Patient matching is a process of comparing data from different health IT systems to see if data sources match and belong to the same patient to obtain a complete record of the patient’s health history and medical care. If they match, those patient’s records are combined to eliminate duplication and confusion.
Patient matching makes it possible to integrate data from different providers and health IT systems, including electronic health records (EHR) and personal health records (PHR). This data integration helps make for a complete picture of a patient’s health and previous care. Patient matching can improve future care through this broader shared data, combined with evidence based medicine.
Patient matching has been identified as a key component of achieving interoperability between United States health IT infrastructure systems. Through the use of data analytics and algorithms, patient matching compares demographics, health indicators and other aspects of patient data from different sources. With adequate information about a patient, similarities can be picked out until a match is more or less certain. The resulting pooled information creates a better data set to support future care decisions. Duplicate records are also eliminated reducing confusion.
The importance of data-to-care decisions means that patient matching accuracy is crucial. Patient identification issues can result in data integrity problems that contribute to poor choices in care resulting in serious complications, even deaths. Patient matching is critical to the interoperability of health IT systems, but is often ambiguous regarding performance and matching rates.