MMHA 6900 Week 7 Quality Measure Set Strategy Example

Eight Numbers for One Heart Failure Program: Choosing and Defending Outcome, Process and Balancing Measures

Student Name

Walden University

MMHA 6900: Healthcare Quality Management

Instructor Name

Month Day, Year

Eight Numbers for One Heart Failure Program: Choosing and Defending Outcome, Process and Balancing Measures

Purpose

Cobalt Ridge Medical Center, the invented hospital whose heart failure readmissions this course has tracked, is launching a heart failure transitions program built on the Week 4 project plan. Leaders want a dashboard. The risk is a dashboard of thirty numbers that nobody can act on. This paper chooses eight measures, explains what each tells the program team and defends the choice against the measures left out. The set follows Donabedian's logic that quality can be judged by outcomes, by the processes that produce them and by the structures that support those processes, and adds balancing measures to catch harm the program might cause elsewhere (Donabedian, 1988).

The Measure Set

Table 1

Heart Failure Transitions Measure Set (Illustrative Baselines)

Two outcomes, four processes and two balancing measures. Every process measure is a step the program can change; every balancing measure watches for a side effect.

Why These Outcomes

Readmission is the outcome the program exists to reduce, and matching it to the public measure lets the team see whether internal gains will show up in reporting (Keenan et al., 2008). Mortality is the outcome that guards patients. After the federal readmissions penalty program began, a study found that 30-day mortality for heart failure patients rose in the period after the program was announced and implemented, raising concern that pressure to avoid readmission might lead to harm (Wadhera et al., 2018). Whatever the cause of that finding, a program that lowers readmissions while raising deaths has failed, so mortality sits beside readmission on every report.

Why These Processes

The four process measures follow the chain the program depends on. An appointment made before discharge is the link the hospital controls most directly; the seven-day visit is what the appointment is for; the 48-hour call catches patients who are struggling before the visit; and guideline medications at discharge reflect the clinical quality of the stay itself. The seven-day visit has the strongest evidence behind it: hospitals with higher rates of early follow-up after heart failure discharge had lower readmission rates (Hernandez et al., 2010). Because process measures change faster than outcomes and occur in every discharge, they are reported monthly, giving the team feedback it can use.

Why These Balancing Measures

Balancing measures look for harm the program could cause in another part of the system. A program under pressure to avoid readmissions might keep patients in observation status or send them home from the emergency department rather than admitting them. Analysis of the national program found that observation stays rose while readmissions fell, although increases in observation did not account for most of the decline (Zuckerman et al., 2016). Tracking observation stays and emergency returns at Cobalt Ridge shows whether any decline in readmissions reflects real improvement. Length of the index stay is the second balancing measure, since one way to lower readmissions is to keep patients longer, which costs beds and money and may not help them.

Measures Left Out

Several measures were proposed and excluded. Patient satisfaction with discharge was left out of this set because the hospital's existing survey already reports it and adding it here would duplicate effort; the team will review it quarterly. A heart failure knowledge score at discharge was excluded because no validated tool was in use and creating one would delay the program. Weight monitoring adherence at home was excluded because the hospital has no reliable data source. Each exclusion is noted so that it can be revisited if a data source appears.

How the Set Will Be Read

The program team will read the measures in a fixed order each month. First, the balancing measures: if observation stays, emergency returns or length of stay have moved sharply, the team investigates before celebrating any readmission change. Second, the process measures, which show whether the program is doing its work. Third, the outcomes, read as rolling twelve-month rates to smooth small monthly numbers. Reading in this order keeps the team from mistaking a shift in where patients go for a real improvement in how they fare. The dashboard will show each measure on a run chart with its baseline and target, so that a reader sees direction as well as level.

Data Owners

Each measure has an owner responsible for its accuracy: the quality department for outcomes and balancing measures, case management for appointments and calls, and pharmacy for guideline medications. Process measures will be checked monthly by sampling ten charts against the automated report, since a measure that drifts from reality can mislead the team more than no measure at all. When a sampled chart disagrees with the report, the owner fixes the data source before the next month's dashboard goes out.

Conclusion

Eight measures give Cobalt Ridge's heart failure program what it needs to steer: two outcomes that say whether patients are better off, four processes that say whether the program is doing its work and two balancing measures that say whether it is causing harm elsewhere. The set is small enough to read in a minute and complete enough to trust.

References

Donabedian, A. (1988). The quality of care: How can it be assessed? JAMA, 260(12), 1743-1748. https://doi.org/10.1001/jama.1988.03410120089033

Hernandez, A. F., Greiner, M. A., Fonarow, G. C., Hammill, B. G., Heidenreich, P. A., Yancy, C. W., Peterson, E. D., & Curtis, L. H. (2010). Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA, 303(17), 1716-1722. https://doi.org/10.1001/jama.2010.533

Keenan, P. S., Normand, S.-L. T., Lin, Z., Drye, E. E., Bhat, K. R., Ross, J. S., Schuur, J. D., Stauffer, B. D., Bernheim, S. M., Epstein, A. J., Wang, Y., Herrin, J., Chen, J., Federer, J. J., Mattera, J. A., Wang, Y., & Krumholz, H. M. (2008). An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure. Circulation: Cardiovascular Quality and Outcomes, 1(1), 29-37. https://doi.org/10.1161/CIRCOUTCOMES.108.802686

Wadhera, R. K., Joynt Maddox, K. E., Wasfy, J. H., Haneuse, S., Shen, C., & Yeh, R. W. (2018). Association of the Hospital Readmissions Reduction Program with mortality among Medicare beneficiaries hospitalized for heart failure, acute myocardial infarction, and pneumonia. JAMA, 320(24), 2542-2552. https://doi.org/10.1001/jama.2018.19232

Zuckerman, R. B., Sheingold, S. H., Orav, E. J., Ruhter, J., & Epstein, A. M. (2016). Readmissions, observation, and the Hospital Readmissions Reduction Program. New England Journal of Medicine, 374(16), 1543-1551. https://doi.org/10.1056/NEJMsa1513024