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They would test the algorithm further with refinements and give the dieticians 10 more patients to look at the next week. This process helped boost confidence in the algorithm to a point where it is now actually placing an order for consults in the electronic medical record (EMR).

"We're finding six to 10 patients a week who have undiagnosed malnutrition. Now, if you think about that from a family member of a child, that's a huge difference. And those things are really impactful in terms of practical AI, and that's kind of spawned other ideas, but that's been kind of one of our great use cases," Higginson explained.

Five years ago, Phoenix Children's Hospital embarked on a journey to harness the power of AI in solving clinical challenges. The traditional approach of relying on biostatisticians to develop algorithms proved to be time-consuming and often inefficient. He said the team might work on an algorithm for several months and find it does not work well in the end. So Higginson's team opted for a different path, utilizing automated machine learning. This approach involves providing a dataset to an AI system that autonomously creates the algorithm, allowing the hospital to start using it within a matter of hours, rather than weeks.

One of the key lessons learned from using AI in healthcare is that getting it right on the first attempt is a rare occurrence. Thus, an iterative approach is essential to fine-tune algorithms over time.

While there are now many vendors selling commercialized AI algorithms, Higginson said many are to generalized for the needs of his hospitals, which another reason why they have decided to develop their own, highly customized algorithms.

"One of the things I've learned with AI over the years is it doesn't translate very well. So I'm always very skeptical of vendors that tell me, 'I've got an AI model that's going to work great,' because geographic factors are a huge influence as well. There are some clinical conditions which obviously translate, but I think we've seen some recent examples where models are trained in one state, lifted somewhere else and don't work," he said.

For example, he said they created AI models on operational things like our donors and managing their employees, which require very local and customized factors that are completely unique. "Understanding how far is too far for an employee to travel into work all depends on the road density, where they are traveling from. I think the concepts and the ideas are transferable. But I would be a little skeptical of taking that black box and just lifting it somewhere else," Higginson explained.

Pediatric healthcare presents unique challenges that often require tailored solutions. At Phoenix Children's Hospital, they've developed their own patient portal, recognizing that pediatric patients and their families have distinct relationships with healthcare providers. This patient portal addresses the complex dynamics of patient relationships within families and guardianship scenarios. This includes who has access in a divorce or foster home situation, and the ages when patient information needs to be shared with the patient.

Moreover, the hospital has adapted to the post-pandemic landscape by embracing telehealth services, which have been particularly well-received by pediatric patients and their caregivers. The implementation of hybrid telehealth, where patients and their caregivers join virtual consultations, has transformed the healthcare experience for families, Higginson said.

Higginson encourages a more general application of AI in healthcare, emphasizing its adaptability to a wide range of scenarios. He used the example of AI helping determine no-show rates to better staff the emergency room. Another example is AI can be used to sift through patient emails to doctors via the patient portal to determine the most appropriate recipient within the healthcare team. This could streamline communication and enhancing efficiency so doctors can practice at the top of their license not not spend a large amount of time sorting basic email requests. Higginson said doctors tell him over and over 80% of these messages are about scheduling, medications and billing which have nothing to do with the physician.

"So how great would it be to take that message that came in and run it through a GPT prompt and ask it, which help desk should this go to?" He said.

Phoenix Children's Hospital's innovative approach to AI demonstrates the immense potential for the technology in healthcare. By adopting a strategic and iterative approach, they have successfully developed clinical algorithms that not only improve patient care, but also enhance the overall healthcare experience for pediatric patients and their families.

Find more HIMSS coverage.

Link:
Leveraging machine learning to rapidly create clinical AI algorithms - HealthExec