MBAX 6332 Week 11 Final Health Policy Paper Example

A Comment to the CMS Administrator on the Hospital-Acquired Condition Reduction Program: What It Provoked, What the Evidence Shows and One Change the Agency Can Make Now

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Walden University

MBAX 6332: Health Policy and Systems Thinking

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Month Day, Year

A Comment to the CMS Administrator on the Hospital-Acquired Condition Reduction Program: What It Provoked, What the Evidence Shows and One Change the Agency Can Make Now

To the Administrator

This paper is addressed, by office, to the head of CMS, the federal agency that runs Medicare, as a comment on the annual inpatient payment proposed rule. Its writer prepares safety data in a composite hospital's quality office, and it draws on the term's analysis of the Hospital-Acquired Condition Reduction Program. It describes the program, maps the reactions it has provoked, weighs the evidence on each and closes with one recommendation that falls within the agency's existing authority.

The Program

The program dates from section 3008 of the Affordable Care Act; its text now sits at 42 U.S.C. § 1395ww(p). Beginning with fiscal 2015 discharges, it has reduced by 1 percent the Medicare inpatient payments of acute care hospitals in the worst-performing quartile on a combined score of patient safety indicators, calculated from claims, and hospital infection measures defined by the CDC's National Healthcare Safety Network. The statute fixes the quartile, the size of the reduction and the hospitals covered; the agency selects and defines the measures, sets the scoring method and updates the program each year through this rule.

The program's aim is sound. Hospital-acquired infections and safety events harm Medicare patients and add cost, and a payment incentive tied to them signals that safety matters. The question is whether the program's design delivers that signal.

Reactions the Program Provoked

Four reactions stand out. First, hospitals invested in prevention, particularly in reducing device use and standardizing infection practices, though much of that work began before the program and was encouraged by other efforts. Second, the burden fell unevenly: about a fifth of participating hospitals lost money in the first year, a share that rose sharply among large teaching centers, hospitals treating complex cases and safety-net institutions (Rajaram et al., 2015). Third, hospitals and their associations raised concerns that the infection measures reward hospitals that test less, a form of the surveillance bias described by Haut and Pronovost (2011). Fourth, at the bedside, the program added a small incentive into decisions about testing, coding and the application of surveillance definitions, decisions made by staff who have no role in designing the program.

What the Evidence Shows

The evidence on effectiveness is discouraging. The largest evaluation, a regression discontinuity study across more than 3,000 hospitals, detected no significant drop in targeted harms, return admissions or deaths after the program's first round of penalties, and cautioned that penalizing hospitals with poorer patients could deepen inequity (Sankaran et al., 2019). Sheetz et al. (2019) saw the conditions the program targets fall in a Michigan surgical registry while conditions outside it fell faster, leaving no sign of added improvement. Earlier experience with relative rankings points the same way: Ryan et al. (2012) found that ranking-based rewards in an earlier Medicare demonstration left the weakest hospitals improving no faster than before.

The evidence on surveillance bias is suggestive rather than conclusive. Higher penalty rates at teaching centers, which order more cultures and scans, fit the surveillance-bias account, but it is also consistent with sicker patients. No published source in this review measures testing intensity directly alongside the program's infection measures, which is itself the gap the recommendation below addresses.

Unintended Consequences

Merton (1936) showed how deliberate social action breeds outcomes nobody planned, and Campbell (1979) warned that indicators relied on for high-stakes decisions tend to be gamed and, in being gamed, distort what they measure. The program shows both risks. A measure that counts only confirmed infections creates pressure, however slight, against confirming them. A fixed quartile ensures that a quarter of hospitals are penalized regardless of improvement, so hospitals cannot know whether their efforts will be rewarded.

Recommendation

The agency should publish, alongside each hospital's infection measures, a testing-intensity figure, for instance cultures drawn relative to inpatient days, and should test whether infection scores ought to be adjusted for testing. To limit the risk of unnecessary testing, testing rates should be published as context and as a flag for any hospital whose infection count and testing rate are both near the bottom, not scored as a measure in their own right. This change falls within the agency's authority over the measures and their presentation, and it would let the agency, hospitals and the public distinguish safe hospitals from hospitals that look less hard.

How the Agency Could Carry It Out

The change could be phased in over three rule cycles. In the first, the agency would propose the testing-intensity measure and ask hospitals and the CDC which existing data could supply it; most hospitals already record every culture and C. difficile test in their laboratory systems, and the surveillance network already collects some information on C. difficile testing methods. In the second cycle, the agency would collect the data for one performance period without publishing hospital results, giving hospitals time to check their figures. In the third, it would publish testing rates as context beside infection measures and report what the data show. The evaluation question is simple: once testing is taken into account, does the gap in penalty rates between teaching and nonteaching hospitals, and between safety-net and other hospitals, narrow? If it does, the agency would have evidence for adjusting scores; if it does not, the concern about surveillance bias would be weakened, which is also worth knowing. Either result would make the program easier to defend.

A Matter for Congress

The fixed quartile cannot be changed by rule. If the agency agrees that relative rankings weaken the program's signal, it could say so in the final rule's discussion, which would inform any future legislative change toward fixed performance thresholds with credit for improvement.

Conclusion

The program's goal, safer hospitals for Medicare patients, deserves support. Its design, as the evidence stands, penalizes some hospitals for reasons unrelated to safety and has not been shown to improve outcomes. Publishing testing intensity is a modest step the agency can take now, and it would produce the evidence needed to judge the program fairly.

References

Campbell, D. T. (1979). Assessing the impact of planned social change. Evaluation and Program Planning, 2(1), 67-90. https://doi.org/10.1016/0149-7189(79)90048-X

Haut, E. R., & Pronovost, P. J. (2011). Surveillance bias in outcomes reporting. JAMA, 305(23), 2462-2463. https://doi.org/10.1001/jama.2011.822

Hospital-acquired conditions, 42 U.S.C. § 1395ww(p) (2024).

Merton, R. K. (1936). The unanticipated consequences of purposive social action. American Sociological Review, 1(6), 894-904. https://doi.org/10.2307/2084615

Rajaram, R., Chung, J. W., Kinnier, C. V., Barnard, C., Mohanty, S., Pavey, E. S., McHugh, M. C., & Bilimoria, K. Y. (2015). Hospital characteristics associated with penalties in the Centers for Medicare & Medicaid Services Hospital-Acquired Condition Reduction Program. JAMA, 314(4), 375-383. https://doi.org/10.1001/jama.2015.8609

Ryan, A. M., Blustein, J., & Casalino, L. P. (2012). Medicare's flagship test of pay-for-performance did not spur more rapid quality improvement among low-performing hospitals. Health Affairs, 31(4), 797-805. https://doi.org/10.1377/hlthaff.2011.0626

Sankaran, R., Sukul, D., Nuliyalu, U., Gulseren, B., Engler, T. A., Arntson, E., Zlotnick, H., Dimick, J. B., & Ryan, A. M. (2019). Changes in hospital safety following penalties in the US Hospital Acquired Condition Reduction Program: Retrospective cohort study. BMJ, 366, l4109. https://doi.org/10.1136/bmj.l4109

Sheetz, K. H., Dimick, J. B., Englesbe, M. J., & Ryan, A. M. (2019). Hospital-acquired condition reduction program is not associated with additional patient safety improvement. Health Affairs, 38(11), 1858-1865. https://doi.org/10.1377/hlthaff.2018.05504