Autopilots in the Operating Room

Clinicians and AI use: where is the professional guidance?

With the introduction of artificial intelligence (AI) to healthcare, there is also a need for professional guidance to support its use. New (2022) reports from National Health Service AI Lab & Health Education England focus on healthcare workers' understanding and confidence in AI clinical decision support systems (AI-CDDSs), and are concerned with developing trust in, and the trustworthiness of these systems. While they offer guidance to aid developers and purchasers of such systems, they offer little specific guidance for the clinical users who will be required to use them in patient care.This paper argues that clinical, professional and reputational safety will be risked if this deficit of professional guidance for clinical users of AI-CDDSs is not redressed. We argue it is not enough to develop training for clinical users without first establishing professional guidance regarding the rights and expectations of clinical users.We conclude with a call to action for clinical regulators: to unite to draft guidance for users of AI-CDDS that helps manage clinical, professional and reputational risks. We further suggest that this exercise offers an opportunity to address fundamental issues in the use of AI-CDDSs; regarding, for example, the fair burden of responsibility for outcomes.

Enhancing the performance of premature ventricular contraction detection in unseen datasets through deep learning with denoise and contrast attention module

Premature ventricular contraction (PVC) is a common and harmless cardiac arrhythmia that can be asymptomatic or cause palpitations and chest pain in rare instances. However, frequent PVCs can lead to more serious arrhythmias, such as atrial fibrillation. Several PVC detection models have been proposed to enable early diagnosis of arrhythmias; however, they lack reliability and generalizability due to the variability of electrocardiograms across different settings and noise levels. Such weaknesses are known to aggravate with new data. Therefore, we present a deep learning model with a novel attention mechanism that can detect PVC accurately, even on unseen electrocardiograms with various noise levels. Our method, called the Denoise and Contrast Attention Module (DCAM), is a two-step process that denoises signals with a convolutional neural network (CNN) in the frequency domain and attends to differences. It focuses on differences in the morphologies and intervals of the remaining beats, mimicking how trained clinicians identify PVCs. Using three different encoder types, we evaluated 1D U-Net with DCAM on six external test datasets. The results showed that DCAM significantly improved the F1-score of PVC detection performance on all six external datasets and enhanced the performance of balancing both the sensitivity and precision of the models, demonstrating its robustness and generalization ability regardless of the encoder type. This demonstrates the need for a trainable denoising process before applying the attention mechanism. Our DCAM could contribute to the development of a reliable algorithm for cardiac arrhythmia detection under real clinical electrocardiograms.

Anaesthesia and patient safety in the socio-technical operating theatre: a narrative review spanning a century

We review the development of technology in anaesthesia over the course of the past century, from the invention of the Boyle apparatus to the modern anaesthetic workstation with artificial intelligence assistance. We define the operating theatre as a socio-technical system, being necessarily comprised of human and technological parts, the ongoing development of which has led to a reduction in mortality during anaesthesia by an order of four magnitudes over a century. The remarkable technological advances in anaesthesia have been accompanied by important paradigm shifts in the approach to patient safety, and we describe the inter-relationship between technology and the human work environment in the development of such paradigm shifts, including the systems approach and organisational resilience. A better understanding of emerging technological advances and their effects on patient safety will allow anaesthesia to continue to be a leader in both patient safety and in the design of equipment and workspaces.

[Targeted hemodynamic monitoring in the operating theatre: what for and by what means?]

Goal directed hemodynamic monitoring and the balance in goal directed therapy between adequate fluid/volume therapy and the application of vasoactive or inotropic drugs are the basic elements of modern perioperative therapy.Surgical procedures should be accompanied by as few side effects and complications as possible. Nevertheless, the number of postoperative complications remains surprisingly high, despite of the modern surgical procedures. Anticipation of potential complications in the perioperative period and their rapid treatment build a core competence of anesthesiological action. Thus, it is clear that anesthesia plays a central role in this balancing act.This article aims to provide an overview of the application of the currently available perioperative goal directed hemodynamic monitoring. The current possibilities are discussed by using a case example and an outlook on the future of hemodynamic monitoring is given.

Patient safety education in anesthesia: current state and future directions

PURPOSE OF REVIEW

Although patient safety is a core component of education in anesthesiology, approaches to implementation of education programs are less well defined. The goal of this review is to describe the current state of education in anesthesia patient safety and the ideal patient safety curriculum.

RECENT FINDINGS

Anesthesiology has been a pioneer in patient safety for decades, with efforts amongst national organizations, such as the American Society of Anesthesiologists and the Anesthesia Patient Safety Foundation to disseminate key standards and guidelines in patient safety. However, few, if any strategies for implementation of a patient safety curriculum in anesthesiology exist.

SUMMARY

Patient safety education is crucial to the field of anesthesiology, particularly with the advancement of surgical and anesthesia technologies and increasing complexity of patients and procedures. The ideal patient safety curriculum in anesthesiology consists of simulation, adverse event investigation and analysis, and participation in process improvement. Efforts in education must adapt with changing technology, shifts in the way anesthesia care is delivered, and threats to physician wellness. Future efforts in education should harness emerging platforms, such as social media, podcasts, and wikis.

Patient monitoring in the nonoperating room anesthesia (NORA) setting: current advances in technology

PURPOSE OF REVIEW

Nonoperating room anesthesia (NORA) procedures continue to increase in type and complexity as procedural medicine makes technical advances. Patients presenting for NORA procedures are also older and sicker than ever. Commensurate with the requirements of procedural medicine, anesthetic monitoring must meet the American Society of Anesthesiologists standards for basic monitoring.

RECENT FINDINGS

There have been improvements in the required monitors that are used for intraoperative patient care. Some of these changes have been with new technologies and others have occurred with software refinements. In addition, specialized monitoring devises have also been introduced into NORA locations (depth of hypnosis, respiratory monitoring, point-of care ultrasound). These additions to the monitoring tools available to the anesthesiologist working in the NORA-environment push the boundaries of procedures which may be accomplished in this setting.

SUMMARY

NORA procedures constitute a growing percentage of total administered anesthetics. There is no difference in the monitoring standard between that of an anesthetic administered in an operating room and a NORA location. Anesthesiologists in the NORA setting must have the same compendium of monitors available as do their colleagues working in the operating suite.

Automation failures and patient safety

PURPOSE OF REVIEW

The goal of automation is to decrease the anesthesiologist's workload and to decrease the possibility of human error. Automated systems introduce problems of its own, however, including loss of situation awareness, leaving the physician out of the loop, and training physicians how to monitor autonomous systems. This review will discuss the growing role of automated systems in healthcare and describe two types of automation failures.

RECENT FINDINGS

An automation surprise occurs when an automated system takes an action that is unexpected by the user. Mode confusion occurs when the operator does not understand what an automated system is programmed to do and may prevent the clinician from fully understanding what the device is doing during a critical event. Both types of automation failures can decrease a clinician's trust in the system. They may also prevent a clinician from regaining control of a failed system (e.g., a ventilator that is no longer working) during a critical event.

SUMMARY

Clinicians should receive generalized training on how to manage automation and should also be required to demonstrate competency before using medical equipment that employs automation, including electronic health records, infusion pumps, and ventilators.