Browsing by Author "McDonald, Clement J."

Now showing 1 - 7 of 7
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    Demonstration of the Indianapolis SPIN Query Tool for De-identified Access to Content of the Indiana Network for Patient Care’s (a Real RHIO) Database
    (American Medical Informatics Association, 2006) McDonald, Clement J.; Blevins, Lonnie; Dexter, Paul; Schadow, Gunther; Hook, John; Abernathy, Greg; Dugan, Tammy; Martin, Andrew; Phillips, Ryan; Davis, Mary; Medicine, School of Medicine
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    Preparing a collection of radiology examinations for distribution and retrieval
    (Oxford University Press, 2016-03) Demner-Fushman, Dina; Kohli, Marc D.; Rosenman, Marc B.; Shooshan, Sonya E.; Rodriguez, Laritza; Antani, Sameer; Thoma, George R.; McDonald, Clement J.; Department of Radiology and Imaging Sciences, IU School of Medicine
    OBJECTIVE: Clinical documents made available for secondary use play an increasingly important role in discovery of clinical knowledge, development of research methods, and education. An important step in facilitating secondary use of clinical document collections is easy access to descriptions and samples that represent the content of the collections. This paper presents an approach to developing a collection of radiology examinations, including both the images and radiologist narrative reports, and making them publicly available in a searchable database. MATERIALS AND METHODS: The authors collected 3996 radiology reports from the Indiana Network for Patient Care and 8121 associated images from the hospitals' picture archiving systems. The images and reports were de-identified automatically and then the automatic de-identification was manually verified. The authors coded the key findings of the reports and empirically assessed the benefits of manual coding on retrieval. RESULTS: The automatic de-identification of the narrative was aggressive and achieved 100% precision at the cost of rendering a few findings uninterpretable. Automatic de-identification of images was not quite as perfect. Images for two of 3996 patients (0.05%) showed protected health information. Manual encoding of findings improved retrieval precision. CONCLUSION: Stringent de-identification methods can remove all identifiers from text radiology reports. DICOM de-identification of images does not remove all identifying information and needs special attention to images scanned from film. Adding manual coding to the radiologist narrative reports significantly improved relevancy of the retrieved clinical documents. The de-identified Indiana chest X-ray collection is available for searching and downloading from the National Library of Medicine (http://openi.nlm.nih.gov/).
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    Protections against the Risk of Airborne SARS-CoV-2 Infection
    (American Society for Microbiology, 2020-06-16) McDonald, Clement J.; Medicine, School of Medicine
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    Report of the AMIA EHR-2020 Task Force on the status and future direction of EHRs
    (Oxford University Press, 2015-09) Payne, Thomas H.; Corley, Sarah; Cullen, Theresa A.; Gandhi, Tejal K.; Harrington, Linda; Kuperman, Gilad J.; Mattison, John E.; McCallie, David P.; McDonald, Clement J.; Tang, Paul C.; Tierney, William M.; Weaver, Charlotte; Weir, Charlene R.; Zaroukian, Michael H.; Department of Medicine, IU School of Medicine
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    Response to Unit conversions between LOINC codes
    (Oxford University Press, 2018-05-01) Vreeman, Daniel J.; Abhyankar, Swapna; McDonald, Clement J.; Medicine, School of Medicine
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    Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery
    (Springer Nature, 2019) Zhang, Xingmin Aaron; Yates, Amy; Vasilevsky, Nicole; Gourdine, J. P.; Callahan, Tiffany J.; Carmody, Leigh C.; Danis, Daniel; Joachimiak, Marcin P.; Ravanmehr, Vida; Pfaff, Emily R.; Champion, James; Robasky, Kimberly; Xu, Hao; Fecho, Karamarie; Walton, Nephi A.; Zhu, Richard L.; Ramsdill, Justin; Mungall, Christopher J.; Köhler, Sebastian; Haendel, Melissa A.; McDonald, Clement J.; Vreeman, Daniel J.; Peden, David B.; Bennett, Tellen D.; Feinstein, James A.; Martin, Blake; Stefanski, Adrianne L.; Hunter, Lawrence E.; Chute, Christopher G.; Robinson, Peter N.; Medicine, School of Medicine
    Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.
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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.