AI-Powered Patient Record Deduplication & Healthcare Data Integration

Overview

About the Client

A multi-facility health network that included 15 outpatient and 12 hospital clinics spread across three states was struggling with the fragmentation of patient information systems. After a merger with two regional healthcare providers, the company faced a number of issues in establishing a single electronic health record (EHR) technology. With more than 2.3 million records of patients scattered across disparate older systems, obsolete databases, and the latest cloud-based platforms, health managers were unable to access all patient histories, which led to repeated tests, errors in medication, and compliance issues as required by HIPAA regulations.

Challenges Faced

The healthcare network was faced with serious problems with data quality management, which posed a threat to patient safety as well as operational efficiency. The patient records had uncoordinated formatting across different systems, including dates that were stored in 14 different formats, medication names that used generic and brand names without standardization, and diagnostic codes that mixed ICD-9 with ICD-10 standards.

The patient record was a mess of duplicates throughout the system, with around 340,000 duplicate entries resulting from spelling variations and missing middle initials, altered birthdates, and inconsistencies in the usage of the suffix. Manual data migration procedures could have taken about 18 months and could have created an unacceptably high risk for a health environment where instant access to accurate information about patients is vital.

The IT team also had to meet additional demands to comply with the requirements of healthcare data governance while consolidating their systems without affecting daily routine clinical activities.

Our Solution

Blueflame Labs deployed comprehensive AI data processing tools specifically developed to consolidate healthcare data. Our automatic data cleaning engine completed the processing of every single one of the 2.3 million records, with specific to healthcare AI models that are trained to recognize medical terms as well as coding standards and patterns for patient identification.

The data mapping layer that is intelligent instantly matched patient data across multiple systems, by analysing numerous data points that included name patterns, birthdate patterns, security numbers, and historic addresses. The software was integrated seamlessly with the existing Epic EHR system, Cerner databases, and custom platforms, without the need to replace the system.

Through the entire implementation, the real-time analytics layer offered insight into the quality of data metrics, including migration progress and the possibility of duplicate matches in clinical validation.

Results

The healthcare network has seen transformative outcomes across clinical operations and the safety of patients:

• 99.7 97% accuracy rate of data after consolidation, confirmed by audits of clinical practices
• 89% reduction in duplicate patient records, eliminating 303,000 duplicate entries
• 67% reduction in redundant diagnostic tests as a result of the complete visibility of patient history
• 43% quicker patient check-in and registration procedures
• $8.4 million savings annually from the elimination of duplicate tests and increased operational efficiency
• 100 percent HIPAA compliance is maintained throughout data migration, with a full audit trail
• Real-time patient data analytics enabling population health management initiatives