How one hospital forecasts patient payment behavior

Predictive Analytics Helps a Hospital Raise Collections Revenue by 12 Percent

Cover

When a patient is uninsured or underinsured, hospitals provide treatment first and seek payment later. This costly and time-consuming collections process is unsuccessful a shocking 85% of the time for self-pay patients.

Hospitals have millions of rows of untapped patient and hospital data, and this case study explains why applying predictive analytics to this data can better identify the patients who are most likely to pay their bills. Read on to learn how MedeAnalytics worked with one hospital to beta-test a solution that could monetize existing data, optimize the payment collections process, and maximize returns from self-pay patients.

Vendor:
IBM
Posted:
Mar 3, 2015
Published:
Feb 28, 2014
Format:
PDF
Type:
Case Study
Already a Bitpipe member? Log in here

Download this Case Study!