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Feinstein M, Katz D, Demaria S, Hofer IS. Remote Monitoring and Artificial Intelligence: Outlook for 2050. Anesth Analg 2024; 138:350-357. [PMID: 38215713 PMCID: PMC10794024 DOI: 10.1213/ane.0000000000006712] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Abstract
Remote monitoring and artificial intelligence will become common and intertwined in anesthesiology by 2050. In the intraoperative period, technology will lead to the development of integrated monitoring systems that will integrate multiple data streams and allow anesthesiologists to track patients more effectively. This will free up anesthesiologists to focus on more complex tasks, such as managing risk and making value-based decisions. This will also enable the continued integration of remote monitoring and control towers having profound effects on coverage and practice models. In the PACU and ICU, the technology will lead to the development of early warning systems that can identify patients who are at risk of complications, enabling early interventions and more proactive care. The integration of augmented reality will allow for better integration of diverse types of data and better decision-making. Postoperatively, the proliferation of wearable devices that can monitor patient vital signs and track their progress will allow patients to be discharged from the hospital sooner and receive care at home. This will require increased use of telemedicine, which will allow patients to consult with doctors remotely. All of these advances will require changes to legal and regulatory frameworks that will enable new workflows that are different from those familiar to today's providers.
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Affiliation(s)
- Max Feinstein
- Department of Anesthesiology Pain and Perioperative Medicine, Icahn School of Medicine at Mount Sinai
| | - Daniel Katz
- Department of Anesthesiology Pain and Perioperative Medicine, Icahn School of Medicine at Mount Sinai
| | - Samuel Demaria
- Department of Anesthesiology Pain and Perioperative Medicine, Icahn School of Medicine at Mount Sinai
| | - Ira S. Hofer
- Department of Anesthesiology Pain and Perioperative Medicine, Icahn School of Medicine at Mount Sinai
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2
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King CR, Gregory S, Fritz BA, Budelier TP, Ben Abdallah A, Kronzer A, Helsten DL, Torres B, McKinnon S, Goswami S, Mehta D, Higo O, Kerby P, Henrichs B, Wildes TS, Politi MC, Abraham J, Avidan MS, Kannampallil T. An Intraoperative Telemedicine Program to Improve Perioperative Quality Measures: The ACTFAST-3 Randomized Clinical Trial. JAMA Netw Open 2023; 6:e2332517. [PMID: 37738052 PMCID: PMC10517374 DOI: 10.1001/jamanetworkopen.2023.32517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 07/30/2023] [Indexed: 09/23/2023] Open
Abstract
Importance Telemedicine for clinical decision support has been adopted in many health care settings, but its utility in improving intraoperative care has not been assessed. Objective To pilot the implementation of a real-time intraoperative telemedicine decision support program and evaluate whether it reduces postoperative hypothermia and hyperglycemia as well as other quality of care measures. Design, Setting, and Participants This single-center pilot randomized clinical trial (Anesthesiology Control Tower-Feedback Alerts to Supplement Treatments [ACTFAST-3]) was conducted from April 3, 2017, to June 30, 2019, at a large academic medical center in the US. A total of 26 254 adult surgical patients were randomized to receive either usual intraoperative care (control group; n = 12 980) or usual care augmented by telemedicine decision support (intervention group; n = 13 274). Data were initially analyzed from April 22 to May 19, 2021, with updates in November 2022 and February 2023. Intervention Patients received either usual care (medical direction from the anesthesia care team) or intraoperative anesthesia care monitored and augmented by decision support from the Anesthesiology Control Tower (ACT), a real-time, live telemedicine intervention. The ACT incorporated remote monitoring of operating rooms by a team of anesthesia clinicians with customized analysis software. The ACT reviewed alerts and electronic health record data to inform recommendations to operating room clinicians. Main Outcomes and Measures The primary outcomes were avoidance of postoperative hypothermia (defined as the proportion of patients with a final recorded intraoperative core temperature >36 °C) and hyperglycemia (defined as the proportion of patients with diabetes who had a blood glucose level ≤180 mg/dL on arrival to the postanesthesia recovery area). Secondary outcomes included intraoperative hypotension, temperature monitoring, timely antibiotic redosing, intraoperative glucose evaluation and management, neuromuscular blockade documentation, ventilator management, and volatile anesthetic overuse. Results Among 26 254 participants, 13 393 (51.0%) were female and 20 169 (76.8%) were White, with a median (IQR) age of 60 (47-69) years. There was no treatment effect on avoidance of hyperglycemia (7445 of 8676 patients [85.8%] in the intervention group vs 7559 of 8815 [85.8%] in the control group; rate ratio [RR], 1.00; 95% CI, 0.99-1.01) or hypothermia (7602 of 11 447 patients [66.4%] in the intervention group vs 7783 of 11 672 [66.7.%] in the control group; RR, 1.00; 95% CI, 0.97-1.02). Intraoperative glucose measurement was more common among patients with diabetes in the intervention group (RR, 1.07; 95% CI, 1.01-1.15), but other secondary outcomes were not significantly different. Conclusions and Relevance In this randomized clinical trial, anesthesia care quality measures did not differ between groups, with high confidence in the findings. These results suggest that the intervention did not affect the targeted care practices. Further streamlining of clinical decision support and workflows may help the intraoperative telemedicine program achieve improvement in targeted clinical measures. Trial Registration ClinicalTrials.gov Identifier: NCT02830126.
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Affiliation(s)
- Christopher R. King
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Stephen Gregory
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Bradley A. Fritz
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Thaddeus P. Budelier
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Arbi Ben Abdallah
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Alex Kronzer
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Daniel L. Helsten
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Brian Torres
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Sherry McKinnon
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Shreya Goswami
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Divya Mehta
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Omokhaye Higo
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Paul Kerby
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Bernadette Henrichs
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Troy S. Wildes
- Department of Anesthesiology, University of Nebraska Medical Center, Omaha
| | - Mary C. Politi
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Joanna Abraham
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
- Institute for Informatics, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Michael S. Avidan
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Thomas Kannampallil
- Department of Anesthesiology, Washington University School of Medicine in St Louis, St Louis, Missouri
- Institute for Informatics, Washington University School of Medicine in St Louis, St Louis, Missouri
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3
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Abraham J, Meng A, Montes de Oca A, Politi M, Wildes T, Gregory S, Henrichs B, Kannampallil T, Avidan MS. An ethnographic study on the impact of a novel telemedicine-based support system in the operating room. J Am Med Inform Assoc 2022; 29:1919-1930. [PMID: 35985294 PMCID: PMC10161534 DOI: 10.1093/jamia/ocac138] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/07/2022] [Accepted: 08/04/2022] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE The Anesthesiology Control Tower (ACT) for operating rooms (ORs) remotely assesses the progress of surgeries and provides real-time perioperative risk alerts, communicating risk mitigation recommendations to bedside clinicians. We aim to identify and map ACT-OR nonroutine events (NREs)-risk-inducing or risk-mitigating workflow deviations-and ascertain ACT's impact on clinical workflow and patient safety. MATERIALS AND METHODS We used ethnographic methods including shadowing ACT and OR clinicians during 83 surgeries, artifact collection, chart reviews for decision alerts sent to the OR, and 10 clinician interviews. We used hybrid thematic analysis informed by a human-factors systems-oriented approach to assess ACT's role and impact on safety, conducting content analysis to assess NREs. RESULTS Across 83 cases, 469 risk alerts were triggered, and the ACT sent 280 care recommendations to the OR. 135 NREs were observed. Critical factors facilitating ACT's role in supporting patient safety included providing backup support and offering a fresh-eye perspective on OR decisions. Factors impeding ACT included message timing and ACT and OR clinician cognitive lapses. Suggestions for improvement included tailoring ACT message content (structure, timing, presentation) and incorporating predictive analytics for advanced planning. DISCUSSION ACT served as a safety net with remote surveillance features and as a learning healthcare system with feedback/auditing features. Supporting strategies include adaptive coordination and harnessing clinician/patient support to improve ACT's sustainability. Study insights inform future intraoperative telemedicine design considerations to mitigate safety risks. CONCLUSION Incorporating similar remote technology enhancement into routine perioperative care could markedly improve safety and quality for millions of surgical patients.
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Affiliation(s)
- Joanna Abraham
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
- Institute for Informatics, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
- Division of Biology and Biomedical Sciences, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Alicia Meng
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Arianna Montes de Oca
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Mary Politi
- Department of Surgery, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Troy Wildes
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Stephen Gregory
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Bernadette Henrichs
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
- Goldfarb School of Nursing, Barnes-Jewish College, St. Louis, Missouri, USA
| | - Thomas Kannampallil
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
- Institute for Informatics, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
- Division of Biology and Biomedical Sciences, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Computer Science & Engineering, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Michael S Avidan
- Department of Anesthesiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
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4
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Abraham J, Meng A, Holzer KJ, Brawer L, Casarella A, Avidan M, Politi MC. Exploring patient perspectives on telemedicine monitoring within the operating room. Int J Med Inform 2021; 156:104595. [PMID: 34627112 PMCID: PMC10627166 DOI: 10.1016/j.ijmedinf.2021.104595] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 09/10/2021] [Accepted: 09/24/2021] [Indexed: 01/04/2023]
Abstract
BACKGROUND Clinical decision support systems and telemedicine for remote monitoring can together support clinicians' intraoperative decision-making and management of surgical patients' care. However, there has been limited investigation on patient perspectives about advanced health information technology use in intraoperative settings, especially an electronic OR (eOR) for remote monitoring and management of surgical patients. PURPOSE Our study objectives were: (1) to identify participant-rated items contributing to patient attitudes, beliefs, and level of comfort with eOR monitoring; and (2) to highlight barriers and facilitators to eOR use. METHODS We surveyed 324 individuals representing surgical patients across the United States using Amazon Mechanical Turk, an online platform supporting internet-based work. The structured survey questions examined the level of agreement and comfort with eOR for remote patient monitoring. We calculated descriptive statistics for demographic variables and performed a Wilcoxon matched-pairs signed-rank test to assess whether participants were more comfortable with familiar clinicians from local hospitals or health systems monitoring their health and safety status during surgery than clinicians from hospitals or health systems in other regions or countries. We also analyzed open-ended survey responses using a thematic approach informed by an eight-dimensional socio-technical model. RESULTS Participants' average age was 34.07 (SD = 10.11). Most were white (80.9%), male (57.1%), and had a high school degree or more (88.3%). Participants reported a higher level of comfort with clinicians they knew monitoring their health and safety than clinicians they did not know, even within the same healthcare system (z = -4.012, p < .001). They reported significantly higher comfort levels with clinicians within the same hospital or health system in the United States than those in a different country (z = -10.230, p < .001). Facilitators and barriers to eOR remote monitoring were prevalent across four socio-technical dimensions: 1) organizational policies, procedures, environment, and culture; 2) people; 3) workflow and communication; and 4) hardware and software. Facilitators to eOR use included perceptions of improved patient safety through a safeguard system and perceptions of streamlined care. Barriers included fears of incorrect eOR patient assessments, decision-making conflicts between care teams, and technological malfunctions. CONCLUSIONS Participants expressed significant support for intraoperative telemedicine use and greater comfort with local telemedicine systems instead of long-distance telemedicine systems. Reservations centered on organizational policies, procedures, environment, culture; people; workflow and communication; and hardware and software. To improve the buy-in and acceptability of remote monitoring by an eOR team, we offer a few evidence-based guidelines applicable to telemedicine use within the context of OR workflow. Guidelines include backup plans for technical challenges, rigid care, and privacy standards, and patient education to increase understanding of telemedicine's potential to improve patient care.
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Affiliation(s)
- Joanna Abraham
- Department of Anesthesiology, Washington University School of Medicine, St Louis, MO, United States; Institute for Informatics, Washington University School of Medicine, St Louis, MO, United States.
| | - Alicia Meng
- Department of Anesthesiology, Washington University School of Medicine, St Louis, MO, United States
| | - Katherine J Holzer
- Department of Anesthesiology, Washington University School of Medicine, St Louis, MO, United States
| | - Luke Brawer
- Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Aparna Casarella
- Brown School at Washington University in St. Louis, St. Louis, MO, United States
| | - Michael Avidan
- Department of Anesthesiology, Washington University School of Medicine, St Louis, MO, United States
| | - Mary C Politi
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, MO, United States
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5
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Budelier TP, King CR, Goswami S, Bansal A, Gregory SH, Wildes TS, Abraham J, McKinnon SL, Cooper A, Kangrga I, Martin JL, Milbrandt M, Evers AS, Avidan MS. Protocol for a proof-of-concept observational study evaluating the potential utility and acceptability of a telemedicine solution for the post-anesthesia care unit. F1000Res 2020; 9:1261. [PMID: 33214879 PMCID: PMC7656276 DOI: 10.12688/f1000research.26794.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/09/2020] [Indexed: 11/23/2022] Open
Abstract
Introduction: The post-anesthesia care unit (PACU) is a clinical area designated for patients recovering from invasive procedures. There are typically several geographically dispersed PACUs within hospitals. Patients in the PACU can be unstable and at risk for complications. However, clinician coverage and patient monitoring in PACUs is not well regulated and might be sub-optimal. We hypothesize that a telemedicine center for the PACU can improve key PACU functions. Objectives: The objective of this study is to demonstrate the potential utility and acceptability of a telemedicine center to complement the key functions of the PACU. These include participation in hand-off activities to and from the PACU, detection of physiological derangements, identification of symptoms requiring treatment, recognition of situations requiring emergency medical intervention, and determination of patient readiness for PACU discharge. Methods and analysis: This will be a single center prospective before-and-after proof-of-concept study. Adults (18 years and older) undergoing elective surgery and recovering in two selected PACU bays will be enrolled. During the initial three-month observation phase, clinicians in the telemedicine center will not communicate with clinicians in the PACU, unless there is a specific patient safety concern. During the subsequent three-month interaction phase, clinicians in the telemedicine center will provide structured decision support to PACU clinicians. The primary outcome will be time to PACU discharge readiness determination in the two study phases. The attitudes of key stakeholders towards the telemedicine center will be assessed. Other outcomes will include detection of physiological derangements, complications, adverse symptoms requiring treatments, and emergencies requiring medical intervention. Registration: This trial is registered on clinicaltrials.gov,
NCT04020887 (16
th July 2019).
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Affiliation(s)
- Thaddeus P Budelier
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Christopher Ryan King
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Shreya Goswami
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Anchal Bansal
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Stephen H Gregory
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Troy S Wildes
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Joanna Abraham
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, 63110, USA.,Institute for Informatics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Sherry L McKinnon
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Amy Cooper
- Department of Perioperative Services, Barnes-Jewish Hospital, St. Louis, MO, 63110, USA
| | - Ivan Kangrga
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Jackie L Martin
- Department of Perioperative Services, Barnes-Jewish Hospital, St. Louis, MO, 63110, USA
| | - Melissa Milbrandt
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Alex S Evers
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, 63110, USA.,Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA.,Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Michael S Avidan
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
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6
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King CR, Abraham J, Kannampallil TG, Fritz BA, Ben Abdallah A, Chen Y, Henrichs B, Politi M, Torres BA, Mickle A, Budelier TP, McKinnon S, Gregory S, Kheterpal S, Wildes T, Avidan MS. Protocol for the Effectiveness of an Anesthesiology Control Tower System in Improving Perioperative Quality Metrics and Clinical Outcomes: the TECTONICS randomized, pragmatic trial. F1000Res 2019; 8:2032. [PMID: 32201572 PMCID: PMC7076336 DOI: 10.12688/f1000research.21016.1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/12/2019] [Indexed: 01/25/2023] Open
Abstract
Introduction: Perioperative morbidity is a public health priority, and surgical volume is increasing rapidly. With advances in technology, there is an opportunity to research the utility of a telemedicine-based control center for anesthesia clinicians that assess risk, diagnoses negative patient trajectories, and implements evidence-based practices. Objectives: The primary objective of this trial is to determine whether an anesthesiology control tower (ACT) prevents clinically relevant adverse postoperative outcomes including 30-day mortality, delirium, respiratory failure, and acute kidney injury. Secondary objectives are to determine whether the ACT improves perioperative quality of care metrics including management of temperature, mean arterial pressure, mean airway pressure with mechanical ventilation, blood glucose, anesthetic concentration, antibiotic redosing, and efficient fresh gas flow. Methods and analysis: We are conducting a single center, randomized, controlled, phase 3 pragmatic clinical trial. A total of 58 operating rooms are randomized daily to receive support from the ACT or not. All adults (eighteen years and older) undergoing surgical procedures in these operating rooms are included and followed until 30 days after their surgery. Clinicians in operating rooms randomized to ACT support receive decision support from clinicians in the ACT. In operating rooms randomized to no intervention, the current standard of anesthesia care is delivered. The intention-to-treat principle will be followed for all analyses. Differences between groups will be presented with 99% confidence intervals; p-values <0.005 will be reported as providing compelling evidence, and p-values between 0.05 and 0.005 will be reported as providing suggestive evidence. Registration: TECTONICS is registered on ClinicalTrials.gov, NCT03923699; registered on 23 April 2019.
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Affiliation(s)
- Christopher R. King
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA
| | - Joanna Abraham
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA
- Institute for Informatics, Washington University in St Louis, St Louis, MO, 63110, USA
| | - Thomas G. Kannampallil
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA
- Institute for Informatics, Washington University in St Louis, St Louis, MO, 63110, USA
| | - Bradley A. Fritz
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA
| | - Arbi Ben Abdallah
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA
| | - Yixin Chen
- Department of Computer Science and Engineering, Washington University in St Louis, St Louis, MO, 63110, USA
| | - Bernadette Henrichs
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA
| | - Mary Politi
- Department of Surgery, Washington University in St Louis, St Louis, MO, 63110, USA
| | - Brian A. Torres
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA
| | - Angela Mickle
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA
| | - Thaddeus P. Budelier
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA
| | - Sherry McKinnon
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA
| | - Stephen Gregory
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA
| | - Sachin Kheterpal
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Troy Wildes
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA
| | - Michael S. Avidan
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA
| | - TECTONICS Research Group
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA
- Institute for Informatics, Washington University in St Louis, St Louis, MO, 63110, USA
- Department of Computer Science and Engineering, Washington University in St Louis, St Louis, MO, 63110, USA
- Department of Surgery, Washington University in St Louis, St Louis, MO, 63110, USA
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, 48109, USA
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7
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Fritz BA, Cui Z, Zhang M, He Y, Chen Y, Kronzer A, Ben Abdallah A, King CR, Avidan MS. Deep-learning model for predicting 30-day postoperative mortality. Br J Anaesth 2019; 123:688-695. [PMID: 31558311 DOI: 10.1016/j.bja.2019.07.025] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Revised: 06/21/2019] [Accepted: 07/22/2019] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Postoperative mortality occurs in 1-2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. We sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality. METHODS We constructed a multipath convolutional neural network model using patient characteristics, co-morbid conditions, preoperative laboratory values, and intraoperative numerical data from patients undergoing surgery with tracheal intubation at a single medical centre. Data for 60 min prior to a randomly selected time point were utilised. Model performance was compared with a deep neural network, a random forest, a support vector machine, and a logistic regression using predetermined summary statistics of intraoperative data. RESULTS Of 95 907 patients, 941 (1%) died within 30 days. The multipath convolutional neural network predicted postoperative 30-day mortality with an area under the receiver operating characteristic curve of 0.867 (95% confidence interval [CI]: 0.835-0.899). This was higher than that for the deep neural network (0.825; 95% CI: 0.790-0.860), random forest (0.848; 95% CI: 0.815-0.882), support vector machine (0.836; 95% CI: 0.802-870), and logistic regression (0.837; 95% CI: 0.803-0.871). CONCLUSIONS A deep-learning time-series model improves prediction compared with models with simple summaries of intraoperative data. We have created a model that can be used in real time to detect dynamic changes in a patient's risk for postoperative mortality.
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Affiliation(s)
- Bradley A Fritz
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, USA.
| | - Zhicheng Cui
- Department of Computer Science and Engineering, Washington University in St Louis, St Louis, MO, USA
| | - Muhan Zhang
- Department of Computer Science and Engineering, Washington University in St Louis, St Louis, MO, USA
| | - Yujie He
- Department of Computer Science and Engineering, Washington University in St Louis, St Louis, MO, USA
| | - Yixin Chen
- Department of Computer Science and Engineering, Washington University in St Louis, St Louis, MO, USA
| | - Alex Kronzer
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, USA
| | - Arbi Ben Abdallah
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, USA
| | - Christopher R King
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, USA
| | - Michael S Avidan
- Department of Anesthesiology, Washington University in St Louis, St Louis, MO, USA
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Safavi KC, Driscoll W, Wiener-Kronish JP. Remote Surveillance Technologies: Realizing the Aim of Right Patient, Right Data, Right Time. Anesth Analg 2019; 129:726-734. [PMID: 31425213 PMCID: PMC6693927 DOI: 10.1213/ane.0000000000003948] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/18/2018] [Indexed: 01/11/2023]
Abstract
The convergence of multiple recent developments in health care information technology and monitoring devices has made possible the creation of remote patient surveillance systems that increase the timeliness and quality of patient care. More convenient, less invasive monitoring devices, including patches, wearables, and biosensors, now allow for continuous physiological data to be gleaned from patients in a variety of care settings across the perioperative experience. These data can be bound into a single data repository, creating so-called data lakes. The high volume and diversity of data in these repositories must be processed into standard formats that can be queried in real time. These data can then be used by sophisticated prediction algorithms currently under development, enabling the early recognition of patterns of clinical deterioration otherwise undetectable to humans. Improved predictions can reduce alarm fatigue. In addition, data are now automatically queriable on a real-time basis such that they can be fed back to clinicians in a time frame that allows for meaningful intervention. These advancements are key components of successful remote surveillance systems. Anesthesiologists have the opportunity to be at the forefront of remote surveillance in the care they provide in the operating room, postanesthesia care unit, and intensive care unit, while also expanding their scope to include high-risk preoperative and postoperative patients on the general care wards. These systems hold the promise of enabling anesthesiologists to detect and intervene upon changes in the clinical status of the patient before adverse events have occurred. Importantly, however, significant barriers still exist to the effective deployment of these technologies and their study in impacting patient outcomes. Studies demonstrating the impact of remote surveillance on patient outcomes are limited. Critical to the impact of the technology are strategies of implementation, including who should receive and respond to alerts and how they should respond. Moreover, the lack of cost-effectiveness data and the uncertainty of whether clinical activities surrounding these technologies will be financially reimbursed remain significant challenges to future scale and sustainability. This narrative review will discuss the evolving technical components of remote surveillance systems, the clinical use cases relevant to the anesthesiologist's practice, the existing evidence for their impact on patients, the barriers that exist to their effective implementation and study, and important considerations regarding sustainability and cost-effectiveness.
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Affiliation(s)
- Kyan C. Safavi
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - William Driscoll
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Jeanine P. Wiener-Kronish
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
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Murray-Torres T, Casarella A, Bollini M, Wallace F, Avidan MS, Politi MC. Anesthesiology Control Tower-Feasibility Assessment to Support Translation (ACTFAST): Mixed-Methods Study of a Novel Telemedicine-Based Support System for the Operating Room. JMIR Hum Factors 2019; 6:e12155. [PMID: 31012859 PMCID: PMC6658281 DOI: 10.2196/12155] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Revised: 02/21/2019] [Accepted: 03/13/2019] [Indexed: 01/26/2023] Open
Abstract
Background Despite efforts to improve patient outcomes, major morbidity and mortality remain common after surgery. Health information technologies that provide decision support for clinicians might improve perioperative and postoperative patient care. Evaluating the usability of these technologies and barriers to their implementation can facilitate their acceptance within health systems. Objective This manuscript describes usability testing and refinement of an innovative telemedicine-based clinical support system, the Anesthesiology Control Tower (ACT). It also reports stakeholders’ perceptions of the barriers and facilitators to implementation of the intervention. Methods Three phases of testing were conducted in an iterative manner. Phase 1 testing employed a think-aloud protocol analysis to identify surface-level usability problems with individual software components of the ACT and its structure. Phase 2 testing involved an extended qualitative and quantitative real-world usability analysis. Phase 3 sought to identify major barriers and facilitators to implementation of the ACT through semistructured interviews with key stakeholders. Results Phase 1 and phase 2 usability testing sessions identified numerous usability problems with the software components of the ACT. The ACT platform was revised in seven iterations in response to these usability concerns. Initial satisfaction with the ACT, as measured by standardized instruments, was below commonly accepted cutoffs for these measures. Satisfaction improved to acceptable levels over the course of revision and testing. A number of barriers to implementation were also identified and addressed during the refinement of the ACT intervention. Conclusions The ACT model can improve the standard of perioperative anesthesia care. Through our thorough and iterative usability testing process and stakeholder assessment of barriers and facilitators, we enhanced the acceptability of this novel technology and improved our ability to implement this innovation into routine practice. International Registered Report Identifier (IRRID) RR2-10.1186/s40814-018-0233-4
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Affiliation(s)
- Teresa Murray-Torres
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, Washington University in St. Louis, St. Louis, MO, United States
| | - Aparna Casarella
- Brown School of Social Work, Washington University in St. Louis, St. Louis, MO, United States
| | - Mara Bollini
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, Washington University in St. Louis, St. Louis, MO, United States
| | - Frances Wallace
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, MA, United States
| | - Michael S Avidan
- Department of Anesthesiology, Washington University School of Medicine in St. Louis, Washington University in St. Louis, St. Louis, MO, United States
| | - Mary C Politi
- Department of Surgery, Division of Public Health Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
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10
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Gregory S, Murray-Torres TM, Fritz BA, Ben Abdallah A, Helsten DL, Wildes TS, Sharma A, Avidan MS. Study protocol for the Anesthesiology Control Tower-Feedback Alerts to Supplement Treatments (ACTFAST-3) trial: a pilot randomized controlled trial in intraoperative telemedicine. F1000Res 2018; 7:623. [PMID: 30026931 PMCID: PMC6039946 DOI: 10.12688/f1000research.14897.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/07/2018] [Indexed: 03/17/2024] Open
Abstract
Background: Each year, over 300 million people undergo surgical procedures worldwide. Despite efforts to improve outcomes, postoperative morbidity and mortality are common. Many patients experience complications as a result of either medical error or failure to adhere to established clinical practice guidelines. This protocol describes a clinical trial comparing a telemedicine-based decision support system, the Anesthesiology Control Tower (ACT), with enhanced standard intraoperative care. Methods: This study is a pragmatic, comparative effectiveness trial that will randomize approximately 12,000 adult surgical patients on an operating room (OR) level to a control or to an intervention group. All OR clinicians will have access to decision support software within the OR as a part of enhanced standard intraoperative care. The ACT will monitor patients in both groups and will provide additional support to the clinicians assigned to intervention ORs. Primary outcomes include blood glucose management and temperature management. Secondary outcomes will include surrogate, clinical, and economic outcomes, such as incidence of intraoperative hypotension, postoperative respiratory compromise, acute kidney injury, delirium, and volatile anesthetic utilization. Ethics and dissemination: The ACTFAST-3 study has been approved by the Human Resource Protection Office (HRPO) at Washington University in St. Louis and is registered at clinicaltrials.gov ( NCT02830126). Recruitment for this protocol began in April 2017 and will end in December 2018. Dissemination of the findings of this study will occur via presentations at academic conferences, journal publications, and educational materials.
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Affiliation(s)
- Stephen Gregory
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Teresa M. Murray-Torres
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Bradley A. Fritz
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Arbi Ben Abdallah
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Daniel L. Helsten
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Troy S. Wildes
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Anshuman Sharma
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Michael S. Avidan
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - ACTFAST Study Group
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
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11
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Gregory S, Murray-Torres TM, Fritz BA, Ben Abdallah A, Helsten DL, Wildes TS, Sharma A, Avidan MS. Study protocol for the Anesthesiology Control Tower-Feedback Alerts to Supplement Treatments (ACTFAST-3) trial: a pilot randomized controlled trial in intraoperative telemedicine. F1000Res 2018; 7:623. [PMID: 30026931 PMCID: PMC6039946 DOI: 10.12688/f1000research.14897.2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/07/2018] [Indexed: 01/15/2023] Open
Abstract
Background: Each year, over 300 million people undergo surgical procedures worldwide. Despite efforts to improve outcomes, postoperative morbidity and mortality are common. Many patients experience complications as a result of either medical error or failure to adhere to established clinical practice guidelines. This protocol describes a clinical trial comparing a telemedicine-based decision support system, the Anesthesiology Control Tower (ACT), with enhanced standard intraoperative care. Methods: This study is a pragmatic, comparative effectiveness trial that will randomize approximately 12,000 adult surgical patients on an operating room (OR) level to a control or to an intervention group. All OR clinicians will have access to decision support software within the OR as a part of enhanced standard intraoperative care. The ACT will monitor patients in both groups and will provide additional support to the clinicians assigned to intervention ORs. Primary outcomes include blood glucose management and temperature management. Secondary outcomes will include surrogate, clinical, and economic outcomes, such as incidence of intraoperative hypotension, postoperative respiratory compromise, acute kidney injury, delirium, and volatile anesthetic utilization. Ethics and dissemination: The ACTFAST-3 study has been approved by the Human Resource Protection Office (HRPO) at Washington University in St. Louis and is registered at clinicaltrials.gov ( NCT02830126). Recruitment for this protocol began in April 2017 and will end in December 2018. Dissemination of the findings of this study will occur via presentations at academic conferences, journal publications, and educational materials.
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Affiliation(s)
- Stephen Gregory
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Teresa M Murray-Torres
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Bradley A Fritz
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Arbi Ben Abdallah
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Daniel L Helsten
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Troy S Wildes
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Anshuman Sharma
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Michael S Avidan
- Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
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