How Technology Has Revolutionized Anesthetic Practice
Thanks to the integration of cutting-edge technology, anesthetic practice is vastly different from times when nitrous oxide, nerve blocks, spinal, ether, and general anesthesia were among the few choices for pain-free surgical procedures and pain management.
The increased safety, enhanced scalability, and efficiency can be attributed mainly to the progress made in monitoring and delivery technologies. Currently, trends are impacting the field of anesthesiology, such as robotic systems, artificial intelligence (AI), wireless fidelity (Wi-Fi), machine learning (ML), and the rise of non-surgical procedures.
This blog explores how technology has transformed anesthetic practice, resulting in better patient outcomes and an enhanced overall experience for patients and healthcare professionals.
Anesthetic Direction: Improved Monitoring and Delivery Methods
One of the most notable advancements in anesthetic practice is the development of advanced monitoring tools. Older methods of anesthesia monitoring relied heavily on the anesthesiologist’s clinical judgment and interpretation of basic physiological parameters like heart rate, blood pressure, and oxygen saturation.
While these methods are still essential, automation monitoring systems provide more accurate, real-time information on vital signs and oxygen saturation. This can be used to fine-tune anesthesia management, prevent complications, and improve patient safety and comfort during the procedure.
The Technology-Driven Anesthetic Practice
Older methods were more invasive—very different from current practice that puts a premium on the patient’s comfort level. For instance, non-invasive devices like wearable patches or finger cuffs have replaced needles for real-time monitoring of cardiac output.
Becoming an anesthesiologist today requires the same sensitivity to clinical signs with the added requirement of being technologically savvy because of the many tools available in and out of the operating room. Here are some of those:
- Depth of Anesthesia Monitors
These devices, such as the Bispectral Index (BIS) monitor, use processed electroencephalogram (EEG) signals to estimate the patient’s level of consciousness. They provide a more accurate assessment of the depth of anesthesia compared to traditional monitoring methods like observing vital signs and patient movement.
- Neuromuscular Monitoring
Acceleromyograph and electromyograph devices help anesthesiologists accurately measure how muscles respond to nerve stimulation. This allows for the correct dose of muscle-relaxing drugs during surgery, ensuring proper muscle relaxation and preventing lasting muscle weakness after surgery.
- Advanced Hemodynamic Monitoring
Techniques like pulse contour analysis, esophageal Doppler, and non-invasive cardiac output monitoring give real-time patient heart and blood flow updates. This helps anesthesiologists adjust treatments like fluids, medications, and other interventions to ensure proper blood flow and oxygen supply.
- Respiratory Monitoring
Capnography and advanced lung monitors give essential information about a patient’s breathing, oxygen levels, and lung function. These tools help anesthesiologists adjust ventilator settings and catch breathing problems early.
- Regional Anesthesia
Point-of-care ultrasound (POCUS) for regional anesthesia is now commonly used for nerve blocks. Using ultrasound helps accurately see nerves, blood vessels, and nearby structures, making nerve blocks safer and more effective.
- Guidance Integrated Systems
These tools combine different monitoring methods, giving anesthesiologists a complete view of a patient’s condition, which helps them make better decisions and improve patient safety during surgery.
- Target-Controlled Infusion Systems
Target-controlled infusion (TCI) systems are computer-assisted systems that help anesthesiologists deliver precise doses of intravenous anesthetic drugs, improving control over the depth of anesthesia and reducing the risk of over- or under-dosing.
- Robotic or Closed-Loop Anesthesia
Robotic or closed-loop anesthetic delivery (CLAD) is gaining traction because of more standardized delivery. Combining the advantages of TCI systems with advanced monitoring, CLAD systems automatically adjust drug infusions based on real-time feedback. This technology aims to improve the safety and efficiency of anesthesia administration.
- Peri-operative and Post-operative Wearables
Wearables used in telemedicine and remote monitoring integrate Wi-Fi and AI to deliver information like vital signs. Telemedicine platforms enable anesthesiologists to provide consultations and remote supervision during anesthesia delivery in rural or underserved areas.
- Artificial Intelligence and Machine Learning
AI and machine learning algorithms are being developed to assist in decision-making, risk assessment, and prediction of patient outcomes. These tools can improve patient safety and the overall quality of anesthesia care.
The New Anesthetic Practice
Technology has significantly transformed anesthetic practice, making it necessary for professionals to keep up with changes or risk becoming outdated. With the advent of telemedicine, health maintenance organizations (HMOs), and the like, documentation is necessary—something made easier when working with technology that can prevent judgment errors.
The Enhanced Recovery After Surgery (ERAS) protocols also impact the role of anesthesiologists. ERAS are evidence-based, multidisciplinary guidelines designed to improve patient recovery and reduce complications after surgery. Anesthesiologists play a huge role in implementing these protocols, which prioritize pain management, fluid management, and early mobilization.
Technology has made the anesthesiologist’s role more significant in the patient’s care from pre-operative to recovery. It has made anesthesiology less invasive and more sensitive in monitoring anesthetic effects and delivery effectiveness, resulting in greater patient satisfaction, increased safety, and faster recovery.
However, even with artificial intelligence and machine learning, the anesthesiologist’s skill remains at the crux of the practice.
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