REFERENCES
1. Zhu C, Attaluri PK, Wirth PJ, Shaffrey EC, Friedrich JB, Rao VK. Current applications of artificial intelligence in billing practices and clinical plastic surgery. Plast Reconstr Surg Glob Open. 2024;12:e5939.
2. Esposito T, Reed R, Adams RC, Fakhry S, Carey D, Crandall ML. Acute care surgery billing, coding and documentation series part 1: basic evaluation and management (E/M), emergency department E/M, prolonged services, adult critical care documentation and coding. Trauma Surg Acute Care Open. 2020;5:e000578.
3. Dotson P. CPT® Codes: what are they, why are they necessary, and how are they developed? Adv Wound Care. 2013;2:583-7.
4. Venkatesh KP, Raza MM, Kvedar JC. Automating the overburdened clinical coding system: challenges and next steps. NPJ Digit Med. 2023;6:16.
5. Abràmoff MD, Roehrenbeck C, Trujillo S, et al. A reimbursement framework for artificial intelligence in healthcare. NPJ Digit Med. 2022;5:72.
6. Varnosfaderani S, Forouzanfar M. The role of AI in hospitals and clinics: transforming healthcare in the 21st century. Bioengineering. 2024;11:337.
7. Khaleghi T, Murat A, Arslanturk S. A tree based approach for multi-class classification of surgical procedures using structured and unstructured data. BMC Med Inform Decis Mak. 2021;21:328.
8. Xu HA, Maccari B, Guillain H, Herzen J, Agri F, Raisaro JL. An end-to-end natural language processing application for prediction of medical case coding complexity: algorithm development and validation. JMIR Med Inform. 2023;11:e38150.
9. Jarvis T, Thornburg D, Rebecca AM, Teven CM. Artificial intelligence in plastic surgery: current applications, future directions, and ethical implications. Plast Reconstr Surg Glob Open. 2020;8:e3200.
10. Tavabi N, Singh M, Pruneski J, Kiapour AM. Systematic evaluation of common natural language processing techniques to codify clinical notes. PLoS One. 2024;19:e0298892.
11. Blanchfield BB, Heffernan JL, Osgood B, Sheehan RR, Meyer GS. Saving billions of dollars - and physicians’ time - by streamlining billing practices. Health Aff. 2010;29:1248-54.
12. 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;319:691-7.
13. Cheng CP, Sicard R, Vujovic D, et al. Replicating current procedural terminology code assignment of rhinology operative notes using machine learning. World J Otorhinolaryngol Head Neck Surg. 2024.
14. Isch EL, Sarikonda A, Sambangi A, et al. Evaluating the efficacy of large language models in CPT coding for craniofacial surgery: a comparative analysis. J Craniofac Surg. 2024.
15. O’Malley GR Jr, Sarwar SA, Cassimatis ND, et al. Can publicly available artificial intelligence successfully identify current procedural terminology codes for common procedures in neurosurgery? World Neurosurg. 2024;183:e860-70.
16. Zaidat B, Tang J, Arvind V, et al. Can a novel natural language processing model and artificial intelligence automatically generate billing codes from spine surgical operative notes? Global Spine J. 2024;14:2022-30.
17. Kim JS, Vivas A, Arvind V, et al. Can natural language processing and artificial intelligence automate the generation of billing codes from operative note dictations? Global Spine J. 2023;13:1946-55.
18. Shost MD, Meade SM, Steinmetz MP, Mroz TE, Habboub G. Surgical classification using natural language processing of informed consent forms in spine surgery. Neurosurg Focus. 2023;54:E10.
19. Zaidat B, Lahoti YS, Yu A, Mohamed KS, Cho SK, Kim JS. Artificially intelligent billing in spine surgery: an analysis of a large language model. Global Spine J. 2023:21925682231224753.
20. Kim JS, Arvind V, Schwartz JT, et al. P72. Natural language processing of operative note dictations to automatically generate CPT codes for billing. Spine J. 2020;20:S181-2.
21. Brat G, Beam A, Salim A, Christopher K. 1160: Using machine learning algorithms to identify open abdomen procedures in administrative databases. Crit Care Med. 2015;43:291-2.
22. Khansa I, Janis JE. A growing epidemic: plastic surgeons and burnout - a literature review. Plast Reconstr Surg. 2019;144:298e-305e.
23. Streu R, Hansen J, Abrahamse P, Alderman AK. Professional burnout among US plastic surgeons: results of a national survey. Ann Plast Surg. 2014;72:346-50.
24. Holzer E, Tschan F, Kottwitz MU, Beldi G, Businger AP, Semmer NK. The workday of hospital surgeons: what they do, what makes them satisfied, and the role of core tasks and administrative tasks; a diary study. BMC Surg. 2019;19:112.
25. Toscano F, O’Donnell E, Broderick JE, et al. How physicians spend their work time: an ecological momentary assessment. J Gen Intern Med. 2020;35:3166-72.
26. Donaldson R, Hallman TG, Qureshi U, et al. Quantifying plastic and reconstructive surgery engagement in the evolution of ICD-10 codes. Plast Reconstr Surg Glob Open. 2024;12:e6304.
27. Siotos C, Cheah MA, Damoulakis G, et al. Trends of medicare reimbursement rates for common plastic surgery procedures. Plast Reconstr Surg. 2021;147:1220-5.
28. Baudry M, Robert CY. A machine learning approach for individual claims reserving in insurance. Appl Stoch Models Bus Ind. 2019;35:1127-55.
29.
30. Kiwan O, Al-Kalbani M, Rafie A, Hijazi Y. Artificial intelligence in plastic surgery, where do we stand? JPRAS Open. 2024;42:234-43.
31. Ankarath S. Navigating complications in hand surgery: a crucial discussion. J Hand Surg Eur Vol. 2024;49:139-41.
32. Wright JD, Chen L, Suzuki Y, Matsuo K, Hershman DL. National estimates of gender-affirming surgery in the US. JAMA Netw Open. 2023;6:e2330348.
33. Fleming NS, Culler SD, McCorkle R, Becker ER, Ballard DJ. The financial and nonfinancial costs of implementing electronic health records in primary care practices. Health Aff. 2011;30:481-9.
34. Landi H. Epic touts new AI applications to streamline charting and bring research insights to the point of care. 2024. Available from: https://www.fiercehealthcare.com/ai-and-machine-learning/epic-touts-new-ai-applications-streamline-charting-and-bring-research. [Last accessed on 3 Jan 2025].
35. Curtiss ET, Susan E. Computer assisted coding: market shares, strategies, and forecasts, worldwide 2016 to 2022. WinterGreen Res 2017. Available from: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Curtiss+E+T+SE.+Computer+Assisted+Coding%3A+Market+Shares%2C+Strategies%2C+and+Forecasts%2C+Worldwide+2016+to+2022.+WinterGreen+Research.+2017&btnG=. [Last accessed on 3 Jan 2025].
36. Centers for Medicare & Medicaid Services. Comprehensive error rate testing (CERT). 2023. Available from: https://www.cms.gov/data-research/monitoring-programs/improper-payment-measurement-programs/comprehensive-error-rate-testing-cert/. [Last accessed on 3 Jan 2025].
37. Ozmen BB, Schwarz GS. Future of artificial intelligence in plastic surgery: toward the development of specialty-specific large language models. J Plast Reconstr Aesthet Surg. 2024;93:70-1.
38. Spoer DL, Kiene JM, Dekker PK, et al. A systematic review of artificial intelligence applications in plastic surgery: looking to the future. Plast Reconstr Surg Glob Open. 2022;10:e4608.
