Care Evolution

7 Clinical data challenges facing payers and how to solve them

1. Inconsistent & non-standardized data formats
   One of the biggest roadblocks for payers is the inconsistency of standardization in the clinical data they receive. Payers often have multiple vendors, each providing data in different formats with inconsistencies in the same format across different data sources. Legacy solutions, like ingestion engines, enable you to send and receive data, but are resource intensive, require high levels of customization and maintenance, causing a high total cost of ownership for already overwhelmed IT teams.

   Solution: Modern SaaS tools like the Convert API handle these variations and inconsistencies out-of-the-box. This simplifies data ingestion, making it faster and easier to feed data into risk adjustment, quality measures, and other departments and systems—fast tracking the entire process from months to hours.

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2. Proprietary, uncoded, & unstructured data
   Another common issue is uncoded data, proprietary and non-standard codes which results in missed insights. This impairs risk adjustment and quality scoring for payers.

   Solution: Adding codes to uncoded, free text and unstructured data ensures that maximum insights can be unlocked from clinical datasets.

   The table below contrasts the original values from an EMR C-CDA file (raw, often uncoded or using non-standard coding) with the enhanced coding provided by the Convert API. The table also highlights which HEDIS Measure can be met by the now standardized coding.

   Data Type	Source Code System	Raw Value (EMR CCDA)	Codes added by the Convert API	HEDIS Measure
   Date TypeImmunization	Source Code SystemCVX	Raw Value (EMR CCDA)08 (HEPATITIS B 0-19 YRS./ENGERIX B 08 )	Codes added by the Convert API49610 (RXNORM) 08 (CVX)	HEDIS MeasurePediatric Immunization (Hep B)
   Date TypeLab	Source Code SystemEPIC Proprietary	Raw Value (EMR CCDA)125306 (CHLAMYDIA TRACHOMATIS IGM ABS)	Codes added by the Convert API134256004 (SNOMED)	HEDIS MeasureChlamydia Screening
   Date TypeProcedure	Source Code SystemFree Text	Raw Value (EMR CCDA)GI-COLONOSCOPY SCREENING (LVL4)	Codes added by the Convert API73761001 (SNOMED)	HEDIS MeasureColon Cancer Screening

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3. Constrained IT resources burdened with unscalable, manual processes
   IT teams face the daunting challenge of managing complex clinical data nuances. Payers spend significant time on manual data mapping and building complex ingestion pipelines, and often lack deep clinical informaticist expertise. Adding additional personnel has added costs but not solved the problem in a scalable manner.

   Solution: IT teams can use the Convert and Terminology APIs as a scalable, automated solution that fills the deep clinical knowledge gap within IT departments. Standardizing data with this simple API call in a matter of hours frees up IT time and resources.

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4. Dirty data (e.g., leading zeros, percentage signs)
   Leading zeros on encounter numbers or extraneous characters can cause errors in data processing and affect downstream analysis by failing edits in the HEDIS engine, pushing more work back on the already overwhelmed IT resources.

   Solution: Encapsulating this logic in a SaaS solution like Convert will automatically clean data before it reaches the HEDIS engine so that IT never gets the call.

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5. Long turnaround time to resolve data errors
   When errors arise in clinical data, it can take days or even weeks to fix, impacting key operations like risk adjustment or quality measures.

   Solution: The Convert API eliminates turnaround time by automating the process of data validation and cleaning, allowing IT teams to focus on optimizing workflows instead of firefighting data errors.

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6. Revealing hidden member data
   Missing data on members, such as vital statistics or immunization records, leads to incomplete health profiles, affecting risk adjustment and quality scoring.

   Solution: The Convert API can find entire encounters, labs, vitals, and immunizations in data that would otherwise be overlooked due to non-standard formats or uncoded data in structured fields with free text.

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7. Missing description for codes
   When the descriptions for codes are missing, it becomes difficult to categorize and analyze data effectively.

   Solution: The Convert API surfaces and then standardizes all descriptions, making them consistent with industry standards, which facilitates more accurate data processing and reporting for risk and quality teams.

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