Identifying those at-risk for hospital readmissions

As a Ministry, OSF HealthCare prioritizes objectives to ensure success and that we can continue serving patients with the greatest care and love as we have for more than 140 years. Among these important goals is working hard to keep patients from needing to be readmitted to the hospital within 30 days of discharge. This means our clinicians not only have to determine who is most at-risk for readmission, they also have to make sure these patients have the understanding, support and ability to care for themselves outside of the hospital.

To make it easier to identify those at-risk for hospital readmissions, OSF HealthCare required nurses to assess patients using a questionnaire that was located within the electronic health record (EHR). However, the approach was found to take a significant amount of nurse time, resulting in more than $3 million in salary and benefits each year.

This led the Healthcare Analytics team, a part of OSF Innovation, to develop an easier way to proactively identify patients needing help to reduce their risk of hospital readmissions.

Focusing resources on the appropriate patients

The group built a predictive model that uses many variables from data within the EHR and automatically identifies at-risk patients in four levels.

  • Low risk: About 55 percent of discharges, with a readmission rate of about 4 percent
  • Medium-low risk: About 22 percent of discharges, with a readmission rate of about 11 percent
  • Medium-high risk: About 16 percent of discharges, with a readmission rate of about 18 percent
  • High risk: About 7 percent of discharges, with a readmission rate of about 30 percent

This made it easier for clinicians to provide case management resources to the most at-risk patients. As the utility and effectiveness of the model was proven, this data was incorporated into clinicians’ daily workflows, reducing the potential barrier to use.

Reducing readmissions

Over the course of a year, this resulted in about 425 fewer readmissions than expected in our medium-high and high-risk patients. The team also found it was able to reduce about 67-percent of nursing assessment activities and decrease the flow into case management by about 44-percent. These staff time reductions translate to a little more than $2 million per year that we can put back into direct patient care.

The model has been in active use for more than three years.  While it started as a way to help direct case management activities inside the hospital, the use of the model has expanded to provide work direction assistance to inpatient case management, ambulatory care management, follow-up phone calls, outpatient palliative care and homecare reporting/monitoring.

Overall, the system-wide readmission rate has remained steady, but we did see a change in the distribution of our population with an increase in medium-high patients and a decrease in those within our low-risk category. Patients categorized in the medium-high and high levels are showing significant differences.