Internet of Things and New Technologies for Tracking Perioperative Patients With an Innovative Model for Operating Room Scheduling: Protocol for a Development and Feasibility Study

Overview

Journal JMIR Res Protoc

Publisher JMIR Publications

Specialty General Medicine

Date 2023 Jul 5

PMID 37405821

Authors

Eleonora Bottani

Valentina Bellini

Monica Mordonini

Mattia Pellegrino

Gianfranco Lombardo

Beatrice Franchi

Michelangelo Craca

Elena Bignami

Affiliations

Soon will be listed here.

Abstract

Background: Management of operating rooms is a critical point in health care organizations because surgical departments represent a significant cost in hospital budgets. Therefore, it is increasingly important that there is effective planning of elective, emergency, and day surgery and optimization of both the human and physical resources available, always maintaining a high level of care and health treatment. This would lead to a reduction in patient waiting lists and better performance not only of surgical departments but also of the entire hospital.

Objective: This study aims to automatically collect data from a real surgical scenario to develop an integrated technological-organizational model that optimizes operating block resources.

Methods: Each patient is tracked and located in real time by wearing a bracelet sensor with a unique identifier. Exploiting the indoor location, the software architecture is able to collect the time spent for every step inside the surgical block. This method does not in any way affect the level of assistance that the patient receives and always protects their privacy; in fact, after expressing informed consent, each patient will be associated with an anonymous identification number.

Results: The preliminary results are promising, making the study feasible and functional. Times automatically recorded are much more precise than those collected by humans and reported in the organization's information system. In addition, machine learning can exploit the historical data collection to predict the surgery time required for each patient according to the patient's specific profile. Simulation can also be applied to reproduce the system's functioning, evaluate current performance, and identify strategies to improve the efficiency of the operating block.

Conclusions: This functional approach improves short- and long-term surgical planning, facilitating interaction between the various professionals involved in the operating block, optimizing the management of available resources, and guaranteeing a high level of patient care in an increasingly efficient health care system.

Trial Registration: ClinicalTrials.gov NCT05106621; https://clinicaltrials.gov/ct2/show/NCT05106621.

International Registered Report Identifier (irrid): DERR1-10.2196/45477.

References

Schwartz P, Woosley R . Predicting the Unpredictable: Drug-Induced QT Prolongation and Torsades de Pointes. J Am Coll Cardiol. 2016; 67(13):1639-1650. DOI: 10.1016/j.jacc.2015.12.063. View

Wu A, Huang C, Weaver M, Urman R . Use of Historical Surgical Times to Predict Duration of Primary Total Knee Arthroplasty. J Arthroplasty. 2016; 31(12):2768-2772. DOI: 10.1016/j.arth.2016.05.038. View

Liu P, Lu L, Zhang J, Huo T, Liu S, Ye Z . Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci. 2021; 41(6):1105-1115. PMC: 8648557. DOI: 10.1007/s11596-021-2474-3. View

Kendale S . The role of artificial intelligence in preoperative medicine. Int Anesthesiol Clin. 2021; 60(1):69-73. DOI: 10.1097/AIA.0000000000000344. View

Bellini V, Guzzon M, Bigliardi B, Mordonini M, Filippelli S, Bignami E . Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization. J Med Syst. 2019; 44(1):20. DOI: 10.1007/s10916-019-1512-1. View

Futoma J, Morris J, Lucas J . A comparison of models for predicting early hospital readmissions. J Biomed Inform. 2015; 56:229-38. DOI: 10.1016/j.jbi.2015.05.016. View

Izad Shenas S, Raahemi B, Hossein Tekieh M, Kuziemsky C . Identifying high-cost patients using data mining techniques and a small set of non-trivial attributes. Comput Biol Med. 2014; 53:9-18. DOI: 10.1016/j.compbiomed.2014.07.005. View

Bahou N, Fenwick C, Anderson G, Van der Meer R, Vassalos T . Modeling the critical care pathway for cardiothoracic surgery. Health Care Manag Sci. 2017; 21(2):192-203. DOI: 10.1007/s10729-017-9401-y. View

Evans R, Burke J, Classen D, Gardner R, Menlove R, Goodrich K . Computerized identification of patients at high risk for hospital-acquired infection. Am J Infect Control. 1992; 20(1):4-10. DOI: 10.1016/s0196-6553(05)80117-8. View

10.

Dai W, Brisimi T, Adams W, Mela T, Saligrama V, Paschalidis I . Prediction of hospitalization due to heart diseases by supervised learning methods. Int J Med Inform. 2014; 84(3):189-97. PMC: 4314395. DOI: 10.1016/j.ijmedinf.2014.10.002. View

11.

Luo L, Zhang F, Yao Y, Gong R, Fu M, Xiao J . Machine learning for identification of surgeries with high risks of cancellation. Health Informatics J. 2018; 26(1):141-155. DOI: 10.1177/1460458218813602. View

12.

Ploscaru V, Popa-Fotea N, Calmac L, Itu L, Mihai C, Bataila V . Artificial intelligence and cloud based platform for fully automated PCI guidance from coronary angiography-study protocol. PLoS One. 2022; 17(9):e0274296. PMC: 9462679. DOI: 10.1371/journal.pone.0274296. View

13.

Miyaguchi N, Takeuchi K, Kashima H, Morita M, Morimatsu H . Predicting anesthetic infusion events using machine learning. Sci Rep. 2021; 11(1):23648. PMC: 8655034. DOI: 10.1038/s41598-021-03112-2. View

14.

Bignami E, Cozzani F, Del Rio P, Bellini V . The role of artificial intelligence in surgical patient perioperative management. Minerva Anestesiol. 2020; 87(7):817-822. DOI: 10.23736/S0375-9393.20.14999-X. View

15.

Smith T, Norasi H, Herbst K, Kendrick M, Curry T, Grantcharov T . Creating a Practical Transformational Change Management Model for Novel Artificial Intelligence-Enabled Technology Implementation in the Operating Room. Mayo Clin Proc Innov Qual Outcomes. 2022; 6(6):584-596. PMC: 9618851. DOI: 10.1016/j.mayocpiqo.2022.09.004. View

16.

Bishara A, Wong A, Wang L, Chopra M, Fan W, Lin A . Opal: an implementation science tool for machine learning clinical decision support in anesthesia. J Clin Monit Comput. 2021; 36(5):1367-1377. PMC: 9275816. DOI: 10.1007/s10877-021-00774-1. View

17.

Hassanzadeh H, Boyle J, Khanna S, Biki B, Syed F . Daily surgery caseload prediction: towards improving operating theatre efficiency. BMC Med Inform Decis Mak. 2022; 22(1):151. PMC: 9172609. DOI: 10.1186/s12911-022-01893-8. View

18.

Mulita F, Verras G, Anagnostopoulos C, Kotis K . A Smarter Health through the Internet of Surgical Things. Sensors (Basel). 2022; 22(12). PMC: 9231158. DOI: 10.3390/s22124577. View

19.

Laskin D, Abubaker A, Strauss R . Accuracy of predicting the duration of a surgical operation. J Oral Maxillofac Surg. 2013; 71(2):446-7. DOI: 10.1016/j.joms.2012.10.009. View

20.

Chen H, Lundberg S, Erion G, Kim J, Lee S . Forecasting adverse surgical events using self-supervised transfer learning for physiological signals. NPJ Digit Med. 2021; 4(1):167. PMC: 8654960. DOI: 10.1038/s41746-021-00536-y. View