Data Linkage
No single data source tells the whole story. Claims data captures diagnoses and procedures but may miss clinical nuance. EHRs capture lab values and physician notes but are limited to a single health system. Linking these sources at the patient level – using privacy-preserving methods such as tokenization – enables analysts to build more accurate and complete datasets for research, targeting and measurement. The integrity of any linked dataset depends on the quality of the matching methodology and the comprehensiveness of the underlying sources.
TL;DR
Data linkage is the process of connecting records across separate datasets, such as claims, pharmacy data, and EHRs, to build a more complete picture of a patient or population.
CATEGORY
Life Sciences
WHO IT IMPACTS
Life sciences researchers, outcomes analysts, health economists, payers and data science teams building integrated patient datasets
RELATED TERMS
DEFINITION
Data linkage (also called data integration or record linkage) is the process of connecting records from two or more separate datasets – such as medical claims, pharmacy claims, EHR data and social determinants of health data – to create a more complete picture of a patient or population.
WHY IT MATTERS
- No single data source tells the whole story, so linking sources closes gaps that any one dataset leaves behind
- Privacy preserving methods like tokenization allow patient level matching without compromising identity
- Enables a full treatment to outcome picture, connecting diagnoses, prescriptions, procedures, and downstream events
- The value of any linked dataset depends heavily on matching quality and how comprehensive the underlying sources are
Example: Linking Rx data (showing what medications patients are filling) with Mx data (showing their diagnoses and procedures) and mortality data may enable a life sciences team to construct a full treatment-to-outcome picture for patients with a given condition – including gaps in care and downstream clinical events.