MMHA 6900 Week 9 Improvement Aim Charter Example

An Aim Built From the Measure's Own Rules: A Charter to Lower Heart Failure Readmissions by a Stated Number and Date

Student Name

Walden University

MMHA 6900: Healthcare Quality Management

Instructor Name

Month Day, Year

An Aim Built From the Measure's Own Rules: A Charter to Lower Heart Failure Readmissions by a Stated Number and Date

Why Build the Aim From the Specification

Improvement aims often fail because they are written in terms the organization cannot measure the same way the public will. Cobalt Ridge, the invented hospital at the center of this term, wants its heart failure readmissions out of the public site's below-national group. An aim such as "reduce readmissions by 20 percent" would be measured with whatever data are handy, probably all heart failure patients returning to Cobalt Ridge, and could improve on paper while the public rate barely moved. This charter builds its aim from the public measure's own specification, so that internal progress predicts public results.

The Measure's Rules

The public heart failure readmission measure is built from Medicare claims for patients aged 65 and older in fee-for-service Medicare, discharged alive after a hospitalization with a principal diagnosis of heart failure. Any unplanned return to an acute hospital, for whatever reason, inside a month of discharge counts against the discharging hospital, and it adjusts for patients' age and coexisting conditions to produce a risk-standardized rate that compares each hospital with an average hospital treating the same patients (Keenan et al., 2008). Several rules shape the aim. Planned readmissions, such as a scheduled procedure, are excluded. Transfers are attributed to the discharging hospital. The public rate pools three years.

The Aim

By the end of the eighteenth month after the program begins, the internally rebuilt 30-day unplanned readmission rate for Medicare fee-for-service heart failure patients aged 65 and older discharged alive from Cobalt Ridge will fall from 24.8 percent to 21.0 percent or lower, measured over a rolling twelve months and counting readmissions to any hospital through regional exchange data, with no increase in 30-day mortality or in observation stays and emergency returns.

Every element of the aim comes from the measure's rules or from the hospital's situation. The population and the any-hospital rule match the public measure. The internal rate is crude rather than risk-standardized, because the hospital cannot reproduce the full adjustment model, but the patient mix has been stable, so changes in the crude rate should track changes in the standardized one. The target of 21.0 percent is near the expected rate for Cobalt Ridge's patient mix, which would move the hospital toward the national rate. The deadline allows twelve months of full program operation after a six-month start. The balancing conditions follow the concern that pressure on readmissions could increase mortality or shift returns to observation (Wadhera et al., 2018; Zuckerman et al., 2016).

Table 1

Aim Elements and Their Source

An auditor could check every element against the measure specification or the hospital's baseline data.

Measurement Plan

The quality department will rebuild the measure monthly, publishing a rolling twelve-month rate and a monthly rate on a run chart. Because monthly counts are small, about 55 eligible discharges, monthly rates will vary widely; the team will use run chart rules rather than reacting to single months, as statistical process control guidance recommends for distinguishing signal from noise (Benneyan et al., 2003). The process measures from the Week 7 measure set, especially seven-day follow-up, will be reported alongside, since they should change first.

Checking the Baseline

Before the program starts, the team will confirm that the internal baseline matches the public picture. It will compare its rebuilt three-year rate with the hospital-specific report Medicare provides, which lists the discharges and readmissions used in the public calculation. If the two differ by more than a percentage point, the team will look for causes, such as missing outside readmissions or differences in which discharges count, and adjust the internal method. A baseline that does not match the public measure would make the aim meaningless, so this check comes first.

Interim Milestones

An eighteen-month aim needs milestones so the team and sponsor can tell whether it is on track. By month six, appointments made before discharge should exceed 70 percent and seven-day follow-up should exceed 50 percent. By month twelve, the rolling readmission rate should be below 23 percent. Missing a milestone does not end the project, but it triggers a review of the design with the quality council, so that problems are caught well before the deadline and the board hears about them from the team rather than from the public report.

Scope, Team and Sponsor

In scope are heart failure discharges from all medical units meeting the measure's population rules. Sponsorship sits with the chief nursing officer; the quality improvement specialist leads day to day and a team that includes a hospitalist, a cardiology nurse practitioner, case management, pharmacy and a patient representative. The team meets every two weeks and reports to the quality committee monthly.

Risks to the Aim

The aim could be missed for reasons outside the program, such as a severe influenza season or the closure of a local primary care practice. The team will record such events on the run chart so that the board can see them. A second risk is that regional exchange data miss some outside readmissions, which would make the internal rate look better than the public one; a yearly reconciliation with Medicare's hospital-specific report will check for that gap.

Conclusion

The aim states a number, a date, a population and balancing conditions, all built from the public measure's own rules and the hospital's baseline. That construction means that if the program reaches its aim internally, Cobalt Ridge can expect its public rate to follow, with the lag the three-year pooling implies.

References

Benneyan, J. C., Lloyd, R. C., & Plsek, P. E. (2003). Statistical process control as a tool for research and healthcare improvement. Quality and Safety in Health Care, 12(6), 458-464. https://doi.org/10.1136/qhc.12.6.458

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