Healthcare Analytics, Population Health Management, Healthcare Big Data

Quality & Governance News

Patient Matching Hampered by Poor Data Standardization

By Jennifer Bresnick

The healthcare industry must develop a national patient matching strategy based on standardized data elements as HIE expands, AHIMA says.

- A dearth of standardized data elements is preventing EHR interoperability and widespread health information exchange (HIE) along the care continuum, states an article published recently in AHIMA’s Perspectives in Health Information Management, and is contributing to the inability to properly match patients across disparate systems.  While better patient matching has been identified by the ONC as a short-term goal for the healthcare industry, the lack of a nation-wide data standardization strategy may limit efforts to improve interoperability, health information exchange, and improved data governance.

“Lack of a standard data set can lead to patient records not being linked to one another in the HIE, resulting in an incomplete health record being available to the provider for the patient being treated, thereby defeating the purpose of the HIE,” write Katherine G. Lusk, MHSM, RHIA and a team of AHIMA co-authors. “Even more concerning is the potential for different patients being identified as the same, resulting in the possibility of improper care rendered on the basis of inaccurate patient information.”

As HIEs grow to encompass larger and larger regions of the country, the potential for errors in patient matching will only increase.  While health information management (HIM) professionals have traditionally taken on the role of correcting patient matching efforts, “manual review will not be sustainable in the future because EHRs have created a vast amount of data that puts an undue budgetary burden on the HIE to employ additional staff responsible for ensuring data integrity.”

There are several possible avenues for improving patient matching based on various forms of standardized data, the authors say.  While many HIEs currently use a unique patient identifier (UPI) for each member of the system, smaller HIEs may run into trouble with this method when merging with other data exchange organizations that may have slightly different ways of identifying their patients.  Organizations may also use widely accepted techniques such as matching patients to US Post Office standardized address data, but patients who experience housing instability or those who simply move house will need to constantly verify and update their information.

HIE organizations may also turn to a more advanced form of analytics and algorithmic patient matching, the article explains.  These algorithms compare data elements from multiple sources to identify matching values, thereby increasing mathematical confidence that the data sources belong to the same patient.  Deterministic algorithms require an exact match of values to identify a positive association, while statistical versions of the technology assign weights and probabilities to near matches in order to detect the likelihood of a correct association with greater sensitivity.

However, without standardized data elements across multiple systems to fuel these algorithms, they will be equally as ineffective as any other method. “The process of capturing data is an operational consideration that cannot be taken lightly,” the article warns.  AHIMA suggests that providers use technologies that eliminate free text fields for patient identity data except for patient names, use industry-wide data standards for their underlying infrastructure, and ensure that each data element is entered in a separate and appropriate field to prevent inaccuracies.

As with other efforts to improve health information exchange and data interoperability, the healthcare industry must agree on the most effective strategies to embrace in pursuit of a nation-wide patient matching system if these efforts are to produce positive results for care quality and patient identification.


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