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Mehta D, Gonzalez XT, Huang G, Abraham J. Machine learning-augmented interventions in perioperative care: a systematic review and meta-analysis. Br J Anaesth 2024:S0007-0912(24)00473-2. [PMID: 39322472 DOI: 10.1016/j.bja.2024.08.007] [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: 07/03/2024] [Revised: 08/01/2024] [Accepted: 08/05/2024] [Indexed: 09/27/2024] Open
Abstract
BACKGROUND We lack evidence on the cumulative effectiveness of machine learning (ML)-driven interventions in perioperative settings. Therefore, we conducted a systematic review to appraise the evidence on the impact of ML-driven interventions on perioperative outcomes. METHODS Ovid MEDLINE, CINAHL, Embase, Scopus, PubMed, and ClinicalTrials.gov were searched to identify randomised controlled trials (RCTs) evaluating the effectiveness of ML-driven interventions in surgical inpatient populations. The review was registered with PROSPERO (CRD42023433163) and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Meta-analysis was conducted for outcomes with two or more studies using a random-effects model, and vote counting was conducted for other outcomes. RESULTS Among 13 included RCTs, three types of ML-driven interventions were evaluated: Hypotension Prediction Index (HPI) (n=5), Nociception Level Index (NoL) (n=7), and a scheduling system (n=1). Compared with the standard care, HPI led to a significant decrease in absolute hypotension (n=421, P=0.003, I2=75%) and relative hypotension (n=208, P<0.0001, I2=0%); NoL led to significantly lower mean pain scores in the post-anaesthesia care unit (PACU) (n=191, P=0.004, I2=19%). NoL showed no significant impact on intraoperative opioid consumption (n=339, P=0.31, I2=92%) or PACU opioid consumption (n=339, P=0.11, I2=0%). No significant difference in hospital length of stay (n=361, P=0.81, I2=0%) and PACU stay (n=267, P=0.44, I2=0) was found between HPI and NoL. CONCLUSIONS HPI decreased the duration of intraoperative hypotension, and NoL decreased postoperative pain scores, but no significant impact on other clinical outcomes was found. We highlight the need to address both methodological and clinical practice gaps to ensure the successful future implementation of ML-driven interventions. SYSTEMATIC REVIEW PROTOCOL CRD42023433163 (PROSPERO).
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Affiliation(s)
- Divya Mehta
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Xiomara T Gonzalez
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Grace Huang
- Medical Education, Washington University School of Medicine, St. Louis, MO, USA
| | - Joanna Abraham
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, USA; Institute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine, St. Louis, MO, USA.
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2
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Cardillo C, Garry C, Katzman JL, Meftah M, Rozell JC, Schwarzkopf R, Lajam C. Factors Affecting Operating Room Scheduling Accuracy for Primary and Revision Total Knee Arthroplasty: A Retrospective Study. Orthopedics 2024; 47:313-319. [PMID: 38976845 DOI: 10.3928/01477447-20240702-01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
BACKGROUND Optimizing operating room (OR) scheduling accuracy is important for improving OR efficiency and maximizing value of total knee arthroplasty (TKA). However, data on factors that may impact TKA OR scheduling accuracy are limited. MATERIALS AND METHODS A retrospective review of 7655 knee arthroplasties (6999 primary TKAs and 656 revision TKAs) performed between January 2020 and May 2023 was conducted. Patient baseline characteristics, surgeon experience (years in practice), as well as actual vs scheduled OR times were collected. Actual OR times that were at least 15% shorter or longer than scheduled OR times were considered to be clinically important. Logistic regression analyses were employed to assess the influence of specific patient and surgeon factors on OR scheduling inaccuracies. RESULTS Using adjusted odds ratio, patients with primary TKA who had a lower body mass index (P<.001) were independently associated with overestimation of scheduled surgical time. Conversely, younger age (P<.001), afternoon procedure start time (P<.001), surgeons with less than 10 years of experience (P=.037), and higher patient body mass index (P<.001) were associated with underestimation of scheduled surgical time. For revision TKA, female sex (P=.021) and morning procedure start time (P=.038) were associated with overestimation of scheduled surgical time, while surgeons with less than 10 years of experience (P=.014) and patients who underwent spinal/epidural/block anesthesia (P=.038) were associated with underestimation of scheduled surgical time. CONCLUSION This study highlights patient, surgeon, and intraoperative variables that impact the accuracy of scheduling for TKA procedures. Health systems should take these variables into consideration when creating OR schedules to fully optimize resources and available space. [Orthopedics. 2024;47(5):313-319.].
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Pardo E, Le Cam E, Verdonk F. Artificial intelligence and nonoperating room anesthesia. Curr Opin Anaesthesiol 2024; 37:413-420. [PMID: 38934202 DOI: 10.1097/aco.0000000000001388] [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: 06/28/2024]
Abstract
PURPOSE OF REVIEW The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient selection, perioperative care, and anesthesia delivery. This review examines AI's growing impact on NORA and how it can optimize our clinical practice in the near future. RECENT FINDINGS AI has already improved various aspects of anesthesia, including preoperative assessment, intraoperative management, and postoperative care. Studies highlight AI's role in patient risk stratification, real-time decision support, and predictive modeling for patient outcomes. Notably, AI applications can be used to target patients at risk of complications, alert clinicians to the upcoming occurrence of an intraoperative adverse event such as hypotension or hypoxemia, or predict their tolerance of anesthesia after the procedure. Despite these advances, challenges persist, including ethical considerations, algorithmic bias, data security, and the need for transparent decision-making processes within AI systems. SUMMARY The findings underscore the substantial benefits of AI in NORA, which include improved safety, efficiency, and personalized care. AI's predictive capabilities in assessing hypoxemia risk and other perioperative events, have demonstrated potential to exceed human prognostic accuracy. The implications of these findings advocate for a careful yet progressive adoption of AI in clinical practice, encouraging the development of robust ethical guidelines, continual professional training, and comprehensive data management strategies. Furthermore, AI's role in anesthesia underscores the need for multidisciplinary research to address the limitations and fully leverage AI's capabilities for patient-centered anesthesia care.
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Affiliation(s)
- Emmanuel Pardo
- Sorbonne University, GRC 29, AP-HP, DMU DREAM, Department of Anesthesiology and Critical Care, Saint-Antoine Hospital, Paris, France
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Varghese C, Harrison EM, O'Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med 2024; 30:1257-1268. [PMID: 38740998 DOI: 10.1038/s41591-024-02970-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.
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Affiliation(s)
- Chris Varghese
- Department of Surgery, University of Auckland, Auckland, New Zealand
| | - Ewen M Harrison
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Greg O'Grady
- Department of Surgery, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Eric J Topol
- Scripps Research Translational Institute, La Jolla, CA, USA.
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5
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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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Al Zoubi F, Kashanian K, Beaule P, Fallavollita P. First deployment of artificial intelligence recommendations in orthopedic surgery. Front Artif Intell 2024; 7:1342234. [PMID: 38362139 PMCID: PMC10867959 DOI: 10.3389/frai.2024.1342234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/17/2024] [Indexed: 02/17/2024] Open
Abstract
Scant research has delved into the non-clinical facets of artificial intelligence (AI), concentrating on leveraging data to enhance the efficiency of healthcare systems and operating rooms. Notably, there is a gap in the literature regarding the implementation and outcomes of AI solutions. The absence of published results demonstrating the practical application and effectiveness of AI in domains beyond clinical settings, particularly in the field of surgery, served as the impetus for our undertaking in this area. Within the realm of non-clinical strategies aimed at enhancing operating room efficiency, we characterize OR efficiency as the capacity to successfully perform four uncomplicated arthroplasty surgeries within an 8-h timeframe. This Community Case Study addresses this gap by presenting the results of incorporating AI recommendations at our clinical institute on 228 patient arthroplasty surgeries. The implementation of a prescriptive analytics system (PAS), utilizing supervised machine learning techniques, led to a significant improvement in the overall efficiency of the operating room, increasing it from 39 to 93%. This noteworthy achievement highlights the impact of AI in optimizing surgery workflows.
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Affiliation(s)
- Farid Al Zoubi
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada
| | - Koorosh Kashanian
- Division of Orthopedic Surgery, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Paul Beaule
- Division of Orthopedic Surgery, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Pascal Fallavollita
- Interdisciplinary School of Health Sciences, University of Ottawa, Ottawa, ON, Canada
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Lv G, Wang Y. Machine learning-based antibiotic resistance prediction models: An updated systematic review and meta-analysis. Technol Health Care 2024; 32:2865-2882. [PMID: 38875058 DOI: 10.3233/thc-240119] [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: 06/16/2024]
Abstract
BACKGROUND The widespread use of antibiotics has led to a gradual adaptation of bacteria to these drugs, diminishing the effectiveness of treatments. OBJECTIVE To comprehensively assess the research progress of antibiotic resistance prediction models based on machine learning (ML) algorithms, providing the latest quantitative analysis and methodological evaluation. METHODS Relevant literature was systematically retrieved from databases, including PubMed, Embase and the Cochrane Library, from inception up to December 2023. Studies meeting predefined criteria were selected for inclusion. The prediction model risk of bias assessment tool was employed for methodological quality assessment, and a random-effects model was utilised for meta-analysis. RESULTS The systematic review included a total of 22 studies with a combined sample size of 43,628; 10 studies were ultimately included in the meta-analysis. Commonly used ML algorithms included random forest, decision trees and neural networks. Frequently utilised predictive variables encompassed demographics, drug use history and underlying diseases. The overall sensitivity was 0.57 (95% CI: 0.42-0.70; p< 0.001; I2= 99.7%), the specificity was 0.95 (95% CI: 0.79-0.99; p< 0.001; I2 = 99.9%), the positive likelihood ratio was 10.7 (95% CI: 2.9-39.5), the negative likelihood ratio was 0.46 (95% CI: 0.34-0.61), the diagnostic odds ratio was 23 (95% CI: 7-81) and the area under the receiver operating characteristic curve was 0.78 (95% CI: 0.74-0.81; p< 0.001), indicating a good discriminative ability of ML models for antibiotic resistance. However, methodological assessment and funnel plots suggested a high risk of bias and publication bias in the included studies. CONCLUSION This meta-analysis provides a current and comprehensive evaluation of ML models for predicting antibiotic resistance, emphasising their potential application in clinical practice. Nevertheless, stringent research design and reporting are warranted to enhance the quality and credibility of future studies. Future research should focus on methodological innovation and incorporate more high-quality studies to further advance this field.
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Affiliation(s)
- Guodong Lv
- Department of STD and AIDS Prevention and Control, Langfang Center for Disease Prevention and Control, Langfang, Hebei, China
| | - Yuntao Wang
- Department of Pharmacy, Langfang Health Vocational College, Langfang, Hebei, China
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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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Ortenzi M, Rapoport Ferman J, Antolin A, Bar O, Zohar M, Perry O, Asselmann D, Wolf T. A novel high accuracy model for automatic surgical workflow recognition using artificial intelligence in laparoscopic totally extraperitoneal inguinal hernia repair (TEP). Surg Endosc 2023; 37:8818-8828. [PMID: 37626236 PMCID: PMC10615930 DOI: 10.1007/s00464-023-10375-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 07/30/2023] [Indexed: 08/27/2023]
Abstract
INTRODUCTION Artificial intelligence and computer vision are revolutionizing the way we perceive video analysis in minimally invasive surgery. This emerging technology has increasingly been leveraged successfully for video segmentation, documentation, education, and formative assessment. New, sophisticated platforms allow pre-determined segments chosen by surgeons to be automatically presented without the need to review entire videos. This study aimed to validate and demonstrate the accuracy of the first reported AI-based computer vision algorithm that automatically recognizes surgical steps in videos of totally extraperitoneal (TEP) inguinal hernia repair. METHODS Videos of TEP procedures were manually labeled by a team of annotators trained to identify and label surgical workflow according to six major steps. For bilateral hernias, an additional change of focus step was also included. The videos were then used to train a computer vision AI algorithm. Performance accuracy was assessed in comparison to the manual annotations. RESULTS A total of 619 full-length TEP videos were analyzed: 371 were used to train the model, 93 for internal validation, and the remaining 155 as a test set to evaluate algorithm accuracy. The overall accuracy for the complete procedure was 88.8%. Per-step accuracy reached the highest value for the hernia sac reduction step (94.3%) and the lowest for the preperitoneal dissection step (72.2%). CONCLUSIONS These results indicate that the novel AI model was able to provide fully automated video analysis with a high accuracy level. High-accuracy models leveraging AI to enable automation of surgical video analysis allow us to identify and monitor surgical performance, providing mathematical metrics that can be stored, evaluated, and compared. As such, the proposed model is capable of enabling data-driven insights to improve surgical quality and demonstrate best practices in TEP procedures.
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Affiliation(s)
- Monica Ortenzi
- Theator Inc., Palo Alto, CA, USA.
- Department of General and Emergency Surgery, Polytechnic University of Marche, Ancona, Italy.
| | | | | | - Omri Bar
- Theator Inc., Palo Alto, CA, USA
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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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Al Zoubi F, Khalaf G, Beaulé PE, Fallavollita P. Leveraging machine learning and prescriptive analytics to improve operating room throughput. Front Digit Health 2023; 5:1242214. [PMID: 37808917 PMCID: PMC10556872 DOI: 10.3389/fdgth.2023.1242214] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/12/2023] [Indexed: 10/10/2023] Open
Abstract
Successful days are defined as days when four cases were completed before 3:45pm, and overtime hours are defined as time spent after 3:45pm. Based on these definitions and the 460 unsuccessful days isolated from the dataset, 465 hours, 22 minutes, and 30 seconds total overtime hours were calculated. To reduce the increasing wait lists for hip and knee surgeries, we aim to verify whether it is possible to add a 5th surgery, to the typical 4 arthroplasty surgery per day schedule, without adding extra overtime hours and cost at our clinical institution. To predict 5th cases, 301 successful days were isolated and used to fit linear regression models for each individual day. After using the models' predictions, it was determined that increasing performance to a 77% success rate can lead to approximately 35 extra cases per year, while performing optimally at a 100% success rate can translate to 56 extra cases per year at no extra cost. Overall, this shows the extent of resources wasted by overtime costs, and the potential for their use in reducing long wait times. Future work can explore optimal staffing procedures to account for these extra cases.
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Affiliation(s)
- Farid Al Zoubi
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada
| | - Georges Khalaf
- The Ottawa-Carleton Institute of Biomedical Engineering (OCIBME), University of Ottawa, Ottawa, ON, Canada
| | - Paul E. Beaulé
- Division of Orthopedic Surgery, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Pascal Fallavollita
- Interdisciplinary School of Health Sciences, University of Ottawa, Ottawa, ON, Canada
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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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Bellini V, Russo M, Lanza R, Domenichetti T, Compagnone C, Maggiore SM, Cammarota G, Pelosi P, Vetrugno L, Bignami EG. Artificial intelligence and "the Art of Kintsugi" in Anesthesiology: ten influential papers for clinical users. Minerva Anestesiol 2023; 89:804-811. [PMID: 37194240 DOI: 10.23736/s0375-9393.23.17279-8] [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: 05/18/2023]
Abstract
Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the present review we chose ten influential papers from the last five years and through Kintsugi, shed the light on recent evolution of artificial intelligence in anesthesiology. A comprehensive search in in Medline, Embase, Web of Science and Scopus databases was conducted. Each author searched the databases independently and created a list of six articles that influenced their clinical practice during this period, with a focus on their area of competence. During a subsequent step, each researcher presented his own list and most cited papers were selected to create the final collection of ten articles. In recent years purely methodological works with a cryptic technology (black-box) represented by the intact and static vessel, translated to a "modern artificial intelligence" in clinical practice and comprehensibility (glass-box). The purposes of this review are to explore the ten most cited papers about artificial intelligence in anesthesiology and to understand how and when it should be integrated in clinical practice.
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Affiliation(s)
- Valentina Bellini
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Michele Russo
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Roberto Lanza
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Tania Domenichetti
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Christian Compagnone
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Salvatore M Maggiore
- Department of Anesthesiology, Critical Care Medicine and Emergency, SS. Annunziata Hospital, Chieti, Italy
- University Department of Innovative Technologies in Medicine and Dentistry, Gabriele D'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Gianmaria Cammarota
- Department of Anesthesia and Intensive Care Medicine, University of Perugia, Perugia, Italy
| | - Paolo Pelosi
- Department of Anesthesia and Intensive Care, IRCCS San Martino Polyclinic Hospital, University of Genoa, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Luigi Vetrugno
- Department of Medical, Oral and Biotechnological Sciences, University of Chieti-Pescara, Chieti, Italy
| | - Elena G Bignami
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy -
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14
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Dang H, Dekkers N, Steyerberg EW, Baldaque-Silva F, Omae M, Haasnoot KJ, van Tilburg L, Nobbenhuis K, van der Kraan J, Langers AM, van Hooft JE, de Graaf W, Koch AD, Didden P, Moons LM, Hardwick JC, Boonstra JJ. Predicting procedure duration of colorectal endoscopic submucosal dissection at Western endoscopy centers. Endosc Int Open 2023; 11:E724-E732. [PMID: 37941732 PMCID: PMC10629487 DOI: 10.1055/a-2122-0419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 06/27/2023] [Indexed: 11/10/2023] Open
Abstract
Background and study aims Overcoming logistical obstacles for the implementation of colorectal endoscopic submucosal dissection (ESD) requires accurate prediction of procedure times. We aimed to evaluate existing and new prediction models for ESD duration. Patients and methods Records of all consecutive patients who underwent single, non-hybrid colorectal ESDs before 2020 at three Dutch centers were reviewed. The performance of an Eastern prediction model [GIE 2021;94(1):133-144] was assessed in the Dutch cohort. A prediction model for procedure duration was built using multivariable linear regression. The model's performance was validated using internal validation by bootstrap resampling, internal-external cross-validation and external validation in an independent Swedish ESD cohort. Results A total of 435 colorectal ESDs were analyzed (92% en bloc resections, mean duration 139 minutes, mean tumor size 39 mm). The performance of current unstandardized time scheduling practice was suboptimal (explained variance: R 2 =27%). We successfully validated the Eastern prediction model for colorectal ESD duration <60 minutes (c-statistic 0.70, 95% CI 0.62-0.77), but this model was limited due to dichotomization of the outcome and a relatively low frequency (14%) of ESDs completed <60 minutes in the Dutch centers. The model was more useful with a dichotomization cut-off of 120 minutes (c-statistic: 0.75; 88% and 17% of "easy" and "very difficult" ESDs completed <120 minutes, respectively). To predict ESD duration as continuous outcome, we developed and validated the six-variable cESD-TIME formula ( https://cesdtimeformula.shinyapps.io/calculator/ ; optimism-corrected R 2 =61%; R 2 =66% after recalibration of the slope). Conclusions We provided two useful tools for predicting colorectal ESD duration at Western centers. Further improvements and validations are encouraged with potential local adaptation to optimize time planning.
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Affiliation(s)
- Hao Dang
- Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, Netherlands
| | - Nik Dekkers
- Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, Netherlands
| | - Ewout W. Steyerberg
- Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
| | - Francisco Baldaque-Silva
- Endoscopy Unit, Center for Upper Digestive Diseases, Karolinska Hospital, Stockholm, Sweden
- Advanced Endoscopy Center Carlos Moreira da Silva, Pedro Hispano Hospital, Matosinhos, Portugal
| | - Masami Omae
- Endoscopy Unit, Center for Upper Digestive Diseases, Karolinska Hospital, Stockholm, Sweden
| | - Krijn J.C. Haasnoot
- Gastroenterology & Hepatology, University Medical Centre Utrecht, Utrecht, Netherlands
| | | | - Kate Nobbenhuis
- Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, Netherlands
| | - Jolein van der Kraan
- Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, Netherlands
| | | | - Jeanin E. van Hooft
- Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, Netherlands
| | - Wilmar de Graaf
- Gastroenterology and Hepatology, Erasmus MC, Rotterdam, Netherlands
| | - Arjun D. Koch
- Gastroenterology and Hepatology, Erasmus MC, Rotterdam, Netherlands
| | - Paul Didden
- Gastroenterology and Hepatology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Leon M.G. Moons
- Gastroenterology and Hepatology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - James C.H. Hardwick
- Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, Netherlands
| | - Jurjen J. Boonstra
- Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, Netherlands
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15
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Spinelli A, Carrano FM, Laino ME, Andreozzi M, Koleth G, Hassan C, Repici A, Chand M, Savevski V, Pellino G. Artificial intelligence in colorectal surgery: an AI-powered systematic review. Tech Coloproctol 2023; 27:615-629. [PMID: 36805890 DOI: 10.1007/s10151-023-02772-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/07/2023] [Indexed: 02/23/2023]
Abstract
Artificial intelligence (AI) has the potential to revolutionize surgery in the coming years. Still, it is essential to clarify what the meaningful current applications are and what can be reasonably expected. This AI-powered review assessed the role of AI in colorectal surgery. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic search of PubMed, Embase, Scopus, Cochrane Library databases, and gray literature was conducted on all available articles on AI in colorectal surgery (from January 1 1997 to March 1 2021), aiming to define the perioperative applications of AI. Potentially eligible studies were identified using novel software powered by natural language processing (NLP) and machine learning (ML) technologies dedicated to systematic reviews. Out of 1238 articles identified, 115 were included in the final analysis. Available articles addressed the role of AI in several areas of interest. In the preoperative phase, AI can be used to define tailored treatment algorithms, support clinical decision-making, assess the risk of complications, and predict surgical outcomes and survival. Intraoperatively, AI-enhanced surgery and integration of AI in robotic platforms have been suggested. After surgery, AI can be implemented in the Enhanced Recovery after Surgery (ERAS) pathway. Additional areas of applications included the assessment of patient-reported outcomes, automated pathology assessment, and research. Available data on these aspects are limited, and AI in colorectal surgery is still in its infancy. However, the rapid evolution of technologies makes it likely that it will increasingly be incorporated into everyday practice.
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Affiliation(s)
- A Spinelli
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy.
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, MI, Italy.
| | - F M Carrano
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy
| | - M E Laino
- Artificial Intelligence Center, Humanitas Clinical and Research Center-IRCCS, Via A. Manzoni 56, 20089, Rozzano, MI, Italy
| | - M Andreozzi
- Department of Clinical Medicine and Surgery, University "Federico II" of Naples, Naples, Italy
| | - G Koleth
- Department of Gastroenterology and Hepatology, Hospital Selayang, Selangor, Malaysia
| | - C Hassan
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy
| | - A Repici
- IRCCS Humanitas Research Hospital, via Manzoni 56, 20089, Rozzano, MI, Italy
| | - M Chand
- Wellcome EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK
| | - V Savevski
- Artificial Intelligence Center, Humanitas Clinical and Research Center-IRCCS, Via A. Manzoni 56, 20089, Rozzano, MI, Italy
| | - G Pellino
- Department of Advanced Medical and Surgical Sciences, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
- Colorectal Surgery, Vall d'Hebron University Hospital, Universitat Autonoma de Barcelona UAB, Barcelona, Spain
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16
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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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17
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von Schudnat C, Schoeneberg KP, Albors-Garrigos J, Lahmann B, De-Miguel-Molina M. The Economic Impact of Standardization and Digitalization in the Operating Room: A Systematic Literature Review. J Med Syst 2023; 47:55. [PMID: 37129717 DOI: 10.1007/s10916-023-01945-0] [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: 07/31/2022] [Accepted: 03/29/2023] [Indexed: 05/03/2023]
Abstract
Hospital face increased resource constraints and competition. This escalates the need for efficiency optimization especially in resource-intense areas, such as the Operating Room (OR). Efficiency cannot happen at expenses of patient outcomes. Innovative digital support systems (DSS) have been introduced into the market to support established standardization methods of intraoperative workflows further. This review aimed to analyze whether applied standardization methods and implemented DSS of intraoperative surgical workflows lead to increasing efficiency and demonstrate economic improvements. A systematic review of intraoperative surgical workflows standardization and digitalization was performed. Journal articles and reviews from 2000 to 2023 were retrieved from EBSCO, PubMed, and Scopus databases, as well as the internal database of Johnson & Johnson. 17 articles showed a significant increase in efficiency through standardization, which led to cost reductions between $70.20 to $3,516 per case without negatively impacting quality. Five additional articles on DSS demonstrated a significant positive impact on efficiency and quality. Reduction in OR-time between 6 to 22% per case was one main contributor. No literature on DSS revealed any correlated economic impact. Selected standardization methods and introduced DSS for intraoperative surgical workflows effectively increase efficiency while maintaining or even improving quality. Demonstrated cost-effectiveness of non-digital standardization methods across surgical areas requires more research on complex and resource-intensive procedures and the economic value of DSS to support hospital management's strategic decisions to overcome the increasing economic burden.
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Affiliation(s)
- Christian von Schudnat
- Department of Business Organization, Faculty of Business Management, Universitat Politecnica de Valencia, Cami de Vera, s/n, 46022, Valencia, Spain.
| | - Klaus-Peter Schoeneberg
- Department of Economic and Social Sciences, Berliner Hochschule für Technik, Berlin, Luxemburger Str. 10, 13353, Berlin, Germany
| | - Jose Albors-Garrigos
- Department of Business Organization, Faculty of Business Management, Universitat Politecnica de Valencia, Cami de Vera, s/n, 46022, Valencia, Spain
| | - Benjamin Lahmann
- Department of Statistics and Operation Analysis, Faculty of Business and Economics, Mendel University Brno, Zemědělská 1, 61300, Brno, Czech Republic
| | - María De-Miguel-Molina
- Department of Business Organization, Faculty of Business Management, Universitat Politecnica de Valencia, Cami de Vera, s/n, 46022, Valencia, Spain
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18
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Wieben AM, Walden RL, Alreshidi BG, Brown SF, Cato K, Coviak CP, Cruz C, D'Agostino F, Douthit BJ, Forbes TH, Gao G, Johnson SG, Lee MA, Mullen-Fortino M, Park JI, Park S, Pruinelli L, Reger A, Role J, Sileo M, Schultz MA, Vyas P, Jeffery AD. Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature. Appl Clin Inform 2023; 14:585-593. [PMID: 37150179 PMCID: PMC10411069 DOI: 10.1055/a-2088-2893] [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: 11/29/2022] [Accepted: 05/03/2023] [Indexed: 05/09/2023] Open
Abstract
OBJECTIVES The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools. METHODS We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021. We extracted data on model development, implementation-related strategies and measures, lessons learned, and challenges and stakeholder involvement. We also assessed whether reports of data science application implementations currently follow the guidelines of the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by AI (DECIDE-AI) framework. RESULTS Of 4,943 articles found in PubMed (NLM) and CINAHL (EBSCOhost), 11 were included in the final review and data extraction. Systems leveraging data science were developed for adult patient populations and were primarily deployed in hospital settings. The clinical domains targeted included mortality/deterioration, utilization/resource allocation, and hospital-acquired infections/COVID-19. The composition of development teams and types of stakeholders involved varied. Research teams more frequently reported on implementation methods than implementation results. Most studies provided lessons learned that could help inform future implementations of data science systems in health care. CONCLUSION In 2021, very few studies report on the implementation of data science-driven applications focused on structural- or outcome-related nurse-sensitive indicators. This gap in the sharing of implementation strategies needs to be addressed in order for these systems to be successfully adopted in health care settings.
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Affiliation(s)
- Ann M. Wieben
- University of Wisconsin-Madison School of Nursing, Madison, Wisconsin, United States
| | - Rachel Lane Walden
- Annette and Irwin Eskind Family Biomedical Library, Vanderbilt University, Nashville, Tennessee, United States
| | - Bader G. Alreshidi
- Medical-Surgical Nursing Department, College of Nursing, University of Hail, Hail, Saudi Arabia
| | | | - Kenrick Cato
- Department of Emergency Medicine, Columbia University School of Nursing, New York, New York, United States
| | - Cynthia Peltier Coviak
- Kirkhof College of Nursing, Grand Valley State University, Allendale, Michigan, United States
| | - Christopher Cruz
- Global Health Technology and Informatics, Chevron, San Ramon, California, United States
| | - Fabio D'Agostino
- Department of Medicine and Surgery, Saint Camillus International University of Health Sciences, Rome, Italy
| | - Brian J. Douthit
- Department of Biomedical Informatics, United States Department of Veterans Affairs, Vanderbilt University, Nashville, Tennessee, United States
| | - Thompson H. Forbes
- Department of Advanced Nursing Practice and Education, East Carolina University College of Nursing, Greenville, North Carolina, United States
| | - Grace Gao
- Atlanta VA Quality Scholars Program, Joseph Maxwell Cleland, Atlanta VA Medical Center, North Druid Hills, Georgia, United States
| | - Steve G. Johnson
- Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, United States
| | | | | | - Jung In Park
- Sue and Bill Gross School of Nursing, University of California, Irvine, United States
| | - Suhyun Park
- College of Nursing and College of Medicine, University of Florida, Gainesville, Florida, United States
| | - Lisiane Pruinelli
- College of Nursing and College of Medicine, University of Florida, Gainesville, Florida, United States
| | | | - Jethrone Role
- Loma Linda University Health, Loma Linda, California, United States
| | - Marisa Sileo
- Boston Children's Hospital, Boston, Massachusetts, United States
| | | | - Pankaj Vyas
- University of Arizona College of Nursing, Tucson, Arizona, United States
| | - Alvin D. Jeffery
- U.S. Department of Veterans Affairs, Vanderbilt University School of Nursing, Tennessee Valley Healthcare System, Nashville, Tennessee, United States
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19
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Cheikh Youssef S, Haram K, Noël J, Patel V, Porter J, Dasgupta P, Hachach-Haram N. Evolution of the digital operating room: the place of video technology in surgery. Langenbecks Arch Surg 2023; 408:95. [PMID: 36807211 PMCID: PMC9939374 DOI: 10.1007/s00423-023-02830-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 02/06/2023] [Indexed: 02/23/2023]
Abstract
PURPOSE The aim of this review was to collate current evidence wherein digitalisation, through the incorporation of video technology and artificial intelligence (AI), is being applied to the practice of surgery. Applications are vast, and the literature investigating the utility of surgical video and its synergy with AI has steadily increased over the last 2 decades. This type of technology is widespread in other industries, such as autonomy in transportation and manufacturing. METHODS Articles were identified primarily using the PubMed and MEDLINE databases. The MeSH terms used were "surgical education", "surgical video", "video labelling", "surgery", "surgical workflow", "telementoring", "telemedicine", "machine learning", "deep learning" and "operating room". Given the breadth of the subject and the scarcity of high-level data in certain areas, a narrative synthesis was selected over a meta-analysis or systematic review to allow for a focussed discussion of the topic. RESULTS Three main themes were identified and analysed throughout this review, (1) the multifaceted utility of surgical video recording, (2) teleconferencing/telemedicine and (3) artificial intelligence in the operating room. CONCLUSIONS Evidence suggests the routine collection of intraoperative data will be beneficial in the advancement of surgery, by driving standardised, evidence-based surgical care and personalised training of future surgeons. However, many barriers stand in the way of widespread implementation, necessitating close collaboration between surgeons, data scientists, medicolegal personnel and hospital policy makers.
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Affiliation(s)
| | | | - Jonathan Noël
- Guy's and St. Thomas' NHS Foundation Trust, Urology Centre, King's Health Partners, London, UK
| | - Vipul Patel
- Adventhealth Global Robotics Institute, 400 Celebration Place, Celebration, FL, USA
| | - James Porter
- Department of Urology, Swedish Urology Group, Seattle, WA, USA
| | - Prokar Dasgupta
- Guy's and St. Thomas' NHS Foundation Trust, Urology Centre, King's Health Partners, London, UK
| | - Nadine Hachach-Haram
- Department of Plastic Surgery, Guy's and St. Thomas' NHS Foundation Trust, King's Health Partners, London, UK
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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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21
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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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22
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Golany T, Aides A, Freedman D, Rabani N, Liu Y, Rivlin E, Corrado GS, Matias Y, Khoury W, Kashtan H, Reissman P. Artificial intelligence for phase recognition in complex laparoscopic cholecystectomy. Surg Endosc 2022; 36:9215-9223. [PMID: 35941306 PMCID: PMC9652206 DOI: 10.1007/s00464-022-09405-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 06/19/2022] [Indexed: 01/06/2023]
Abstract
BACKGROUND The potential role and benefits of AI in surgery has yet to be determined. This study is a first step in developing an AI system for minimizing adverse events and improving patient's safety. We developed an Artificial Intelligence (AI) algorithm and evaluated its performance in recognizing surgical phases of laparoscopic cholecystectomy (LC) videos spanning a range of complexities. METHODS A set of 371 LC videos with various complexity levels and containing adverse events was collected from five hospitals. Two expert surgeons segmented each video into 10 phases including Calot's triangle dissection and clipping and cutting. For each video, adverse events were also annotated when present (major bleeding; gallbladder perforation; major bile leakage; and incidental finding) and complexity level (on a scale of 1-5) was also recorded. The dataset was then split in an 80:20 ratio (294 and 77 videos), stratified by complexity, hospital, and adverse events to train and test the AI model, respectively. The AI-surgeon agreement was then compared to the agreement between surgeons. RESULTS The mean accuracy of the AI model for surgical phase recognition was 89% [95% CI 87.1%, 90.6%], comparable to the mean inter-annotator agreement of 90% [95% CI 89.4%, 90.5%]. The model's accuracy was inversely associated with procedure complexity, decreasing from 92% (complexity level 1) to 88% (complexity level 3) to 81% (complexity level 5). CONCLUSION The AI model successfully identified surgical phases in both simple and complex LC procedures. Further validation and system training is warranted to evaluate its potential applications such as to increase patient safety during surgery.
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Affiliation(s)
| | | | | | | | - Yun Liu
- Google Health, Tel Aviv, Israel
| | | | | | | | - Wisam Khoury
- Department of Surgery, Rappaport Faculty of Medicine, Carmel Medical Center, Technion, Haifa, Israel
| | - Hanoch Kashtan
- Department of Surgery, Rabin Medical Center, The Sackler School of Medicine, Tel-Aviv University, Petah Tikva, Israel
| | - Petachia Reissman
- Department of Surgery, The Hebrew University School of Medicine, Sharee Zedek Medical Center, Jerusalem, Israel.
- Digestive Disease Institute, Shaare-Zedek Medical Center, The Hebrew University School of Medicine, P.O. Box 3235, 91031, Jerusalem, Israel.
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23
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Tang R, Luo R, Tang S, Song H, Chen X. Machine learning in predicting antimicrobial resistance: a systematic review and meta-analysis. Int J Antimicrob Agents 2022; 60:106684. [PMID: 36279973 DOI: 10.1016/j.ijantimicag.2022.106684] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 10/17/2022] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Antimicrobial resistance (AMR) is a global health threat; rapid and timely identification of AMR improves patient prognosis and reduces inappropriate antibiotic use. METHODS Relevant literature in PubMed, Web of Science, Embase and Institute of Electrical and Electronics Engineers prior to 28 September 2021 was searched. Any study that deployed machine learning (ML) or a risk score as a tool to predict AMR was included in the final review; there were 25 studies that employed the ML algorithm to predict AMR. RESULTS Extended spectrum β-lactamases, methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem resistance were the most common outcomes in studies with a specific resistance pattern. The most common algorithms in ML prediction were logistic regression (n = 14 studies), decision tree (n = 14) and random forest (n = 7). The area under the curve (AUC) range for ML prediction was 0.48-0.93. The pooled AUC for ML prediction was 0.82 (0.78-0.85). Compared with risk score, higher specificity [87% (82-91) vs. 37% (25-51)] was indicated for ML prediction, but not sensitivity [67% (62-72) vs. 73% (67-79)]. CONCLUSIONS Machine learning might be a potential technology for AMR prediction; however, retrospective methodology for model development, nonstandard data processing and scarcity of validation in a randomised controlled trial or real-world study limit the application of these models in clinical practice.
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Affiliation(s)
- Rui Tang
- Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, China.
| | - Rui Luo
- Department of Pain Medicine, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Shiwei Tang
- Department of Pharmacy, People's Hospital of Xinjin District, Chengdu, China
| | - Haoxin Song
- Department of Pharmacy, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Xiujuan Chen
- Department of Medical Big Data Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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24
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Plana D, Shung DL, Grimshaw AA, Saraf A, Sung JJY, Kann BH. Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review. JAMA Netw Open 2022; 5:e2233946. [PMID: 36173632 PMCID: PMC9523495 DOI: 10.1001/jamanetworkopen.2022.33946] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
IMPORTANCE Despite the potential of machine learning to improve multiple aspects of patient care, barriers to clinical adoption remain. Randomized clinical trials (RCTs) are often a prerequisite to large-scale clinical adoption of an intervention, and important questions remain regarding how machine learning interventions are being incorporated into clinical trials in health care. OBJECTIVE To systematically examine the design, reporting standards, risk of bias, and inclusivity of RCTs for medical machine learning interventions. EVIDENCE REVIEW In this systematic review, the Cochrane Library, Google Scholar, Ovid Embase, Ovid MEDLINE, PubMed, Scopus, and Web of Science Core Collection online databases were searched and citation chasing was done to find relevant articles published from the inception of each database to October 15, 2021. Search terms for machine learning, clinical decision-making, and RCTs were used. Exclusion criteria included implementation of a non-RCT design, absence of original data, and evaluation of nonclinical interventions. Data were extracted from published articles. Trial characteristics, including primary intervention, demographics, adherence to the CONSORT-AI reporting guideline, and Cochrane risk of bias were analyzed. FINDINGS Literature search yielded 19 737 articles, of which 41 RCTs involved a median of 294 participants (range, 17-2488 participants). A total of 16 RCTS (39%) were published in 2021, 21 (51%) were conducted at single sites, and 15 (37%) involved endoscopy. No trials adhered to all CONSORT-AI standards. Common reasons for nonadherence were not assessing poor-quality or unavailable input data (38 trials [93%]), not analyzing performance errors (38 [93%]), and not including a statement regarding code or algorithm availability (37 [90%]). Overall risk of bias was high in 7 trials (17%). Of 11 trials (27%) that reported race and ethnicity data, the median proportion of participants from underrepresented minority groups was 21% (range, 0%-51%). CONCLUSIONS AND RELEVANCE This systematic review found that despite the large number of medical machine learning-based algorithms in development, few RCTs for these technologies have been conducted. Among published RCTs, there was high variability in adherence to reporting standards and risk of bias and a lack of participants from underrepresented minority groups. These findings merit attention and should be considered in future RCT design and reporting.
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Affiliation(s)
| | - Dennis L Shung
- Department of Medicine, Yale University, New Haven, Connecticut
| | - Alyssa A Grimshaw
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, New Haven, Connecticut
| | - Anurag Saraf
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Joseph J Y Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Benjamin H Kann
- Artificial Intelligence in Medicine Program, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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25
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Lam TYT, Cheung MFK, Munro YL, Lim KM, Shung D, Sung JJY. Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review. J Med Internet Res 2022; 24:e37188. [PMID: 35904087 PMCID: PMC9459941 DOI: 10.2196/37188] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 06/13/2022] [Accepted: 07/29/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND The number of artificial intelligence (AI) studies in medicine has exponentially increased recently. However, there is no clear quantification of the clinical benefits of implementing AI-assisted tools in patient care. OBJECTIVE This study aims to systematically review all published randomized controlled trials (RCTs) of AI-assisted tools to characterize their performance in clinical practice. METHODS CINAHL, Cochrane Central, Embase, MEDLINE, and PubMed were searched to identify relevant RCTs published up to July 2021 and comparing the performance of AI-assisted tools with conventional clinical management without AI assistance. We evaluated the primary end points of each study to determine their clinical relevance. This systematic review was conducted following the updated PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. RESULTS Among the 11,839 articles retrieved, only 39 (0.33%) RCTs were included. These RCTs were conducted in an approximately equal distribution from North America, Europe, and Asia. AI-assisted tools were implemented in 13 different clinical specialties. Most RCTs were published in the field of gastroenterology, with 15 studies on AI-assisted endoscopy. Most RCTs studied biosignal-based AI-assisted tools, and a minority of RCTs studied AI-assisted tools drawn from clinical data. In 77% (30/39) of the RCTs, AI-assisted interventions outperformed usual clinical care, and clinically relevant outcomes improved with AI-assisted intervention in 70% (21/30) of the studies. Small sample size and single-center design limited the generalizability of these studies. CONCLUSIONS There is growing evidence supporting the implementation of AI-assisted tools in daily clinical practice; however, the number of available RCTs is limited and heterogeneous. More RCTs of AI-assisted tools integrated into clinical practice are needed to advance the role of AI in medicine. TRIAL REGISTRATION PROSPERO CRD42021286539; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=286539.
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Affiliation(s)
- Thomas Y T Lam
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, Hong Kong
- Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong., Hong Kong, Hong Kong
| | - Max F K Cheung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Yasmin L Munro
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Kong Meng Lim
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Dennis Shung
- Department of Medicine (Digestive Diseases), Yale School of Medicine, New Haven, CT, United States
| | - Joseph J Y Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
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26
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Dean A, Meisami A, Lam H, Van Oyen MP, Stromblad C, Kastango N. Quantile regression forests for individualized surgery scheduling. Health Care Manag Sci 2022; 25:682-709. [PMID: 35980502 DOI: 10.1007/s10729-022-09609-0] [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: 10/09/2019] [Accepted: 07/15/2022] [Indexed: 11/29/2022]
Abstract
Determining the optimal surgical case start times is a challenging stochastic optimization problem that shares a key feature with many other healthcare operations problems. Namely, successful problem solutions require using a vast array of available historical data to create distributions that accurately capture a case duration's uncertainty for integration into an optimization model. Distribution fitting is the conventional approach to generate these distributions, but it can only employ a limited, aggregate portion of the detailed patient features available in Electronic Medical Records systems today. If all the available information can be taken advantage of, then distributions individualized to every case can be constructed whose precision would support higher quality solutions in the presence of uncertainty. Our individualized stochastic optimization framework shows how the quantile regression forest (QRF) method predicts individualized distributions that are integrable into sample-average approximation, robust optimization, and distributionally robust optimization models for problems like surgery scheduling. In this paper, we present some related theoretical performance guarantees for each formulation. Numerically, we also study our approach's benefits relative to three other traditional models using data from Memorial Sloan Kettering Cancer Center in New York, NY, USA.
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Affiliation(s)
- Arlen Dean
- University of Michigan, Ann Arbor, MI, USA.
| | | | - Henry Lam
- Columbia University, New York, NY, USA
| | | | | | - Nick Kastango
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
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27
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A Case Study of Multiple Maintenance Efficacy in Gynaecological Surgery Assessed by Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8574000. [PMID: 35979051 PMCID: PMC9377963 DOI: 10.1155/2022/8574000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/18/2022] [Accepted: 07/22/2022] [Indexed: 11/18/2022]
Abstract
Deep learning is a new learning concept and a highly effective way of learning, which is still being explored in the field of nursing education. This paper analyses the effectiveness of interventions in perioperative gynaecological care using humanised care in the operating theatre and the impact of this model of care on patients’ psychological well-being and sleep quality. A deep learning-based vision robot was designed to provide higher quality of care for our human care and simplify our approach to gynaecological surgery. The anxiety and depression scores of the two groups were significantly improved after and before care, and the scores of the observation group were lower than those of the control group, with a statistically significant difference (
). The humanised care for gynaecological surgery patients in the perioperative period is more conducive to the improvement of their negative emotions and at the same time can improve the sleep quality of patients, so it can be further promoted.
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28
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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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29
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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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Jiao Y, Xue B, Lu C, Avidan MS, Kannampallil T. Continuous real-time prediction of surgical case duration using a modular artificial neural network. Br J Anaesth 2022; 128:829-837. [PMID: 35090725 PMCID: PMC9074795 DOI: 10.1016/j.bja.2021.12.039] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/07/2021] [Accepted: 12/24/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Real-time prediction of surgical duration can inform perioperative decisions and reduce surgical costs. We developed a machine learning approach that continuously incorporates preoperative and intraoperative information for forecasting surgical duration. METHODS Preoperative (e.g. procedure name) and intraoperative (e.g. medications and vital signs) variables were retrieved from anaesthetic records of surgeries performed between March 1, 2019 and October 31, 2019. A modular artificial neural network was developed and compared with a Bayesian approach and the scheduled surgical duration. Continuous ranked probability score (CRPS) was used as a measure of time error to assess model accuracy. For evaluating clinical performance, accuracy for each approach was assessed in identifying cases that ran beyond 15:00 (commonly scheduled end of shift), thus identifying opportunities to avoid overtime labour costs. RESULTS The analysis included 70 826 cases performed at eight hospitals. The modular artificial neural network had the lowest time error (CRPS: mean=13.8; standard deviation=35.4 min), which was significantly better (mean difference=6.4 min [95% confidence interval: 6.3-6.5]; P<0.001) than the Bayesian approach. The modular artificial neural network also had the highest accuracy in identifying operating theatres that would overrun 15:00 (accuracy at 1 h prior=89%) compared with the Bayesian approach (80%) and a naïve approach using the scheduled duration (78%). CONCLUSIONS A real-time neural network model using preoperative and intraoperative data had significantly better performance than a Bayesian approach or scheduled duration, offering opportunities to avoid overtime labour costs and reduce the cost of surgery by providing superior real-time information for perioperative decision support.
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Affiliation(s)
- York Jiao
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, MO, USA.
| | - Bing Xue
- Department of Computer Science and Engineering, Washington University in St Louis, St Louis, MO, USA
| | - Chenyang Lu
- Department of Computer Science and Engineering, Washington University in St Louis, St Louis, MO, USA
| | - Michael S Avidan
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Thomas Kannampallil
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, MO, USA; Institute for Informatics, Washington University School of Medicine in St Louis, St Louis, MO, USA
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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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Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:349-361. [PMID: 34862559 DOI: 10.1007/978-3-030-85292-4_39] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Applications of machine learning (ML) in translational medicine include therapeutic drug creation, diagnostic development, surgical planning, outcome prediction, and intraoperative assistance. Opportunities in the neurosciences are rich given advancement in our understanding of the brain, expanding indications for intervention, and diagnostic challenges often characterized by multiple clinical and environmental factors. We present a review of ML in neuro-oncology, epilepsy, Alzheimer's disease, and schizophrenia to highlight recent progression in these field, optimizing machine learning capabilities in their current forms. Supervised learning models appear to be the most commonly incorporated algorithm models for machine learning across the reviewed neuroscience disciplines with primary aim of diagnosis. Accuracy ranges are high from 63% to 99% across all algorithms investigated. Machine learning contributions to neurosurgery, neurology, psychiatry, and the clinical and basic science neurosciences may enhance current medical best practices while also broadening our understanding of dynamic neural networks and the brain.
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Coiera E, Braithwaite J. Turbulence health systems: engineering a rapidly adaptive health system for times of crisis. BMJ Health Care Inform 2021; 28:bmjhci-2021-100363. [PMID: 34417204 PMCID: PMC8382666 DOI: 10.1136/bmjhci-2021-100363] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 08/03/2021] [Indexed: 11/26/2022] Open
Affiliation(s)
- Enrico Coiera
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
| | - Jeffrey Braithwaite
- Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
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35
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Dexter F, Epstein RH. Simply Adjusting for Schedulers' Bias in Estimated Case Durations Can Accomplish the Same Objectives of Improving Predictions as Use of Machine Learning. JAMA Surg 2021; 156:1074-1075. [PMID: 34259804 DOI: 10.1001/jamasurg.2021.3126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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Strömblad CT, Wilson RS. Simply Adjusting for Schedulers' Bias in Estimated Case Durations Can Accomplish the Same Objectives of Improving Predictions as Use of Machine Learning-Reply. JAMA Surg 2021; 156:1075. [PMID: 34259825 DOI: 10.1001/jamasurg.2021.3129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Affiliation(s)
- Christopher T Strömblad
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.,Department of Strategy and Innovation, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Roger S Wilson
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
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