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Integrating AI with Patient Monitoring Equipment: Enhancing Veterinary Anaesthesia Management


Artificial intelligence (AI) is becoming an indispensable tool in many sectors, from healthcare and finance to entertainment and transportation (Panesar & Panesar 2020). One of the many areas in which AI could be promising is anaesthesiology. By automating certain tasks, enhancing decision-making, and providing more accurate predictions, AI could transform the field and improve patient safety.


While patient monitoring equipment plays a critical role in observing vital signs and other physiological parameters during an anaesthetic, the integration of artificial intelligence (AI) with these systems could further enhance anaesthesiologists' decision-making capabilities (Bellini et al 2022). For example, AI could potentially help analyse real-time patient data to alert anaesthesiologists about potential complications, suggest appropriate interventions, and facilitate prompt action. Machine learning, the most widely applied arm of AI in medicine, confers the ability to analyse large volumes of data, find associations, and predict outcomes with ongoing learning by the computer. It involves algorithm creation, testing and analyses with the ability to perform cognitive functions including association between variables, pattern recognition, and prediction of outcomes (Singh & Nath 2022).


The integration of AI algorithms with the patient's monitoring equipment could offer several advantages:

  1. Data integration: AI could integrate previously entered data such as patient's history, signalment, pre-medication etc. with the data from various monitoring variables, such as blood pressure, pulse-oximetry, capnography and ECG.

  2. Alerting and decision support: AI could alert anaesthesiologists to any significant changes in patient data that may suggest potential complications. In addition, AI could provide decision support by suggesting appropriate interventions based on the patient's condition, vital signs, and the surgery being performed. This could help anaesthesiologists respond more effectively to evolving situations (Cote & Kim 2019). For example, if the AI system detects significant changes in the patient's heart rhythm (e.g., ventricular tachycardia or fibrillation), a sudden drop in blood pressure, a decrease in oxygen saturation, or a reduction in EtCO2 levels, it may recognize these as early signs of an impending cardiopulmonary arrest. The AI system can then immediately alert the anaesthesiologist, providing crucial information about the patient's condition and suggesting appropriate interventions.

  3. Integration with electronic anaesthetic and patient's records: AI-enhanced monitoring systems could be integrated with the patient's records (Dawoodbhoy et al 2021), allowing anaesthesiologists to access relevant medical history, lab results, and other pertinent information. This could help make quicker informed decisions and tailor our approach to the patient's specific needs.

The integration of AI with patient monitoring equipment has the potential to significantly enhance anaesthesia management by providing real-time data analysis, decision support, and predictive capabilities. As AI continues to advance, these systems will likely play an increasingly important role in improving patient safety, anaesthesiologist decision-making, and overall patients' outcomes (Choudhury 2022).


According to a recent study (Tasdogan 2020), a small proportion, 2.9%, of the anaesthesiologists surveyed believe that AI has the potential to completely replace physicians in the near future. This suggests that the majority of anaesthesiologists acknowledge the growing influence of AI, yet they maintain the perspective that the human touch and expertise will continue to play a crucial role in healthcare. As AI technologies continue to evolve, it will be important for practitioners to strike a balance between embracing these advancements and preserving the essential human elements of their profession.



References:


1. Bellini V, Pelosi P, Valente M, Gaddi AV, Baciarello M, Bignami E. Using artificial intelligence techniques to support clinical decisions in perioperative medicine. Perioperative Care and Operating Room Management. 2022 Mar 1;26:100236.


2. Choudhury A. Toward an ecologically valid conceptual framework for the use of artificial intelligence in clinical settings: need for systems thinking, accountability, decision-making, trust, and patient safety considerations in safeguarding the technology and clinicians. JMIR Human Factors. 2022 Jun 21;9(2):e35421.


3. Cote CD, Kim PJ. Artificial intelligence in anesthesiology: Moving into the future. University of Toronto Medical Journal. 2019 Jan 1;96(1).


4. Dawoodbhoy FM, Delaney J, Cecula P, Yu J, Peacock I, Tan J, Cox B. AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units. Heliyon. 2021 May 1;7(5):e06993.


5. Panesar A, Panesar H. Artificial intelligence and machine learning in global healthcare. Handbook of Global Health. 2020:1-39.


6. Singh M, Nath G. Artificial intelligence and anesthesia: A narrative review. Saudi J Anaesth. 2022 Jan-Mar;16(1):86-93.


7. Tasdogan AM. Knowledge, Attitudes and Perspectives of Anesthesiologists on Artificial Intelligence. EJMI 2020;4(1):1–6.


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