Artificial Intelligence for Perioperative Medicine: Perioperative Intelligence

A bibliometric analysis of perioperative medicine and artificial intelligence

BACKGROUND

Artificial intelligence holds the potential to transform perioperative medicine by leveraging complex datasets to predict risks and optimise patient management in response to rising surgical volumes and patient complexity.

AIM

This bibliometric analysis aims to analyse trends, contributions, collaborations and research hotspots in artificial intelligence and perioperative medicine.

METHODS

A Scopus search on 11 October 2024 identified articles on artificial intelligence in perioperative medicine. Relevant peer-reviewed studies were screened by two reviewers, with a third resolving discrepancies. Data were analysed using VOSviewer, Biblioshiny and Microsoft Excel.

RESULTS

A total of 240 articles were included; 84% of articles were published after 2018, indicating rapid recent growth. The United States, China and Italy led contributions. Single-country publications comprised 76.6% of the dataset, reflecting limited international collaboration. Key research areas included perioperative risk prediction, intraoperative monitoring, blood management and echocardiography.

CONCLUSION

Artificial intelligence in perioperative medicine is rapidly advancing but requires increased international collaboration to fully realise its potential.

Patient selection for nonoperating room anesthesia

PURPOSE OF REVIEW

Given the rapid growth of nonoperating room anesthesia (NORA) in recent years, it is essential to review its unique challenges as well as strategies for patient selection and care optimization.

RECENT FINDINGS

Recent investigations have uncovered an increasing prevalence of older and higher ASA physical status patients in NORA settings. Although closed claim data regarding patient injury demonstrate a lower proportion of NORA cases resulting in a claim than traditional operating room cases, NORA cases have an increased risk of claim for death. Challenges within NORA include site-specific differences, limitations in ergonomic design, and increased stress among anesthesia providers. Several authors have thus proposed strategies focusing on standardizing processes, site-specific protocols, and ergonomic improvements to mitigate risks.

SUMMARY

Considering the unique challenges of NORA settings, meticulous patient selection, risk stratification, and preoperative optimization are crucial. Embracing data-driven strategies and leveraging technological innovations (such as artificial intelligence) is imperative to refine quality control methods in targeted areas. Collaborative efforts led by anesthesia providers will ensure personalized, well tolerated, and improved patient outcomes across all phases of NORA care.

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.

Artificial intelligence and regional anesthesiology education curriculum development: navigating the digital noise

Artificial intelligence (AI) has demonstrated a disruptive ability to enhance and transform clinical medicine. While the dexterous nature of anesthesiology work offers some protections from AI clinical assimilation, this technology will ultimately impact the practice and augment the ability to provide an enhanced level of safe and data-driven care. Whether predicting difficulties with airway management, providing perioperative or critical care risk assessments, clinical-decision enhancement, or image interpretation, the indications for AI technologies will continue to grow and are limited only by our collective imagination on how best to deploy this technology.An essential mission of academia is education, and challenges are frequently encountered when working to develop and implement comprehensive and effectively targeted curriculum appropriate for the diverse set of learners assigned to teaching faculty. Curriculum development in this context frequently requires substantial efforts to identify baseline knowledge, learning needs, content requirement, and education strategies. Large language models offer the promise of targeted and nimble curriculum and content development that can be individualized to a variety of learners at various stages of training. This technology has not yet been widely evaluated in the context of education deployment, but it is imperative that consideration be given to the role of AI in curriculum development and how best to deploy and monitor this technology to ensure optimal implementation.

[Artificial intelligence in intensive care medicine]

The integration of artificial intelligence (AI) into intensive care medicine has made considerable progress in recent studies, particularly in the areas of predictive analytics, early detection of complications, and the development of decision support systems. The main challenges remain availability and quality of data, reduction of bias and the need for explainable results from algorithms and models. Methods to explain these systems are essential to increase trust, understanding, and ethical considerations among healthcare professionals and patients. Proper training of healthcare professionals in AI principles, terminology, ethical considerations, and practical application is crucial for the successful use of AI. Careful assessment of the impact of AI on patient autonomy and data protection is essential for its responsible use in intensive care medicine. A balance between ethical and practical considerations must be maintained to ensure patient-centered care while complying with data protection regulations. Synergistic collaboration between clinicians, AI engineers, and regulators is critical to realizing the full potential of AI in intensive care medicine and maximizing its positive impact on patient care. Future research and development efforts should focus on improving AI models for real-time predictions, increasing the accuracy and utility of AI-based closed-loop systems, and overcoming ethical, technical, and regulatory challenges, especially in generative AI systems.

Academic anaesthesiology: a global perspective on training, support, and future development of early career researchers

As anaesthesiologists face increasing clinical demands and a limited and competitive funding environment for academic work, the sustainability of academic anaesthesiologists has never been more tenuous. Yet, the speciality needs academic anaesthesiologists in many roles, extending beyond routine clinical duties. Anaesthesiologist educators, researchers, and administrators are required not only to train future generations but also to lead innovation and expansion of anaesthesiology and related specialities, all to improve patient care. This group of early career researchers with geographically distinct training and practice backgrounds aim to highlight the diversity in clinical and academic training and career development pathways for anaesthesiologists globally. Although multiple routes to success exist, one common thread is the need for consistent support of strong mentors and sponsors. Moreover, to address inequitable opportunities, we emphasise the need for diversity and inclusivity through global collaboration and exchange that aims to improve access to research training and participation. We are optimistic that by focusing on these fundamental principles, we can help build a more resilient and sustainable future for academic anaesthesiologists around the world.

Expanding the Role of the Surgical Preoperative Evaluation Clinic: Impact on Risk and Quality Outcome Measures

Objective

To prove that inpatient-adjusted surgical risk and quality outcome measures can be considerably impacted by interventions to improve documentation in the preoperative evaluation (POE) clinic.

Patients and Methods

We designed a quality improvement project with a multidisciplinary team in our POE clinic to more accurately reflect surgical risk and impact expected surgical quality outcomes through improved documentation. Interventions included an improved patient record acquisition process and extensive POE provider education regarding patient comorbidities' documentation. For patients admitted after their planned operations, POE clinic comprehensive evaluation notes were linked to inpatient History and Physical notes. High complexity patients seen from October 1, 2018 to December 31, 2018 were the preintervention cohort, and the patients seen from January 1, 2019 to December 31, 2019 were the postintervention cohort.

Results

The primary outcome measures included the total number of coded diagnoses per encounter and the number of coded hierarchical condition categories per encounter. The secondary outcomes included the calculated severity of illness, risk of mortality, case-mix index, and risk-adjustment factor. Postintervention results show statistically significant increases in all primary outcomes with a P<.05. All secondary outcome measures reported positive change.

Conclusion

Our interventions confirm that a comprehensive POE and thorough documentation provide a more accurate clinical depiction of the preoperative patient, which in turn impacts quality outcomes in inpatient surgical settings. These results are impactful for direct and indirect patient care and publicly reported hospital and provider level performance data.