# Applications of Natural Language Processing Tools in Orthopaedic Surgery: A Scoping Review
## 4 Discussion
Historically, orthopaedic clinicians have depended on manual chart reviews, data accumulation, and synthesis as the gold standard for gathering adverse outcomes or diagnostic data. Efforts to enhance the efficiency of these processes have included the transition of healthcare systems from paper-based to electronic medical records, allowing all the information to be found on one interface. This has allowed clinicians to streamline these processes by allowing computer-generated searches of adverse outcomes or diagnoses from relevant investigations as required. However, this digital transition has its set of challenges, primarily due to the proliferation of unstructured text, vast data quantities, and user interfaces that might not effectively capture pertinent data. There is clear emerging evidence that NLP offers a solution for evaluating vast swathes of digital healthcare data in a highly accurate and efficient manner. Integration of these technologies could theoretically improve the time orthopaedic surgeons and trainees spend in auditing and data collection for research. This is supported by five additional studies which demonstrate NLP is able to extract with high accuracy various outcome measures and important operative details [21; 22; 23; 24; 25]. In almost all of these cases, hundreds to thousands of separate documents were evaluated with these tools, a task that would be laborious and highly prone to human error, particularly if factors including fatigue, burnout and staff shortages are considered. The allure of automated, swift data extraction, processing, and synthesis through NLP is undeniable. Nevertheless, it is pivotal to acknowledge the need for further refinement and training of these tools to hone their precision. Our scoping review identified various preliminary studies that explored unique applications of NLP in orthopaedic surgery including in the assessment of the patient experience, as an informative resource for patients, in the prediction of readmission, in assistance with billing/coding procedures and in triaging of patients [15; 16; 17; 18; 19; 20; 26]. With the exception of predicting readmission and triage, NLP was adequately and efficiently used for these purposes. The ability of NLP to assess the patient experience and provide advice may represent the beginnings of a new paradigm shift in the way we harness digital tools outside of the acute care setting. For the former, sentiment analysis of patient experience can be streamlined and better represented with NLPs [15; 16]. This may allow not only orthopaedic departments but also various quality and safety units within hospital systems to best understand how our health services can improve the delivery of healthcare. Furthermore, the ability of ChatGPT to provide answers to frequently asked questions associated with hip and knee arthroplasty may be a useful adjunct for surgeons to present information in more accessible and palatable ways for patients [17; 18]. This is particularly relevant in an environment where diverse caseloads of patients exist. More streamlined generation of CPT codes from operative notes by NLP tools is of great benefit to health services, particularly as this would improve the workload and efficiency of clinical coders [26]. Importantly, allowing surgeons to be prompted with specific CPT codes based on their operation notes, may yield more accurate and comprehensive documentation. The downstream impacts of this include improved healthcare funding, more appropriate remuneration of clinicians and better identification of areas for resource allocation. Notably, when it came to predicting readmission and triage, [PERSON] et al. and [PERSON] et al., respectively, found that NLP was unable to assist with these processes with high accuracy [19; 20]. Specifically, [PERSON] et al. demonstrated low to moderate AUC scores when using NLP to predict 90-day readmission and [PERSON] et al. demonstrated NLP was unable to be validated for triaging patients with neck or lower back pain in operative or nonoperative populations [19; 20].