Well Informed

by Ronna L. Del Rosario

Applications of AI in Healthcare

The realm of technology remains a burgeoning one, continuously providing opportunities for innovation and creativity while also greatly impacting the way our modern society functions in the day-to-day. AI, in particular, has come a long way over the years, and this is especially evident when one explores the various ways that AI has been applied to the healthcare field.

            In Emergency Room (ER) medicine, one specific tool that is gaining significant traction and usage among healthcare providers is TriageGo Clinical Decision Support (Beckman Coulter, Inc., 2023). With this innovative AI healthcare tool, providers such as triage nurses and clinical staff can maintain existing workflows while integrating TriageGo. The results of implementing TriageGo include reliable adverse-outcome prediction and improved consistency and standardization. Factors that distinguish it from other CDS (Clinical Decision Support) software tools include its capacity to be “. . . embedded into the EHR workflow – no extra clicks or pop-ups . . . (Beckman, Coulter, Inc., 2023).” Additionally, its recommendations are made in real-time so during the actual delivery of care. Most importantly, its ML model allows it to learn from the particular ED’s data, so it can be customized to differing patient populations. The implications of TriageGo’s features can expedite emergency care; enhance decision-making the emergent and triage environments; save time, money, and resources; and most importantly, prevent and treat deteriorating health conditions.

            In the field of mental health, Wysa is transforming the way that mental health services are delivered through the power of AI (Wysa, 2023). While Wysa’s primary method of delivery mental health services is by way is via AI Chat conversations, structured programs and on-demand self-care, the option to work with a live professional, and customized escalation pathways when mental health crises seem imminent. An intriguing aspect of this digital mental health tool is that there is even a dedicated application for employees in participating health plans called Wysa at Work. With such tools like Wysa, individuals may be better equipped to utilize enhanced coping mechanisms and navigate through life’s often difficult situations.

  In the field of clinical documentation, DAX Copilot is one such tool that enables physicians to more accurately and efficiently document patient interactions in real-time. Per the DAX Copilot page (Microsoft, 2024), this AI assistant asserts  “. . . improve[d] clinician and patient satisfaction, operational efficiencies, and financial outcomes. . .” Unique features include the ability to capture a multi-party conversation ambiently, the capacity to create high-quality clinical documentation automatically, seamless integration with over 200 EHRs (Electronic Health Records), customizable templates, and scalability across healthcare organizations (Microsoft, 2024). With such a myriad of benefits that accompany it, it will be interesting to see if DAX Copilot continues to be adopted by providers across the nation.

Benefits and Challenges of AI-Enabled Health Technology

As was discussed in the previous section, the realm of AI has much to offer in terms of applications in healthcare, significantly impacting diverse fields from the areas of mental health to clinical documentation. Additionally, Deloitte (2024) mentions 4 benefits of AI in the healthcare realm which are the following: “1. Improve provider and clinician productivity and quality of care, 2. Enhance patient engagement in their own care and streamline patient access to care, 3. Accelerate the speed and reduce the cost to develop new pharmaceutical treatments, and 4. Personalize medical treatments by leveraging analytics to mine significant, previously untapped stores of non-codified clinical data.”

Equally important, it is vital to acknowledge and gain a thorough understanding of the existing challenges that come with the territory. The U. S. Government Accountability Office (GAO) (2022) alludes to three primary challenges that accompany further improvements where AI integration in healthcare is concerned: “1. Demonstrating real-world performance across diverse clinical settings and in rigorous studies, 2. Meeting clinical needs, such as developing technologies that integrate into clinical workflows, and 3. Addressing regulatory gaps, such as providing clear guidance for the development of adaptive algorithms.” Moreover, AI must be used in ethical ways that do not take advantage of underrepresented or disenfranchised populations or disclose sensitive patient information.

Future & Prospects of AI in Healthcare

In terms of AI’s future applications in healthcare, I believe that the forecast is bright. From the various scholarly articles and resource materials I have read in my Health Informatics course, it is clear that both the benefits and accompanying challenges of AI-powered health technology are becoming more well-known. Hopefully, such knowledge can be transformed into even more innovative solutions to address the increasingly complex health issues experienced by patients in our modern world.

The future that I foresee would entail AI working to better complement the care that only human empathy, kindness, and consideration can provide to others. One way that I see AI augmenting healthcare professionals’ productivity and attention to what truly matters to their patients is through increased automation processes that allow for a less cumbersome and tedious experience when it comes to performing more mundane and administrative tasks.

Though the road to a more symbiotic working relationship between humans and AI is a long one, increased communication and collaboration between healthcare professionals, patients, information science professionals, regulatory agencies, governmental entities, and other key stakeholders will play a significant role in influencing a more positive and transformative shift in the healthcare landscape. Additionally, when used for good, I believe that AI in healthcare will improve patient outcomes; support medical professionals in their continued development, education, and training;  and work toward changing the healthcare ecosystem to one that can better support the health and wellness of individuals worldwide.

References

Beckman Coulter, Inc. (2023). TriageGo Clinical Decision Support: FAQs. Beckman Coulter. https://media.beckmancoulter.com/-/media/diagnostics/products/solutions/triagego/docs/cds-triagego-fl-us-en—final.pdf?rev=fd32c0c3a1134966abf24ec59d35ed21 

Deloitte Touche Tohmatsu Limited.  (2024). The future of artificial intelligence in health care: Emerging applications of AI in health care. Deloitte. https://www2.deloitte.com/us/en/pages/life-sciences-and-health-care/articles/future-of-artificial-intelligence-in-health-care.html

Microsoft. (2024). DAX Copilot. Nuance. https://www.nuance.com/healthcare/dragon-ai-clinical-solutions/dax-copilot.html

U. S. Government Accountability Office (GAO). (2022, September 29). Artificial Intelligence in health care: Benefits and challenges of machine learning technologies for medical diagnostics. GAO. https://www.gao.gov/products/gao-22-104629

Wysa, LTD. (2023). How does it work? Wysa. https://www.wysa.com/