hospitals proactively identifying adverse events are more likely to find them) and 2) inadequate risk adjustment (i.e., medically complex and patients from underserved communities are not appropriately accounted for in administrative data).

Critically, neither of these activities—proactive identification of errors or serving the underserved—should be penalized as they are consistent with the ACA’s overall goals. Instead, quality improvement program metrics should include these factors and reward institutions for these important, often voluntary activities.

Unintended consequences are a risk of every policy and when they disincentivize the very thing the policy is meant to address, in this case improving and , it is time to re-evaluate and make some adjustments.

commentary by Megan Douglas


IMPORTANCE: In fiscal year (FY) 2015, the Centers for Medicare & Medicaid Services (CMS) instituted the Hospital-Acquired Condition (HAC) Reduction Program, which reduces payments to the lowest-performing hospitals. However, it is uncertain whether this program accurately measures quality and fairly penalizes hospitals.

OBJECTIVE: To examine the characteristics of hospitals penalized by the HAC Reduction Program and to evaluate the association of a summary score of hospital characteristics related to quality with penalization in the HAC program.

DESIGN, SETTING, AND PARTICIPANTS: Data for hospitals participating in the FY2015 HAC Reduction Program were obtained from CMS’ Hospital Compare and merged with the 2014 American Hospital Association Annual Survey and FY2015 Medicare Impact File. Logistic regression models were developed to examine the association between hospital characteristics and HAC program penalization. An 8-point hospital quality summary score was created using hospital characteristics related to volume, accreditations, and offering of advanced care services. The relationship between the hospital quality summary score and HAC program penalization was examined. Publicly reported process-of-care and outcome measures were examined from 4 clinical areas (surgery, acute myocardial infarction, heart failure, pneumonia), and their association with the hospital quality summary score was evaluated.

EXPOSURES: Penalization in the HAC Reduction Program.

MAIN OUTCOMES AND MEASURES: Hospital characteristics associated with penalization.

RESULTS: Of the 3284 hospitals participating in the HAC program, 721 (22.0%) were penalized. Hospitals were more likely to be penalized if they were accredited by the Joint Commission (24.0% accredited, 14.4% not accredited; odds ratio [OR], 1.33; 95% CI, 1.04-1.70); they were major teaching hospitals (42.3%; OR, 1.58; 95% CI, 1.09-2.29) or very major teaching hospitals (62.2%; OR, 2.61; 95% CI, 1.55-4.39; vs nonteaching hospitals, 17.0%); they cared for more complex patient populations based on case mix index (quartile 4 vs quartile 1: 32.8% vs 12.1%; OR, 1.98; 95% CI, 1.44-2.71); or they were safety-net hospitals vs non-safety-net hospitals (28.3% vs 19.9%; OR, 1.36; 95% CI, 1.11-1.68). Hospitals with higher hospital quality summary scores had significantly better performance on 9 of 10 publicly reported process and outcomes measures compared with hospitals that had lower quality scores (all P???.01 for trend). However, hospitals with the highest quality score of 8 were penalized significantly more frequently than hospitals with the lowest quality score of 0 (67.3% [37/55] vs 12.6% [53/422]; P?

CONCLUSIONS AND RELEVANCE: Among hospitals participating in the HAC Reduction Program, hospitals that were penalized more frequently had more quality accreditations, offered advanced services, were major teaching institutions, and had better performance on other process and outcome measures. These paradoxical findings suggest that the approach for assessing hospital penalties in the HAC Reduction Program merits reconsideration to ensure it is achieving the intended goals.

Rajaram R. JAMA. 2015;  314 (4): 375-383. PMID: 26219055

Megan Douglas, JD
About Megan Douglas, JD

Megan Douglas is the Associate Director of Health Information Technology Policy in the National Center for Primary Care at Morehouse School of Medicine in Atlanta, GA. She is a licensed attorney and focuses on health policy and its impact on individuals from underserved communities. She was a 2012-13 Health Policy Leadership Fellow under Dr. David Satcher, 16th Surgeon General of the United States. Megan has worked on health policy issues related to neurodevelopmental disabilities, HIV and AIDS discrimination, racial and ethnic health disparities, and individuals identifying as LGBTQ. In her current role, she is looking at the impact of Health Information Technology (HIT) policies on healthcare providers who serve underserved communities and is identifying ways to leverage HIT to improve health outcomes for the underserved. Contact: Website | Facebook | Twitter | Google+ | More Posts

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