Electronic Health Records

Displaying 51 - 59 of 59CSV
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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Stephens, A. B., Wynn, C. S., Hofstetter, A. M., Kolff, C., Pena, O., Kahn, E., Dasgupta, B., Natarajan, K., Vawdrey, D. K., Lane, M. M., Robbins-Milne, L., Ramakrishnan, R., Holleran, S., & Stockwell, M. S. (2021). Effect of Electronic Health Record Reminders for Routine Immunizations and Immunizations Needed for Chronic Medical Conditions. Applied Clinical Informatics, 12(05), 1101–1109. CLOCKSS. https://doi.org/10.1055/s-0041-1739516
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Metser, G., Bradley, C., Moise, N., Liyanage-Don, N., Kronish, I., & Ye, S. (2021). Gaps and Disparities in Primary Prevention Statin Prescription During Outpatient Care. The American Journal of Cardiology, 161, 36–41. https://doi.org/10.1016/j.amjcard.2021.08.070
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Koleck, T. A., Topaz, M., Tatonetti, N. P., George, M., Miaskowski, C., Smaldone, A., & Bakken, S. (2021). Characterizing shared and distinct symptom clusters in common chronic conditions through natural language processing of nursing notes. Research in Nursing & Health, 44(6), 906–919. Portico. https://doi.org/10.1002/nur.22190
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Rudin, R. S., Perez, S., Rodriguez, J. A., Sousa, J., Plombon, S., Arcia, A., Foer, D., Bates, D. W., & Dalal, A. K. (2021). User-centered design of a scalable, electronic health record-integrated remote symptom monitoring intervention for patients with asthma and providers in primary care. Journal of the American Medical Informatics Association, 28(11), 2433–2444. https://doi.org/10.1093/jamia/ocab157
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Columbia Affiliation
Moy, A. J., Aaron, L., Cato, K. D., Schwartz, J. M., Elias, J., Trepp, R., & Rossetti, S. C. (2021). Characterizing Multitasking and Workflow Fragmentation in Electronic Health Records among Emergency Department Clinicians: Using Time-Motion Data to Understand Documentation Burden. Applied Clinical Informatics, 12(05), 1002–1013. CLOCKSS. https://doi.org/10.1055/s-0041-1736625
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Patra, B. G., Sharma, M. M., Vekaria, V., Adekkanattu, P., Patterson, O. V., Glicksberg, B., Lepow, L. A., Ryu, E., Biernacka, J. M., Furmanchuk, A., George, T. J., Hogan, W., Wu, Y., Yang, X., Bian, J., Weissman, M., Wickramaratne, P., Mann, J. J., Olfson, M., … Pathak, J. (2021). Extracting social determinants of health from electronic health records using natural language processing: a systematic review. Journal of the American Medical Informatics Association, 28(12), 2716–2727. https://doi.org/10.1093/jamia/ocab170
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Havrilla, J. M., Zhao, M., Liu, C., Weng, C., Helbig, I., Bhoj, E., & Wang, K. (2021). Clinical Phenotypic Spectrum of 4095 Individuals with Down Syndrome from Text Mining of Electronic Health Records. Genes, 12(8), 1159. https://doi.org/10.3390/genes12081159
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