CASE STUDY

Patient Readmission Analytics Helps a Healthcare Organization Reduce Readmission Rates by Identifying High-Risk Cohorts

May 11, 2020

About the Client

The client is a well-known healthcare services provider based out of Belgium.

Patient Readmission Analytics Engagement Summary

Patient Readmission Analytics

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Business Challenge

Recent changes in healthcare systems across the globe have prompted healthcare organizations to redesign their workflows to better manage patients and avoid government penalties. In an effort to do so, healthcare service providers are now focusing on reducing patient readmission rates by implementing analytics in their organizational workflows. The predictive models used by the client, in this case, were isolated along the care continuum focusing only on certain silos and not the entire healthcare workflow. They approached Quantzig, looking to leverage patient readmission analytics to fully integrate predictive models into their workflows and devise readmission reduction strategies to drive impactful transformations.

By integrating patient readmission analytics into their workflow, the healthcare services provider wanted to achieve four main goals centered around reducing patient readmissions, including:

  • Improve the performance of predictive models
  • Predict and identify high-risk patient cohorts
  • Obtain near real-time insights using an automated, easy-to-understand, cross-continuum tool
  • Recommend actions in the best interest of the patients

To take advantage of our cutting-edge patient analytics solutions and turn your patient data into insights that help you reduce readmission rates, request a FREE proposal.


Solution Offered

Implementing a practical predictive model for analyzing high-risk patient groups is not an easy task given the complexity of the healthcare ecosystem. Even before a healthcare organization can do so, they must develop a comprehensive picture of the patient’s journey and analyze patient data on a granular level. Adopting such an approach will help healthcare service providers gain critical insights into the factors leading to a patient’s readmission.

A detailed analysis of the challenges faced by the client revealed that to reduce patient readmission rates using analytics, the client had to build and integrate a predictive analytics-driven strategy into their current workflow. To do so, our healthcare provider analytics experts devised predictive models that consider the entire patient readmission journey, as well as inputs from the whole patient care team and the patients, and leverage the capabilities of patient readmission analytics to deliver accessible, easy-to-use tools with meaningful visualizations on interactive dashboards.

To understand readmissions, we adopted a comprehensive approach that leveraged patient data from readmission logs and discharge logs, including critical touchpoints in the process. The detailed breakup of the three-pronged patient care analytics approach is as outlined below:

Phase 1: Patient Data Analysis

To develop a predictive model, we gathered relevant historical patient data sets from various sources in the first phase. Our patient readmission analytics team also had to analyze data to identify inputs that could impact the target outcome. To do so, we applied advanced machine learning techniques to map the relationship between patient attributes and the target outcomes. Though different events could be predicted using such an approach, we focused our analysis on the aforementioned business priorities which are mostly around patient readmission risks.

Phase 2: Design and Development of Patient Readmission Predictive Models

The patient readmission prediction models helped the client to monitor and identify patients at a higher probability of being readmitted after an initial stay at the hospital. This helped the healthcare services provider to recommend special plans for the high-risk cohorts by identifying and categorizing the patients into similar groups.

Phase 3: Data Dashboarding and Reporting

The final phase of this patient readmission analytics engagement focused on communicating the outcomes on advanced analytics dashboards with interactive visualizations and easy to use interfaces.

Value Delivered

Apart from helping the client generate individualized predictions for patient readmission rates, Quantzig’s patient care analytics solutions also helped them:

  • Reduce patient readmission rate by 59%
  • Save millions in medical service costs, approx. $7 million
  • Optimize resource usage
  • Provide predictive care to high-risk cohorts
  • Increase patient satisfaction and improve overall rating
  • Avoid potential government imposed medical penalties

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Patient Readmission Analytics Solutions: What sets us apart?

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