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Factors Associated with Hospital Readmissions

By March 30, 2016Commentary

It has been a while since we have posted on a hospital readmissions study.  The Journal for Healthcare Quality carried a study examining factors that appeared associate with readmission likelihood.   (Readm. Article)   Research in this area continues because of serious flaws in the various readmission reduction incentive programs.  The authors used a database maintained by Premier, a large GPO, and covering 611 acute care hospitals and, unlike, much readmissions research, patients from all payers, not just Medicare.  A variety of correlations were examined in regard to hospitalizations and readmissions within 30 days from 2008 to 2010.  The primary clinical conditions leading to hospitalization were one factor, as was principal diagnosis, comorbidity score, cancer status, chronic disease history, age, sex, race, income, admission source, admission type, payer type, discharge disposition, and distance between home and hospital.  The point of the exercise is to see how well any one or a combination of these factors identifies readmission risk.  The honest answer is not very well.  As a whole, the model, depending on disease, explains less than half the likelihood of readmission.  Certain individual factors have stronger association with readmissions and certainly combinations of these factors would indicate higher risk for readmission, but many of these are just consistent with common sense.  Being older and having more comorbidities, for example, means a higher readmission risk, duh.  Interestingly, being closer to the hospital increased the likelihood of readmission.  Being discharged to a facility like a SNF also increased it, but that is probably because of the association with being sicker.  Aggregating data to create an analysis across all hospitals also allows comparison of an individual hospital to that aggregated predicted risk.  About 27% of hospitals are “underperformers” according to the model; that is, they have significantly higher readmission rates than the characteristics of the patients would suggest should occur.  The research clearly supports the idea that factors not currently accounted for in the model CMS uses to punish hospitals for “excessive” readmissions for Medicare beneficiaries are important determinants of readmission, many beyond the control of the hospital.  But is also clear that no model yet has been developed that would really be helpful in precisely determining which patients are most at risk of readmission.


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