July 20, 2023 -In anesthesiology, as in all medical specialties, clinicians strive to support patient safety and improve outcomes. Some anesthesiology professionals are investigating how advanced technologies like artificial intelligence (AI) and machine learning (ML) may positively impact the field.
Anesthesia patients generate massive amounts of data that could be used to bolster these efforts. But capturing and analyzing high-quality big data presents a challenge for health systems and providers.
Research into AIs clinical applications and current limitations in anesthesiology practice suggests that these tools may demonstrate utility in various areas, including depth of anesthesia monitoring, control of anesthesia, event and risk prediction, ultrasound guidance, pain management, and operating room logistics.
Other studies analyzing trends in AI and anesthesia indicate that ML tools, robots, clinical decision support systems, and other technologies may play a significant role in anesthetic care in the future. Some even suggest that the combination of AI, nanotechnology, and genomic medicine may one day advance the quality of anesthesia practice.
But how can anesthesia teams navigate the hype around these tools and work to leverage them appropriately and effectively?
Anesthesiology has historically been at the forefront of patient safety initiatives, with anesthesiologists working to establish reliable processes and implement technologies that can help reduce adverse outcomes like morbidity and mortality, noted Desire Chappell, CRNA, vice president of clinical quality at Northstar Anesthesia in Irving, Texas, during an interview with HealthITAnalytics.
However, taking anesthesia to the next level in terms of enhancing patient safety requires access to high-quality data on patients and outcomes. Limited access to such data can hinder efforts to fine-tune intraoperative practices that may improve postoperative outcomes.
Additional factors, like staff shortages and patient complexity, can create hurdles, resulting in a need for advanced technologies to support known anesthesia-related safety measures.
We know the things that save lives, and we know the things that improve care, but having us, as people on teams, practice in that way reliably and at all times is the challenge, explained Jonathan Tan, MD, vice chair of Analytics and Clinical Effectiveness at the Childrens Hospital Los Angeles (CHLA), who serves as an assistant professor of Clinical Anesthesiology at CHLA and the Keck School of Medicine at the University of Southern California. [Because of these] factors, I think there's huge opportunity for us to scale the way we practice more safely by using technology, including artificial intelligence and machine learning.
Lack of standardization within the anesthesiology specialty is also a limitation that can potentially affect patient outcomes and safety, Chappell stated.
I think that because we have been able to do anesthesia, [which] can be done in a lot of different ways, that we haven't traditionally looked at variation as an issue potentially with patient outcomes and patient safety, she said. But the more we standardize, the better patient outcomes are.
This is where AI and ML come in.
AI may be able to address the issues described above by identifying nuances in the data and helping standardize patient care.
[In anesthesiology,] there's actually so much information and data coming our way, Tan said. It's an extraordinary amount of information that's being generated every second, probably [more information at a higher density] at a given moment than the rest of the hospital from a vital sign and patient standpoint.
Ingesting and analyzing that data while caring for patients and fulfilling their other responsibilities can create a significant cognitive load for anesthesia professionals.
In this case, AI can augment the practice of anesthesiology by helping to reduce that cognitive load and allowing providers to focus on more important aspects of patient care. By leveraging AI in this assistive capacity, rather than replacing clinicians' experience and expertise, Tan and Chappell indicated that care teams can prioritize the essential human connection between patients and providers.
Chappell likened using AI to navigate the wealth of anesthesiology data to using a GPS while driving.
[When] you're driving your car somewhere, and you're using your navigation tool, even though you think you know how to get there, you don't necessarily know what the traffic patterns are," she said. "You don't know if there's been an accident.
[AI] is just helping you as a tool to navigate where you're trying to go, she added. We need tools to help us get to where we're going more efficiently, to have a little bit more information that is deciphered in a different way, and I think that AI can help us do that.
But Chappell and Tan agreed that these technologies could never replace the human aspect of their work or the need for meaningful connections between anesthesia teams and their patients.
The purpose of technology, and the purpose of AI and other tools like that, is to actually free up our ability to spend more time with the patient, to spend more time at the bedside, to be physically and emotionally there to care for patients, Tan noted. And in the complex world that we live in now, I think that those tools are more important than ever to be able to help us do our job better by being at the bedside with the patient.
AI and other technological advancements have their place in anesthesiology and healthcare more broadly. Still, the key lies in identifying high-value use cases for these tools and integrating them effectively in clinical workflows, Chappell noted. In doing so, care teams can meaningfully leverage technology to improve patient care.
AI has diverse applications in anesthesiology. Some are already in use, while others will come onto the market in the near future, particularly for use cases related to patient experience, procedure guidance, risk assessment, and intraoperative optimization, said Tan.
He indicated that AI could facilitate communication between healthcare providers and families before surgery, which may reduce the burden on staff. These tools can also provide practice guidance for anesthesiologists during certain procedures by identifying important anatomical structures to target or avoid using ultrasound or airway devices.
Additionally, AI and ML tools can support pre-surgical risk stratification by flagging patient risk factors beyond those captured by current evidence-based scoring systems. Postoperatively, the technology can help support clinical decision-making by identifying the patients at the highest risk for readmission or mortality following surgery.
During surgery, these tools can be integrated into closed-loop systems that automate the delivery of medications and fluids based on patient parameters, such as weight or BMI, said Chappell. This integration can help support and optimize hemodynamic stability during surgery, which is critical to maintaining patient safety and improving outcomes.
Across use cases, Tan emphasized the importance of not only focusing on the technology itself but also on integrating it into hospitals.
When we talk about artificial intelligence [in anesthesiology], we're often talking about the technology itself, the tools, the data science, and there's a lot of focus on that, he stated. But I think a lot of that involvement is already pretty far along. And the other half of [this] is actually the implementation and bringing that technology to hospitals to reduce variation in care.
Bringing AI into clinical settings would require increased education for clinicians about the basics of AI and how it applies to the practice of anesthesiology, Tan explained.
Implementing these tools also necessitates considering the impact of change management, or the transformation of a health systems processes and technologies, on anesthesia providers, Chappell stated.
We have to remember that as new technology comes out, that each one of us as anesthesia providers and clinicians, we're individuals, too, she explained.
She added that when stakeholders leverage implementation science to deploy new technologies, they can forget how challenging change management can be for some.
There are a lot of people who get really super excited about the shiny, bright new tool that comes out, and then there are a lot of people who are very on the other side of the change management curve, Chappell continued. I think that as we go forward to adopt this, we need to be really sensitive to that and think of the human factors of the people who have to use this in practice, what's the best way that we get adoption for the long term, and sustainable adoption of new tools could potentially have such a huge impact on patient safety.
Tan further underscored that achieving the next level of patient safety in anesthesiology requires a collaborative ecosystem of health systems, care teams, patient safety scientists, data scientists, and specialists in implementation science and change management working to support the use of AI and ML in the field.
Editors Note: This article was updated to correct Desire Chappells credentials at 3:00pm ET on July 24, 2023.
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