Telehealth, Robotics, and Closed Loop Anesthesia: Is This the Future?

Educational Objectives

The goal of this program is to improve the use of digital technology in anesthesia. After hearing and assimilating this program, the clinician will be better able to:

1. Compare outcomes between telehealth and in-person preoperative and postoperative assessments.
2. Review the current and future uses of artificial intelligence and machine learning in anesthesiology.

Summary

Telehealth (TH): a 2004 study of remote preoperative anesthesia evaluation from Ontario, Canada used a TH device that allowed the physician to see patients and hear their heartbeats; 8 of the 10 patients were able to go directly to surgery, and 2 patients needed further evaluation; study by Applegate et al (2013) compared remote and in-person anesthesia evaluation and the ability to predict difficult airways; both types of evaluation had a similarly low predictive value for difficult airways, but patients and providers were highly satisfied with TH, and the benefits of TH were patient-centric (ie, lower cost and less time needed); a 2016 survey of postoperative patients at the University of Texas Medical Branch found that 97% preferred the TH; a 2019 systematic review found a high degree of patient satisfaction with TH preoperative assessment; study of TH outcomes at the University of California, Los Angeles preoperative clinic were assessed over 27 mo and found that the rate of case cancellation was similar between TH and in-person assessments; the American Heart Association Get With the Guidelines investigators found no difference in post-resuscitation survival or survival to discharge with TH critical care among >27,000 cardiac arrests in the intensive care unit (ICU)

Implementation of a TH ICU at Houston Methodist Hospital: the virtual physician is off site and the nurses are in the virtual “op center,” where they monitor physiologic parameters and algorithms; observed mortality is lower than expected and has not changed over the 5 quarters of observation since implementation, but the length of stay is higher than expected; the rate of nocturnal code blue and unplanned extubations may be decreasing (although these outcomes require a longer period of monitoring)

Robotics and automation: robots are intelligent machines that help perform tasks; artificial intelligence (AI) is human intelligence simulated by machines; machine learning (ML) is a form of AI in which machines learn without explicit instructions; the US Food and Drug Administration (FDA) approved the robotic sedation machine (Sedasys robot), but the manufacturer took it off the market in 2016 due to poor sales; the robotic sedation machine was designed to administer propofol for colonoscopy and endoscopy without a clinician present, which was met with strong resistance among clinicians

Artificial intelligence: >$10 billion was spent in 2021 on AI health care applications; radiology has the most FDA-approved AI devices; a robotic assisted surgery device (daVinci robot) creates video recordings that are subjected to AI analysis of surgeon performance; this analysis predicts the postoperative length of stay and outcomes, eg, urinary incontinence 3 and 6 mo after radical prostatectomy; Hofer et al (2020) found that deep neural networks to train a ML application performed better than the American Society of Anesthesiologists (ASA) Physical Status Classification System for predicting mortality, acute kidney injury, reintubation, and other consequential outcomes; computer vision is used in the operating room to assess staff efficiency and send notifications

Closed-loop anesthesia (CLA): Hemmerling et al (2010) assessed propofol administration using an infusion pump that self-corrected the dose to the bispectral index (BIS) and found that the BIS stayed ≤10% of the desired value; a 2016 study that compared CLA delivery with manual delivery of propofol with a target BIS of 50 found that the CLA delivery group maintained BIS ≤10% of the target 81% of the time (compared with 55% of the time in the manual delivery group); a meta-analysis of 12 studies found automation is superior to manual delivery of propofol; the induction dose of propofol differed, the recovery time was similar, and the maintenance of target depth of anesthesia was better with automated delivery; multicenter study (Wang et al [2021]) of patients >60 yr of age who underwent noncardiac surgery used automated delivery of propofol and remifentanil (titrated to the BIS), fluid (titrated to stroke volume), and ventilation (titrated to end tidal CO₂); the automated delivery yielded less time with an extremely low BIS, less hypocapnia, and a lower fluid balance; the primary outcome of cognitive dysfunction on the Montreal Cognitive Assessment scale differed by 1 point between the CLA group and the control group, and the clinical significance of this difference is unclear

Readings

Applegate RL 2nd, Gildea B, Patchin R, et al. Telemedicine pre-anesthesia evaluation: a randomized pilot trial. Telemed J E Health. 2013 Mar; 19(3):211-6; Connor CW. Artificial intelligence and machine learning in anesthesiology. Anesthesiology. 2019 Dec; 131(6):1346-59; Hemmerling TM, Charabati S, Zaouter C, et al. A randomized controlled trial demonstrates that a novel closed-loop propofol system performs better hypnosis control than manual administration. Can J Anesth/J Can Anesth. 2010;57: 725–735; Hofer IS, Lee C, Gabel E, et al. Development and validation of a deep neural network model to predict post-op mortality, acute kidney injury, and reintubation using a single feature set. NPJ Digit Med. 2020; 3: 58; Hung AJ, Chen J, Ghodoussipour S, et al. A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy. BJU Int. 2019 Sep;124(3):487-495; Joosten A, Rinehart J, Bardaji A, et al. Anesthetic management using multiple closed-loop systems and delayed neurocognitive recovery: a randomized controlled trial. Anesthesiology. 2020 Feb;132(2):253-66; Lee RS, Ma R, Pham S, et al. Machine learning to delineate surgeon and clinical factors that anticipate positive surgical margins after robot-assisted radical prostatectomy. J Endourol. 2022 Sep; 36(9):1192-1198; Peltan ID, Guidry D, Brown K, et al. Telemedical intensivist consultation during in-hospital cardiac arrest resuscitation: a simulation-based, randomized controlled trial. Chest. 2022 Jul; 162(1):111-119; Quesada N, Júdez D, Martínez Ubieto J, et al. Bispectral index monitoring reduces the dosage of propofol and adverse events in sedation for endobronchial ultrasound. Respiration. 2016; 92(3):166-75; Schoen DC, Prater K. Role of telehealth in pre-anesthetic evaluations. AANA J. 2019 Feb;87(1):43-49; Udeh C, Udeh B, Rahman N, et al. Telemedicine/virtual ICU: where are we and where are we going?. Methodist Debakey Cardiovasc J. 2018; 14(2):126. doi:10.14797/mdcj-14-2-126; Wang D, Song Z, Zhang C, et al. Bispectral index monitoring of the clinical effects of propofol closed-loop target-controlled infusion: systematic review and meta-analysis of randomized controlled trials. Medicine. 2021 Jan 1; 100(4).

Disclosures

For this program, the following relevant financial relationships were disclosed and mitigated to ensure that no commercial bias has been inserted into this content: Dr. Steadman was a consultant for UpToDate. Members of the planning committee reported nothing relevant to disclose.

Acknowledgements

Dr. Steadman was recorded at the Texas Society of Anesthesiologists 2022 Annual Meeting, held September 8-11, 2022, in Round Rock, TX, and presented by Texas Society of Anesthesiologists. For more information on future CME activities from this presenter, please visit tsa.org. Audio Digest thanks the speakers and presenters for their cooperation in the production of this program.

CME/CE INFO

Accreditation:

The Audio- Digest Foundation is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

The Audio- Digest Foundation designates this enduring material for a maximum of 0.75 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Audio Digest Foundation is accredited as a provider of continuing nursing education by the American Nurses Credentialing Center's (ANCC's) Commission on Accreditation. Audio Digest Foundation designates this activity for 0.75 CE contact hours.

Lecture ID:

AN644502

Expiration:

This CME course qualifies for AMA PRA Category 1 Credits™ for 3 years from the date of publication.

Instructions:

To earn CME/CE credit for this course, you must complete all the following components in the order recommended: (1) Review introductory course content, including Educational Objectives and Faculty/Planner Disclosures; (2) Listen to the audio program and review accompanying learning materials; (3) Complete posttest (only after completing Step 2) and earn a passing score of at least 80%. Taking the course Pretest and completing the Evaluation Survey are strongly recommended (but not mandatory) components of completing this CME/CE course.

Estimated time to complete this CME/CE course:

Approximately 2x the length of the recorded lecture to account for time spent studying accompanying learning materials and completing tests.