Browsing by Subject "medical records systems"

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    Supporting interoperability of genetic data with LOINC
    (Oxford, 2015) Deckard, Jamalynne; McDonald, Clement J.; Vreeman, Daniel J.; Department of Medicine, IU School of Medicine
    Electronic reporting of genetic testing results is increasing, but they are often represented in diverse formats and naming conventions. Logical Observation Identifiers Names and Codes (LOINC) is a vocabulary standard that provides universal identifiers for laboratory tests and clinical observations. In genetics, LOINC provides codes to improve interoperability in the midst of reporting style transition, including codes for cytogenetic or mutation analysis tests, specific chromosomal alteration or mutation testing, and fully structured discrete genetic test reporting. LOINC terms follow the recommendations and nomenclature of other standards such as the Human Genome Organization Gene Nomenclature Committee’s terminology for gene names. In addition to the narrative text they report now, we recommend that laboratories always report as discrete variables chromosome analysis results, genetic variation(s) found, and genetic variation(s) tested for. By adopting and implementing data standards like LOINC, information systems can help care providers and researchers unlock the potential of genetic information for delivering more personalized care.
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    Which veterans enroll in a VA health information exchange program?
    (Oxford Academic, 2017-01) Dixon, Brian E.; Ofner, Susan; Perkins, Susan M.; Myers, Laura J.; Rosenman, Marc B.; Zillich, Alan J.; French, Dustin D.; Weiner, Michael; Haggstrom, David A.; Biostatistics, School of Public Health
    Objective: To characterize patients who voluntarily enrolled in an electronic health information exchange (HIE) program designed to share data between Veterans Health Administration (VHA) and non-VHA institutions. Materials and Methods: Patients who agreed to participate in the HIE program were compared to those who did not. Patient characteristics associated with HIE enrollment were examined using a multivariable logistic regression model. Variables selected for inclusion were guided by a health care utilization model adapted to explain HIE enrollment. Data about patients’ sociodemographics (age, gender), comorbidity (Charlson index score), utilization (primary and specialty care visits), and access (distance to VHA medical center, insurance, VHA benefits) were obtained from VHA and HIE electronic health records. Results: Among 57 072 patients, 6627 (12%) enrolled in the HIE program during its first year. The likelihood of HIE enrollment increased among patients ages 50–64, of female gender, with higher comorbidity, and with increasing utilization. Living in a rural area and being unmarried were associated with decreased likelihood of enrollment. Discussion and Conclusion: Enrollment in HIE is complex, with several factors involved in a patient’s decision to enroll. To broaden HIE participation, populations less likely to enroll should be targeted with tailored recruitment and educational strategies. Moreover, inclusion of special populations, such as patients with higher comorbidity or high utilizers, may help refine the definition of success with respect to HIE implementation.