cirpublications.com-feVZ-1790880683-w681883871 1790880683 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 2023 3 2 Oliver Grant David Clark Sophia Nguyen 2023 10.68159/w681883871 https://cirpublications.com/pub/journal/2/article/w681883871 Johnson M, Albizri A, Harfouche A. Responsible artificial intelligence in healthcare: predicting and preventing insurance claim denials for economic and social wellbeing. Inf Syst Front. 2023;25(6):2179-95. 10.1007/s10796-022-10311-4 Pal S, Gaur M, Chaudhuri R, Kalaivanan R, Chetan KV, Praneeth BH, et al. Driving impact in claims denial management using artificial intelligence. In: Singh PK, Pawłowski W, Tanwar S, Sur A, Sharma R, editors. Advances in Computing and Data Sciences. Cham: Springer International Publishing; 2022. p. 107-20. 10.1007/978-3-031-16364-7_9 Chaudhuri R, Parsa SP, Nagpal D, R K. Time to response prediction for following up on account receivables in healthcare revenue cycle management. In: Singh PK, Pawłowski W, Tanwar S, Sur A, Sharma R, editors. Advances in Computing and Data Sciences. Cham: Springer International Publishing; 2022. p. 124-37. 10.1007/978-3-031-16364-7_11 Permai SD, Herdianto K. Prediction of health insurance claims using logistic regression and XGBoost methods. Procedia Comput Sci. 2023;227:1012-9. 10.1016/j.procs.2023.10.598 Kaushik K, Bhardwaj A, Dwivedi AD, Singh R. Machine learning-based regression framework to predict health insurance premiums. Int J Environ Res Public Health. 2022;19(13):7898. 10.3390/ijerph19137898 Drewe-Boss P, Enders D, Walker J, Ohler U. Deep learning for prediction of population health costs. BMC Med Inform Decis Mak. 2022;22(1):32. 10.1186/s12911-022-01748-z 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/s41746-022-00691-2 Diao X, Huo Y, Zhao S, Yuan J, Cui M, Wang Y, et al. Automated ICD coding for primary diagnosis via clinically interpretable machine learning. Int J Med Inform. 2021;153:104543. 10.1016/j.ijmedinf.2021.104543 Campbell S, Giadresco K. Computer-assisted clinical coding: a narrative review of the literature on its benefits, limitations, implementation and impact on clinical coding professionals. Health Inf Manag J. 2020;49(1):5-18. 10.1177/1833358319851585 Pine KH, Landon LA, Bossen C, VanGelder ME. Innovations in clinical documentation integrity practice: continual adaptation in a data-intensive healthcare organisation. Health Inf Manag J. 2023;52(2):119-24. 10.1177/18333583221074707 Lenert LA, Lane S, Wehbe R. Could an artificial intelligence approach to prior authorization be more human? J Am Med Inform Assoc. 2023;30(5):989-94. Park SH, Choi J, Byeon JS. Key principles of clinical validation, device approval, and insurance coverage decisions of artificial intelligence. Korean J Radiol. 2021;22(3):442-53. 10.3348/kjr.2020.0981 Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319(13):1317-8. 10.1001/jama.2017.18391 Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719-31. 10.1038/s41551-018-0305-z Wiens J, Saria S, Sendak M, Ghassemi M, Liu VX, Doshi-Velez F, et al. Do no harm: a roadmap for responsible machine learning for health care. Nat Med. 2019;25(9):1337-40. 10.1038/s41591-019-0548-6 Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. A review of challenges and opportunities in machine learning for health. AMIA Summits Transl Sci Proc. 2020;2020:191-200. Nguyen AN, Truran D, Kemp M, Koopman B, Conlan D, O’Dwyer J, et al. Computer-assisted diagnostic coding: effectiveness of an NLP-based approach using SNOMED CT to ICD-10 mappings. AMIA Annu Symp Proc. 2018;2018:807-16. Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2(1):56-67. 10.1038/s42256-019-0138-9 Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng. 2018;2(10):749-60. 10.1038/s41551-018-0304-0 Adadi A, Berrada M. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access. 2018;6:52138-60. 10.1109/ACCESS.2018.2870052 Guidotti R, Monreale A, Ruggieri S, Turini F, Giannotti F, Pedreschi D. A survey of methods for explaining black box models. ACM Comput Surv. 2018;51(5):93. 10.1145/3236009 Arrieta AB, Díaz-Rodríguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, et al. Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion. 2020;58:82-115. 10.1016/j.inffus.2019.12.012 Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK, Ashrafian H, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health. 2020;2(10):e537-e548. 10.1016/S2589-7500(20)30218-1 Norgeot B, Quer G, Beaulieu-Jones BK, Torkamani A, Dias R, Gianfrancesco M, et al. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat Med. 2020;26(9):1320-4. 10.1038/s41591-020-1041-y Markus AF, Kors JA, Rijnbeek PR. The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies. J Biomed Inform. 2021;113:103655. 10.1016/j.jbi.2020.103655 Tjoa E, Guan C. A survey on explainable artificial intelligence (XAI): toward medical XAI. IEEE Trans Neural Netw Learn Syst. 2021;32(11):4793-813. 10.1109/TNNLS.2020.3027314 Vellido A. The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Comput Appl. 2020;32(24):18069-83. 10.1007/s00521-019-04051-w Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1(1):18. 10.1038/s41746-018-0029-1 Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J Biomed Health Inform. 2018;22(5):1589-604. 10.1109/JBHI.2017.2767063