Unique Patient Identifier Solution
Eliminate duplicate patient records with advanced analytics
How are Universal Patient Identifiers created?
In an ideal world, your patient records would be 100% correct, up-to-date, and without duplication. You would have complete confidence that you have a single, accurate, and current view of who your patient is—the “golden record.”
However, traditional matching solutions, like those embedded within the most common Electronic Health Records (EHR), base matching decisions only on the patient demographic data provided, which can contain about 30% of out-of-date, incomplete or inaccurate data.
By combining the power of Experian’s consumer demographic information with more than 40 years of experience across matching methodologies, Experian Health is committed using our data for good to build a connected and simplified healthcare system for all. Through industry partnerships, like our work with the National Council for Prescription Drug Programs (NCPDP) and leading HIS platforms like Epic and Cerner, we are setting the foundation for the adoption and facilitation of Universal Patient Identifiers (UPIs).
Patient safety
- 86% of all clinicians have witnessed a medical error caused by patient misidentification
- More than half of all deaths that are attributed to medical errors are the result of identity errors
Cost to health systems
- The average hospital has 800,000 records with a duplicate record creation rate of 8 to 12% per year
- The estimated cost of remediation is $96 per duplicate pair, which is $3M to $4M to remediate per year
Effect on revenue cycle
- 1/3 of denied claims stem from patient misidentification, costing hospitals $1.5 million on average in 2017
- Repeated services because of duplicate records can cost $1,950 for each patient per hospital stay
Cost to pharmacies
- 77% of all prescriptions filled are submitted electronically
- 50% fallout for manual review, costing about $1 per record to remediate
What are the limitations of an enterprise master patient index (EMPI)?
Universal Patient Identifiers created from original-source referential data gives the most complete view of patients
Most duplicate medical records are created in the very early stages of the patient journey during scheduling or registration. Experian’s Universal Identity Manager platform remediates matching challenges within your enterprise by leveraging the power of Experian reference data. This provides a more complete understanding of who a patient is, despite the data gaps or errors that may exist in patient rosters. It also offers points of justification in support of patient identity merge decisions, so you confidently know how well they match and what linkages between records lead to the match decision.
Universal Identity Manager is an algorithmic engine tuned to meet the specific data needs of the healthcare industry. It lays the foundation for safer, more accurate patient data exchanges across the care continuum by assigning a Universal Patient Identifier (UPI) to each patient, which is maintained in a master person index. A UPI is more secure and unique than a matching algorithm, which is dependent on having Social Security numbers that can be vulnerable to errors.
Safety
• Prevent incorrect treatments
• Fortify Patient Safety initiatives
Quality
• Improve patient record quality and prevent medical identity theft
• Interface with multiple disparate clinical workflows and integrate data sources
• Accurately match, govern and protect patient data across data sources
Administrative
• Prevent duplicate patient records through real-time search and alert processes
• Decrease costs associated with medical billing errors and redundant or unnecessary data testing and clean-up
• Real-time insight into merge decisions
Clinical
• Assign a Universal Patient Identifier (UPI) to create a complete, longitudinal patient record across the enterprise and the continuum of care
Case Study
ACS Medical Business Solutions helps clients close gaps in patient identity records to facilitate claim submissions over $20 million.
Case Study: Council of State and Territorial Epidemiologists (CSTE)
Blog: 9 common causes of patient misidentification.
Blog: The new and improved way to match patient data.