AI & Patient Care Trends: Q&A with Ajay K. Gupta & Dani Bowie

A select group of industry leaders will convene at the Healthcare Council Annual Conference on October 9, 2024, at the Congressional Country Club in Bethesda, Maryland to discuss emerging healthcare trends. One of the sessions at the conference includes a panel discussion on “Emerging Trends in AI and the Impact on Patient Care.” In advance of the conference, the moderator, Ajay K. Gupta, CISSP, MBA, co-founder and CEO of HSR.health, and Dani Bowie, DNP, RN, NE-BC, SVP of solutions design for Workforce AI at Aya Healthcare, sat down for a brief Q&A to share their journeys with AI and what they see on the horizon for this technology in the context of improving patient care.

Q: AI has been around for a while. Why do you think it is now being widely talked about in healthcare?

Dani: Artificial intelligence is an umbrella term for many different forms of machine capable learning. And while it may seem like AI is new for healthcare, research in AI’s application has been around for over fifty years. However, there’s a notable gap in which it takes an average of 17 years for research evidence to reach clinical practice. So, while it may seem that AI is everywhere, especially with the large-scale consumerization of such tools like ChatGPT and others, much research around AI’s applications are just starting to make more headway into clinical practice.

Ajay: Research is the process of discovering and validating new ideas which can take time. This is because the speed at which the ideas are translated to practice is determined in part by risk tolerance, and healthcare organizations often have a low tolerance for risk. Now, we must recognize that AI, and technology as a whole, has increased the rate of change. This presents an opportunity to be more engaged in testing potential solutions so what ultimately comes to market is aligned with what we really need at the clinical point-of-care. I don’t think we can leave this to the developers. We have to engage with the developers of AI solutions so we can better ensure that these solutions — safely — meet the specific needs of our health systems and patients.

Q: How did your journey with AI start?

Dani: My journey with AI started with my doctoral work in 2015 as I was looking to create solutions to solve the nurse staffing and scheduling problem. The literature revealed that nurse staffing and scheduling is one of the most complex optimization problems that can be solved mathematically! The manual and complex process that consumes 60% of a nursing manager’s time could be aided by predictive modeling, machine learning, regression models and logic rules. The literature was abundant, but because most AI models were not incorporated into existing scheduling and staffing technology, there was limited application of these models in practice.

Ajay: I’ve always been curious and driven by a desire to solve problems. AI fascinated me as the ultimate tool for tackling challenges once thought impossible. Given its power, I’ve always believed it should be applied to the most complex issues we face: health problems — which are costly and impact our very lives. My journey began with exploring how data could improve healthcare outcomes, and I quickly realized AI’s transformative potential. From predictive modeling to automating decision-making, AI unlocks innovative solutions that directly enhance patient care. By identifying problems, analyzing data and developing actionable insights, we can now predict the spread and severity of diseases — whether chronic, infectious or social — making these critical insights accessible to all organizations impacted by health risks.

Q: What are some scenarios in which you believe AI can improve patient care?

Dani: This brings me back to my doctoral journey and passion for solving the nurse staffing and scheduling process. Today’s many nurse schedules are based on a monthly average census number or outdated models, generally leading to over or understaffing on a regular basis. Using AI, we can create more optimal staffing models with greater accuracy. We’ve built Workforce AI that uses several predictive (ML /AI) algorithms, producing 300-plus mathematical models to predict patient volumes to build proactive hiring plans and staffing recommendations. With better staffing, we can positively impact nurse retention and job satisfaction, which will lead to improved patient care.

Ajay: AI is a solution that is sold as having broad application to our overall way of life, and I think that can be true in healthcare. It can play a role in all aspects of healthcare, from diagnosis, such as detecting cardiac conditions from ECG data before symptoms appear, to automating routine tasks, such as documentation of patient visit notes or even for reading medical images — all freeing up physicians to spend time with patients. 

AI can speed along progress towards personalized treatment plans using patient data to identify the most effective therapies. This is an area where greater coordination of research will be beneficial as these results from AI will need validation.

Additionally, it can also predict health risks into the future, allowing health systems and even entire communities to mitigate or prepare for tomorrow’s challenges. These applications make care more proactive, efficient and tailored to individual needs.

This conversation provides a preview of the insights and perspectives on AI’s impact in healthcare that will be explored further during this session. To hear more from Dani and Ajay, as well as their fellow panelists, Myla Maloney, chief growth officer of PINC AI Applied Sciences, Premier, Inc.; Deanna Hanisch, vice president of Health IT at Johns Hopkins Health System; Janusz Wojtusiak, professor and director of programs in health informatics and director of the Machine Learning and Inference Laboratory; and Molly K. McCarthy, MBA, BSN, RN-NI, health technology advisory and strategist, learn more about the conference here: https://healthcare-council.org/annual-fall-conference/.