EHR-Claims Linked Datasets for Pharma: Uses and Limits
What EHR-claims linked datasets are, how tokenization builds them, what pharma uses them for, and the limits that shape what they can show.
The short answer
EHR-claims linked datasets connect de-identified electronic health record data with claims data for the same patients, using privacy-preserving tokens. The combination gives clinical detail from EHRs, such as lab values and notes-derived information, plus the fuller view of care and prescriptions from claims. Pharma uses them for patient journey research, targeting insight, and outcome measurement, but coverage gaps, linkage errors, and privacy rules limit what they can show.
Claims data and EHR data each answer some questions well and others poorly. Linked datasets combine them. They are increasingly common in pharma analytics, and understanding their limits keeps conclusions honest.
What each source contributes
| Claims | EHR | |
|---|---|---|
| Coverage of care | Across providers billed to the payer | Only providers in contributing systems |
| Clinical detail | Diagnosis and procedure codes | Labs, vitals, notes-derived data, medication orders |
| Prescriptions | Filled prescriptions | Prescribed orders, not always filled |
| Timeliness | Lag due to claims processing | Often faster |
| Gaps | Uninsured, cash pay, some plans missing | Care outside contributing systems |
See claims vs. EHR vs. lab data for a deeper comparison.
How linking works
- Each data holder creates tokens from patient identifiers using the same tokenization method.
- Identifiers are removed; tokens remain.
- Records with matching tokens are joined.
- The linked dataset is reviewed for re-identification risk, usually under expert determination.
Uses in pharma
- Patient journey research: time from symptoms to diagnosis to treatment, with clinical context.
- Diagnosis and testing gaps: comparing lab results with diagnosis codes. See biomarker testing gaps.
- HCP insights: which HCPs see eligible patients, in aggregate.
- Outcome measurement: with exposure data linked through tokens, aggregated results on starts and persistence.
Limits
- Partial overlap. Only patients present in both sources are fully linked.
- Linkage errors. Token mismatches create false links or missed links.
- Representativeness. Contributing systems and payers may skew by region or population.
- Privacy constraints. Small groups are suppressed; some analyses are not allowed.
- Coding differences. EHR and claims may record the same event differently.
Questions to ask providers
- What share of patients in my condition are linked across both sources?
- Which EHR systems and payers contribute, and how does that skew coverage?
- How is linkage accuracy measured?
- What de-identification method and minimum cell sizes apply?
- How current is the data?
Common mistakes
- Treating a linked dataset as nationally representative.
- Ignoring patients present in only one source.
- Over-reading small subgroups.
- Using linked data findings for identified targeting.
Practical takeaway
Before relying on a linked dataset for a launch decision, ask for the share of patients in your condition who appear in both sources and how that group compares with the rest. If it is small or skewed, cross-check key findings with a second source.
Frequently asked questions
What is an EHR-claims linked dataset?
A de-identified dataset in which EHR records and claims records belonging to the same patients have been joined, usually through tokens.
Why link EHR and claims?
Claims show care and prescriptions across providers but lack clinical detail. EHRs have clinical detail but only within the systems that contribute data. Linking adds both.
Can linked data be used for ad targeting?
Not directly for identified targeting. It is used for research, audience design insight, and aggregated measurement under privacy rules.
Sources
- HHS, Guidance Regarding Methods for De-identification of PHI
- ONC, United States Core Data for Interoperability
- HL7 FHIR Overview
External guidance and platform documentation change. Links were current at publication; check them again before relying on them for a decision.
Editorial note. Analysis and frameworks are the author's own and do not represent Acxiom or any current or former employer, client, or named platform. Examples labeled hypothetical or illustrative are not results from real campaigns. Nothing here is legal, regulatory, or medical advice.
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