Automated Clinical Notes Annotation - Improving EHR Management and Clinical Decision Making

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AUTOMATED CLINICAL NOTES ANNOTATION — IMPROVING EHR MANAGEMENT AND CLINICAL DECISION MAKING

References: Medicine Io. The Computer-Based Patient Record: An Essential Technology for Health Care, Revised Edition. Washington, DC: The National Academies Press; 1997. 2 Wrenn JO, Stein DM, Bakken S, Stetson PD. Quantifying clinical narrative redundancy in an electronic health record. Journal of the American Medical Informatics Association : JAMIA. 2010;17(1):49-53. 3 Haugen MB, Tegen A, Warner D. Fundamentals of the legal health record and designated record set. Journal of AHIMA. 2011;82(2):44-49. 4 Weber GM, Mandl KD, Kohane IS. Finding the missing link for big biomedical data. Jama. 2014;311(24):2479-2480. 5 Institute of Medicine Committee on Quality of Health Care in A. In: Kohn LT, Corrigan JM, Donaldson MS, eds. To Err is Human: Building a Safer Health System. Washington (DC): National Academies Press (US) Copyright 2000 by the National Academy of Sciences. All rights reserved.; 2000. 6 Demner-Fushman D, Chapman WW, McDonald CJ. What can natural language processing do for clinical decision support? Journal of biomedical informatics. 2009;42(5):760-772. 7 Botsis T, Hartvigsen G, Chen F, Weng C. Secondary Use of EHR: Data Quality Issues and Informatics Opportunities. Summit on translational bioinformatics. 2010;2010:1-5. 8 O’Leary KJ, Liebovitz DM, Feinglass J, et al. Creating a better discharge summary: improvement in quality and timeliness using an electronic discharge summary. Journal of hospital medicine. 2009;4(4):219-225. 9 Otero Varela L, Wiebe N, Niven DJ, et al. Evaluation of interventions to improve electronic health record documentation within the inpatient setting: a protocol for a systematic review. Systematic Reviews. 2019;8(1):54. 10 Tseng P, Kaplan RS, Richman BD, Shah MA, Schulman KA. Administrative Costs Associated With Physician Billing and Insurance-Related Activities at an Academic Health Care System. JAMA. 2018;319(7):691-697. 11 Khor RC, Nguyen A, O’Dwyer J, et al. Extracting tumour prognostic factors from a diverse electronic record dataset in genito-urinary oncology. International journal of medical informatics. 2019;121:53-57. 12 Kim E, Rubinstein SM, Nead KT, Wojcieszynski AP, Gabriel PE, Warner JL. The Evolving Use of Electronic Health Records (EHR) for Research. Seminars in Radiation Oncology. 2019;29(4):354-361. 13 Hernandez-Boussard T, Kourdis PD, Seto T, et al. Mining Electronic Health Records to Extract Patient-Centered Outcomes Following Prostate Cancer Treatment. AMIA Annual Symposium proceedings AMIA Symposium. 2017;2017:876-882. 14 Manion FJ, Harris MR, Buyuktur AG, Clark PM, An LC, Hanauer DA. Leveraging EHR data for outcomes and comparative effectiveness research in oncology. Current oncology reports. 2012;14(6):494-501. 15 Coiera E. The fate of medicine in the time of AI. Lancet (London, England). 2018;392(10162):2331-2332. 16 Barcelona Declaration for the Proper Development and Usage of Artificial Intelligence in Europe. March 2017. 17 Yu KH, Kohane IS. Framing the challenges of artificial intelligence in medicine. BMJ quality & safety. 2019;28(3):238-241. 18 Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nature biomedical engineering. 2018;2(10):719-731. 19 Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology. 2017;2(4):230-243. 20 Leyh-Bannurah SR, Tian Z, Karakiewicz PI, et al. Deep Learning for Natural Language Processing in Urology: State-of-the-Art Automated Extraction of Detailed Pathologic Prostate Cancer Data From Narratively Written Electronic Health Records. JCO clinical cancer informatics. 2018;2:1-9. 21 Zeng Z, Espino S, Roy A, et al. Using natural language processing and machine learning to identify breast cancer local recurrence. BMC bioinformatics. 2018;19(Suppl 17):498. 22 Jiang M, Chen Y, Liu M, et al. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. Journal of the American Medical Informatics Association : JAMIA. 2011;18(5):601-606. 23 Peleg M, Tu S. Decision support, knowledge representation and management in medicine. Yearbook of medical informatics. 2006:72-80. 24 Wagholikar KB, MacLaughlin KL, Henry MR, et al. Clinical decision support with automated text processing for cervical cancer screening. Journal of the American Medical Informatics Association : JAMIA. 2012;19(5):833-839. 25 Nykanen P, Chowdhury S, Wigertz O. Evaluation of decision support systems in medicine. Computer methods and programs in biomedicine. 1991;34(2-3):229-238. 26 Clarke K, O’Moore R, Smeets R, et al. A methodology for evaluation of knowledge-based systems in medicine. Artificial Intelligence in Medicine. 1994;6(2):107-121. 27 Magrabi F, Ammenwerth E, McNair JB, et al. Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications. Yearbook of medical informatics. 2019;28(1):128-134. 28 Gensheimer MF, Henry AS, Wood DJ, et al. Automated Survival Prediction in Metastatic Cancer Patients Using High-Dimensional Electronic Medical Record Data. Journal of the National Cancer Institute. 2019;111(6):568-574. 29 Lindsay WD, Ahern CA, Tobias JS, et al. Automated data extraction and ensemble methods for predictive modeling of breast cancer outcomes after radiation therapy. Medical physics. 2019;46(2):1054-1063. 30 Nakatsugawa M, Cheng Z, Kiess A, et al. The Needs and Benefits of Continuous Model Updates on the Accuracy of RT-Induced Toxicity Prediction Models Within a Learning Health System. International journal of radiation oncology, biology, physics. 2019;103(2):460-467. 31 HealthITSecurity. The difference between big data and smart data in healthcare. HealthIT Anal. 2016. 32 Humphreys BL, Lindberg DA. The UMLS project: making the conceptual connection between users and the information they need. Bull Med Libr Assoc. 1993;81(2):170-177. 33 (GATE) GAfTE. https://gate.ac.uk/. Accessed February 2020. 34 (UIMA) UIMa. https://uima.apache.org/. Accessed February 2020. 35 (OHNLP) OHNLP. http://www.ohnlp.org/. Accessed February 2020. 36 Warner JL, Levy MA, Neuss MN, Warner JL, Levy MA, Neuss MN. ReCAP: Feasibility and Accuracy of Extracting Cancer Stage Information From Narrative Electronic Health Record Data. Journal of oncology practice. 2016;12(2):157-158; e169-157. 37 Warner JL, Anick P, Hong P, Xue N. Natural language processing and the oncologic history: is there a match? Journal of oncology practice. 2011;7(4):e15-e19. 38 Gregg JR, Lang M, Wang LL, et al. Automating the Determination of Prostate Cancer Risk Strata From Electronic Medical Records. JCO clinical cancer informatics. 2017;1. 39 Chen PH, Zafar H, Galperin-Aizenberg M, Cook T. Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports. Journal of digital imaging. 2018;31(2):178-184. 1

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