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WP Using Analytics to Reduce Readmission

Identify High-Risk Patients

Today, 5% of the patient population accounts for about 50% of the healthcare costs. In 2015, readmission penalties cost hospitals $428 million, and rates continue to climb. ARGO has collaborated with Dr. Susan McBride, Professor and Director of Nursing Informatics, at Texas Tech University to create a state of the art 30-day preventable readmission risk model, building upon decades of nursing and bioinformatics research.

This white paper will outline ARGO’s research and findings, including

  • The challenge caregivers face in identify high-risk patients;
  • A brief background on existing readmission risk models;
  • The design and methods of ARGO’s research; and
  • Results and a discussion of the impact of these findings.

ARGO aims for this model to help caregivers allocate their resources for the appropriate patients, make an impact on their care needs, and avoid readmissions. With the help of predictive algorithms, organizations can work toward better reimbursement contracts and provide true value-based care.