Case Study: Predictive Analytics for Hospital Readmissions

The emergency department (ED) of a large urban hospital was experiencing significant challenges with patient flow. Patients were waiting long hours to be seen by a physician, resulting in low patient satisfaction scores and increased risk of adverse outcomes. The hospital leadership team decided to undertake a patient flow analysis to identify bottlenecks and inefficiencies in the process.

Background:

Hospital readmissions are a costly and burdensome issue for both patients and healthcare systems. According to the Centers for Medicare and Medicaid Services (CMS), around 20% of Medicare patients are readmitted within 30 days of discharge, costing billions of dollars each year. In addition to the financial burden, readmissions can also indicate poor quality of care and result in patient dissatisfaction.

Challenge:

A large hospital system in the United States sought to reduce its readmission rates by implementing a predictive analytics solution. The hospital system had access to large amounts of patient data, including electronic health records (EHRs), demographics, and clinical data, but lacked the tools to extract meaningful insights from this data.

Solution

The hospital system partnered with a predictive analytics firm to develop a readmission prediction model. The firm analyzed the hospital's patient data and identified variables that were most predictive of readmission, including age, gender, diagnosis, length of stay, comorbidities, and prior hospitalizations. Using these variables, the firm developed a machine learning model that could predict the likelihood of readmission for each patient.

To implement the solution, the hospital integrated the readmission prediction model into its EHR system. When a patient was discharged, the model would generate a risk score indicating the probability of readmission within 30 days. This risk score was then communicated to the patient's care team, who could use this information to develop a targeted care plan to reduce the risk of readmission.

Below diagram illustrates the complexity of patient flows between various units of a large hospital. By improving patient flows, a hospital can save money and boost patient and provider satisfaction.

Results

After implementing these interventions, the hospital conducted a follow-up patient flow analysis. The analysis revealed significant improvements in patient wait times, triage times, and treatment times. Specifically:

Wait times decreased by an average of 20 minutes.

Triage times decreased by an average of 15 minutes.

Treatment times decreased by an average of 30 minutes.

Patient satisfaction scores increased by 15%.

Conclusion

Through the use of patient flow analysis and data analytics, the hospital was able to identify inefficiencies in its emergency department process and implement targeted interventions to improve patient flow. This resulted in improved patient satisfaction, decreased wait times, and increased efficiency in the ED. The hospital leadership team continues to monitor patient flow using data analytics and adjusts processes as needed to maintain optimal performance.

Have a Question ?

Whether you are a small business owner or a large enterprise, our data analytics solutions can help you stay ahead of the curve and make informed decisions that drive business growth and success. Contact us today to learn more about how our services can benefit your organization!