ROBOTIC ANESTHESIA 

Physician anesthesiologist at Stanford at Associated Anesthesiologists Medical Group
Richard Novak, MD is a Stanford physician board-certified in anesthesiology and internal medicine.Dr. Novak is an Adjunct Clinical Professor in the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University, the Medical Director at Waverley Surgery Center in Palo Alto, California, and a member of the Associated Anesthesiologists Medical Group in Palo Alto, California.
email rjnov@yahoo.com
phone 650-465-5997

How soon will we see robotic anesthesia in our hospitals and surgery centers? In the past three decades the high-tech revolution introduced the internet, the laptop computer, the iPhone, Google, and global positioning satellites. Most of these discoveries originated in Silicon Valley, just miles outside Stanford University Hospital where I’ve been working for the past 42 years. Our medical world inside the hospital has changed more slowly. We’ve seen advances in noninvasive surgery, fiberoptic scopes, transplantation science, cancer therapeutics, and mega healthcare delivery companies. But what’s new in anesthesia the last 30 years? Relatively little. The Glidescope, sugammadex, ultrasound-guided blocks, and the time-consuming Electronic Medical Record arrived, but we typically administer the same medications, use the same airway tubes, and watch the same vital signs monitors as we did in the 1990s. 

Why have there been no new anesthetics? Let me tell you a story: A former Stanford Chairman of Anesthesiology and friend of mine left the university in 2006 to become a pharmaceutical company executive, first at Novartis and then at AstraZeneca. Ten years ago, when I asked him what new anesthesia drugs were in the pipeline, he answered, “None, and there probably will be very few new ones. The drugs you have now are inexpensive generic drugs, and they work very well. The research and development costs to bring a new anesthetic drug to market are prohibitively expensive, and unless that new drug is markedly better, it will not push the inexpensive generic drugs out of use.”

Is the same true for anesthesia devices? Are proposed anesthetic robots too expensive to design, test, and manufacture? Can they be brought to market to assist current anesthesia providers? Can they be brought to market to replace any anesthesia providers? Keep these economic questions in mind as we review the current science of robotic anesthesia.

vanished and vanishing jobs

Jobs have already disappeared in many industries. ATMs replaced bank tellers. Automated garbage trucks replaced garbage men. In the near future automated cars and trucks will replace drivers. In medicine, computerized artificial intelligence for the analysis of digital images is superior to the human eye, placing the jobs of radiologists, pathologists, and dermatologists in peril. 

Will we live to see anesthesiologists replaced by technology? The following three pictures depict fictional anesthesia robots:

fictional medical robots

But this is what real anesthesia robots look like:

real anesthesia robots

An outline of the types of robotic anesthesia is as follows:

  1. PHARMACOLOGIC ROBOTS
  2. MECHANICAL ROBOTS PERFORMING PROCEDURES
  3. DECISION SUPPORT ROBOTS

  1. PHARMACOLOGIC ROBOTS:

In 2012 a United States national marketing firm contacted me to seek my opinion regarding an automated device to infuse propofol. The device was the Sedasys®-Computer-Assisted Personalized Sedation System, developed by Johnson and Johnson/Ethicon. The system incorporated an automated propofol infusion device, along with standard ASA monitors, including end-tidal CO2, into a device to be used to provide conscious sedation for GI endoscopy.

The SEDASYS system

The Sedasys unit infused an initial dose of propofol (typically 30 – 50 mg in young patients) over 3 minutes, and then began a maintenance infusion of propofol at a pre-programmed rate (usually 50 mcg/kg/min).  If the monitors detected signs of over-sedation, that is, falling oxygen saturation, depressed respiratory rate, or a failure of the end-tidal CO2 curve, then the propofol infusion was stopped automatically.  In addition, the machine talked to the patient, and at intervals asked the patient to squeeze a hand-held gripper device.  If the patient was non-responsive and did not squeeze, the propofol infusion was automatically stopped.

The planned strategy was to have gastroenterologists complete a weekend educational course to learn: that Sedasys was not appropriate if the patient is ASA 3 or 4 or had severe medical problems; that Sedasys was not appropriate if the patient had risk factors such as morbid obesity, a difficult airway, or sleep apnea; and gastroenterologists were taught the airway skills of chin lift, jaw thrust, oral airway use, nasal airway use, and bag-mask ventilation. 

I did not recommend the device be FDA-approved, as I saw the potential of inappropriate patients with obesity or sleep apnea slipping through the screening process, as well as the risk that an over-sedated patient could lose their airway and the gastroenterologist would not be able to rescue them, seeing as propofol has no reversal agent. 

With only one prospective clinical trial, the United States Food and Drug Administration did approve the device in 2013. There was limited clinical use of Sedasys, and Ethicon announced in March 2016 that it was pulling Sedasys from the market. 

The failure of Sedasys was attributed to three factors:

  1. If a patient became too “light” during a procedure, the Sedasys system was not capable of increasing the depth of the sedation.
  2. Both patients and endoscopists expected deep general anesthesia, not moderate sedation. 
  3. Gastroenterologists were ill-equipped to shoulder the responsibility of general anesthesia and airway management. 

From the failure of Sedasys it was clear that further refinement in technology and drug use was needed. That refinement was the development of closed-loop devices. A closed-loop control system is a set of mechanical or electronic devices that automatically regulates a process variable to a desired state or set point without human interaction. The cruise-control on your automobile is an example of closed-loop feedback control of driving speed.

In anesthesia, closed-loop devices can infuse the medications propofol and remifentanil, with the rate of the infusions guided by a bispectral (BIS) monitor of EEG (electroencephalography) activity.  Propofol is an ultra-short-acting hypnotic drug, and remifentanil is an ultra-short-acting narcotic. Administered together, these drugs induce total intravenous anesthesia (TIVA).

A closed-loop system can infuse these two drugs automatically. A BIS monitor calculates a score between 0 and 100 for the patient’s level of unconsciousness, with a score of 100 corresponding to wide awake and 0 corresponding to a flat EEG. A score of 40 – 60 is considered an optimal amount of anesthesia depth. A computer controls the infusion rates of two automated infusion pumps containing propofol and remifentanil. The infusion rates depend on whether the measured BIS score is higher or lower than the 40- 60 range. Researchers in Vancouver, Canada expanded this technology into a device called the iControl-RP, where the initials RP stand for remifentanil and propofol. In addition to the BIS monitor, the iControl-RP monitored the vital signs of blood oxygen level, heart rate, respiratory rate, and blood pressure to determine how much anesthesia to deliver.

iControl-RP robot

In a single-blind randomized study published in Anesthesiology in 2015, 42 patients were randomized to the closed-loop iControl-RP group or to a manual group. The results showed the percentage of time with BIS40-60 was greater in the closed-loop group (87%) vs. the manual group (72%). The number of perioperative adverse events and the length of stay in the postanesthesia care unit were similar. The conclusion of the study was that automated control of hypnosis and analgesia guided by the BIS was clinically feasible.

This study led to an article in the The Washington Post in 2015,  in which one of the machine’s co-developers, Dr. Mark Ansermino said, “We are convinced the machine can do better than human anesthesiologists.” The device had been used on 250 patients at that time. The iControl-RP team struggled to find a corporate backer for its project. Dr. Ansermino told The Washington Post, “Most big companies view this as too risky.” He believed a device like this was inevitable. “I think eventually this will happen,” Ansermino said, “whether we like it or not.”

A second pharmacologic robot named McSleepy used three syringe pumps to control the three components of general anesthesia (hypnosis, analgesia, and neuromuscular block) in an automated closed-loop anesthesia drug delivery system. Each component had specific monitoring: BIS; AnalgoScore (an-AL-go-score = a pain score derived from the heart rate and mean arterial pressure) which was used as the control variable to titrate the effective dose of remifentanil; and the train of four (TOF), which was a measure of the twitch strength of a muscle when its peripheral nerve was electrically stimulated.

McSleepy robot

A 2013 study in the British Journal of Anaesthesia  looked at 186 patients managed by McSleepy, in which the McSleepy system showed better control of hypnosis than manually administered anesthesia (see graphs below). 

The control of depth of anesthesia under McSleepy (blue) or manual (green)

The McSleepy system also showed faster extubation times than manually administered anaesthesia. 

A second McSleepy study in the British Journal of Anaesthesia in 2013 showed an application in telemedicine.  The remote control of general anesthetics was successfully performed between two different countries (Canada and Italy). Twenty patients underwent elective thyroid surgeries, with a master-computer in Montreal and a slave-computer in Pisa, demonstrating the feasibility of remote telemedicine control of anesthesia administration.

II.  MECHANICAL ANESTHESIA ROBOTS

Ma’s mask ventilation robot

The first example is a machine designed to provide mask ventilation, as described in the paper “Novel Anesthesia Airway Management Robot for Robot Assisted Non-invasive Positive Pressure Mask Ventilation,” Published by Dr. Ma et al, from China. Ma designed a robot equipped with two snake arms and a mask-fastening mechanism to facilitate trachea airway management for anesthesia. (PIC) The two snake arms were designed to lift a patient’s jaw. The mask-fastening mechanism was used to fasten and hold the mask onto a patient’s face. A joystick control unit managed both the lifting and fastening force. To date this system has not been used on humans, but the device was proposed as a method to perform non-invasive mask positive pressure ventilation via a robotic system.

The Kepler Intubating System

In 2012 Dr. Hemmerling at McGill University in Montreal published a paper in Current Opinions in Anaesthesiology, describing the Kepler Intubation System. The Kepler Intubation System consisted of a remote-control joystick and intubation cockpit, linked to a standard videolaryngoscope via a robotic arm. (PIC) Ninety intubations were performed on a mannequin with this device. The first group of 30 intubations was performed with the operator in direct view of the mannequin. The second group of 30 intubations was performed with the operator unable to see the mannequin. The third group of 30 intubations were performed via semiautomated intubations during which the robotic system replayed a tracing of a previously recorded intubation maneuver. All intubations were successful on the first attempt, with the average intubation times between 41 and 51 seconds for all three groups. The study concluded that a robotic intubation system can complete successful remote intubation within 40 to 60 seconds.

The Magellan Nerve Block System

In 2013 Dr. Hemmerling published the study “First Robotic Ultrasound-Guided Nerve Blocks in Humans Using the Magellan System” in Anesthesia & Analgesia. The Magellan system consisted of three main components: a joystick, a robotic arm, and a software control system. After localization of the sciatic nerve by ultrasound, 35 ml of bupivacaine 0.25% was injected by the robot. Thirteen patients were enrolled. The nerve blocks were successful in all patients. The nerve performance time was 164 seconds by the robotic system, and 189 seconds by a human practitioner. The Magellan System was the first robotic ultrasound-guided nerve block system tested on humans.  

III.  DECISION SUPPORT ROBOTS

A decision-support robot can recognize a crucial clinical situation that requires human intervention and, when allowed by the attending clinician, may administer treatment. It seems likely that cognitive robots which follow algorithms can increase patient safety.

In August 2021 Dr. Alexandre Joosten, an anesthesia professor in Brussels, Belgium and Paris, France, published “Computer-assisted Individualized Hemodynamic Management Reduces Intraoperative Hypotension in Intermediate- and High-risk Surgery: A Randomized Controlled Trial” in Anesthesiology.  This study tested the hypothesis that computer-assisted hemodynamic management could reduce intraoperative low blood pressure in patients undergoing intermediate- to high-risk surgery. This prospective randomized single-blinded study included 38 patients undergoing abdominal or orthopedic surgery. All patients had an indwelling radial arterial catheter to monitor blood pressure continuously. A closed-loop system titrated a norepinephrine infusion based on the blood pressure, and a second separate decision support system infused mini-fluid challenges when low blood pressures were recorded. Results showed the time of intraoperative hypotension was 1.2% in the computer-assisted group compared to 21.5% in the manually adjusted goal-directed therapy group (P < 0.001). The incidence of minor postoperative complications was the same between groups (42 vs. 58%; P = 0.330). The mean stroke volume index and cardiac index were both significantly higher in the computer-assisted group than in the manually adjusted goal-directed therapy group (P < 0.001). The study’s conclusion was that this closed-loop system resulted in a significant decrease in the percentage of intraoperative time with a low mean arterial pressure.

VOICE-ACTIVATED DEVICES

Voice-activated devices are gaining traction in healthcare. The story “Amazon’s Alexa Is Now a Healthcare Provider” was published by Medscape on February 17, 2022.

Alexa at bedside

The article described how thousands of Alexa-enabled devices are in use in hundreds of hospitals in America. Amazon’s Alexa functions as a digital personal assistant whose voice-powered innovation connects patients with their healthcare team members. Patients who are confined to bed can use their voice to communicate directly to a nurse’s smartphone. An Alexa device is positioned near the bed at Cedars-Sinai Medical Center in Los Angeles, making it easy to call for nursing help. (PIC) Alexa can also connect healthcare providers to their patients. Doctors or nurses can appear virtually in a patient’s room on the Alexa Show’s video screen and assess the needs of that patient. I expect voice-activation to link healthcare providers with medical robots in the future.

PROBLEMS WITH ROBOTS REPLACING ANESTHESIA

The medical publications referenced above demonstrate that robotic anesthesia devices exist, yet none of them are in common use at this time. The current and proposed robotic devices are only small steps toward replacing anesthesiologists, because anesthetizing patients requires far more expertise than merely titrating drug levels or performing a solitary mechanical procedure. 

Anesthesia management consists of a wide variety of skills:

  • preoperative assessment of a patient’s medical problems 
  • successful mask ventilation of an unconscious patient (in most cases) followed by placement of an airway tube
  • diagnosis and treatment of any medical complication that occurs as a result of the anesthesia or the surgical procedure
  • removal of the airway tube at the conclusion of most surgeries, and 
  • the diagnosis and treatment of postoperative medical complications

Successful robotic anesthesia devices may eventually eliminate the repetitive aspects of anesthesia management. You may see robots assisting anesthesia providers in the coming decades, depending on the economic viability of the technology. 

Will the intrusion of a robot into anesthesia care be a welcome event? When you’re a patient, do you desire a caring, empathetic human attending to you, or do you desire an algorithm? 

Or in the future, will you desire both?

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READ ABOUT RICK NOVAK’S FICTION WRITING AT RICK NOVAK.COM.

DOCTOR VITA IS COMING

Physician anesthesiologist at Stanford at Associated Anesthesiologists Medical Group
Richard Novak, MD is a Stanford physician board-certified in anesthesiology and internal medicine.Dr. Novak is an Adjunct Clinical Professor in the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University, the Medical Director at Waverley Surgery Center in Palo Alto, California, and a member of the Associated Anesthesiologists Medical Group in Palo Alto, California.
email rjnov@yahoo.com
phone 650-465-5997
artificial_intelligence_ai_healthcare

My name is Rick Novak, and I’m a double-boarded anesthesiologist and internal medicine doctor and a writer of medical fiction. I’m here to talk about Doctor Vita, a vision of the future of Artificial Intelligence in Medicine.

I’m an Adjunct Clinical Professor of Anesthesiology, Perioperative and Pain Medicine at Stanford and the Deputy Chief of the department. I don’t tout myself as an expert in AI technology, but I am an expert in taking care of patients, which I’ve done in clinics, operating rooms, intensive care units, and emergency rooms at Stanford and in Silicon Valley for over 30 years.

AI is already prevalent in our daily life. Smartphones verbally direct us to our destination through mazes of highways and traffic. Self-driving cars are in advanced testing phases. The Amazon Echo brings us Alexa, an AI-powered personal assistant who follows verbal commands in our homes.Artificial intelligence in medicine (AIM) will grow in importance in the decades to come and will change anesthesia practice, surgical practice, perioperative medicine in clinics, and the interpretation of imaging. AI is already prevalent in our daily life. Smartphones verbally direct us to our destination through mazes of highways and traffic. Self-driving cars are in advanced testing phases. The Amazon Echo brings us Alexa, an AI-powered personal assistant who follows verbal commands in our homes. AIM advances are paralleling these inventions in three clinical arenas:

Surgical Robot

1. Operating rooms: Anesthesia robots fall into two groups: manual robots and pharmacological robots. Manual robots include the Kepler Intubation System intubating robot:

designed to utilized video laryngoscopy and a robotic arm to place an endotracheal tube, the use of the DaVinci surgical robot to perform regional anesthetic blockade, and the use of the Magellan robot to place peripheral nerve blocks.

Magellan robot for placing regional anesthetic blocks

Pharmacological robots include the McSleepy intravenous sedation machine, designed to administer propofol, narcotic, and muscle relaxant:

McSleepy anesthesia robot

and the iControl-RP machine, described in The Washington Post as a closed-loop system intravenous anesthetic delivery system which makes its own decisions regarding the IV administration of remifentanil and propofol. This device monitors the patient’s EEG level of consciousness via a BIS monitor device as well as traditional vital signs. One of the machine’s developers, Mark Ansermino MD stated, “We are convinced the machine can do better than human anesthesiologists.” The current example of surgical robot technology in the operating room is the DaVinci operating robot. This robot is not intended to have an independent existence, but rather enables the surgeon to see inside the body in three dimensions and to perform fine motor procedures at a higher level. The good news for procedural physicians is that it’s unlikely any AIM robot will be able to independently master manual skills such as complex airway management or surgical excision. No device on the horizon can be expected to replace anesthesiologists. Anesthetizing patients requires preoperative assessment of all medical problems from the history, physical examination, and laboratory evaluation; mask ventilation of an unconscious patient; placement of an airway tube; observation of all vital monitors during surgery; removal of the airway tube at the conclusion of most surgeries; and the diagnosis and treatment of any complication during or following the anesthetic.

IBM Watson AI Robot

2. Clinics: In a clinic setting a desired AIM application would be a computer to input information on a patient’s history, physical examination, and laboratory studies, and via deep learning establish a diagnosis with a high percentage of success. IBM’s Watson computer has been programmed with over 600,000 medical evidence reports, 1.5 million patient medical records, and two million pages of text from medical journals. Equipped with more information than any human physician could ever remember, Watson is projected to become a diagnostic machine superior to any doctor. AIM machines can input new patient information into a flowchart, also known as a branching tree. A flowchart will mimic the process a physician carries out when asking a patient a series of increasingly more specific questions. Once each diagnosis is established with a reasonable degree of medical certainty, an already-established algorithm for treatment of that diagnosis can be applied. Because anesthesiology involves preoperative clinic assessment and perioperative medicine, the role of AIM in clinics is relevant to our field.

Artificial Intelligence and X-ray Interpretation

3. Diagnosis of images: Applications of image analysis in medicine include machine learning for diagnosis in radiology, pathology, and dermatology. The evaluation of digital X-rays, MRIs, or CT scans requires the assessment of arrays of pixels. Future computer programs may be more accurate than human radiologists. The model for machine learning is similar to the process in which a human child learns–a child sees an animal and his parents tell him that animal is a dog. After repeated exposures the child learns what a dog looks like. Early on the child may be fooled into thinking that a wolf is a dog, but with increasing experience the child can discern with almost perfect accuracy what is or is not a dog. Deep learning is a radically different method of programming computers which requires a massive database entry, much like the array of dogs that a child sees in the example above, until a computer can learn the skill of pattern matching. An AIM computer which masters deep learning will probably not give yes or no answers, but rather a percentage likelihood of a diagnosis, i.e. a radiologic image has a greater than a 99% chance of being normal, or a skin lesion has a greater than 99% chance of being a malignant melanoma. In pathology, computerized digital diagnostic skills will be applied to microscopic diagnose. In dermatology, machine learning will be used to diagnosis skin cancers, based on large learned databases of digital photographs. Imaging advances will not directly affect anesthesiologists, but if you’re a physician who makes his or her living by interpreting digital images, you should have real concern about AIM taking your job in the future.

There’s currently a shortage of over seven million physicians, nurses and other health workers worldwide. Can AIM replace physicians? Contemplate the following . . . 

All medical knowledge is available on the Internet:

Most every medical diagnosis and treatment can be written as a decision tree algorithm:

Voice interaction software is excellent:

The physical exam is of less diagnostic importance than scans and lab tests which can be digitalized:

Computers are cheaper than the seven-year post-college education required to train a physician:

versus an inexpensive computer:

There is a need for cheaper, widespread healthcare, and the concept of an automated physician is no longer the domain of science fiction. Most sources project an AIM robot doctor will likely look like a tablet computer. For certain applications such as clinical diagnosis or new image retrieval, the AIM robot will have a camera, perhaps on a retractable arm so that the camera can approach various aspects of a patient’s anatomy as indicated. Individual patients will need to sign in to the computer software system via retinal scanners, fingerprint scanners, or face recognition programs, so that the computer can retrieve the individual patient’s EHR data from an Internet cloud. It’s possible individual patients will be issued a card, not unlike a debit or credit card, which includes a chip linking them to their EHR data.

What will be the economics of AI in medicine? Who will pay for it? America spends 17.8% of its Gross National Product on healthcare, and this number is projected to reach 20% by 2025. Entrepreneurs realize that healthcare is a multi-billion dollar industry, and the opportunity to earn those healthcare dollars is alluring.

It’s inevitable that AI will change current medical practice. Vita is the Latin word for “life.” I’ve coined the name “Doctor Vita” for the AI robot which will someday do many of the tasks currently managed by human physicians.

These machines will breathe new life into our present healthcare systems. In all likelihood these improvements will be more powerful and more wonderful than we could imagine. A bold prediction: AI will change medicine more than any development since the invention of anesthesia in 1849. Doctor Vita from All Things That Matter Press describes a fictional University of Silicon Valley Medical Center staffed by both AI doctors and human doctors. How physicians interact with these machines will be a leading question for our future. AI in medicine will arrive in decades to come. Michael Crichton wrote Jurassic Parkin 1990, 29 years ago, and we still do not see genetically recreated dinosaurs roaming the Earth. But we will see AI in medicine within 29 years. You can bet on it.

Here’s a dilemma: In 2018 and 2019 autopilots drove two Boeing 737 Max airplanes to crashes despite the best efforts of human pilots to correct their course. To date there have been 3 deaths of drivers in self-driving Tesla automobiles. What will happen when AI intersects with medicine and we have machines directing medical care? In the spirit of Jules Verne, this century’s trip around the world, to the center of the earth, to the moon, or beneath the ocean’s surface is the coming of Artificial Intelligence in Medicine.

For the bibliography click here.

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The most popular posts for laypeople on The Anesthesia Consultant include:

How Long Will It Take To Wake Up From General Anesthesia?

Why Did Take Me So Long To Wake From General Anesthesia?

Will I Have a Breathing Tube During Anesthesia?

What Are the Common Anesthesia Medications?

How Safe is Anesthesia in the 21st Century?

Will I Be Nauseated After General Anesthesia?

What Are the Anesthesia Risks For Children?

LEARN MORE ABOUT RICK NOVAK’S FICTION WRITING AT RICK NOVAK.COM BY CLICKING ON THE PICTURE BELOW:

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