Search

Building clinical registries is an important step in clinical research and improvement of patient care quality. Natural Language Processing (NLP) methods have shown promising results in extracting valuable information from unstructured clinical notes. However, the structure and nature of clinical notes are very different from regular text that state-of-the-art NLP models are trained and tested on, and they have their own set of challenges. In this study, we propose Sentence Extractor with Keywords (SE-K), an efficient and interpretable classification approach for extracting information from clinical notes and show that it outperforms more computationally expensive methods in text classification. Following the Institutional Review Board (IRB) approval, we used SE-K and two embedding based NLP approaches (Sentence Extractor with Embeddings (SE-E) and Bidirectional Encoder Representations from Transformers (BERT)) to develop comprehensive registry of anterior cruciate ligament surgeries from 20 years of unstructured clinical data at a multi-site tertiary-care regional children's hospital. The low-resource approach (SE-K) had better performance (average AUROC of 0.94 ± 0.04) than the embedding-based approaches (SE-E: 0.93 ± 0.04 and BERT: 0.87 ± 0.09) for out of sample validation, in addition to minimum performance drop between test and out-of-sample validation. Moreover, the SE-K approach was at least six times faster (on CPU) than SE-E (on CPU) and BERT (on GPU) and provides interpretability. Our proposed approach, SE-K, can be effectively used to extract relevant variables from clinic notes to build large-scale registries, with consistently better performance compared to the more resource-intensive approaches (e.g., BERT). Such approaches can facilitate information extraction from unstructured notes for registry building, quality improvement and adverse event monitoring., Competing Interests: Declaration of competing interest No conflicts of interests relevant to the topic of this study., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Proper codification of medical diagnoses and procedures is essential for optimized health care management, quality improvement, research, and reimbursement tasks within large healthcare systems. Assignment of diagnostic or procedure codes is a tedious manual process, often prone to human error. Natural Language Processing (NLP) has been suggested to facilitate this manual codification process. Yet, little is known on best practices to utilize NLP for such applications. With Large Language Models (LLMs) becoming more ubiquitous in daily life, it is critical to remember, not every task requires that level of resource and effort. Here we comprehensively assessed the performance of common NLP techniques to predict current procedural terminology (CPT) from operative notes. CPT codes are commonly used to track surgical procedures and interventions and are the primary means for reimbursement. Our analysis of 100 most common musculoskeletal CPT codes suggest that traditional approaches can outperform more resource intensive approaches like BERT significantly (P-value = 4.4e-17) with average AUROC of 0.96 and accuracy of 0.97, in addition to providing interpretability which can be very helpful and even crucial in the clinical domain. We also proposed a complexity measure to quantify the complexity of a classification task and how this measure could influence the effect of dataset size on model's performance. Finally, we provide preliminary evidence that NLP can help minimize the codification error, including mislabeling due to human error., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Tavabi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Professional society conferences are integral to the medical profession. However, airline travel is a major contributor to greenhouse gas production, and the environmental impact of in-person attendance at an orthopaedic conference has yet to be described. With growing concern about the climate crisis, we sought to quantify the carbon footprint of in-person attendance to help potential attendees more consciously consider in-person attendance, inform strategies to minimize greenhouse gas emissions during travel to annual meetings, and increase awareness about and momentum for efforts in orthopaedic surgery to reduce the carbon footprint of society conferences., Questions/purposes: (1) What was the magnitude of greenhouse gas production resulting from all-in-person 2019 American Orthopaedic Foot and Ankle Society (AOFAS) annual meeting attendance in Chicago, IL, USA? (2) What was the magnitude of greenhouse gas production resulting from the all-virtual 2020 AOFAS annual meeting, and how does it compare with the 2019 AOFAS annual meeting carbon footprint? (3) To what extent could an alternative in-person meeting model with four or seven hubs decrease greenhouse gas production resulting from round-trip air travel compared with the 2019 AOFAS annual meeting?, Methods: A list of the postal codes and countries of all 1271 registered participants attending the four-day 2019 AOFAS annual meeting in Chicago, IL, USA, was obtained from AOFAS headquarters. The 2019 conference was chosen because it was the last pre-COVID meeting and thus attendance was more likely to resemble that at prepandemic in-person conferences than more recent meetings because of pandemic travel restrictions. We estimated carbon dioxide-equivalent (CO 2 e) production from round-trip air travel using a publicly available internet-based calculator (Myclimate: https://co2.myclimate.org/en/flight_calculators/new ). Emissions produced by the conference venue, car travel, and hotel stays were estimated using published Environmental Protection Agency emission factors. To estimate emissions produced by the all-virtual 2020 AOFAS annual meeting (assuming an equal number of attendees as in 2019), we used the framework published by Faber and summed estimated network data transfer emissions, personal computer and monitor emissions, and server-related emissions. Using the 2019 registrant list, we modeled four-hub and seven-hub in-person meeting alternatives to determine potential decreased round-trip air travel greenhouse gas production. Meeting hub locations were selected by visualizing the geographic distribution of the 2019 registrants and selecting reasonable meeting locations that would minimize air travel for the greatest number of attendees. Registrants were assigned to the nearest hub location. Myclimate was again used to estimate CO 2 e production for round-trip air travel for the hub meeting models., Results: The total estimated emissions of the all-in-person 2019 AOFAS annual meeting (when accounting for travel, conference space, and hotel stays) was 1565 tons CO 2 e (median 0.61 tons per attendee, range 0.02 to 7.7 tons). The total estimated emissions of the all-virtual 2020 meeting (when accounting for network data transfer emissions, personal computer and monitor emissions, and server-related emissions) was 34 tons CO 2 e (median 0.03 tons per attendee). This corresponds to a 97.8% decrease in CO 2 e emissions compared with the in-person conference. The model of a four-hub in-person meeting alternative with meetings in Chicago, Santiago, London, and Tokyo predicted an estimated 54% decrease in CO 2 e emissions from round-trip air travel. The seven-hub meeting model with meetings in Chicago; Washington, DC; Dallas; Los Angeles; Santiago; London; and Tokyo was predicted to diminish the CO 2 e emissions of round-trip air travel by an estimated 71%., Conclusion: The 2019 AOFAS annual meeting had an enormous carbon footprint and resulted in many individuals exceeding their annual allotted carbon budget (2.5 tons) according to the Paris Agreement. Hosting the meeting virtually greatly reduced the annual meeting carbon footprint, and our hub-based meeting models identified potential in-person alternatives for reducing the carbon footprint of conference attendance., Clinical Relevance: Professional societies must consider our responsibility to decarbonizing the healthcare sector by considering innovative approaches-perhaps such as our multihub proposals-to decarbonize carbon-intensive annual meetings without stalling academic progress., Competing Interests: Each author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research ® editors and board members are on file with the publication and can be viewed on request., (Copyright © 2023 by the Association of Bone and Joint Surgeons.)

Background: Several anatomic risk factors have been identified in the pathogenesis of patellofemoral instability. The literature is sparce regarding how these anatomic risk factors change during skeletal growth and development., Hypothesis: The anatomic risk factors associated with patellar instability change significantly during skeletal growth and maturation with different patterns in male versus female patients., Study Design: Cross-sectional study; Level of evidence, 3., Methods: Magnetic resonance imaging data from 240 unique, asymptomatic knees (7-18 years of age; 50% female) were used to measure patellar height (Caton-Deschamps index), lateral patellar tilt angle, trochlear height, trochlear groove depth, trochlear sulcus angle, and tibial tubercle-trochlear groove (TT-TG) distance. Linear regression was used to test the associations between age and anatomic findings. Two-way analysis of variance with Holm-Šídák post hoc test was used to compare anatomic characteristics between sexes in 3 age groups: prepubertal school-aged children (7-10 years old), early adolescents (11-14 years old), and late adolescents (15-18 years old)., Results: Patellar height (female sex), lateral patellar tilt angle (male sex), and trochlear sulcus angle (both sexes) decreased with age ( P < .001). Trochlear height, depth, and TT-TG distance increased with age in both male and female participants ( P < .02). Male participants had a larger sulcus angle (by 5.3°± 1.6° at age 11-14 years) and greater trochlear height (by >5 mm across medial, central, and lateral regions at age 15-18 years) than age-matched female participants ( P < .01). We found no other sex-related differences in quantified anatomic features ( P > .1)., Conclusion: The findings partially support our hypothesis indicating significant age-related changes in all quantified features, which were not different between male and female participants except for trochlear sulcus angle in early adolescence and trochlear height in late adolescence. In general, the majority of anatomic risk factors for patellar instability change with maturity in a direction that assists in reducing the risk of patellar instability and/or dislocation. The only outlier is the TT-TG distance, which increased by age, and in our oldest cohort of patients, the mean fell below the normal adult range. The current observations highlight the importance of age in the interpretation of risk for injury as well as the need for further studies to identify intrinsic and extrinsic factors that may result in abnormal development of these anatomic features during skeletal growth and maturation.