Real-World Matching Performance of Deidentified Record-Linking Tokens.

OBJECTIVE: Our objective was to evaluate tokens commonly used by clinical research consortia to aggregate clinical data across institutions. METHODS: This study compares tokens alone and token-based matching algorithms against manual annotation for 20,002 record pairs extracted from the University of Texas Houston's clinical data warehouse (CDW) in terms of entity resolution. RESULTS: The highest precision achieved was 99.9% with a token derived from the first name, last name, gender, and date-of-birth. The highest recall achieved was 95.5% with an algorithm involving tokens that reflected combinations of first name, last name, gender, date-of-birth, and social security number. DISCUSSION: To protect the privacy of patient data, information must be removed from a health care dataset to obscure the identity of individuals from which that data were derived. However, once identifying information is removed, records can no longer be linked to the same entity to enable analyses. Tokens are a mechanism to convert patient identifying information into Health Insurance Portability and Accountability Act-compliant deidentified elements that can be used to link clinical records, while preserving patient privacy. CONCLUSION: Depending on the availability and accuracy of the underlying data, tokens are able to resolve and link entities at a high level of precision and recall for real-world data derived from a CDW.

Spirometry use in Patients with Sickle Cell Disease with and without asthma and acute chest syndrome: A Multicenter Study.

A de-identified data repository of electronic medical record (EMR) data, i2b2 (Informatics for Integrating Biology and the Bedside), including 4 geographically diverse academic medical centers, was queried to determine the use of diagnostic spirometry testing in African American children and young adults 5-34 years old with sickle cell disease (SCD) with or without a documented history of asthma and/or acute chest syndrome (ACS). A total of 2,749 patients were identified with SCD, of these 577 had asthma and 409 had ACS. Cross-referencing the CPT code for diagnostic spirometry showed that for patients identified as having SCD, a history or ACS, and a diagnosis of asthma, only 31% across all 4 centers had spirometry. Having an asthma diagnosis was associated with ACS. Among SCD patients with asthma, the proportion with ACS for the four centers was 47%, 75%, 38%, and 36% respectively. The bivariate association between asthma and ACS for each Center was significant for each (p<.001). To summarize, only one third of patients with co-morbid SCD, ACS, and asthma received the spirometry procedure as recommended in evidence-based guidelines, suggesting limited testing for changes in pulmonary function. Future studies to determine barriers and facilitators to implementation of pulmonary testing in SCD are warranted.

The Building Blocks of Interoperability. A Multisite Analysis of Patient Demographic Attributes Available for Matching.

BACKGROUND: Patient matching is a key barrier to achieving interoperability. Patient demographic elements must be consistently collected over time and region to be valuable elements for patient matching. OBJECTIVES: We sought to determine what patient demographic attributes are collected at multiple institutions in the United States and see how their availability changes over time and across clinical sites. METHODS: We compiled a list of 36 demographic elements that stakeholders previously identified as essential patient demographic attributes that should be collected for the purpose of linking patient records. We studied a convenience sample of 9 health care systems from geographically distinct sites around the country. We identified changes in the availability of individual patient demographic attributes over time and across clinical sites. RESULTS: Several attributes were consistently available over the study period (2005-2014) including last name (99.96%), first name (99.95%), date of birth (98.82%), gender/sex (99.73%), postal code (94.71%), and full street address (94.65%). Other attributes changed significantly from 2005-2014: Social security number (SSN) availability declined from 83.3% to 50.44% (p<0.0001). Email address availability increased from 8.94% up to 54% availability (p<0.0001). Work phone number increased from 20.61% to 52.33% (p<0.0001). CONCLUSIONS: Overall, first name, last name, date of birth, gender/sex and address were widely collected across institutional sites and over time. Availability of emerging attributes such as email and phone numbers are increasing while SSN use is declining. Understanding the relative availability of patient attributes can inform strategies for optimal matching in healthcare.