cirpublications.com-KLC4-1790883433-t203262807 1790883433 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Health Informatics and Digital Systems J. Health Inform. Digit. Syst. 3149-8973 2024 4 1 Ahmed Benali Karim Boudiaf Samir Touati 2024 10.68159/t203262807 https://cirpublications.com/pub/journal/2/article/t203262807 Arndt BG, Beasley JW, Watkinson MD, Temte JL, Tuan WJ, Sinsky CA, et al. Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations. Ann Fam Med. 2017;15(5):419-26. 10.1370/afm.2121 Tai-Seale M, Olson CW, Li J, Chan AS, Morikawa C, Durbin M, et al. Electronic health record logs indicate that physicians split time evenly between seeing patients and desktop medicine. Health Aff (Millwood). 2017;36(4):655-62. 10.1377/hlthaff.2016.0811 Rule A, Bedrick S, Chiang MF, Hribar MR. Length and redundancy of outpatient progress notes across a decade at an academic medical center. JAMA Netw Open. 2021;4(7):e2115334. 10.1001/jamanetworkopen.2021.15334 Moy AJ, Schwartz JM, Chen R, Sadri S, Lucas E, Cato KD, et al. Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review. J Am Med Inform Assoc. 2021;28(5):998-1008. Holmgren AJ, Downing NL, Bates DW, Shanafelt TD, Milstein A, Sharp CD, et al. Assessment of electronic health record use between US and non-US health systems. JAMA Intern Med. 2021;181(2):251-9. 10.1001/jamainternmed.2020.7421 Kreimeyer K, Foster M, Pandey A, Arya N, Halford G, Jones SF, et al. Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review. J Biomed Inform. 2017;73:14-29. 10.1016/j.jbi.2017.07.012 Wang Y, Wang L, Rastegar-Mojarad M, Moon S, Shen F, Afzal N, et al. Clinical information extraction applications: a literature review. J Biomed Inform. 2018;77:34-49. 10.1016/j.jbi.2017.11.011 Mullenbach J, Wiegreffe S, Duke J, Sun J, Eisenstein J. Explainable prediction of medical codes from clinical text. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2018;1:1101-11. Li F, Yu H. ICD coding from clinical text using multi-filter residual convolutional neural network. Proc AAAI Conf Artif Intell. 2020;34(05):8180-7. 10.1609/aaai.v34i05.6406 Dong H, Falis M, Whiteley W, Alex B, Matterson J, Ji S, et al. Automated clinical coding: what, why, and where we are? NPJ Digit Med. 2022;5(1):159. 10.1038/s41746-022-00691-7 Ji S, Hölttä M, Marttinen P. Does the magic of BERT apply to medical code assignment? A quantitative study. Comput Biol Med. 2021;139:104998. 10.1016/j.compbiomed.2021.104998 Baumel T, Nassour-Kassis J, Cohen R, Elhadad M, Elhadad N. Multi-label classification of patient notes: case study on ICD code assignment. In: AAAI Workshops; 2018. p. 409-16. Chen PF, Wang SM, Liao WC, Kuo LC, Chen KC, Lin YC, et al. Automatic ICD-10 coding and training system: deep neural network based on supervised learning. JMIR Med Inform. 2021;9(8):e23230. 10.2196/23230 Zhou L, Blackley SV, Kowalski L, Doan R, Acker WW, Landman AB, et al. Analysis of errors in dictated clinical documents assisted by speech recognition software and professional transcriptionists. JAMA Netw Open. 2018;1(3):e180530. 10.1001/jamanetworkopen.2018.0530 Quiroz JC, Laranjo L, Kocaballi AB, Berkovsky S, Rezazadegan D, Coiera E. Challenges of developing a digital scribe to reduce clinical documentation burden. NPJ Digit Med. 2019;2(1):114. 10.1038/s41746-019-0190-1 Finley G, Edwards E, Robinson A, Brenndoerfer M, Sadoughi N, Fone J, et al. An automated medical scribe for documenting clinical encounters. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations; 2018. p. 11-5. Spasic I, Nenadic G. Clinical text data in machine learning: systematic review. JMIR Med Inform. 2020;8(3):e17984. 10.2196/17984 Enarvi S, Amoia M, Teba MD, Delaney B, Diehl F, Hahn S, et al. Generating medical reports from patient-doctor conversations using sequence-to-sequence models. In: Proceedings of the First Workshop on Natural Language Processing for Medical Conversations; 2020. p. 22-30. Krishna K, Khosla S, Bigham JP, Lipton ZC. Generating SOAP notes from doctor-patient conversations using modular summarization techniques. In: Proc ACL-IJCNLP; 2021. p. 4958-72. Coutinho I, Martins B. Transformer-based models for ICD-10 coding of death certificates with Portuguese text. J Biomed Inform. 2022;136:104232. 10.1016/j.jbi.2022.104232 Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural language processing of clinical notes on chronic diseases: systematic review. JMIR Med Inform. 2019;7(2):e12239. 10.2196/12239 Vu T, Nguyen DQ, Nguyen A. A label attention model for ICD coding from clinical text. arXiv [Preprint]. 2020 Jul 13:arXiv:2007.06351. Chen Y, Chen H, Lu X, Duan H, He S, An J. Automatic ICD-10 coding: deep semantic matching based on analogical reasoning. Heliyon. 2023;9(4):e15178. 10.1016/j.heliyon.2023.e15178 Sonabend A, Cai W, Ahuja Y, Ananthakrishnan A, Xia Z, Yu S, et al. Automated ICD coding via unsupervised knowledge integration (UNITE). Int J Med Inform. 2020;139:104135. 10.1016/j.ijmedinf.2020.104135 Masud JH, Kuo CC, Yeh CY, Yang HC, Lin MC. Applying deep learning model to predict diagnosis code of medical records. Diagnostics (Basel). 2023;13(13):2297. 10.3390/diagnostics13132297 Hu S, Teng F, Huang L, Yan J, Zhang H. An explainable CNN approach for medical codes prediction from clinical text. BMC Med Inform Decis Mak. 2021;21(Suppl 9):256. 10.1186/s12911-021-01642-3 Berdahl CT, Moran GJ, McBride O, Santini AM, Verzhbinsky IA, Schriger DL. Concordance between electronic clinical documentation and physicians’ observed behavior. JAMA Netw Open. 2019;2(9):e1911390. 10.1001/jamanetworkopen.2019.11390 Cohen GR, Friedman CP, Ryan AM, Richardson CR, Adler-Milstein J. Variation in physicians’ electronic health record documentation and potential patient harm from that variation. J Gen Intern Med. 2019;34(11):2355-67. 10.1007/s11606-019-05215-5 Zhao S, Diao X, Xia Y, Huo Y, Cui M, Wang Y, et al. Automated ICD coding for coronary heart diseases by a deep learning method. Heliyon. 2023;9(3):e13998. 10.1016/j.heliyon.2023.e13998