Artificial Intelligence: Building a Hub for Clinical Informatics

UT Southwestern’s Clinical Informatics Center is leveraging AI and big data to advance research, patient care, and training.

The widespread adoption of artificial intelligence (AI) and large language model technologies such as ChatGPT and Bard reveal the importance of thoughtful and ethical leadership, research, and education as the technology evolves. Machine learning (ML) and natural language processing continue to drive discovery, care, and health care decision-making in a rapidly evolving, global context.

UT Southwestern established the Clinical Informatics Center in 2019 to develop and implement clinical informatics solutions for health care providers, including AI models, and to train practitioners in the field.

Led by Christoph Lehmann, M.D., Professor of Pediatrics, Bioinformatics, and Public Health, and Associate Dean of Clinical Informatics, the Center has grown to include more than 31 faculty members, who are united by the goal of harnessing the power of informatics, AI, and big data to improve quality, safety, and cost in health care.

Applying Machine Learning Models

The Center is actively working to integrate ML models into clinical workflows using the appropriate care, ethical considerations, and human oversight. ML models can be applied to a variety of health care datasets, including electronic health records, text databases, pathology slides, imaging and diagnostic tests, genetic information, and many others.

A prime example is the development of a plainlanguage health information text generator that uses a large language model similar to ChatGPT. The project was pioneered by John Hanna, M.D., a Clinical Informatics fellow working in collaboration with Nelly Estefanie Garduno-Rapp, M.D., a graduate student in the Master of Science in Health Informatics program, and Jonathan Reeder, M.D., Assistant Professor of Emergency Medicine and one of the course directors.

With one click, the model takes complex medical writing like a progress note copied into a text field and generates a plain-language summary that can be understood by patients.

Other examples of models in development at UT Southwestern include a clinical decision support tool designed to predict iron-deficiency anemia as early as three months in advance, a model predicting patients who will benefit from surgery for hyperparathyroidism to reduce the risk of bone fracture, and a model that uses electroencephalogram data to predict early hypoxic ischemic encephalopathy, or oxygen deprivation during birth.

The ultimate decision-maker for AI tools applied in the clinical setting must be a human.

Shaping Priorities for AI Research

While Dr. Lehmann is excited about AI’s increasing role in supporting staff making clinical decisions, he cautions that using such technologies requires a great deal of responsibility and oversight to ensure that they are used ethically.

“Performance of AI models tends to degrade over time. Thus, the ultimate decision-maker for AI tools applied in the clinical setting must be a human,” Dr. Lehmann says.

He notes that many of the ML tools under development are intended to be algorithms that support clinicians in their decision-making, rather than independent actors. One of his ambitions for the Center is to become a national advocate for patients and physicians to help ensure that AI systems entering the market are up to the highest quality and ethical standards.

“We hope to develop measures so that the professionals managing these new tools integrated into daily clinical care know the performance benchmarks they need to reach,” he adds. “This goal includes educating the next generation of engineers and clinicians who will be working with these tools.”

New Frontiers in Education

Ultimately, educating future physicians and trainees is key to expanding access to the best and most innovative AI and big-data tools. As public awareness grows and more programs prioritize education around these technologies, Dr. Lehmann expects that more clinicians and researchers will attempt to incorporate them into their workflows.

Christoph Lehmann, M.D., is Professor of Pediatrics, Bioinformatics, and Public Health, Director of the Clinical Informatics Center, and Associate Dean of Clinical Informatics. His research focuses on improving clinical information technology and clinical decision support.