Ensuring EHR data integrity and accuracy for HEDIS quality measures

How HEDIS measures are negatively impacted by raw EHR data

The rise in electronic health record (EHR) data has made utilizing patient medical records an increasingly complex task, which impacts downstream calculations such as HEDIS quality measure performance. Teams looking to extract insights from medical records must sift through vast amounts of information, often encountering:

Disorganized records from numerous sources, with duplicate information scattered across hundreds of pages.
Uncoded data buried within free-text fields, making it difficult to extract and analyze crucial information, especially when the records span hundreds of pages.
Inconsistent coding and formats, which don’t always translate into the codes HEDIS teams use. This can cause ingestion failures, slowing down workflows and limiting discovery of gap-closing data.
Manual Processes, which introduce risks of inconsistency and human error. Without robust validation and cleansing processes, and links to the original raw source data, information hidden in EHR data may get left behind, resulting in misguided HEDIS measures.

Despite the emergence of tools to manage clinical datasets, few automated solutions exist to address these issues and the inability to fully utilize EHR datasets can put HEDIS quality measure performance metrics at risk.

3 Ways to quickly improve your HEDIS quality measures

Ensuring EHR data integrity and accuracy are paramount to improving HEDIS quality measure performance. This can be done quickly, without changing existing processes, using the Orchestrate APIs:

EHR Data Integration & Enrichment for HEDIS
Seamless integration of EHR data requires translating it to more usable formats and the code systems used by HEDIS. The right technology can ensure that all EHR data is captured efficiently, summarized into categories so it’s easy to find, and converted into the file formats and code systems needed by HEDIS teams.
Implement Data Validation & Cleansing Procedures
Ensuring EHR data is accurate and complete is foundational to improving HEDIS performance metrics. Maintaining links to the original raw source data is also necessary in order to pass audits.
Incorporate Automation
Automating EHR data integration and enrichment improves efficiency and reduces the potential for human error. This allows HEDIS teams to focus on analyzing data more efficiently to close care gaps rather than data management.

The Orchestrate APIs enrich raw clinical EHR datasets with code reference systems and HEDIS value set names:

Type	Raw Clinical Data	Codes Added by the Convert API	HEDIS Value Set Name
TypeImmunization	Raw Clinical DataHEPATITIS B 0-19 YRS./ENGERIX B08 (Free Text)	Codes Added by the Convert API49610 (RXNORM) 08 (CVX)	HEDIS Value Set NameHepatitis B Immunization
TypeDiagnosis	Raw Clinical DataFinal: Type 1 diabetes mellitus without complications (Free Text)	Codes Added by the Convert APIE10.9 (ICD-10-CM)	HEDIS Value Set NameDiabetes Mellitus Without Complications
TypeProcedure	Raw Clinical DataCHLAMYDIA TRACHOMATIS IGM ABS 125306 (EPIC code)	Codes Added by the Convert API134256004 (SNOMED)	HEDIS Value Set NameChlamydia Tests
TypeProcedure	Raw Clinical DataGI-COLONOSCOPY SCREENING (LVL4) (Free text)	Codes Added by the Convert API73761001 (SNOMED)	HEDIS Value Set NameColonoscopy
TypeCrosswalk	Raw Clinical Data19515-808-52 (NDC)	Codes Added by the Convert API150 (CVX)	HEDIS Value Set NameInfluenza Immunization
TypeCrosswalk	Raw Clinical Data2642200 (RxNorm)	Codes Added by the Convert API150 (CVX)	HEDIS Value Set NameInfluenza Immunization
TypeCrosswalk	Raw Clinical DataCHLAMYDIA TRACHOMATIS IGM ABS 6920-3 (LOINC)	Codes Added by the Convert API134256004 (SNOMED)	HEDIS Value Set NameChlamydia Tests

Learn more about how APIs quickly unlock value for HEDIS teams.

Developers at a computer By implementing these improvements, HEDIS teams can perform more efficient and accurate medical record reviews without losing data to ingestion failures, uncoded free text, or issues with code translation and file format inconsistencies. The Orchestrate APIs make it possible to implement immediately without creating new infrastructure. Payers and health plans that adopt the Orchestrate APIs for EHR data integration and cleaning can ensure data integrity and improve performance without disrupting operations, freeing up resources for other enhancements.