A surprising finding in our study was the lack of association between social determinants of health, such as alcohol and drug abuse, and readmission risk. We posit several reasons that may account for this finding. First, the population served by the medical center may be too small or homogeneous with respect to social determinants of health to detect a difference in readmission risk. Second, markers available in the electronic medical record to determine social needs may be too crude to distinguish degrees of vulnerability that increase the risk of readmission. We do not discount the importance of social determinants of health as predictors of readmission risk, but we do acknowledge the limitations of the data incorporated in our model.
Predictive models are useful only if they can be incorporated into workflow to identify high-risk patients. Prior to developing and using our model, we used LACE inconsistently because it required length of stay as 1 of the variables. Because the variables in our model are collected and recorded routinely at admission in our electronic medical record, the readmission risk score is calculated and displayed in a daily high-risk patient report. This automated process has afforded a more consistent and reliable approach to readmission risk assessment than previous efforts to assess the LACE index. Case managers use the high-risk patient report to identify patients who require enhanced care coordination and discharge planning. Since the introduction of this predictive model, we have noted a 10% reduction in the hospital’s 30-day readmission rate.
This project was subject to several limitations. Because data on admissions to other facilities were unavailable, we may have underestimated the risk of readmission to other facilities. Our results may not be generalizable to other organizations, although we believe that the methods are readily replicable. The performance of the model and its replication with a validation cohort are strengths of the approach.
Conclusion
We created a hospital service–specific 30-day readmission prediction tool whose performance improved incrementally over the widely used LACE index. This research suggests that readmission prediction is highly context-specific and that organizations would do well to examine the readmission risk factors most pertinent to the populations they serve. We believe that “customized” readmission risk prediction models for particular services in specific hospitals may offer a superior method to identify high-risk patients who may benefit from individualized care planning. Future research is needed to understand how best to capture information about the attributes of vulnerable populations, so that this information can be incorporated into future risk models.
Corresponding author: Karen Freund, MD, Tufts Medical Center, 800 Washington St., Boston, MA 02111; kfreund@tuftsmedicalcenter.org.
Financial disclosures: None.
Funding for this work was provided by the Commonwealth of Massachusetts, Executive Office of Health and Human Services.