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Li Y, Zeng H, Fu J. Preovulatory progesterone levels are the top indicator for ovulation prediction based on machine learning model evaluation: a retrospective study. J Ovarian Res 2024; 17:169. [PMID: 39169388 PMCID: PMC11337897 DOI: 10.1186/s13048-024-01495-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 08/14/2024] [Indexed: 08/23/2024] Open
Abstract
BACKGROUND Accurately predicting ovulation timing is critical for women undergoing natural cycle-frozen embryo transfer. However, the precise predicting of the ovulation timing remains challenging due to the lack of consensus among different clinics regarding the definition of this significant event. OBJECTIVE To compare the effectiveness of preovulatory serum progesterone levels (P4) versus luteinizing hormone levels (LH) in predicting ovulation time using two machine learning models. METHODS 771 patients who underwent autologous natural cycle-frozen embryo transfer between January 2015 and February 2022 were recruited. Utilizing variables including follicle diameters, preovulatory serum levels of LH, E2, and P4, two machine learning models were constructed to predict the ovulation time, the importance of the variables in predicting ovulation timing was further ranked. RESULTS Two machine learning models have the capability to accurately predict the timing of ovulation, specifically within 72, 48, or 24 h. The overall accuracy rates of the validation dataset, as determined by the classification trees and random forest models, were found to be 78.83% and 85.28% respectively. Notably, when predicting ovulation within 24 h, the accuracy rate of P4 ≥ 0.65ng/ml exceeded 92%. Furthermore, it was important to consider LH or E2 levels in conjunction with P4 when assessing ovulation timing in cases where P4<0.65ng/ml. CONCLUSIONS Preovulatory serum P4 levels are better predictors of ovulation timing than LH levels and could be used as an alternative in clinical settings, and the model we developed can be used to pinpoint the day of ovulation. Ongoing research and advancements in technology are anticipated to enhance and refine the ovulation method.
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Affiliation(s)
- Yumei Li
- Reproductive Medicine Center, Xiangya Hospital Central South University, Changsha, 410008, China.
| | - Hong Zeng
- Reproductive Medicine Center, Xiangya Hospital Central South University, Changsha, 410008, China
| | - Jing Fu
- Reproductive Medicine Center, Xiangya Hospital Central South University, Changsha, 410008, China
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2
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Wang K, Wang X, Xu C, Bai L. Bibliometric Research on Surgical Scheduling Management from the Perspective of Web of Science. J Multidiscip Healthc 2024; 17:3715-3726. [PMID: 39100902 PMCID: PMC11297594 DOI: 10.2147/jmdh.s458410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 07/17/2024] [Indexed: 08/06/2024] Open
Abstract
Objective Reasonable surgical scheduling management is crucial to optimize the utilization rate of operating room. This study aims to understand the context, frontier and hot spots of surgical scheduling management research, in order to provide reference for surgical scheduling optimization. Methods Literature on operation scheduling management collected in Web of Science core collection database was searched from the database establishment to June 21, 2023. HisCite Pro 2.1 software was used to analyze the publication time, countries, research institutions, journals, authors, keywords and highly cited papers. Results A total of 1383 literatures were included, and research institutions in the United States, Canada and other countries played a leading role in this field. Among them, the combination of machine algorithm and system model optimization to improve the accuracy of surgical duration prediction is the future research focus in this field. Conclusion Improving operation efficiency is one of the key issues in operating room management. Managers should find the best operation scheduling plan from a more detailed and comprehensive perspective to improve operation efficiency.
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Affiliation(s)
- Ke Wang
- Operating Room, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, People’s Republic of China
| | - Xuelu Wang
- Operating Room, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, People’s Republic of China
| | - Chenying Xu
- Operating Room, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, People’s Republic of China
| | - Lina Bai
- Operating Room, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, People’s Republic of China
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Yeramosu T, Krivicich LM, Puzzitiello RN, Guenthner G, Salzler MJ. Concomitant Procedures, Black Race, Male Sex, and General Anesthesia Show Fair Predictive Value for Prolonged Rotator Cuff Repair Operative Time: Analysis of the NSQIP Database Using Machine Learning. Arthroscopy 2024:S0749-8063(24)00516-4. [PMID: 39069020 DOI: 10.1016/j.arthro.2024.07.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 07/10/2024] [Accepted: 07/12/2024] [Indexed: 07/30/2024]
Abstract
PURPOSE The purpose of this study is to develop machine learning models using the American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database to predict prolonged operative time (POT) for rotator cuff repair (RCR). Furthermore, this study aims to use the trained machine learning (ML) models, cross-referenced with traditional multivariate logistic regression (MLR), to determine the key perioperative variables that may predict POT for RCR. METHODS Data were obtained from a large, national database (NSQIP) from 2021. Patients with unilateral RCR procedures were included. Demographic, preoperative, and operative variables were analyzed. A multivariable logistic regression (MLR) model and various other machine learning techniques, including random forest (RF) and artificial neural network (ANN), were compared using area under the curve (AUC), calibration, Brier score, and decision curve analysis. Feature importance was identified from the overall best-performing model. RESULTS A total of 6,690 patients met inclusion criteria. The random forest (RF) ML model had the highest AUC upon internal validation (0.706) and the lowest Brier score (0.15), outperforming the other models. The RF model also demonstrated strong performance upon assessment of the calibration curves (Slope = 0.86, Intercept = 0.08) and decision curve analysis. The model identified concomitant procedure, specifically labral repair and biceps tenodesis, as the most important variable for determining POT, followed by age <30 years, Black or African American race, male sex, and general anesthesia. CONCLUSIONS Despite the advanced machine learning models used in this study, the NSQIP dataset was only able to fairly predict POT following RCR. The RF model identified concomitant procedures, specifically labral repair and biceps tenodesis, as the most important variables for determining POT. Additionally, demographic factors such as age <30 years, Black race, and general anesthesia were significant predictors. While male sex was identified as important in the RF model, the MLR model indicated that its predictive value is primarily in conjunction with specific procedures like biceps tenodesis and subacromial decompression.
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Affiliation(s)
- Teja Yeramosu
- Virginia Commonwealth University School of Medicine, Richmond, VA, U.S.A
| | - Laura M Krivicich
- Department of Orthopaedic Surgery, Tufts Medical Center, Boston, Massachusetts, U.S.A
| | - Richard N Puzzitiello
- Department of Orthopaedic Surgery, Tufts Medical Center, Boston, Massachusetts, U.S.A
| | - Guy Guenthner
- Department of Orthopaedic Surgery, Tufts Medical Center, Boston, Massachusetts, U.S.A
| | - Matthew J Salzler
- Department of Orthopaedic Surgery, Tufts Medical Center, Boston, Massachusetts, U.S.A.
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4
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Caserta M, Romero AG. A novel approach to forecast surgery durations using machine learning techniques. Health Care Manag Sci 2024:10.1007/s10729-024-09681-8. [PMID: 38985398 DOI: 10.1007/s10729-024-09681-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 06/13/2024] [Indexed: 07/11/2024]
Abstract
This study presents a methodology for predicting the duration of surgical procedures using Machine Learning (ML). The methodology incorporates a new set of predictors emphasizing the significance of surgical team dynamics and composition, including experience, familiarity, social behavior, and gender diversity. By applying ML techniques to a comprehensive dataset of over 77,000 surgeries, we achieved a 24% improvement in the mean absolute error (MAE) over a model that mimics the current approach of the decision maker. Our results also underscore the critical role of surgeon experience and team composition dynamics in enhancing prediction accuracy. These advancements can lead to more efficient operational planning and resource allocation in hospitals, potentially reducing downtime in operating rooms and improving healthcare delivery.
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Affiliation(s)
- Marco Caserta
- IE Business School, IE University, Paseo de la Castellana 259E, Madrid, 28046, Madrid, Spain.
| | - Antonio García Romero
- IE Business School, IE University, Paseo de la Castellana 259E, Madrid, 28046, Madrid, Spain
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5
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Mathis M, Steffner KR, Subramanian H, Gill GP, Girardi NI, Bansal S, Bartels K, Khanna AK, Huang J. Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology. J Cardiothorac Vasc Anesth 2024; 38:1211-1220. [PMID: 38453558 PMCID: PMC10999327 DOI: 10.1053/j.jvca.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 01/30/2024] [Accepted: 02/05/2024] [Indexed: 03/09/2024]
Abstract
Artificial intelligence- (AI) and machine learning (ML)-based applications are becoming increasingly pervasive in the healthcare setting. This has in turn challenged clinicians, hospital administrators, and health policymakers to understand such technologies and develop frameworks for safe and sustained clinical implementation. Within cardiac anesthesiology, challenges and opportunities for AI/ML to support patient care are presented by the vast amounts of electronic health data, which are collected rapidly, interpreted, and acted upon within the periprocedural area. To address such challenges and opportunities, in this article, the authors review 3 recent applications relevant to cardiac anesthesiology, including depth of anesthesia monitoring, operating room resource optimization, and transthoracic/transesophageal echocardiography, as conceptual examples to explore strengths and limitations of AI/ML within healthcare, and characterize this evolving landscape. Through reviewing such applications, the authors introduce basic AI/ML concepts and methodologies, as well as practical considerations and ethical concerns for initiating and maintaining safe clinical implementation of AI/ML-based algorithms for cardiac anesthesia patient care.
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Affiliation(s)
- Michael Mathis
- Department of Anesthesiology, University of Michigan Medicine, Ann Arbor, MI
| | - Kirsten R Steffner
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA
| | - Harikesh Subramanian
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, PA
| | - George P Gill
- Department of Anesthesiology, Cedars Sinai, Los Angeles, CA
| | | | - Sagar Bansal
- Department of Anesthesiology and Perioperative Medicine, University of Missouri School of Medicine, Columbia, MO
| | - Karsten Bartels
- Department of Anesthesiology, University of Nebraska Medical Center, Omaha, NE
| | - Ashish K Khanna
- Department of Anesthesiology, Section on Critical Care Medicine, School of Medicine, Wake Forest University, Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, NC
| | - Jiapeng Huang
- Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY.
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6
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Elsaqa M, El Tayeb MM, Yano S, Papaconstantinou HT. Operative Time Accuracy in the Era of Electronic Health Records: Addressing the Elephant in the Room. J Healthc Manag 2024; 69:132-139. [PMID: 38467026 DOI: 10.1097/jhm-d-23-00073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
GOAL Accurate prediction of operating room (OR) time is critical for effective utilization of resources, optimal staffing, and reduced costs. Currently, electronic health record (EHR) systems aid OR scheduling by predicting OR time for a specific surgeon and operation. On many occasions, the predicted OR time is subject to manipulation by surgeons during scheduling. We aimed to address the use of the EHR for OR scheduling and the impact of manipulations on OR time accuracy. METHODS Between April and August 2022, a pilot study was performed in our tertiary center where surgeons in multiple surgical specialties were encouraged toward nonmanipulation for predicted OR time during scheduling. The OR time accuracy within 5 months before trial (Group 1) and within the trial period (Group 2) were compared. Accurate cases were defined as cases with total length (wheels-in to wheels-out) within ±30 min or ±20% of the scheduled duration if the scheduled time is ≥ or <150 min, respectively. The study included single and multiple Current Procedural Terminology code procedures, while procedures involving multiple surgical specialties (combo cases) were excluded. PRINCIPAL FINDINGS The study included a total of 8,821 operations, 4,243 (Group 1) and 4,578 (Group 2), (p < .001). The percentage of manipulation dropped from 19.8% (Group 1) to 7.6% (Group 2), (p < .001), while scheduling accuracy rose from 41.7% (Group 1) to 47.9% (Group 2), (p = .0001) with a significant reduction of underscheduling percentage (38.7% vs. 31.7%, p = .0001) and without a significant difference in the percentage of overscheduled cases (15% vs. 17%, p = .22). Inaccurate OR hours were reduced by 18% during the trial period (2,383 hr vs. 1,954 hr). PRACTICAL APPLICATIONS The utilization of EHR systems for predicting OR time and reducing manipulation by surgeons helps improve OR scheduling accuracy and utilization of OR resources.
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Affiliation(s)
- Mohamed Elsaqa
- Baylor Scott & White Medical Center, Temple, Texas and Alexandria University Faculty of Medicine, Alexandria, Egypt
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Chen X, He J, Peng L, Lin L, Cheng P, Xiao Y, Liu S. Impact of a Task-Grabbing System for surgical technicians on operating room efficiency. Sci Rep 2024; 14:4296. [PMID: 38383755 PMCID: PMC10881986 DOI: 10.1038/s41598-024-54524-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 02/13/2024] [Indexed: 02/23/2024] Open
Abstract
The purpose of this study was to evaluate the effect of the Task-Grabbing System on operating room efficiency. Based on the competition-driven concept of the 'Uber' app, an Task-Grabbing System was designed for task allocation and quality assessment. We implemented the Task-Grabbing System in our hospital operating room and compared the differences in consecutive operation preparation time, turnover time, and task completion time performed by surgical technicians for tasks such as patient pick-up, operating room cleaning, medical equipment recovery, three-piece set delivery, as well as blood gas analysis and intraoperative specimen submission before (October 2019) and after (December 2019) the implementation of the Task-Grabbing System. After the implementation of the Task-Grabbing System, the consecutive operation preparation time was reduced from the average of 43.56-38.55 min (P < 0.05), and the turnover time was decreased from the average of 14.25-12.61 min (P < 0.05). And the respective time consuming of surgical technicians for patients picking up, operating room cleaning, medical facilities recovering, the three-piece set delivering, blood gas analysis sending and intraoperative specimen submitting was significantly shortened (P < 0.05). The Task-Grabbing System could improve the operating room efficiency and effectively mobilize the enthusiasm and initiative of the surgical technicians.
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Affiliation(s)
- Xiuwen Chen
- Teaching and Research Section of Clinical Nursing, Department of Operating Room, Xiangya Hospital, Central South University, Changsha, China
- Xiangya School of Nursing, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jiqun He
- Teaching and Research Section of Clinical Nursing, Department of Operating Room, Xiangya Hospital, Central South University, Changsha, China
| | - Luofang Peng
- Teaching and Research Section of Clinical Nursing, Department of Operating Room, Xiangya Hospital, Central South University, Changsha, China
| | - Li Lin
- Teaching and Research Section of Clinical Nursing, Department of Operating Room, Xiangya Hospital, Central South University, Changsha, China
| | - Pengfei Cheng
- School of Business, Hunan University of Science and Technology, Xiangtan, China
| | - Yao Xiao
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
| | - Shiqing Liu
- Department of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
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Bellini V, Russo M, Domenichetti T, Panizzi M, Allai S, Bignami EG. Artificial Intelligence in Operating Room Management. J Med Syst 2024; 48:19. [PMID: 38353755 PMCID: PMC10867065 DOI: 10.1007/s10916-024-02038-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/05/2024] [Indexed: 02/16/2024]
Abstract
This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Michele Russo
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Tania Domenichetti
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Matteo Panizzi
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Simone Allai
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy
| | - Elena Giovanna Bignami
- Anesthesiology, Intensive Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, 43126, Italy.
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Riahi V, Hassanzadeh H, Khanna S, Boyle J, Syed F, Biki B, Borkwood E, Sweeney L. Improving preoperative prediction of surgery duration. BMC Health Serv Res 2023; 23:1343. [PMID: 38042831 PMCID: PMC10693694 DOI: 10.1186/s12913-023-10264-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 11/01/2023] [Indexed: 12/04/2023] Open
Abstract
BACKGROUND Operating rooms (ORs) are one of the costliest units in a hospital, therefore the cumulative consequences of any kind of inefficiency in OR management lead to a significant loss of revenue for the hospital, staff dissatisfaction, and patient care disruption. One of the possible solutions to improving OR efficiency is knowing a reliable estimate of the duration of operations. The literature suggests that the current methods used in hospitals, e.g., a surgeon's estimate for the given surgery or taking the average of only five previous records of the same procedure, have room for improvement. METHODS We used over 4 years of elective surgery records (n = 52,171) from one of the major metropolitan hospitals in Australia. We developed robust Machine Learning (ML) approaches to provide a more accurate prediction of operation duration, especially in the absence of surgeon's estimation. Individual patient characteristics and historic surgery information attributed to medical records were used to train predictive models. A wide range of algorithms such as Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were tested for predicting operation duration. RESULTS The results show that the XGBoost model provided statistically significantly less error than other compared ML models. The XGBoost model also reduced the total absolute error by 6854 min (i.e., about 114 h) compared to the current hospital methods. CONCLUSION The results indicate the potential of using ML methods for reaching a more accurate estimation of operation duration compared to current methods used in the hospital. In addition, using a set of realistic features in the ML models that are available at the point of OR scheduling enabled the potential deployment of the proposed approach.
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Affiliation(s)
- Vahid Riahi
- The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Melbourne, VIC, Australia.
| | - Hamed Hassanzadeh
- The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia
| | - Sankalp Khanna
- The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia
| | - Justin Boyle
- The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia
| | - Faraz Syed
- Fiona Stanley Hospital, Western Australia Health, Perth, WA, Australia
| | - Barbara Biki
- Fiona Stanley Hospital, Western Australia Health, Perth, WA, Australia
| | - Ellen Borkwood
- Fiona Stanley Hospital, Western Australia Health, Perth, WA, Australia
| | - Lianne Sweeney
- Fiona Stanley Hospital, Western Australia Health, Perth, WA, Australia
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Zaribafzadeh H, Webster WL, Vail CJ, Daigle T, Kirk AD, Allen PJ, Henao R, Buckland DM. Development, Deployment, and Implementation of a Machine Learning Surgical Case Length Prediction Model and Prospective Evaluation. Ann Surg 2023; 278:890-895. [PMID: 37264901 PMCID: PMC10631498 DOI: 10.1097/sla.0000000000005936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
OBJECTIVE To implement a machine learning model using only the restricted data available at case creation time to predict surgical case length for multiple services at different locations. BACKGROUND The operating room is one of the most expensive resources in a health system, estimated to cost $22 to $133 per minute and generate about 40% of hospital revenue. Accurate prediction of surgical case length is necessary for efficient scheduling and cost-effective utilization of the operating room and other resources. METHODS We introduced a similarity cascade to capture the complexity of cases and surgeon influence on the case length and incorporated that into a gradient-boosting machine learning model. The model loss function was customized to improve the balance between over- and under-prediction of the case length. A production pipeline was created to seamlessly deploy and implement the model across our institution. RESULTS The prospective analysis showed that the model output was gradually adopted by the schedulers and outperformed the scheduler-predicted case length from August to December 2022. In 33,815 surgical cases across outpatient and inpatient platforms, the operational implementation predicted 11.2% fewer underpredicted cases and 5.9% more cases within 20% of the actual case length compared with the schedulers and only overpredicted 5.3% more. The model assisted schedulers to predict 3.4% more cases within 20% of the actual case length and 4.3% fewer underpredicted cases. CONCLUSIONS We created a unique framework that is being leveraged every day to predict surgical case length more accurately at case posting time and could be potentially utilized to deploy future machine learning models.
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Affiliation(s)
- Hamed Zaribafzadeh
- Department of Biostatistics and Bioinformatics, and Department of Surgery, Duke University, Durham, NC
| | | | | | - Thomas Daigle
- Duke Health Technology Solutions, Duke University Health System, Durham, NC
| | | | | | - Ricardo Henao
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC
| | - Daniel M. Buckland
- Department of Surgery, Duke University, Durham, NC
- Department of Emergency Medicine and Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC
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11
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Spence C, Shah OA, Cebula A, Tucker K, Sochart D, Kader D, Asopa V. Machine learning models to predict surgical case duration compared to current industry standards: scoping review. BJS Open 2023; 7:zrad113. [PMID: 37931236 PMCID: PMC10630142 DOI: 10.1093/bjsopen/zrad113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Surgical waiting lists have risen dramatically across the UK as a result of the COVID-19 pandemic. The effective use of operating theatres by optimal scheduling could help mitigate this, but this requires accurate case duration predictions. Current standards for predicting the duration of surgery are inaccurate. Artificial intelligence (AI) offers the potential for greater accuracy in predicting surgical case duration. This study aimed to investigate whether there is evidence to support that AI is more accurate than current industry standards at predicting surgical case duration, with a secondary aim of analysing whether the implementation of the models used produced efficiency savings. METHOD PubMed, Embase, and MEDLINE libraries were searched through to July 2023 to identify appropriate articles. PRISMA extension for scoping reviews and the Arksey and O'Malley framework were followed. Study quality was assessed using a modified version of the reporting guidelines for surgical AI papers by Farrow et al. Algorithm performance was reported using evaluation metrics. RESULTS The search identified 2593 articles: 14 were suitable for inclusion and 13 reported on the accuracy of AI algorithms against industry standards, with seven demonstrating a statistically significant improvement in prediction accuracy (P < 0.05). The larger studies demonstrated the superiority of neural networks over other machine learning techniques. Efficiency savings were identified in a RCT. Significant methodological limitations were identified across most studies. CONCLUSION The studies suggest that machine learning and deep learning models are more accurate at predicting the duration of surgery; however, further research is required to determine the best way to implement this technology.
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Affiliation(s)
- Christopher Spence
- Academic Surgical Unit, South West London Elective Orthopaedic Centre, Epsom, Surrey, UK
| | - Owais A Shah
- Academic Surgical Unit, South West London Elective Orthopaedic Centre, Epsom, Surrey, UK
| | - Anna Cebula
- Academic Surgical Unit, South West London Elective Orthopaedic Centre, Epsom, Surrey, UK
| | - Keith Tucker
- Academic Surgical Unit, South West London Elective Orthopaedic Centre, Epsom, Surrey, UK
| | - David Sochart
- Academic Surgical Unit, South West London Elective Orthopaedic Centre, Epsom, Surrey, UK
| | - Deiary Kader
- Academic Surgical Unit, South West London Elective Orthopaedic Centre, Epsom, Surrey, UK
| | - Vipin Asopa
- Academic Surgical Unit, South West London Elective Orthopaedic Centre, Epsom, Surrey, UK
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Kendale S, Bishara A, Burns M, Solomon S, Corriere M, Mathis M. Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study. JMIR AI 2023; 2:e44909. [PMID: 38875567 PMCID: PMC11041482 DOI: 10.2196/44909] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 06/14/2023] [Accepted: 07/02/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Accurate projections of procedural case durations are complex but critical to the planning of perioperative staffing, operating room resources, and patient communication. Nonlinear prediction models using machine learning methods may provide opportunities for hospitals to improve upon current estimates of procedure duration. OBJECTIVE The aim of this study was to determine whether a machine learning algorithm scalable across multiple centers could make estimations of case duration within a tolerance limit because there are substantial resources required for operating room functioning that relate to case duration. METHODS Deep learning, gradient boosting, and ensemble machine learning models were generated using perioperative data available at 3 distinct time points: the time of scheduling, the time of patient arrival to the operating or procedure room (primary model), and the time of surgical incision or procedure start. The primary outcome was procedure duration, defined by the time between the arrival and the departure of the patient from the procedure room. Model performance was assessed by mean absolute error (MAE), the proportion of predictions falling within 20% of the actual duration, and other standard metrics. Performance was compared with a baseline method of historical means within a linear regression model. Model features driving predictions were assessed using Shapley additive explanations values and permutation feature importance. RESULTS A total of 1,177,893 procedures from 13 academic and private hospitals between 2016 and 2019 were used. Across all procedures, the median procedure duration was 94 (IQR 50-167) minutes. In estimating the procedure duration, the gradient boosting machine was the best-performing model, demonstrating an MAE of 34 (SD 47) minutes, with 46% of the predictions falling within 20% of the actual duration in the test data set. This represented a statistically and clinically significant improvement in predictions compared with a baseline linear regression model (MAE 43 min; P<.001; 39% of the predictions falling within 20% of the actual duration). The most important features in model training were historical procedure duration by surgeon, the word "free" within the procedure text, and the time of day. CONCLUSIONS Nonlinear models using machine learning techniques may be used to generate high-performing, automatable, explainable, and scalable prediction models for procedure duration.
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Affiliation(s)
- Samir Kendale
- Department of Anesthesia, Critical Care & Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Andrew Bishara
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA, United States
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, United States
| | - Michael Burns
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Stuart Solomon
- Department of Anesthesiology, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Matthew Corriere
- Department of Surgery, Section of Vascular Surgery, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Michael Mathis
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States
- Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States
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von Hertzberg-Boelch S, Mueller L, Stratos I, Arnholdt J, Holzapfel B, Rudert M. Which patient-specific parameters correlate with operation time for total hip arthroplasty? - A retrospective analysis of the direct anterior approach. INTERNATIONAL ORTHOPAEDICS 2023; 47:1975-1979. [PMID: 37269401 PMCID: PMC10345041 DOI: 10.1007/s00264-023-05841-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/14/2023] [Indexed: 06/05/2023]
Abstract
PURPOSE The current study aims to identify patient-specific factors that correlate with operation time for total hip arthroplasty (THA) performed via the direct anterior approach (DAA). METHODS In this retrospective study, patient-specific factors were tabulated from the charts and measured from preoperative templating radiographs. These factors were correlated with operation time by bivariate analysis. Significant factors were used for stepwise multiple regression analysis. RESULTS Nine hundred-sixty procedures were included. BMI (R = 0.283), the distance from the superior iliac spine to the greater trochanter (DAA Plane) (R = - 0.154), patients age (R = 0.152) and the abdominal fat flap (R = 0.134) showed the strongest correlations (p < 0.005) with operation time. The multiple regression model including BMI, Kellgren and Lawrence Score, Age, DAA Plane and the Canal to Calcar ratio had the best predictive accuracy (corrected R2 = 0.122). CONCLUSIONS Patient-specific factors that make the entry into the femur difficult correlate significantly with operation time of THA via the DAA.
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Affiliation(s)
- Sebastian von Hertzberg-Boelch
- Department of Orthopaedic Surgery, Julius-Maximilian University, Würzburg, Germany.
- LVR Klinik für Orthopädie Viersen, Viersen, Germany.
| | - Laura Mueller
- Department of Orthopaedic Surgery, Julius-Maximilian University, Würzburg, Germany
| | - Ioannis Stratos
- Department of Orthopaedic Surgery, Julius-Maximilian University, Würzburg, Germany
| | - Joerg Arnholdt
- Department of Orthopaedics and Trauma Surgery, Musculoskeletal University Center Munich (MUM), University Hospital, Munich, Germany
| | - Boris Holzapfel
- Department of Orthopaedics and Trauma Surgery, Musculoskeletal University Center Munich (MUM), University Hospital, Munich, Germany
| | - Maximilian Rudert
- Department of Orthopaedic Surgery, Julius-Maximilian University, Würzburg, Germany
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Aljaffary A, AlAnsari F, Alatassi A, AlSuhaibani M, Alomran A. Assessing the Precision of Surgery Duration Estimation: A Retrospective Study. J Multidiscip Healthc 2023; 16:1565-1576. [PMID: 37309537 PMCID: PMC10257906 DOI: 10.2147/jmdh.s403756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 05/30/2023] [Indexed: 06/14/2023] Open
Abstract
Background and Objectives The operating room (OR) is considered the highest source of cost and earnings. Therefore, measuring OR efficiency, which means how time and resources are allocated precisely for their intended purposes in the operating room is crucial. Both overestimation and underestimation negatively impact OR efficiency Therefore, hospitals defined metrics to Measuring OR Effeciency. Many studies have discussed OR efficiency and how surgery scheduling accuracy plays a vital role in increasing OR efficiency. This study aims to evaluate OR efficiency using surgery duration accuracy. Methods This retrospective, quantitative study was conducted at King Abdulaziz Medical City. We extracted data on 97,397 surgeries from 2017 to 2021 from the OR database. The accuracy of surgery duration was identified by calculating the duration of each surgery in minutes by subtracting the time of leaving the OR from the time of entering the OR. Based on the scheduled duration, the calculated durations were categorized as either underestimation or overestimation. Descriptive and bivariate analyses (Chi-square test) were performed using the Statistical Package for the Social Sciences (SPSS) software. Results Sixty percent out of the 97,397 surgeries performed were overestimated compared to the time scheduled by the surgeons. Patient characteristics, surgical division, and anesthesia type showed statistically significant differences (p <0.05) in their OR estimation. Conclusion Significant proportion of procedures have overestimated. This finding provides insight into the need for improvement. Recommendations It is recommended to enhance the surgical scheduling method using machine learning (ML) models to include patient characteristics, department, anesthesia type, and even the performing surgeon increases the accuracy of duration estimation. Then, evaluate the performance of an ML model in future studies.
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Affiliation(s)
- Afnan Aljaffary
- Health Information Management and Technology Department, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Fatimah AlAnsari
- Health Information Management and Technology Department, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Abdulaleem Alatassi
- Preoperative Quality and Patient Safety Department, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Mohammed AlSuhaibani
- Operating Room Services Department, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Ammar Alomran
- Department of Orthopedic, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
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Miller LE, Goedicke W, Crowson MG, Rathi VK, Naunheim MR, Agarwala AV. Using Machine Learning to Predict Operating Room Case Duration: A Case Study in Otolaryngology. Otolaryngol Head Neck Surg 2023; 168:241-247. [PMID: 35133897 DOI: 10.1177/01945998221076480] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/07/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Optimizing operating room (OR) efficiency depends on accurate case duration estimates. Machine learning (ML) methods have been used to predict OR case durations in other subspecialties. We hypothesize that ML methods improve projected case lengths over existing non-ML techniques for otolaryngology-head and neck surgery cases. METHODS Deidentified patient information from otolaryngology surgical cases at 1 academic institution were reviewed from 2016 to 2020. Variables collected included patient, surgeon, procedure, and facility data known preoperatively so as to capture all realistic contributors. Available case data were divided into a training and testing data set. Several ML algorithms were evaluated based on best performance of predicted case duration when compared to actual case duration. Performance of all models was compared by the average root mean squared error and mean absolute error (MAE). RESULTS In total, 50,888 otolaryngology surgical cases were evaluated with an average case duration of 98.3 ± 86.9 minutes. Most cases were general otolaryngology (n = 16,620). Case features closely associated with OR duration included procedure performed, surgeon, subspecialty of case, and postoperative destination of the patient. The best-performing ML models were CatBoost and XGBoost, which reduced operative time MAE by 9.6 minutes and 8.5 minutes compared to current methods, respectively. DISCUSSION The incorporation of other easily identifiable features beyond procedure performed and surgeon meaningfully improved our operative duration prediction accuracy. CatBoost provided the best-performing ML model. IMPLICATIONS FOR PRACTICE ML algorithms to predict OR case time duration in otolaryngology can improve case duration accuracy and result in financial benefit.
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Affiliation(s)
- Lauren E Miller
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - William Goedicke
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Matthew G Crowson
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Vinay K Rathi
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Matthew R Naunheim
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Aalok V Agarwala
- Department of Anesthesia, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
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16
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Leung T, Harjai B, Simpson S, Du AL, Tully JL, George O, Waterman R. An Ensemble Learning Approach to Improving Prediction of Case Duration for Spine Surgery: Algorithm Development and Validation. JMIR Perioper Med 2023; 6:e39650. [PMID: 36701181 PMCID: PMC9912154 DOI: 10.2196/39650] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 11/29/2022] [Accepted: 12/25/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Estimating surgical case duration accurately is an important operating room efficiency metric. Current predictive techniques in spine surgery include less sophisticated approaches such as classical multivariable statistical models. Machine learning approaches have been used to predict outcomes such as length of stay and time returning to normal work, but have not been focused on case duration. OBJECTIVE The primary objective of this 4-year, single-academic-center, retrospective study was to use an ensemble learning approach that may improve the accuracy of scheduled case duration for spine surgery. The primary outcome measure was case duration. METHODS We compared machine learning models using surgical and patient features to our institutional method, which used historic averages and surgeon adjustments as needed. We implemented multivariable linear regression, random forest, bagging, and XGBoost (Extreme Gradient Boosting) and calculated the average R2, root-mean-square error (RMSE), explained variance, and mean absolute error (MAE) using k-fold cross-validation. We then used the SHAP (Shapley Additive Explanations) explainer model to determine feature importance. RESULTS A total of 3189 patients who underwent spine surgery were included. The institution's current method of predicting case times has a very poor coefficient of determination with actual times (R2=0.213). On k-fold cross-validation, the linear regression model had an explained variance score of 0.345, an R2 of 0.34, an RMSE of 162.84 minutes, and an MAE of 127.22 minutes. Among all models, the XGBoost regressor performed the best with an explained variance score of 0.778, an R2 of 0.770, an RMSE of 92.95 minutes, and an MAE of 44.31 minutes. Based on SHAP analysis of the XGBoost regression, body mass index, spinal fusions, surgical procedure, and number of spine levels involved were the features with the most impact on the model. CONCLUSIONS Using ensemble learning-based predictive models, specifically XGBoost regression, can improve the accuracy of the estimation of spine surgery times.
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Affiliation(s)
| | - Bhavya Harjai
- Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, San Diego, CA, United States
| | - Sierra Simpson
- Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, San Diego, CA, United States
| | - Austin Liu Du
- School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Jeffrey Logan Tully
- Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, San Diego, CA, United States
| | - Olivier George
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - Ruth Waterman
- Department of Anesthesiology, University of California, San Diego, San Diego, CA, United States
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Eshghali M, Kannan D, Salmanzadeh-Meydani N, Esmaieeli Sikaroudi AM. Machine learning based integrated scheduling and rescheduling for elective and emergency patients in the operating theatre. ANNALS OF OPERATIONS RESEARCH 2023:1-24. [PMID: 36694896 PMCID: PMC9851122 DOI: 10.1007/s10479-023-05168-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
As the only largest source of revenue and cost in a hospital, the operation room (OR) scheduling problem is a hot research topic. Nonetheless, an integrated model is the missing key to managing and improving the efficiency of ORs. This paper presents a fully integrated model regarding three concepts: meditating elective patients and emergency patients together, considering ORs and downstream units, and proposing hierarchical weekly, daily, and rescheduling models. Due to the inherent randomness in emergency patient arrival, a random forest machine learning model and geographical information systems are used to obtain the emergency patient surgery duration and arrival time, respectively. According to the machine learning model in weekly and daily scheduling, initially, fixed capacity is reserved for emergency patients. When an emergency patient arrives, the surgery starts if a reserved OR is available. Otherwise, the first available OR will be dedicated to the patient due to an emergency patient's higher priority than an elective patient. In this case, it is needed to reschedule the OT schedule for the remaining patient. Moreover, the three-phase model guarantees that an emergency patient assigns to an OR within a specific time limit. To solve the models, genetic algorithm and particle swarm optimization are developed and compared. In addition, a real-world case study is undertaken at a hospital. The results of comparing the proposed approach to the hospital's current scheduling show that the three-phase model had a considerable positive effect on the ORs schedule.
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Affiliation(s)
- Masoud Eshghali
- Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ 85721 USA
| | - Devika Kannan
- Centre for Sustainable Supply Chain Engineering, Department of Technology and Innovation, University of Southern Denmark, 5230 Odense M, Denmark
- School of Business, Woxsen University, Sadasivpet, Telangana India
| | - Navid Salmanzadeh-Meydani
- Centre for Sustainable Supply Chain Engineering, Department of Technology and Innovation, University of Southern Denmark, 5230 Odense M, Denmark
- Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
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18
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Witvoet S, de Massari D, Shi S, Chen AF. Leveraging large, real-world data through machine-learning to increase efficiency in robotic-assisted total knee arthroplasty. Knee Surg Sports Traumatol Arthrosc 2023:10.1007/s00167-023-07314-1. [PMID: 36650339 DOI: 10.1007/s00167-023-07314-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 01/04/2023] [Indexed: 01/19/2023]
Abstract
PURPOSE Increased operative time can be due to patient, surgeon and surgical factors, and may be predicted by machine learning (ML) modeling to potentially improve staff utilization and operating room efficiency. The purposes of our study were to: (1) determine how demographic, surgeon, and surgical factors affected operative times, and (2) train a ML model to estimate operative time for robotic-assisted primary total knee arthroplasty (TKA). METHODS A retrospective study from 2007 to 2020 was conducted including 300,000 unilateral primary TKA cases. Demographic and surgical variables were evaluated using Wilcoxon/Kruskal-Wallis tests to determine significant factors of operative time as predictors in the ML models. For the ML analysis of robotic-assisted TKAs (> 18,000), two algorithms were used to learn the relationship between selected predictors and operative time. Predictive model performance was subsequently assessed on a test data set comparing predicted and actual operative time. Root mean square error (RMSE), R2 and percentage of predictions with an error < 5/10/15 min were computed. RESULTS Males, BMI > 40 kg/m2 and cemented implants were associated with increased operative time, while age > 65yo, cementless, and high surgeon case volume had reduced operative time. Robotic-assisted TKA increased operative time for low-volume surgeons and decreased operative time for high-volume surgeons. Both ML models provided more accurate operative time predictions than standard time estimates based on surgeon historical averages. CONCLUSIONS This study demonstrated that greater surgeon case volume, cementless fixation, manual TKA, female, older and non-obese patients reduced operative time. ML prediction of operative time can be more accurate than historical averages, which may lead to optimized operating room utilization. LEVEL OF EVIDENCE III.
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Affiliation(s)
| | | | - Sarah Shi
- Stryker Corporation, Mahwah, NJ, USA
| | - Antonia F Chen
- Department of Orthopaedics, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.
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Jansson M, Ohtonen P, Alalääkkölä T, Heikkinen J, Mäkiniemi M, Lahtinen S, Lahtela R, Ahonen M, Jämsä S, Liisantti J. Artificial intelligence-enhanced care pathway planning and scheduling system: content validity assessment of required functionalities. BMC Health Serv Res 2022; 22:1513. [PMID: 36510176 PMCID: PMC9746075 DOI: 10.1186/s12913-022-08780-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 11/02/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) and machine learning are transforming the optimization of clinical and patient workflows in healthcare. There is a need for research to specify clinical requirements for AI-enhanced care pathway planning and scheduling systems to improve human-AI interaction in machine learning applications. The aim of this study was to assess content validity and prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system. METHODS A prospective content validity assessment was conducted in five university hospitals in three different countries using an electronic survey. The content of the survey was formed from clinical requirements, which were formulated into generic statements of required AI functionalities. The relevancy of each statement was evaluated using a content validity index. In addition, weighted ranking points were calculated to prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system. RESULTS A total of 50 responses were received from clinical professionals from three European countries. An item-level content validity index ranged from 0.42 to 0.96. 45% of the generic statements were considered good. The highest ranked functionalities for an AI-enhanced care pathway planning and scheduling system were related to risk assessment, patient profiling, and resources. The highest ranked functionalities for the user interface were related to the explainability of machine learning models. CONCLUSION This study provided a comprehensive list of functionalities that can be used to design future AI-enhanced solutions and evaluate the designed solutions against requirements. The relevance of statements concerning the AI functionalities were considered somewhat relevant, which might be due to the low level or organizational readiness for AI in healthcare.
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Affiliation(s)
- Miia Jansson
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
| | - Pasi Ohtonen
- Research Unit of Surgery, Anesthesia and Intensive Care, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - Timo Alalääkkölä
- Testing and Innovations, Oulu University Hospital, Oulu, Finland
| | - Juuso Heikkinen
- Division of Orthopedic and Trauma Surgery, Department of Surgery, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | | | - Sanna Lahtinen
- Department of Anesthesiology, Oulu University Hospital, Oulu, Finland
- MRC Oulu, Research Group of Anesthesiology, Oulu, Finland
| | - Riikka Lahtela
- Department of Anesthesiology, Oulu University Hospital, Oulu, Finland
| | - Merja Ahonen
- Department of Anesthesiology, Oulu University Hospital, Oulu, Finland
- MRC Oulu, Research Group of Anesthesiology, Oulu, Finland
| | - Sirpa Jämsä
- Sense Organ Diseases Centre, Oulu University Hospital, Oulu, Finland
| | - Janne Liisantti
- Department of Anesthesiology, Oulu University Hospital, Oulu, Finland
- MRC Oulu, Research Group of Anesthesiology, Oulu, Finland
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20
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Yeo I, Klemt C, Melnic CM, Pattavina MH, De Oliveira BMC, Kwon YM. Predicting surgical operative time in primary total knee arthroplasty utilizing machine learning models. Arch Orthop Trauma Surg 2022; 143:3299-3307. [PMID: 35994094 DOI: 10.1007/s00402-022-04588-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 08/10/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Prolonged surgical operative time is associated with postoperative adverse outcomes following total knee arthroplasty (TKA). Increasing operating room efficiency necessitates the accurate prediction of surgical operative time for each patient. One potential way to increase the accuracy of predictions is to use advanced predictive analytics, such as machine learning. The aim of this study is to use machine learning to develop an accurate predictive model for surgical operative time for patients undergoing primary total knee arthroplasty. METHODS A retrospective chart review of electronic medical records was conducted to identify patients who underwent primary total knee arthroplasty at a tertiary referral center. Three machine learning algorithms were developed to predict surgical operative time and were assessed by discrimination, calibration and decision curve analysis. Specifically, we used: (1) Artificial Neural Networks (ANNs), (2) Random Forest (RF), and (3) K-Nearest Neighbor (KNN). RESULTS We analyzed the surgical operative time for 10,021 consecutive patients who underwent primary total knee arthroplasty. The neural network model achieved the best performance across discrimination (AUC = 0.82), calibration and decision curve analysis for predicting surgical operative time. Based on this algorithm, younger age (< 45 years), tranexamic acid non-usage, and a high BMI (> 40 kg/m2) were the strongest predictors associated with surgical operative time. CONCLUSIONS This study shows excellent performance of machine learning models for predicting surgical operative time in primary total knee arthroplasty. The accurate estimation of surgical duration is important in enhancing OR efficiency and identifying patients at risk for prolonged surgical operative time. LEVEL OF EVIDENCE Level III, case control retrospective analysis.
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Affiliation(s)
- Ingwon Yeo
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Christian Klemt
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Christopher M Melnic
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Meghan H Pattavina
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Bruna M Castro De Oliveira
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Young-Min Kwon
- Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
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Gabriel RA, Harjai B, Simpson S, Goldhaber N, Curran BP, Waterman RS. Machine Learning-Based Models Predicting Outpatient Surgery End Time and Recovery Room Discharge at an Ambulatory Surgery Center. Anesth Analg 2022; 135:159-169. [PMID: 35389380 PMCID: PMC9172889 DOI: 10.1213/ane.0000000000006015] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Days before surgery, add-ons may be scheduled to fill unused surgical block time at an outpatient surgery center. At times, outpatient surgery centers have time limitations for end of block time and discharge from the postanesthesia care unit (PACU). The objective of our study was to develop machine learning models that predicted the following composite outcome: (1) surgery finished by end of operating room block time and (2) patient was discharged by end of recovery room nursing shift. We compared various machine learning models to logistic regression. By evaluating various performance metrics, including F1 scores, we hypothesized that models using ensemble learning will be superior to logistic regression.
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Affiliation(s)
- Rodney A Gabriel
- From the Department of Anesthesiology, University of California, San Diego, La Jolla, California.,Division of Biomedical Informatics, Department of Medicine, University of California, San Diego, La Jolla, California.,Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, California
| | - Bhavya Harjai
- Division of Perioperative Informatics, Department of Anesthesiology, University of California, San Diego, La Jolla, California
| | - Sierra Simpson
- Department of Psychiatry, University of California, San Diego, La Jolla, California
| | - Nicole Goldhaber
- Department of Surgery, University of California, San Diego, La Jolla, California
| | - Brian P Curran
- From the Department of Anesthesiology, University of California, San Diego, La Jolla, California
| | - Ruth S Waterman
- From the Department of Anesthesiology, University of California, San Diego, La Jolla, California
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Lam SSW, Zaribafzadeh H, Ang BY, Webster W, Buckland D, Mantyh C, Tan HK. Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study. Healthcare (Basel) 2022; 10:healthcare10071191. [PMID: 35885718 PMCID: PMC9319102 DOI: 10.3390/healthcare10071191] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/21/2022] [Accepted: 06/21/2022] [Indexed: 11/26/2022] Open
Abstract
The scheduling of operating room (OR) slots requires the accurate prediction of surgery duration. We evaluated the performance of existing Moving Average (MA) based estimates with novel machine learning (ML)-based models of surgery durations across two sites in the US and Singapore. We used the Duke Protected Analytics Computing Environment (PACE) to facilitate data-sharing and big data analytics across the US and Singapore. Data from all colorectal surgery patients between 1 January 2012 and 31 December 2017 in Singapore and, 1 January 2015 to 31 December 2019 in the US were used, and 7585 cases and 3597 single and multiple procedure cases from Singapore and US were included. The ML models were based on categorical gradient boosting (CatBoost) models trained on common data fields shared by both institutions. The procedure codes were based on the Table of Surgical Procedure (TOSP) (Singapore) and the Current Procedural Terminology (CPT) codes (US). The two types of codes were mapped by surgical experts. The CPT codes were then transformed into the relative value unit (RVU). The ML models outperformed the baseline MA models. The MA, scheduled durations and procedure codes were found to have higher loadings as compared to surgeon factors. We further demonstrated the use of the Duke PACE in facilitating data-sharing and big data analytics.
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Affiliation(s)
- Sean Shao Wei Lam
- Health Services and Systems Research, Duke-NUS Medical School, Health Services Research Centre, Singapore Health Services, Singapore 169856, Singapore;
- SingHealth Duke-NUS Global Health Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore 168753, Singapore;
- Correspondence: ; Tel.: +65-65762617
| | - Hamed Zaribafzadeh
- Department of Surgery, Duke University School of Medicine, Durham, NC 27710, USA; (H.Z.); (W.W.); (D.B.); (C.M.)
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA
| | - Boon Yew Ang
- Health Services and Systems Research, Duke-NUS Medical School, Health Services Research Centre, Singapore Health Services, Singapore 169856, Singapore;
| | - Wendy Webster
- Department of Surgery, Duke University School of Medicine, Durham, NC 27710, USA; (H.Z.); (W.W.); (D.B.); (C.M.)
| | - Daniel Buckland
- Department of Surgery, Duke University School of Medicine, Durham, NC 27710, USA; (H.Z.); (W.W.); (D.B.); (C.M.)
- Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA
| | - Christopher Mantyh
- Department of Surgery, Duke University School of Medicine, Durham, NC 27710, USA; (H.Z.); (W.W.); (D.B.); (C.M.)
| | - Hiang Khoon Tan
- SingHealth Duke-NUS Global Health Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore 168753, Singapore;
- Division of Surgery and Surgical Oncology, Singapore General Hospital, Singapore 168753, Singapore
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Motesharei A, Batailler C, De Massari D, Vincent G, Chen AF, Lustig S. Predicting robotic-assisted total knee arthroplasty operating time. Bone Jt Open 2022; 3:383-389. [PMID: 35532348 PMCID: PMC9134836 DOI: 10.1302/2633-1462.35.bjo-2022-0014.r1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Aims No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model. Methods A retrospective study was conducted on 1,061 TKAs performed from January 2016 to December 2019 with an image-based robotic-assisted system. Demographic data included age, sex, height, and weight. The femoral and tibial mechanical axis and the osteophyte volume were calculated from CT scans. These inputs were used to develop a predictive model aimed to predict operating time based on demographic data only, and demographic and 3D patient anatomy data. Results The key factors for predicting operating time were the surgeon and patient weight, followed by 12 anatomical parameters derived from CT scans. The predictive model based only on demographic data showed that 90% of predictions were within 15 minutes of actual operating time, with 73% within ten minutes. The predictive model including demographic data and CT scans showed that 94% of predictions were within 15 minutes of actual operating time and 88% within ten minutes. Conclusion The primary factors for predicting robotic-assisted TKA operating time were surgeon, patient weight, and osteophyte volume. This study demonstrates that incorporating 3D patient-specific data can improve operating time predictions models, which may lead to improved operating room planning and efficiency. Cite this article: Bone Jt Open 2022;3(5):383–389.
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Affiliation(s)
| | - Cecile Batailler
- Orthopedic Surgery Department, Croix-Rousse Hospital, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
| | | | | | - Antonia F. Chen
- Department of Orthopaedic Surgery, Brigham & Women’s Hospital, Boston, Massachusetts, USA
| | - Sébastien Lustig
- Orthopedic Surgery Department, Croix-Rousse Hospital, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
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24
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Automatic Surgery and Anesthesia Emergence Duration Prediction Using Artificial Neural Networks. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:2921775. [PMID: 35463687 PMCID: PMC9023179 DOI: 10.1155/2022/2921775] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/29/2022] [Accepted: 03/16/2022] [Indexed: 12/29/2022]
Abstract
Cost control is becoming increasingly important in hospital management. Hospital operating rooms have high resource consumption because they are a major part of a hospital. Thus, the optimal use of operating rooms can lead to high resource savings. However, because of the uncertainty of the operation procedures, it is difficult to arrange for the use of operating rooms in advance. In general, the durations of both surgery and anesthesia emergence determine the time requirements of operating rooms, and these durations are difficult to predict. In this study, we used an artificial neural network to construct a surgery and anesthesia emergence duration-prediction system. We propose an intelligent data preprocessing algorithm to balance and enhance the training dataset automatically. The experimental results indicate that the prediction accuracies of the proposed serial prediction systems are acceptable in comparison to separate systems.
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Abbas A, Mosseri J, Lex JR, Toor J, Ravi B, Khalil EB, Whyne C. Machine learning using preoperative patient factors can predict duration of surgery and length of stay for total knee arthroplasty. Int J Med Inform 2022; 158:104670. [PMID: 34971918 DOI: 10.1016/j.ijmedinf.2021.104670] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/29/2021] [Accepted: 12/16/2021] [Indexed: 01/25/2023]
Abstract
BACKGROUND Total knee arthroplasty (TKA) is one of the most resource-intensive, high-volume surgical procedures. Two drivers of the cost of TKAs are duration of surgery (DOS) and postoperative inpatient length of stay (LOS). The ability to predict TKA DOS and LOS has substantial implications for hospital finances, scheduling, and resource allocation. The goal of this study was to predict DOS and LOS for elective unilateral TKAs using machine learning models (MLMs) based on preoperative factors. METHODS The American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database was queried for unilateral TKAs from 2014 to 2019. The dataset was split into training, validation, and testing based on year. Models (linear, tree-based, and multilayer perceptron (MLP)) were fitted to the training set in scikit-learn and PyTorch, with hyperparameters tuned on the validation set. The models were trained to minimize the mean squared error (MSE). Models with the best performance on the validation set were evaluated on the testing set according to 1) MSE, 2) buffer accuracy, and 3) classification accuracy, with results compared to a mean regressor. RESULTS A total of 302,300 patients were included in this study. During validation, the PyTorch MLPs had the best MSEs for DOS (0.918) and LOS (0.715). During testing, the PyTorch MLPs similarly performed best based on MSEs for DOS (0.896) and LOS (0.690). While the scikit-learn MLP yielded the best 30-minute buffer accuracy for DOS (78.8%), the PyTorch MLP provided the best 1-day buffer accuracy for LOS (75.2%). Nearly all the ML models were more accurate than the mean regressors for both DOS and LOS. CONCLUSION Conventional and deep learning models performed better than mean regressors for predicting DOS and LOS of unilateral elective TKA patients based on preoperative factors. Future work should include operational factors to improve overall predictions.
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Affiliation(s)
- Aazad Abbas
- Temerty Faculty of Medicine, University of Toronto, 1 King's College Circle, Toronto, ON M5S 1A8, Canada; Orthopaedic Biomechanics Lab, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada.
| | - Jacob Mosseri
- Orthopaedic Biomechanics Lab, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON M5S 3G8, Canada.
| | - Johnathan R Lex
- Orthopaedic Biomechanics Lab, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Division of Orthopaedic Surgery, University of Toronto, 149 College Street Room 508-A, Toronto, ON M5T 1P5, Canada
| | - Jay Toor
- Orthopaedic Biomechanics Lab, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Division of Orthopaedic Surgery, University of Toronto, 149 College Street Room 508-A, Toronto, ON M5T 1P5, Canada.
| | - Bheeshma Ravi
- Division of Orthopaedic Surgery, University of Toronto, 149 College Street Room 508-A, Toronto, ON M5T 1P5, Canada; Division of Orthopaedic Surgery, Sunnybrook Health Science Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada.
| | - Elias B Khalil
- Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON M5S 3G8, Canada.
| | - Cari Whyne
- Orthopaedic Biomechanics Lab, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada; Division of Orthopaedic Surgery, University of Toronto, 149 College Street Room 508-A, Toronto, ON M5T 1P5, Canada; Division of Orthopaedic Surgery, Sunnybrook Health Science Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada.
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Affiliation(s)
- Samir Kendale
- Department of Anesthesia, Critical Care & Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
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Klumpp M, Hintze M, Immonen M, Ródenas-Rigla F, Pilati F, Aparicio-Martínez F, Çelebi D, Liebig T, Jirstrand M, Urbann O, Hedman M, Lipponen JA, Bicciato S, Radan AP, Valdivieso B, Thronicke W, Gunopulos D, Delgado-Gonzalo R. Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals. Healthcare (Basel) 2021; 9:961. [PMID: 34442098 PMCID: PMC8393951 DOI: 10.3390/healthcare9080961] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/28/2021] [Accepted: 07/03/2021] [Indexed: 01/21/2023] Open
Abstract
The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human-computer interaction, data management, and communication in AI implementation projects.
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Affiliation(s)
- Matthias Klumpp
- Fraunhofer Institute for Material Flow and Logistics (IML), Josef-von-Fraunhofer-Str. 2-4, 44227 Dortmund, Germany; (M.H.); (O.U.)
- Department of Business Administration, Georg-August-University of Göttingen, Platz der Göttinger Sieben 3, 37073 Göttingen, Germany
| | - Marcus Hintze
- Fraunhofer Institute for Material Flow and Logistics (IML), Josef-von-Fraunhofer-Str. 2-4, 44227 Dortmund, Germany; (M.H.); (O.U.)
| | - Milla Immonen
- VTT Technical Research Centre of Finland Ltd., Kaitoväylä 1, 90571 Oulu, Finland;
| | - Francisco Ródenas-Rigla
- Polibienestar Research Institute, University of Valencia, Carrer del Serpis 29, 46022 València, Spain;
| | - Francesco Pilati
- Department of Industrial Engineering, University of Trento, Via Sommarive 9, 38123 Trento, Italy;
| | | | - Dilay Çelebi
- Department of Management Engineering, Istanbul Technical University, Macka, Beşiktaş, 34367 İstanbul, Turkey;
| | - Thomas Liebig
- TU Dortmund, Artificial Intelligence Unit, Otto-Hahn-Straße 12, 44221 Dortmund, Germany;
- Materna Information & Communications SE, Artificial Intelligence Unit, Voßkuhle 37, 44141 Dortmund, Germany
| | - Mats Jirstrand
- Fraunhofer-Chalmers Centre & Fraunhofer Center for Machine Learning, Chalmers Science Park, 41288 Gothenburg, Sweden;
| | - Oliver Urbann
- Fraunhofer Institute for Material Flow and Logistics (IML), Josef-von-Fraunhofer-Str. 2-4, 44227 Dortmund, Germany; (M.H.); (O.U.)
| | - Marja Hedman
- Heart Center, Kuopio University Hospital and Institute of Clinical Medicine, University of Eastern Finland, Ritva Jauhiainen-Bruun, 70029 Kuopio, Finland;
| | - Jukka A. Lipponen
- Department of Applied Physics, University of Eastern Finland, Yliopistonranta 1, 70210 Kuopio, Finland;
| | - Silvio Bicciato
- Interdepartmental Center for Stem Cells and Regenerative Medicine (CIDSTEM), Department of Life Sciences, University of Modena and Reggio Emilia, Via Gottardi 100, 41125 Modena, Italy;
| | - Anda-Petronela Radan
- Department of Obstetrics and Gynecology, University Hospital of Bern, Murtenstraße 11, 3008 Bern, Switzerland;
| | - Bernardo Valdivieso
- La Fe University Hospital Valencia, Avinguda de Fernando Abril Martorell 106, 46026 València, Spain;
| | - Wolfgang Thronicke
- ATOS Information Technology GmbH, Fürstenallee 11, 33102 Paderborn, Germany;
| | - Dimitrios Gunopulos
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Panepistimioupolis, Ilisia, 15784 Athens, Greece;
| | - Ricard Delgado-Gonzalo
- Centre Suisse d’Électronique et de Microtechnique CSEM, Jaquet Droz 1, 2002 Neuchâtel, Switzerland;
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Jiao Y, Sharma A, Ben Abdallah A, Maddox TM, Kannampallil T. Probabilistic forecasting of surgical case duration using machine learning: model development and validation. J Am Med Inform Assoc 2020; 27:1885-1893. [PMID: 33031543 PMCID: PMC7727362 DOI: 10.1093/jamia/ocaa140] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 05/18/2020] [Accepted: 06/11/2020] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Accurate estimations of surgical case durations can lead to the cost-effective utilization of operating rooms. We developed a novel machine learning approach, using both structured and unstructured features as input, to predict a continuous probability distribution of surgical case durations. MATERIALS AND METHODS The data set consisted of 53 783 surgical cases performed over 4 years at a tertiary-care pediatric hospital. Features extracted included categorical (American Society of Anesthesiologists [ASA] Physical Status, inpatient status, day of week), continuous (scheduled surgery duration, patient age), and unstructured text (procedure name, surgical diagnosis) variables. A mixture density network (MDN) was trained and compared to multiple tree-based methods and a Bayesian statistical method. A continuous ranked probability score (CRPS), a generalized extension of mean absolute error, was the primary performance measure. Pinball loss (PL) was calculated to assess accuracy at specific quantiles. Performance measures were additionally evaluated on common and rare surgical procedures. Permutation feature importance was measured for the best performing model. RESULTS MDN had the best performance, with a CRPS of 18.1 minutes, compared to tree-based methods (19.5-22.1 minutes) and the Bayesian method (21.2 minutes). MDN had the best PL at all quantiles, and the best CRPS and PL for both common and rare procedures. Scheduled duration and procedure name were the most important features in the MDN. CONCLUSIONS Using natural language processing of surgical descriptors, we demonstrated the use of ML approaches to predict the continuous probability distribution of surgical case durations. The more discerning forecast of the ML-based MDN approach affords opportunities for guiding intelligent schedule design and day-of-surgery operational decisions.
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Affiliation(s)
- York Jiao
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Anshuman Sharma
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Arbi Ben Abdallah
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Thomas M Maddox
- Division of Cardiology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri, USA
- Healthcare Innovation Lab, BJC HealthCare/Washington University School of Medicine, St. Louis, Missouri, USA
| | - Thomas Kannampallil
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, USA
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
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Calegari R, Fogliatto FS, Lucini FR, Anzanello MJ, Schaan BD. Surgery scheduling heuristic considering OR downstream and upstream facilities and resources. BMC Health Serv Res 2020; 20:684. [PMID: 32703210 PMCID: PMC7379827 DOI: 10.1186/s12913-020-05555-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 07/19/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Surgical theater (ST) operations planning is a key subject in the healthcare management literature, particularly the scheduling of procedures in operating rooms (ORs). The OR scheduling problem is usually approached using mathematical modeling and made available to ST managers through dedicated software. Regardless of the large body of knowledge on the subject, OR scheduling models rarely consider the integration of OR downstream and upstream facilities and resources or validate their propositions in real life, rather using simulated scenarios. We propose a heuristic to sequence surgeries that considers both upstream and downstream resources required to perform them, such as surgical kits, post anesthesia care unit (PACU) beds, and surgical teams (surgeons, nurses and anesthetists). METHODS Using hybrid flow shop (HFS) techniques and the break-in-moment (BIM) concept, the goal is to find a sequence that maximizes the number of procedures assigned to the ORs while minimizing the variance of intervals between surgeries' completions, smoothing the demand for downstream resources such as PACU beds and OR sanitizing teams. There are five steps to the proposed heuristic: listing of priorities, local scheduling, global scheduling, feasibility check and identification of best scheduling. RESULTS Our propositions were validated in a high complexity tertiary University hospital in two ways: first, applying the heuristic to historical data from five typical ST days and comparing the performance of our proposed sequences to the ones actually implemented; second, pilot testing the heuristic during ten days in the ORs, allowing a full rotation of surgical specialties. Results displayed an average increase of 37.2% in OR occupancy, allowing an average increase of 4.5 in the number of surgeries performed daily, and reducing the variance of intervals between surgeries' completions by 55.5%. A more uniform distribution of patients' arrivals at the PACU was also observed. CONCLUSIONS Our proposed heuristic is particularly useful to plan the operation of STs in which resources are constrained, a situation that is common in hospital from developing countries. Our propositions were validated through a pilot implementation in a large hospital, contributing to the scarce literature on actual OR scheduling implementation.
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Affiliation(s)
- Rafael Calegari
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, 99, 5° andar, Porto Alegre, 90035-190, Brazil
| | - Flavio S Fogliatto
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, 99, 5° andar, Porto Alegre, 90035-190, Brazil.
| | - Filipe R Lucini
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, AB, Calgary, AB, T2N 4N1, Canada
| | - Michel J Anzanello
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, 99, 5° andar, Porto Alegre, 90035-190, Brazil
| | - Beatriz D Schaan
- Endocrinology Division, Hospital de Clínicas de Porto Alegre / Federal University of Rio Grande do Sul, Av Ramiro Barcelos, 2350, 4° andar, Porto Alegre, 90035-903, Brazil
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The Times they Are a-Changin’ – Healthcare 4.0 Is Coming! J Med Syst 2019; 44:40. [DOI: 10.1007/s10916-019-1513-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 11/26/2019] [Indexed: 10/25/2022]
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Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization. J Med Syst 2019; 44:20. [PMID: 31823034 DOI: 10.1007/s10916-019-1512-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 11/26/2019] [Indexed: 01/09/2023]
Abstract
We conducted a systematic review of literature to better understand the role of new technologies in the perioperative period; in particular we focus on the administrative and managerial Operating Room (OR) perspective. Studies conducted on adult (≥ 18 years) patients between 2015 and February 2019 were deemed eligible. A total of 19 papers were included. Our review suggests that the use of Machine Learning (ML) in the field of OR organization has many potentials. Predictions of the surgical case duration were obtain with a good performance; their use could therefore allow a more precise scheduling, limiting waste of resources. ML is able to support even more complex models, which can coordinate multiple spaces simultaneously, as in the case of the post-anesthesia care unit and operating rooms. Types of Artificial Intelligence could also be used to limit another organizational problem, which has important economic repercussions: cancellation. Random Forest has proven effective in identifing surgeries with high risks of cancellation, allowing to plan preventive measures to reduce the cancellation rate accordingly. In conclusion, although data in literature are still limited, we believe that ML has great potential in the field of OR organization; however, further studies are needed to assess the effective role of these new technologies in the perioperative medicine.
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Abstract
PURPOSE OF REVIEW Operating rooms are critical financial centers for hospital systems, with surgical care representing about a third of all health care spending. However, not all of the costs are appropriate or necessary, as there are sometimes significant inefficiencies in how operating rooms are utilized. RECENT FINDINGS Recent innovations utilizing patient-centered data, systems principles from manufacturing industries, and enhanced communication processes have made significant improvements in improving operating room efficiency. By focusing on improving communication, standardizing processes, and embracing a learning health system with innovations, significant improvements in operating room efficiency can be seen to improve outcomes and costs for the health system and patient.
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Affiliation(s)
- Daniel J Lee
- Division of Urology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. .,Perelman Center for Advanced Medicine, 3400 Civic Center Blvd, West Pavilion 3rd Floor, Philadelphia, PA, 19104, USA.
| | - James Ding
- Division of Urology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas J Guzzo
- Division of Urology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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