[eMPG - a New Concept for Digital Medical Device Instruction]. / eMPG ­ die digitale Medizinprodukteeinweisung in der Zukunft.

Errors in the use of medical devices are common and pose a threat to patient safety. The field of anaesthesiology is particularly affected by this issue, as many of the medical devices used in this field are directly responsible for the monitoring and maintenance of a patient's vital functions during anaesthesia. To minimize application errors, instruction in the use of medical devices has been legally required since 1985. Inadequate instructions can put the patient as well as the user at risk. Training courses that provide users with information on the safe use of medical devices significantly improve patient safety. The traditional instruction concept has limitations in its ability to adequately train employees on the use of medical devices. The digital device instruction project presented in this article aims to address this issue by providing medical device instructions at any time, thus increasing the safety of the operation of medical devices.

"Alexa, Cycle The Blood Pressure": A Voice Control Interface Method for Anesthesia Monitoring.

BACKGROUND: Anesthesia monitors and devices are usually controlled with some combination of dials, keypads, a keyboard, or a touch screen. Thus, anesthesiologists can operate their monitors only when they are physically close to them, and not otherwise task-loaded with sterile procedures such as line or block placement. Voice recognition technology has become commonplace and may offer advantages in anesthesia practice such as reducing surface contamination rates and allowing anesthesiologists to effect changes in monitoring and therapy when they would otherwise presently be unable to do so. We hypothesized that this technology is practicable and that anesthesiologists would consider it useful. METHODS: A novel voice-driven prototype controller was designed for the GE Solar 8000M anesthesia patient monitor. The apparatus was implemented using a Raspberry Pi 4 single-board computer, an external conference audio device, a Google Cloud Speech-to-Text platform, and a modified Solar controller to effect commands. Fifty anesthesia providers tested the prototype. Evaluations and surveys were completed in a nonclinical environment to avoid any ethical or safety concerns regarding the use of the device in direct patient care. All anesthesiologists sampled were fluent English speakers; many with inflections from their first language or national origin, reflecting diversity in the population of practicing anesthesiologists. RESULTS: The prototype was uniformly well-received by anesthesiologists. Ease-of-use, usefulness, and effectiveness were assessed on a Likert scale with means of 9.96, 7.22, and 8.48 of 10, respectively. No population cofactors were associated with these results. Advancing level of training (eg, nonattending versus attending) was not correlated with any preference. Accent of country or region was not correlated with any preference. Vocal pitch register did not correlate with any preference. Statistical analyses were performed with analysis of variance and the unpaired t -test. CONCLUSIONS: The use of voice recognition to control operating room monitors was well-received anesthesia providers. Additional commands are easily implemented on the prototype controller. No adverse relationship was found between acceptability and level of anesthesia experience, pitch of voice, or presence of accent. Voice recognition is a promising method of controlling anesthesia monitors and devices that could potentially increase usability and situational awareness in circumstances where the anesthesiologist is otherwise out-of-position or task-loaded.

A novel, cassette-based nitric oxide delivery system with an advanced feedback control algorithm accurately delivers nitric oxide via the anesthesia machine independent of fresh gas flow rate and volatile anesthetic agent.

Nitric oxide (NO), a selective pulmonary vasodilator, can be delivered via conventional ICU and anesthesia machine ventilators. Anesthesia machines are designed for rebreathing of circulating gases, reducing volatile anesthetic agent quantity used. Current cylinder- and ionizing-based NO delivery technologies use breathing circuit flow to determine NO delivery and do not account for recirculated gases; therefore, they cannot accurately dose NO at FGF below patient minute ventilation (MV). A novel, cassette-based NO delivery system (GENOSYL® DS, Vero Biotech Inc.) uses measured NO concentration in the breathing circuit as an input to an advanced feedback control algorithm, providing accurate NO delivery regardless of FGF and recirculation of gases. This study evaluated GENOSYL® DS accuracy with different anesthesia machines, ventilation parameters, FGFs, and volatile anesthetics. GENOSYL® DS was tested with GE Aisys and Dräger Fabius anesthesia machines to determine NO dose accuracy with FGF < patient MV, and with a Getinge Flow-i anesthesia machine to determine NO dose accuracy when delivering various volatile anesthetic agents. Neonatal and adult mechanical ventilation parameters and circuits were used. GENOSYL® DS maintained accurate NO delivery with all three anesthesia machines, at low FGF with recirculation of gases, and with all volatile anesthetic agents at different concentrations. Measured NO2 levels remained acceptable at ≤ 1 ppm with set NO dose ≤ 40 ppm. GENOSYL® DS, with its advanced feedback control algorithm, is the only NO delivery system capable of accurately dosing NO with anesthesia machines with rebreathing ventilation parameters (FGF < MV) regardless of anesthetic agent.

What are standard monitoring devices for anesthesia in future?

Monitoring the patient's physiological functions is critical in clinical anesthesia. The latest version of the Japanese Society of Anesthesiologists' Guidelines for Safe Anesthesia Monitoring, revised in 2019, covers various factors, including electroencephalogram monitoring, oxygenation, ventilation, circulation, and muscle relaxation. However, with recent advances in monitoring technologies, the information provided has become more detailed, requiring practitioners to update their knowledge. At a symposium organized by the Journal of Anesthesia in 2023, experts across five fields discussed their respective topics: anesthesiologists need to interpret not only the values displayed on processed electroencephalogram monitors but also raw electroencephalogram data in the foreseeable future. In addition to the traditional concern of preventing hypoxemia, monitoring for potential hyperoxemia and the effects of mechanical ventilation itself will become increasingly important. The importance of using AI analytics to predict hypotension, assess nociception, and evaluate microcirculation may increase. With the recent increase in the availability of neuromuscular monitoring devices in Japan, it is important for anesthesiologists to become thoroughly familiar with the features of each device to ensure its effective use. There is a growing desire to develop and introduce a well-organized, integrated "single screen" monitor.

Comparison of automated and manual control methods in minimal flow anesthesia.

PURPOSE: New-generation anesthesia machines administer inhalation anesthetics and automatically control the fresh gas flow (FGF) rate. This study compared the administration of minimal flow anesthesia (MFA) using the automatically controlled anesthesia (ACA) module of the Mindray A9 (Shenzhen, China) anesthesia machine versus manual control by an anesthesiologist. METHODS: We randomly divided 76 patients undergoing gynecological surgery into an ACA group (Group ACA) and a manually controlled anesthesia group (Group MCA). In Group MCA, induction was performed with a mixture of 40-60% O2 and air with a 4 L/min FGF until the minimum alveolar concentration (MAC) reached 1. Next, MFA was initiated with 0.5 L/min FGF. The target fraction of inspired oxygen (FiO2) value was 35-40%. In Group ACA, the MAC was defined as 1, and the FiO2 was adjusted to 35%. Depth of anesthesia, anesthetic agent (AA) consumption, time to achieve target end-tidal AA concentration, awakening times, and number of ventilator adjustments were analyzed. RESULTS: The two groups showed no statistically significant differences in depth of anesthesia or AA consumption (Group ACA: 19.1 ± 4.9 ml; Group MCA: 17.2 ± 4.5; p-value = 0.076). The ACA mode achieved the MAC target of 1 significantly faster (Group ACA: 218 ± 51 s; Group MCA: 314 ± 169 s). The number of vaporizer adjustments was 15 in the ACA group and 217 in the MCA group. CONCLUSION: The ACA mode was more advantageous than the MCA mode, reaching target AA concentrations faster and requiring fewer adjustments to achieve a constant depth of anesthesia.

Advances in pediatric perioperative care using artificial intelligence.

PURPOSE OF THIS REVIEW: This article explores how artificial intelligence (AI) can be used to evaluate risks in pediatric perioperative care. It will also describe potential future applications of AI, such as models for airway device selection, controlling anesthetic depth and nociception during surgery, and contributing to the training of pediatric anesthesia providers. RECENT FINDINGS: The use of AI in healthcare has increased in recent years, largely due to the accessibility of large datasets, such as those gathered from electronic health records. Although there has been less focus on pediatric anesthesia compared to adult anesthesia, research is on- going, especially for applications focused on risk factor identification for adverse perioperative events. Despite these advances, the lack of formal external validation or feasibility testing results in uncertainty surrounding the clinical applicability of these tools. SUMMARY: The goal of using AI in pediatric anesthesia is to assist clinicians in providing safe and efficient care. Given that children are a vulnerable population, it is crucial to ensure that both clinicians and families have confidence in the clinical tools used to inform medical decision- making. While not yet a reality, the eventual incorporation of AI-based tools holds great potential to contribute to the safe and efficient care of our patients.

Effect of a non-reactive absorbent with or without environmentally oriented electronic feedback on anesthesia provider's fresh gas flow rates: A greening initiative.

STUDY OBJECTIVE: To examine the effects of a non-reactive carbon dioxide absorbent (AMSORB® Plus) versus a traditional carbon dioxide absorbent (Medisorb™) on the FGF used by anesthesia providers and an electronic educational feedback intervention using Carestation™ Insights (GE HealthCare) on provider-specific change in FGF. DESIGN: Prospective, single-center cohort study set in a greening initiative. SETTING: Operating room. PARTICIPANTS: 157 anesthesia providers (i.e., anesthesiology trainees, certified registered nurse anesthetists, and solo anesthesiologists). INTERVENTIONS: Intervention #1 was the introduction of AMSORB® Plus into 8 Aisys CS2, Carestation™ Insights-enabled anesthesia machines (GE HealthCare) at the study site. At the end of week 6, anesthesia providers were educated and given an environmentally oriented electronic feedback strategy for the next 12 weeks of the study (Intervention #2) using Carestation™ Insights data. MEASUREMENTS: The dual primary outcomes were the difference in average daily FGF during maintenance anesthesia between machines assigned to AMSORB® Plus versus Medisorb™ and the provider-specific change in average fresh gas flows after 12 weeks of feedback and education compared to the historical data. MAIN RESULTS: Over the 18-week period, there were 1577 inhaled anesthetics performed in the 8 operating rooms (528 for intervention 1, 1049 for intervention 2). There were 1001 provider days using Aisys CS2 machines and 7452 provider days of historical data from the preceding year. Overall, AMSORB® Plus was not associated with significantly less FGF (mean - 80 ml/min, 97.5% confidence interval - 206 to 46, P = .15). The environmentally oriented electronic feedback intervention was not associated with a significant decrease in provider-specific mean FGF (-112 ml/min, 97.5% confidence interval - 244 to 21, P = .059). CONCLUSIONS: This study showed that introducing a non-reactive absorbent did not significantly alter FGF. Using environmentally oriented electronic feedback relying on data analytics did not result in significantly reduced provider-specific FGF.

A survey of operating theatre staff on the impact of automated medication dispensing systems in operating theatres in an Australian hospital.

There is a lack of published literature investigating the impact of anaesthesia-specific automated medication dispensing systems on theatre staff. This study aimed to investigate the perspectives of theatre staff from multiple disciplines on their experience using anaesthesia stations three years after implementation at our Western Australian quaternary hospital institution. A web-based survey was distributed to 440 theatre staff, which included consultant anaesthetists, anaesthetic trainees, nurses, anaesthetic technicians and pharmacists, and 118 responses were received (response rate 26.8%). Eighty-one percent of the anaesthetic medical staff responders reported that the anaesthesia stations were fit for purpose and 66.67% of the anaesthetic medical staff reported that they were user friendly. Sixty-seven percent of anaesthetic medical staff agreed that controlled medication (e.g. schedule 8 and schedule 4 recordable) transactions were more efficient with the anaesthesia stations, and 66.67% agreed that the anaesthesia stations improved accountability for these transactions. Sixty-seven percent of anaesthetic medical staff preferred to use anaesthesia stations and 21.2% of all the responders preferred a manual medication trolley (P ≤ 0.001). This survey of user experience with anaesthesia stations was found to be predominantly positive with the majority of theatre staff and anaesthetic medical staff preferring anaesthesia stations.

The World Federation of Societies of Anaesthesiologists Minimum Capnometer Specifications 2021-A Guide for Health Care Decision Makers.

Capnometry, the measurement of respiratory carbon dioxide, is regarded as a highly recommended safety technology in intubated and nonintubated sedated and/or anesthetized patients. Its utility includes confirmation of initial and ongoing placement of an airway device as well as in detecting gas exchange, bronchospasm, airway obstruction, reduced cardiac output, and metabolic changes. The utility applies prehospital and throughout all phases of inhospital care. Unfortunately, capnometry devices are not readily available in many countries, especially those that are resource-limited. Constraining factors include cost, durability of devices, availability of consumables, lack of dependable power supply, difficulty with cleaning, and maintenance. There is, thus, an urgent need for all stakeholders to come together to develop, market, and distribute appropriate devices that address costs and other requirements. To foster this process, the World Federation of Societies of Anaesthesiologists (WFSA) has developed the "WFSA-Minimum Capnometer Specifications 2021." The intent of the specifications is to set the minimum that would be acceptable from industry in their attempts to reduce costs while meeting other needs in resource-constrained regions. The document also includes very desirable and preferred options. The intent is to stimulate interest and engagement among industry, clinical providers, professional associations, and ministries of health to address this important patient safety need. The WFSA-Minimum Capnometer Specifications 2021 is based on the International Organization for Standardization (ISO) capnometer specifications. While industry is familiar with such specifications and their presentation format, most clinicians are not; therefore, this article serves to more clearly explain the requirements. In addition, the specifications as described can be used as a purchasing guide by clinicians.

Comparing Ventilation Parameters for COVID-19 Patients Using Both Long-Term ICU and Anesthetic Ventilators in Times of Shortage.

In the first months of the COVID-19 pandemic in Europe, many patients were treated in hospitals using mechanical ventilation. However, due to a shortage of ICU ventilators, hospitals worldwide needed to deploy anesthesia machines for ICU ventilation (which is off-label use). A joint guidance was written to apply anesthesia machines for long-term ventilation. The goal of this research is to retrospectively evaluate the differences in measurable ventilation parameters between the ICU ventilator and the anesthesia machine as used for COVID-19 patients. In this study, we included 32 patients treated in March and April 2020, who had more than 3 days of mechanical ventilation, either in the regular ICU with ICU ventilators (Hamilton S1), or in the temporary emergency ICU with anesthetic ventilators (Aisys, GE). The data acquired during regular clinical treatment was collected from the Patient Data Management Systems. Available ventilation parameters (pressures and volumes: PEEP, Ppeak, Pinsp, Vtidal), monitored parameters EtCO2, SpO2, derived compliance C, and resistance R were processed and analyzed. A sub-analysis was performed to compare closed-loop ventilation (INTELLiVENT-ASV) to other ventilation modes. The results showed no major differences in the compared parameters, except for Pinsp. PEEP was reduced over time in the with Hamilton treated patients. This is most likely attributed to changing clinical protocol as more clinical experience and literature became available. A comparison of compliance between the 2 ventilators could not be made due to variances in the measurement of compliance. Closed loop ventilation could be used in 79% of the time, resulting in more stable EtCO2. From the analysis it can be concluded that the off-label usage of the anesthetic ventilator in our hospital did not result in differences in ventilation parameters compared to the ICU treatment in the first 4 days of ventilation.

The repurposed use of anesthesia machines to ventilate critically ill patients with coronavirus disease 2019 (COVID-19).

BACKGROUND: The surge of critically ill patients due to the coronavirus disease-2019 (COVID-19) overwhelmed critical care capacity in areas of northern Italy. Anesthesia machines have been used as alternatives to traditional ICU mechanical ventilators. However, the outcomes for patients with COVID-19 respiratory failure cared for with Anesthesia Machines is currently unknow. We hypothesized that COVID-19 patients receiving care with Anesthesia Machines would have worse outcomes compared to standard practice. METHODS: We designed a retrospective study of patients admitted with a confirmed COVID-19 diagnosis at a large tertiary urban hospital in northern Italy. Two care units were included: a 27-bed standard ICU and a 15-bed temporary unit emergently opened in an operating room setting. Intubated patients assigned to Anesthesia Machines (AM group) were compared to a control cohort treated with standard mechanical ventilators (ICU-VENT group). Outcomes were assessed at 60-day follow-up. A multivariable Cox regression analysis of risk factors between survivors and non-survivors was conducted to determine the adjusted risk of death for patients assigned to AM group. RESULTS: Complete daily data from 89 mechanically ventilated patients consecutively admitted to the two units were analyzed. Seventeen patients were included in the AM group, whereas 72 were in the ICU-VENT group. Disease severity and intensity of treatment were comparable between the two groups. The 60-day mortality was significantly higher in the AM group compared to the ICU-vent group (12/17 vs. 27/72, 70.6% vs. 37.5%, respectively, p = 0.016). Allocation to AM group was associated with a significantly increased risk of death after adjusting for covariates (HR 4.05, 95% CI: 1.75-9.33, p = 0.001). Several incidents and complications were reported with Anesthesia Machine care, raising safety concerns. CONCLUSIONS: Our results support the hypothesis that care associated with the use of Anesthesia Machines is inadequate to provide long-term critical care to patients with COVID-19. Added safety risks must be considered if no other option is available to treat severely ill patients during the ongoing pandemic. CLINICAL TRIAL NUMBER: Not applicable.

Anesthesia screen use may impact operating room communication practices in otolaryngology.

OBJECTIVES: Failures in communication are a leading contributor to medical error. There is increasing attention on cultivating robust communication practices in the Operating Room (OR) to mitigate against patient injury and optimize efficient patient care. Few studies have evaluated how surgical equipment may introduce barriers to team dynamics. DESIGN: We conducted a pilot observational study to examine the relationship between anesthesia screen drapes (which are used inconsistently) and the frequency of verbal exchanges between surgical and anesthesia members. 25 procedures spanning various procedures in Otolaryngology were covertly observed, 12 of which employed a screen. Verbal exchanges were recorded across three stages of the surgery: pre-procedure (before the draping), procedure (drapes placed throughout) and post-procedure (after the removal of the draping). Speaker and content of the exchange was noted as well as various features about the procedure. RESULTS: Decreases in rates of exchanges were most pronounced during the procedure stage, although they did not reach significance on T-testing (p = 0.0719). After controlling for attending, table orientation and number of professionals, regression analysis did reveal a statistically significant decrease in rates of verbal exchanges during the procedure in the presence of the anesthesia screen (7.17 (± 6.33) versus 2.23 (± 1.00), p = 0.0318). Differences were also significant among surgeon-initiated and patient-care-related exchanges (p = 0.0168 and p = 0.0432, respectively). Decreases in anesthesiologist-initiated and non-clinical exchanges did not reach significance (p = 0.1530 and p = 0.5120, respectively). CONCLUSION: This pilot study suggests that anesthesia screens may negatively impact communication practices in the OR.

Smart Portable Pen for Continuous Monitoring of Anaesthetics in Human Serum With Machine Learning.

Continuous monitoring of anaesthetics infusion is demanded by anaesthesiologists to help in defining personalized dose, hence reducing risks and side effects. We propose the first piece of technology tailored explicitly to close the loop between anaesthesiologist and patient with continuous drug monitoring. Direct detection of drugs is achieved with electrochemical techniques, and several options are present in literature to measure propofol (widely used anaesthetics). Still, the sensors proposed do not enable in-situ detection, they do not provide this information continuously, and they are based on bulky and costly lab equipment. In this paper, we present a novel smart pen-shaped electronic system for continuous monitoring of propofol in human serum. The system consists of a needle-shaped sensor, a quasi digital front-end, a smart machine learning data processing, in a single wireless battery-operated embedded device featuring Bluetooth Low Energy (BLE) communication. The system has been tested and characterized in real, undiluted human serum, at 37  °C. The device features a limit of detection of 3.8  µM, meeting the requirement of the target application, with an electronics system 59% smaller and 81% less power consuming w.r.t. the state-of-the-art, using a smart machine learning classification for data processing, which guarantees up to twenty continuous measure.

Coronavirus Disease 2019: Anesthesia Machine Circuit Pressure During Use as an Improvised Intensive Care Unit Ventilator.

BACKGROUND: Use of anesthesia machines as improvised intensive care unit (ICU) ventilators may occur in locations where waste anesthesia gas suction (WAGS) is unavailable. Anecdotal reports suggest as much as 18 cm H2O positive end-expiratory pressure (PEEP) being inadvertently applied under these circumstances, accompanied by inaccurate pressure readings by the anesthesia machine. We hypothesized that resistance within closed anesthesia gas scavenging systems (AGSS) disconnected from WAGS may inadvertently increase circuit pressures. METHODS: An anesthesia machine was connected to an anesthesia breathing circuit, a reference manometer, and a standard bag reservoir to simulate a lung. Ventilation was initiated as follows: volume control, tidal volume (TV) 500 mL, respiratory rate 12, ratio of inspiration to expiration times (I:E) 1:1.9, fraction of inspired oxygen (Fio2) 1.0, fresh gas flow (FGF) rate 2.0 liters per minute (LPM), and PEEP 0 cm H2O. After engaging the ventilator, PEEP and peak inspiratory pressure (PIP) were measured by the reference manometer and the anesthesia machine display simultaneously. The process was repeated using prescribed PEEP levels of 5, 10, 15, and 20 cm H2O. Measurements were repeated with the WAGS disconnected and then were performed again at FGF of 4, 6, 8, 10, and 15 LPM. This process was completed on 3 anesthesia machines: Dräger Perseus A500, Dräger Apollo, and the GE Avance CS2. Simple linear regression was used to assess differences. RESULTS: Utilizing nonparametric Bland-Altman analysis, the reference and machine manometer measurements of PIP demonstrated median differences of -0.40 cm H2O (95% limits of agreement [LOA], -1.00 to 0.55) for the Dräger Apollo, -0.40 cm H2O (95% LOA, -1.10 to 0.41) for the Dräger Perseus, and 1.70 cm H2O (95% LOA, 0.80-3.00) for the GE Avance CS2. At FGF 2 LPM and PEEP 0 cm H2O with the WAGS disconnected, the Dräger Apollo had a difference in PEEP of 0.02 cm H2O (95% confidence interval [CI], -0.04 to 0.08; P = .53); the Dräger Perseus A500, <0.0001 cm H2O (95% CI, -0.11 to 0.11; P = 1.00); and the GE Avance CS2, 8.62 cm H2O (95% CI, 8.55-8.69; P < .0001). After removing the hose connected to the AGSS and the visual indicator bag on the GE Avance CS2, the PEEP difference was 0.12 cm H2O (95% CI, 0.059-0.181; P = .0002). CONCLUSIONS: Displayed airway pressure measurements are clinically accurate in the setting of disconnected WAGS. The Dräger Perseus A500 and Apollo with open scavenging systems do not deliver inadvertent continuous positive airway pressure (CPAP) with WAGS disconnected, but the GE Avance CS2 with a closed AGSS does. This increase in airway pressure can be mitigated by the manufacturer's recommended alterations. Anesthesiologists should be aware of the potential clinically important increases in pressure that may be inadvertently delivered on some anesthesia machines, should the WAGS not be properly connected.

Patient-maintained propofol sedation for adult patients undergoing surgical or medical procedures: a scoping review of current evidence and technology.

Patient-maintained propofol sedation (PMPS) is the delivery of procedural propofol sedation by target-controlled infusion with the patient exerting an element of control over their target-site propofol concentration. This scoping review aims to establish the extent and nature of current knowledge regarding PMPS from both a clinical and technological perspective, thereby identifying knowledge gaps to guide future research. We searched MEDLINE, EMBASE, and OpenGrey databases, identifying 17 clinical studies for analysis. PMPS is described in the context of healthy volunteers and in orthopaedic, general surgical, dental, and endoscopic clinical settings. All studies used modifications to existing commercially-available infusion devices to achieve prototype systems capable of PMPS. The current literature precludes rigorous generalisable conclusions regarding the safety or comparative clinical effectiveness of PMPS, however cautious acknowledgement of efficacy in specific clinical settings is appropriate. Based on the existing literature, together with new standardised outcome reporting recommendations for sedation research and frameworks designed to assess novel health technologies research, we have made recommendations for future pharmacological, clinical, behavioural, and health economic research on PMPS. We conclude that high-quality experimental clinical trials with relevant comparator groups assessing the impact of PMPS on standardised patient-orientated outcome measures are urgently required.

Controlling Anesthesia Hardware With Simple Hand Gestures: Thumbs Up or Thumbs Down?

BACKGROUND: Modern consumer electronic devices and automobiles are often controlled by interfaces that sense physical gestures and spoken commands. In contrast, patient monitors and anesthesia devices are typically equipped with panel-mounted buttons, dials, and keyboards. The increased use of noncontact gesture-based interfaces in anesthesia may improve patient safety through more intuitive and prompter control of equipment and also through reduced rates of surface contamination. A novel gesture-based controller was designed and retrofitted to a standard GE Solar 8000M patient monitor. This type of technical innovation is rare, due to closely held proprietary input control systems on commercially produced clinical equipment. Nevertheless, we hypothesized that anesthesiologists would find a contactless gesture interface straightforward to use. METHODS: A gesture-based interface system was developed to control a Solar 8000M patient monitor using a millimeter-wave radar sensor. The system was programmed to detect noncontact "rotate" and "press" gestures to control the patient monitor by implementing a virtual trim knob for interface control. Fifty anesthesiologists tested a prototype interface and evaluated usability by completing a short questionnaire incorporating modified Likert scales. These evaluations were performed in a nonpatient care environment so that respondents were not adversely task loaded during assessment, also allaying any ethical or safety concerns regarding use of this novel interface for patient management. RESULTS: Anesthesia hardware was controlled reliably with 2 distinct gestures above the gesture sensor. The gesture-based interface generally was well received by anesthesiologists (8.09; confidence interval, 8.06-8.12 on a 10-point scale), who preferred the simpler "press" gesture to the "rotate" gesture (8.45; 8.39-8.51 vs 7.73; 7.67-7.79 on a 10-point scale; P = .005). The correlation between the preference scores for the 2 gestures from each anesthesiologist was strong (Pearson r = 0.49; 0.25-0.68; P < .001). Advancing level of training (resident, fellow, attending 1-10 years, attending >10 years) was not correlated with preference scores for either gesture (Spearman ρ = -0.02; -0.30 to 0.26; P = .87 for "press" and Spearman ρ = 0.08; -0.20 to 0.35; P = .58 for "rotate"). CONCLUSIONS: The use of gesture sensing for controlling anesthesia equipment was well received by a cohort of anesthesiologists. Even though the simpler "press" gesture was preferred over the "rotate" gesture, the intrarespondent correlation indicates that the preference for gestures as a whole is the stronger effect. No adverse relationship was found between acceptability and anesthesia experience level. Gesture sensing is a promising new area to simplify and improve the interaction between the anesthesiologist and the anesthesia workstation.