Exploring Technological Innovation and Its Incorporation Into Clinical Practice

If we were to visit the Wood Library-Museum of Anesthesiology, we could explore the past and compare how the specialty has evolved for the better. We no longer intubate with metal tubes; we can monitor oxygen levels reliably and noninvasively; and the precordial stethoscope is wholly replaced by technology displayed on our ventilators. The care we provide to patients has improved markedly due to these innovative breakthroughs. One can only imagine how the practice of anesthesiology will change over the next few decades.

Technology Innovation and Adoption

While it is challenging to predict what technology will have a positive effect, the investment required to find the next disruptive technology can be immense. The life cycle of technology has many phases, including development and adoption, and the latter is the primary barrier to its commercial success. The transition from a lab or a garage to the clinical environment requires regulatory approval (compare with the absence of target-controlled infusion pumps), communication of the technology, distribution to hospitals and finally progressive adoption of the technology by the clinical user—us.

And are we ever picky, and for a good reason, too! When a tool is awkward to use, we become frustrated, make more errors and potentially put patients at risk for harm. Examples are too numerous to count: being locked out of a drug dispenser, incorrectly programming an infusion pump to give too much (or too little) medications, missing a key lab value because it is drowned out in a sea of other lab values—when was the last time you interpreted “MCHC” in a laboratory results section? Even our clinical documentation is often frustrating, leading to copy-and-pasted notes, incomplete documentation and occasionally miscommunication of the clinical care rendered.

Usability

The “usability” of technology had often been an afterthought of technology development. While not novel, the recognition that tools should be easy to use was a relatively overlooked concept. Recently, there has been an increased expectation from clinicians, regulatory bodies and even device manufacturers that more user-friendly devices should be the norm. This expectation includes all the different types of devices available for clinical care, from pure hardware (e.g., intravenous catheters and laryngoscope blades) to electronic devices (e.g., video laryngoscopes and processed electroencephalogram monitors) to strict software applications, including electronic health and anesthesia records.

Intelligent Applications

These latter elements will be the focus of exciting technologies that will significantly alter how we document and deliver care. One such technology is generative artificial intelligence, which many readers may already be familiar with; one well-known application of generative AI is ChatGPT (developed by OpenAI). Generative AI can synthesize a text when given a query, such as “How do you change a tire?” Although it still has some issues regarding the reliability of the data being synthesized, with many reported incidents of hallucinating or fabricating manuscripts and case law, it still can be remarkably reliable. When we asked the Google version of generative AI (Google Bard) what it sees as future innovation in the field of anesthesiology, its response included the following:

• New and safer anesthetic drugs: Researchers are developing new anesthetic drugs that are more effective and have fewer side effects. For example, some drugs are being developed that target specific receptors in the brain, which could lead to more precise anesthesia and faster recovery times.
• Personalized anesthesia: Anesthesiologists are increasingly using personalized approaches to anesthesia, taking into account each patient’s individual medical history and needs. This trend is likely to continue in the future, as we learn more about how different people respond to different anesthetic drugs and techniques.

• AI and machine learning: AI and machine learning are already being used in some areas of anesthesiology, such as monitoring patients’ vital signs and predicting their risk for complications. In the future, AI and machine learning could be used to develop even more sophisticated tools for anesthesiologists, such as systems that can help them choose the best anesthetic drugs and techniques for each patient.
• Robotics: Robots are already being used in some types of surgery, and they are likely to become even more common in the future. This could lead to new developments in anesthesiology, such as robotic systems that can administer anesthesia and monitor patients more precisely than humans can.
• Telemedicine: Telemedicine is becoming increasingly popular in many areas of healthcare, and it is likely to play a bigger role in anesthesiology in the future. For example, telemedicine could be used to provide preoperative consultations and postoperative care to patients in remote areas.

It then went into detailed examples of the key areas of development, including closed-loop anesthesia, “smart anesthesia machines,” wearable technology and nanotechnology. We could not have written this better ourselves.

Future Anesthesia Practice

Let’s explore one possible evolution in our care using these latest AI tools. Before a patient presents to an anesthesia clinic or even the holding bay, the AI applications identify key elements requiring further review. When a patient is being evaluated, it listens in the background and generates a comprehensive preoperative evaluation with the discussion of the plan and risks reviewed. During the anesthetic, while the anesthesiologist is overseeing multiple rooms, the AI application detects potential harmful events, such as persistent hypotension or potentially dangerous administration of medications, while also communicating key events to the supervising anesthesiologist. In the recovery room, a patient with uncontrolled pain requiring large amounts of opioids will get flagged sooner to be reviewed by the anesthesiology team. The anesthesiologist would be able to provide better care while only having a fraction of the interaction with the electronic health record that we currently have. Who wouldn’t want that?

Conclusion

The workflow described above is much closer to reality than we realize. Before the rollout of this technology, several additional challenges will need to be addressed. First, there should be a focus on understanding how these technologies will interact in a patient care environment where medical outcomes appear to remain influenced by patient demographics. Second, physicians must remain front and center of these new technologies to ensure these innovations stay relevant to patient care. This requires a collaborative approach between product developers and clinicians.

Ramaswamy and Sarraf are conference co-chairs for the STA.