Key Academic References
Below are categorized key references in patient safety, operating room technology, benchmarking, artificial intelligence in surgery, and future directions in surgical data science. Each citation is listed for clarity and ease of browsing.
Foundational Patient Safety:
1. Makary MA, Daniel M. Medical error—the third leading cause of death in the US. BMJ. 2016;353:i2139.
2. Haynes AB, Weiser TG, Berry WR, et al. A Surgical Safety Checklist to Reduce Morbidity and Mortality in a Global Population. N Engl J Med. 2009;360:491-499.
3. Leape LL, Brennan TA, Laird N, et al. The nature of adverse events in hospitalized patients: Results of the Harvard Medical Practice Study II. N Engl J Med. 1991;324:377-384.
OR Black Box and Surgical Data Science:
4. Møller KE, Sørensen JL, Topperzer MK, et al. Implementation of an Innovative Technology Called the OR Black Box: A Feasibility Study. J Surg Res. 2023;282:56-64.
5. Al Abbas AI, Meier J, Daniel W, et al. Impact of team performance on the surgical safety checklist on patient outcomes: an operating room black box analysis. Surg Endosc. 2024;38(10):5613-5622.
6. Al Abbas AI, Sankaranarayanan G, Polanco PM, et al. The Operating Room Black Box: Understanding Adherence to Surgical Checklists. Ann Surg. 2022;276(6):995-1001.
7. Boet S, Etherington C, Lam S, et al. Implementation of the Operating Room Black Box Research Program at the Ottawa Hospital Through Patient, Clinical, and Organizational Engagement: Case Study. J Med Internet Res. 2021;23(3):e15443.
Quality Benchmarking in Surgery:
8. Staiger RD, Schwandt H, Puhan MA, Clavien PA. Improving surgical outcomes through benchmarking. Br J Surg. 2019;106(1):59-64.
9. McLeod M, Leung K, Pramesh CS, et al. Quality indicators in surgical oncology: systematic review of measures used to compare quality across hospitals. BJS Open. 2024;8(2):zrae009.
10. Merath K, Chen Q, Bagante F, et al. Benchmarking: a novel measuring tool for outcome comparisons in surgery. Int J Surg. 2023;109(7):2131-2142.
AI, Machine Learning, and Surgical Video Analysis
11. Eppler MB, Sayegh AS, Maas M, et al. Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis. J Clin Med. 2023;12(4):1687.
12. Levin M, McKechnie T, Kruse CC, et al. Surgical data recording in the operating room: a systematic review of modalities and metrics. Br J Surg. 2021;108(6):613-621.
13. Garrow CR, Kowalewski KF, Li L, et al. Machine Learning for Surgical Phase Recognition: A Systematic Review. Ann Surg. 2021;273(4):684-693.
14. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial Intelligence in Surgery: Promises and Perils. Ann Surg. 2018;268(1):70-76.
Surgical Data Science and Future Directions
15. Mascagni P, Padoy N. OR black box and surgical control tower: Recording and streaming data and analytics to improve surgical care. J Visc Surg. 2021;158(3):S18-S25.
16. Maier-Hein L, Vedula SS, Speidel S, et al. Surgical data science for next-generation interventions. Nat Biomed Eng. 2017;1:691-696.
17. Jue J, Shah NA, Mackey TK. An Interdisciplinary Review of Surgical Data Recording Technology Features and Legal Considerations. Surg Innov. 2020;27(2):220-228.
18. Grantcharov TP, Rosenberg J, et al. [Multiple foundational publications from over 220 peer-reviewed works on surgical simulation, safety, and the Black Box concept]