In clinical operations, teamwork can be the crucial factor that determines the final outcome. Prior studies have shown that sufficient collaboration is the key factor that determines the outcome of an operation. To understand how the team practices teamwork during the operation, we collected CliniDial from simulations of medical operations. CliniDial includes the audio data and its transcriptions, the simulated physiology signals of the patient manikins, and how the team operates from two camera angles. We annotate behavior codes following an existing framework to understand the teamwork process for CliniDial. We pinpoint three main characteristics of our dataset, including its label imbalances, rich and natural interactions, and multiple modalities, and conduct experiments to test existing LLMs' capabilities on handling data with these characteristics. Experimental results show that CliniDial poses significant challenges to the existing models, inviting future effort on developing methods that can deal with real-world clinical data. We open-source the codebase at https://github.com/MichiganNLP/CliniDial

Large Language Models (LLMs) are emerging as powerful tools in healthcare, particularly for complex, domain-specific tasks. This study describes the development and evaluation of the PErioperative AI CHatbot (PEACH), a secure LLM-based system integrated with local perioperative guidelines to support preoperative clinical decision-making. PEACH was embedded with 35 institutional perioperative protocols in the secure Claude 3.5 Sonet LLM framework within Pair Chat (developed by Singapore Government) and tested in a silent deployment with real-world data. Accuracy, safety, and usability were assessed. Deviations and hallucinations were categorized based on potential harm, and user feedback was evaluated using the Technology Acceptance Model (TAM). Updates were made after the initial silent deployment to amend one protocol. In 240 real-world clinical iterations, PEACH achieved a first-generation accuracy of 97.5% (78/80) and an overall accuracy of 96.7% (232/240) across three iterations. The updated PEACH demonstrated improved accuracy of 97.9% (235/240), with a statistically significant difference from the null hypothesis of 95% accuracy (p = 0.018, 95% CI: 0.952-0.991). Minimal hallucinations and deviations were observed (both 1/240 and 2/240, respectively). Clinicians reported that PEACH expedited decisions in 95% of cases, and inter-rater reliability ranged from kappa 0.772-0.893 within PEACH and 0.610-0.784 among attendings. PEACH is an accurate, adaptable tool that enhances consistency and efficiency in perioperative decision-making. Future research should explore its scalability across specialties and its impact on clinical outcomes.

Automated anesthesia promises to enable more precise and personalized anesthetic administration and free anesthesiologists from repetitive tasks, allowing them to focus on the most critical aspects of a patient's surgical care. Current research has typically focused on creating simulated environments from which agents can learn. These approaches have demonstrated good experimental results, but are still far from clinical application. In this paper, Policy Constraint Q-Learning (PCQL), a data-driven reinforcement learning algorithm for solving the problem of learning anesthesia strategies on real clinical datasets, is proposed. Conservative Q-Learning was first introduced to alleviate the problem of Q function overestimation in an offline context. A policy constraint term is added to agent training to keep the policy distribution of the agent and the anesthesiologist consistent to ensure safer decisions made by the agent in anesthesia scenarios. The effectiveness of PCQL was validated by extensive experiments on a real clinical anesthesia dataset. Experimental results show that PCQL is predicted to achieve higher gains than the baseline approach while maintaining good agreement with the reference dose given by the anesthesiologist, using less total dose, and being more responsive to the patient's vital signs. In addition, the confidence intervals of the agent were investigated, which were able to cover most of the clinical decisions of the anesthesiologist. Finally, an interpretable method, SHAP, was used to analyze the contributing components of the model predictions to increase the transparency of the model.

The case experience of anesthesiologists is one of the leading causes of accidental dural punctures and failed epidurals - the most common complications of epidural analgesia used for pain relief during delivery. We designed a bimanual haptic simulator to train anesthesiologists and optimize epidural analgesia skill acquisition. We present an assessment study conducted with 22 anesthesiologists of different competency levels from several Israeli hospitals. Our simulator emulates the forces applied to the epidural (Touhy) needle, held by one hand, and those applied to the Loss of Resistance (LOR) syringe, held by the other one. The resistance is calculated based on a model of the epidural region layers parameterized by the weight of the patient. We measured the movements of both haptic devices and quantified the results' rate (success, failed epidurals, and dural punctures), insertion strategies, and the participants' answers to questionnaires about their perception of the simulation realism. We demonstrated good construct validity by showing that the simulator can distinguish between real-life novices and experts. Face and content validity were examined by studying users' impressions regarding the simulator's realism and fulfillment of purpose. We found differences in strategies between different level anesthesiologists, and suggest trainee-based instruction in advanced training stages.

Patient's vital signs, which are displayed on monitors, make the anesthesiologist's visual attention (VA) a key component in the safe management of patients under general anesthesia; moreover, the distribution of said VA and the ability to acquire specific cues throughout the anesthetic, may have a direct impact on patient's outcome. Currently, most studies employ wearable eye-tracking technologies to analyze anesthesiologists' visual patterns. Albeit being able to produce meticulous data, wearable devices are not a sustainable solution for large-scale or long-term use for data collection in the operating room (OR). Thus, by utilizing a novel eye-tracking method in the form of deep learning models that process monitor-mounted webcams, we collected continuous behavioral data and gained insight into the anesthesiologist's VA distribution with minimal disturbance to their natural workflow. In this study, we collected OR video recordings using the proposed framework and compared different visual behavioral patterns. We distinguished between baseline VA distribution during uneventful periods to patterns associated with active phases or during critical, unanticipated incidents. In the future, such a platform may serve as a crucial component of context-aware assistive technologies in the OR.

A new study by researchers at MIT and Massachusetts General Hospital (MGH) suggests the day may be approaching when advanced artificial intelligence systems could assist anesthesiologists in the operating room. In a special edition of Artificial Intelligence in Medicine, the team of neuroscientists, engineers, and physicians demonstrated a machine learning algorithm for continuously automating dosing of the anesthetic drug propofol. Using an application of deep reinforcement learning, in which the software's neural networks simultaneously learned how its dosing choices maintain unconsciousness and how to critique the efficacy of its own actions, the algorithm outperformed more traditional software in sophisticated, physiology-based simulations of patients. It also closely matched the performance of real anesthesiologists when showing what it would do to maintain unconsciousness given recorded data from nine real surgeries. The algorithm's advances increase the feasibility for computers to maintain patient unconsciousness with no more drug than is needed, thereby freeing up anesthesiologists for all the other responsibilities they have in the operating room, including making sure patients remain immobile, experience no pain, remain physiologically stable, and receive adequate oxygen, say co-lead authors Gabe Schamberg and Marcus Badgeley.

Artificial intelligence (AI)-based scheduling significantly improves physician engagement and reduces burnout by creating fair and flexible schedules that support work-life balance -; even during the COVID-19 pandemic -; according to research being presented at the American Society of Anesthesiologists' ADVANCE 2022, the Anesthesiology Business Event. Studies show half of all physicians experience burnout during their career, driven by factors including workload, job demands, work-life integration and schedule control and flexibility. In the new study, the AI-based scheduling software granted more vacation days, reduced ungranted vacation days and provided flexibility and predictability, compared to the previous staff-created scheduling system, resulting in significantly improved engagement scores from anesthesiologists within six months. These scores reflect the physician's level of engagement with the health care organization. The higher the engagement score, the better the relationship the physician has with the organization, leading to enhanced patient care, improved patient safety, lower costs, improved efficiency, and greater physician satisfaction and retention.

Anesthestic drugs act on the brain, but most anesthesiologists rely on heart rate, respiratory rate, and movement to infer whether surgery patients remain unconscious to the desired degree. In a new study, a research team based at MIT and Massachusetts General Hospital shows that a straightforward artificial intelligence approach, attuned to the kind of anesthetic being used, can yield algorithms that assess unconsciousness in patients based on brain activity with high accuracy and reliability. "One of the things that is foremost in the minds of anesthesiologists is'Do I have somebody who is lying in front of me who may be conscious and I don't realize it?' Being able to reliably maintain unconsciousness in a patient during surgery is fundamental to what we do," says senior author Emery N. Brown, the Edward Hood Taplin Professor in The Picower Institute for Learning and Memory and the Institute for Medical Engineering and Science at MIT, and an anesthesiologist at MGH. "This is an important step forward." More than providing a good readout of unconsciousness, Brown adds, the new algorithms offer the potential to allow anesthesiologists to maintain it at the desired level while using less drug than they might administer when depending on less direct, accurate, and reliable indicators.

A prominent Chinese venture capitalist, Kai-Fu Lee, has been proselytizing for the advancement of artificial intelligence for some time. He is the chief executive of an influential investment firm called Sinovation Ventures based in Beijing that specializes in artificial intelligence, also known as AI, and as such he has an interest in promoting the idea that it will change our world. However, he may end up scaring us more than astonishing us. You see, Lee now claims that up to 40 percent of jobs will be "displaceable" because of AI within 15 to 25 years. This past weekend, Lee appeared on a well-known American news television show to discuss AI, whereupon he stunned the reporter with his claim.

On the occasion of my sixtieth birthday, my friend Lenny visited me from Toronto. He is seven years older than me, and he gave me some sound advice: respect the limitations of your body. Lenny said that he no longer climbs ladders, even though he is a yoga instructor and his balance is good--climbing ladders just seems like a risky thing for a sixtysomething to do. The advice came just after I had binge-watched the first season of "Westworld," a TV series about machines gaining human consciousness (something that I, like many cognitive neuroscience professors, have been teaching for over ten years). In the world of the show, the bodies of the robots, unlike your body and mine, are easily repaired. A vast robot-repair shop remanufactures and reattaches severed limbs, and efficiently closes gaping wounds. For the past few years, I've been on a kick that I call the "pre-mortem": thinking ahead to what could go wrong and putting systems in place to minimize the damage if they do go wrong. For instance, I got a landline, in case the cell networks go down in a natural disaster such as an earthquake. I've taken cell-phone photos of my passport and credit cards, in case they get lost. I taped an emergency-phone-number list to the inside of the kitchen cabinet that is nearest the phone, and I put a combination-lock box in the back of my house to hold a front-door key, in case I lock myself out. I must have struck a chord with this idea, because my TED talk about it went viral. My wife, Heather, and I have our bedroom upstairs, and there is only one way out in case of a fire--down the stairs and out the front door.