Electronic Health Records

Displaying 1 - 43 of 43CSV
Basile, A. O., Verma, A., Tang, L. A., Serper, M., Scanga, A., Farrell, A., Destin, B., Carr, R. M., Anyanwu‐Ofili, A., Rajagopal, G., Krikhely, A., Bessler, M., Reilly, M. P., Ritchie, M. D., Tatonetti, N. P., & Wattacheril, J. (2024). Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine‐based approach in diverse healthcare systems. Clinical and Translational Science, 18(1). Portico. https://doi.org/10.1111/cts.70105
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Villa, C. H., Biondich, P., Draper, N. L., Dores, G. M., Storch, E., Chada, K., Wong, H., Whitaker, B., Obidi, J., Vossoughi, S., Soares, A., Schilling, L. M., Natarajan, K., Goodman, M., Purkayastha, S., Zucker, R., Falconer, T., Williams, N., Reich, C., … Shoaibi, A. (2024). Patterns of platelet use evaluated in EHR networks of the Biologics Effectiveness and Safety Initiative, 2012–2018. Transfusion. Portico. https://doi.org/10.1111/trf.18114
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Zolnoori, M., Zolnour, A., Vergez, S., Sridharan, S., Spens, I., Topaz, M., Noble, J. M., Bakken, S., Hirschberg, J., Bowles, K., Onorato, N., & McDonald, M. V. (2024). Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocae300
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Bear Don’t Walk, O. J., Pichon, A., Reyes Nieva, H., Sun, T., Li, J., Joseph, J., Kinberg, S., Richter, L. R., Crusco, S., Kulas, K., Ahmed, S. A., Snyder, D., Rahbari, A., Ranard, B. L., Juneja, P., Demner-Fushman, D., & Elhadad, N. (2024). Contextualized race and ethnicity annotations for clinical text from MIMIC-III. Scientific Data, 11(1). https://doi.org/10.1038/s41597-024-04183-2
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Scroggins, J. K., Hulchafo, I. I., Harkins, S., Scharp, D., Moen, H., Davoudi, A., Cato, K., Tadiello, M., Topaz, M., & Barcelona, V. (2024). Identifying stigmatizing and positive/preferred language in obstetric clinical notes using natural language processing. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocae290
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Conderino, S., Anthopolos, R., Albrecht, S. S., Farley, S. M., Divers, J., Titus, A. R., & Thorpe, L. E. (2024). Addressing Information Biases Within Electronic Health Record Data to Improve the Examination of Epidemiologic Associations With Diabetes Prevalence Among Young Adults: Cross-Sectional Study. JMIR Medical Informatics, 12, e58085–e58085. https://doi.org/10.2196/58085
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Coombes, B. J., Sanchez-Ruiz, J. A., Fennessy, B., Pazdernik, V. K., Adekkanattu, P., Nuñez, N. A., Lepow, L., Melhuish Beaupre, L. M., Ryu, E., Talati, A., Mann, J. J., Weissman, M. M., Olfson, M., Pathak, J., Charney, A. W., & Biernacka, J. M. (2024). Clinical associations with treatment resistance in depression: An electronic health record study. Psychiatry Research, 342, 116203. https://doi.org/10.1016/j.psychres.2024.116203
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Cristina Dos Santos, F., D’Agostino, F., Härkönen, M., Nantschev, R., Christensen, B., Müller-Staub, M., & De Groot, K. (2024). Improving the quality of nursing care through standardized nursing languages: Call to action across European countries. International Journal of Medical Informatics, 192, 105627. https://doi.org/10.1016/j.ijmedinf.2024.105627
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Columbia Affiliation
Landau, A. Y., Blanchard, A., Kulkarni, P., Althobaiti, S., Idnay, B., Patton, D. U., Cato, K., & Topaz, M. (2024). Harnessing the Power of Machine Learning and Electronic Health Records to Support Child Abuse and Neglect Identification in Emergency Department Settings. Digital Health and Informatics Innovations for Sustainable Health Care Systems. https://doi.org/10.3233/shti240740
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Zhang, L., Richter, L. R., Wang, Y., Ostropolets, A., Elhadad, N., Blei, D. M., & Hripcsak, G. (2024). Causal fairness assessment of treatment allocation with electronic health records. Journal of Biomedical Informatics, 155, 104656. https://doi.org/10.1016/j.jbi.2024.104656
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Barcelona, V., Scharp, D., Idnay, B. R., Moen, H., Cato, K., & Topaz, M. (2024). Identifying stigmatizing language in clinical documentation: A scoping review of emerging literature. PLOS ONE, 19(6), e0303653. https://doi.org/10.1371/journal.pone.0303653
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Flanagan, S. V., Braman, S., Puelle, R., Gleason, J. A., Spayd, S. E., Navas-Acien, A., & Chillrud, S. (2024). Inclusion of Drinking Water Source and Testing Questions into Electronic Medical Records: Advancing Environmental Medicine and Public Health Outreach in New Jersey. Journal of Public Health Management & Practice, 30(4), E184–E187. https://doi.org/10.1097/phh.0000000000001968
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Columbia Affiliation
Scroggins, J. K., Hulchafo, I. I., Topaz, M., Cato, K., & Barcelona, V. (2024). Addressing bias in preterm birth research: The role of advanced imputation techniques for missing race and ethnicity in perinatal health data. Annals of Epidemiology, 94, 120–126. https://doi.org/10.1016/j.annepidem.2024.05.003
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Columbia Affiliation
Li, Y., Ye, J., Huang, Y., Wu, J., Liu, X., Ahmed, S., & Osterman, T. (2024). Minimal Common Oncology Data Elements Genomics Pilot Project: Enhancing Oncology Research Through Electronic Health Record Interoperability at Vanderbilt University Medical Center. JCO Clinical Cancer Informatics, 8. https://doi.org/10.1200/cci.23.00249
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Raza, M., Abud, D. G., Wang, J., & Shariff, J. A. (2024). Ease and practicability of the 2017 classification of periodontal diseases and conditions: a study of dental electronic health records. BMC Oral Health, 24(1). https://doi.org/10.1186/s12903-024-04385-5
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Liu, Y., Joly, R., Reading Turchioe, M., Benda, N., Hermann, A., Beecy, A., Pathak, J., & Zhang, Y. (2024). Preparing for the bedside—optimizing a postpartum depression risk prediction model for clinical implementation in a health system. Journal of the American Medical Informatics Association, 31(6), 1258–1267. https://doi.org/10.1093/jamia/ocae056
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Columbia Affiliation
Obidi, J., Sridhar, G., Dores, G. M., Whitaker, B., Villa, C. H., Storch, E., Chada, K., Schilling, L. M., Natarajan, K., Biondich, P., Soares, A., Spotnitz, M., Falconer, T., Purkayastha, S., Draper, N. L., Wong, H., Stagg, M., Reich, C., Anderson, S., & Shoaibi, A. (2024). Patterns of red blood cell utilization: Harnessing electronic health records data from the Information Standard for Blood and Transplant (ISBT) 128 system within the Biologics Effectiveness and Safety (BEST) initiative. Transfusion, 64(6), 998–1007. Portico. https://doi.org/10.1111/trf.17852
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Rider, N. L., Truxton, A., Ohrt, T., Margolin-Katz, I., Horan, M., Shin, H., Davila, R., Tenembaum, V., Quinn, J., Modell, V., Modell, F., Orange, J. S., Branner, A., & Senerchia, C. (2024). Validating inborn error of immunity prevalence and risk with nationally representative electronic health record data. Journal of Allergy and Clinical Immunology, 153(6), 1704–1710. https://doi.org/10.1016/j.jaci.2024.01.011
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Casillan, A., Florido, M. E., Galarza-Cornejo, J., Bakken, S., Lynch, J. A., Chung, W. K., Mittendorf, K. F., Berner, E. S., Connolly, J. J., Weng, C., Holm, I. A., Khan, A., Kiryluk, K., Limdi, N. A., Petukhova, L., Sabatello, M., & Wynn, J. (2023). Participant-guided development of bilingual genomic educational infographics for Electronic Medical Records and Genomics Phase IV study. Journal of the American Medical Informatics Association, 31(2), 306–316. https://doi.org/10.1093/jamia/ocad207
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Ostropolets, A., Hripcsak, G., Husain, S. A., Richter, L. R., Spotnitz, M., Elhussein, A., & Ryan, P. B. (2023). Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records. Journal of the American Medical Informatics Association, 31(1), 119–129. https://doi.org/10.1093/jamia/ocad202
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Keloth, V. K., Zhou, S., Lindemann, L., Zheng, L., Elhanan, G., Einstein, A. J., Geller, J., & Perl, Y. (2023). Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients. BMC Medical Informatics and Decision Making, 23(S1). https://doi.org/10.1186/s12911-023-02136-0
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Wang, Y., Stroh, J. N., Hripcsak, G., Low Wang, C. C., Bennett, T. D., Wrobel, J., Der Nigoghossian, C., Mueller, S. W., Claassen, J., & Albers, D. J. (2023). A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation. Journal of Biomedical Informatics, 148, 104547. https://doi.org/10.1016/j.jbi.2023.104547
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Schlosser Metitiri, K. R., & Perotte, A. (2023). Delay Between Actual Occurrence of Patient Vital Sign and the Nominal Appearance in the Electronic Health Record: Single-Center, Retrospective Study of PICU Data, 2014–2018. Pediatric Critical Care Medicine, 25(1), 54–61. https://doi.org/10.1097/pcc.0000000000003398
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Albers, D., Sirlanci, M., Levine, M., Claassen, J., Nigoghossian, C. D., & Hripcsak, G. (2023). Interpretable physiological forecasting in the ICU using constrained data assimilation and electronic health record data. Journal of Biomedical Informatics, 145, 104477. https://doi.org/10.1016/j.jbi.2023.104477
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Connolly, J. J., Berner, E. S., Smith, M., Levy, S., Terek, S., Harr, M., Karavite, D., Suckiel, S., Holm, I. A., Dufendach, K., Nelson, C., Khan, A., Chisholm, R. L., Allworth, A., Wei, W.-Q., Bland, H. T., Clayton, E. W., Soper, E. R., Linder, J. E., … Sabatello, M. (2023). Education and electronic medical records and genomics network, challenges, and lessons learned from a large-scale clinical trial using polygenic risk scores. Genetics in Medicine, 25(9), 100906. https://doi.org/10.1016/j.gim.2023.100906
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Clayton, E. W., Smith, M. E., Anderson, K. C., Chung, W. K., Connolly, J. J., Fullerton, S. M., McGowan, M. L., Peterson, J. F., Prows, C. A., Sabatello, M., & Holm, I. A. (2023). Studying the impact of translational genomic research: Lessons from eMERGE. The American Journal of Human Genetics, 110(7), 1021–1033. https://doi.org/10.1016/j.ajhg.2023.05.011
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Moy, A. J., Cato, K. D., Withall, J., Kim, E. Y., Tatonetti, N., & Rossetti, S. C. (2023). Using Time Series Clustering to Segment and Infer Emergency Department Nursing Shifts from Electronic Health Record Log Files. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022, 805–814.

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Kneifati-Hayek, J. Z., Applebaum, J. R., Schechter, C. B., Dal Col, A., Salmasian, H., Southern, W. N., & Adelman, J. S. (2023). Effect of restricting electronic health records on clinician efficiency: substudy of a randomized clinical trial. Journal of the American Medical Informatics Association, 30(5), 953–957. https://doi.org/10.1093/jamia/ocad025
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Moy, A. J., Hobensack, M., Marshall, K., Vawdrey, D. K., Kim, E. Y., Cato, K. D., & Rossetti, S. C. (2023). Understanding the perceived role of electronic health records and workflow fragmentation on clinician documentation burden in emergency departments. Journal of the American Medical Informatics Association, 30(5), 797–808. https://doi.org/10.1093/jamia/ocad038
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Lefchak, B., Bostwick, S., Rossetti, S., Shen, K., Ancker, J., Cato, K., Abramson, E. L., Thomas, C., Gerber, L., Moy, A., Sharma, M., & Elias, J. (2023). Assessing Usability and Ambulatory Clinical Staff Satisfaction with Two Electronic Health Records. Applied Clinical Informatics, 14(03), 494–502. CLOCKSS. https://doi.org/10.1055/a-2074-1665
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Reuland, B. D., Redman, C. T., Kneifati-Hayek, J. Z., Fernandes, Y., Kosber, R., Ortuno-Garcia, C., Crossman, D. J., Salmasian, H., Chen, A. R., Barchi, D. J., Applebaum, J. R., Green, R. A., & Adelman, J. S. (2022). Observation and Patients’ Perceptions of Incorporating Their Photograph Into the Electronic Health Record. Journal of Patient Safety, 18(5), 377–381. https://doi.org/10.1097/pts.0000000000001024
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Song, J., Topaz, M., Landau, A. Y., Klitzman, R., Shang, J., Stone, P., McDonald, M., & Cohen, B. (2022). Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers. PLOS ONE, 17(7), e0270220. https://doi.org/10.1371/journal.pone.0270220
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Elser, H., Rowland, S. T., Tartof, S. Y., Parks, R. M., Bruxvoort, K., Morello-Frosch, R., Robinson, S. C., Pressman, A. R., Wei, R. X., & Casey, J. A. (2022). Ambient temperature and risk of urinary tract infection in California: A time-stratified case-crossover study using electronic health records. Environment International, 165, 107303. https://doi.org/10.1016/j.envint.2022.107303
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Hobensack, M., Ojo, M., Barrón, Y., Bowles, K. H., Cato, K., Chae, S., Kennedy, E., McDonald, M. V., Rossetti, S. C., Song, J., Sridharan, S., & Topaz, M. (2022). Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians. Journal of the American Medical Informatics Association, 29(5), 805–812. https://doi.org/10.1093/jamia/ocac023
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Spotnitz, M., Ostropolets, A., Castano, V. G., Natarajan, K., Waldman, G. J., Argenziano, M., Ottman, R., Hripcsak, G., Choi, H., & Youngerman, B. E. (2022). Patient characteristics and antiseizure medication pathways in newly diagnosed epilepsy: Feasibility and pilot results using the common data model in a single-center electronic medical record database. Epilepsy & Behavior, 129, 108630. https://doi.org/10.1016/j.yebeh.2022.108630
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McGuinness, J. E., Zhang, T. M., Cooper, K., Kelkar, A., Dimond, J., Lorenzi, V., Crew, K. D., & Kukafka, R. (2022). Extraction of Electronic Health Record Data using Fast Healthcare Interoperability Resources for Automated Breast Cancer Risk Assessment. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021, 843–852.

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Perotte, R., Hajicharalambous, C., Sugalski, G., & Underwood, J. P. (2022). Characterization of Electronic Health Record Documentation Shortcuts: Does the use of dotphrases increase efficiency in the Emergency Department?. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021, 969–978.

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Senathirajah, Y., Cho, H., Fawcett, J., Mondejar, K. M., Cato, K., Broadwell, P., & Yoon, S. (2022). Application of Natural Language Processing to Learn Insights on the Clinician’s Lived Experience of Electronic Health Records. Informatics and Technology in Clinical Care and Public Health. https://doi.org/10.3233/shti210864
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