Overview of Data Linkage Methods for Integrating Separate Health Data Sources

Health data sources across healthcare service deliveries are notoriously disconnected hampering good use of data. Hospitals, family doctors, pharmacists, and health insurers all have their own data, while the data may contain information about the same patients. Also, industries offering healthcare and wellness services host and maintain their own data repositories about the patients on their services. Lastly, governmental organizations collect register and survey data on public health, healthcare utilization, and health outcome. Linking health data of individuals, events, and locations at various aggregation levels from different sources can be extremely insightful. More information can be pulled from linked data than from every data source separately. Bringing patient data together for which unique personal identifiers exist, such as social security numbers, is rather straightforward. In many practices, such identifiers are simply lacking meaning that one has to resort to variables that are not necessarily unique to a person which makes the task of linking data far more challenging. To make things worse, these linking variables come with errors due to misspellings, coding differences, or transcription mistakes. Nevertheless, data linkage needs to be done flawlessly as connecting the wrong patient records or missing valuable connections between patient records can result in biased analyses on linked datasets. This chapter provides a state-of-the-art survey in data linkage technology within healthcare. It will give (1) an overview of the various methods in data linkage including deterministic and probabilistic approaches (2) and a synthesis of healthcare use cases in which data linkage is essential with a discussion on the legal and privacy challenges of using data linkage in healthcare.