Medicare Readmission Penalty

The examination of the readmission penalty to Detroit hospital service areas and N. E. Central region hospitals show both Michigan and Illinois had high rates of readmission in both 2013 and 2014. Detroit city had the highest readmission penalty in 2013 of 0.93% +/- 0.13. Chicago followed Detroit with a readmission rate of 0.60% +/- 0.29. 

These metropolis area scores skew the balance of the state of Michigan and Illinois rates dramatically, which seems to agree with statements physicians have made that large regional referral centers attract a large number of sick patients, which affects statistics and readmissions in these areas (Greene 2012). Everyone in the medical field is interested in tracking these rates.

Kaiser has conducted studies of readmission rates, correlating low-income patients with congestive heart failure, and the readmission rates throughout the country. (Interactive Readmission Rates) Readmission costs are high and financial pressure from Medicare to discharge patients quickly may increase readmissions. The readmission rate is an important outcome indicator of patient care and increased health cost computed from routine statistics; however inconsistent definitions, the difficulty planning for high-risk discharge and readmission of patients to other hospitals, along with the mix of discharge types compromise the usability of statistical data (Halfon 2006).  There are a variety of resources being used in an attempt to tackle this problem.

One of the most widely agreed-upon areas is the need to identify patients who are most likely to return. Transition programs, intensive social service and programs like “LACE” which track a patient’s length of stay, acuity (sickness of patient at admission), comorbidity (multiple health problems) and emergency visits in the past six months, are being experimented with in hospitals (Reform in Action, n.d.). Quantitative tools for addressing hospital readmissions are being tested (Lagoe 2012). These tools help define identification of at-risk patients and create spreadsheets for tracking target populations. Statistical tools using validated predictive algorithms (Gruneir 2011) and physician-led programs with hands-on nurses and social workers conducting intensive follow-ups of target patients are all working to get a handle on this problem.

The Pearson correlation in this analysis demonstrates the general theory that higher populations of low-income patients, combined with metropolitan regional hospitals attracting extremely sick and high-risk patients, will produce a higher ranking of readmission rate. Continued work with predictive variables, definitions, and identification of tracking factors such as patients that readmit to other hospitals, which is at high risk for return, regional centers vs. small community and suburban hospitals will continue to perfect the outcome of this type of study. 

References

Interactive:  Readmission Rates And Poverty Levels For Individual Hospitals - Kaiser Health News. (n.d.). Kaiser Health News. Retrieved September 21, 2013, from http://www.kaiserhealthnews.org/Stories/2011/December/20/Readmission-Rate-Table.aspx

Greene, J. (n.d.). crainsdetroit.com. Crains Detroit Business. Retrieved September 21, 2013, from www.crainsdetroit.com/article/20121209/NEWS/312099997/hospitals-face-reimbursement-penalties-over-readmission-rates

Gruneir, a., Dhalla, I., VanWalraven, C., Fischer, H., Rochon, P., & Anderson, G. (n.d.). Unplanned readmissions after hospital discharge among patients identified as being at high risk for readmission using a validated predictive algorithm. National Center for Biotechnology Information. Retrieved September 21, 2013, from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3148002/?tool=pubmed

Halfon, P., Petre-Rohrbach, I., Meylan, D., Marazzi, A., & Burnand, B. (2006). Validation of the Potentially Avoidable Hospital Readmission Rate as a Routine Indicator of the Quality of Hospital Care. Medical Care, 44(11), 972-981. Retrieved September 20, 2013, from the JSTOR database.

Lagoe, R., Nanno, D., & Luziani, M. (n.d.). BMC Research Notes | Full text | Quantitative tools for addressing hospital readmissions. BioMed Central | The Open Access Publisher. Retrieved September 21, 2013, from http://www.biomedcentral.com/1756-0500/5/620

Reform in Action: How the U.S. Health Care System Can Reduce Avoidable Readmissions. (n.d.). rwf.org. Retrieved September 20, 2013, from www.rwjf.org/content/dam/farm/reports/issue_briefs/2013/rwjf404499