Aligning Artificial Intelligence Prediction Targets with Clinical Workflows Using Human Centered Design Methods

Artificial intelligence models in healthcare often fail to improve patient outcomes despite strong predictive performance because they are frequently developed with limited understanding of clinical workflows and system implementation. We demonstrate a human-centered design approach to define prediction targets before model development, ensuring alignment with actionable clinical interventions. Using pediatric acute kidney injury as a case study, we convened a multidisciplinary working group and applied three complementary methods: user stories to elicit role-specific prediction targets, a People, Environment, Technology, and Tasks (PETT) Scan to analyze sociotechnical system factors, and process mapping to identify workflow leverage points. This approach revealed that different clinical roles require distinct prediction targets, with shared barriers including inadequate monitoring practices, poor visibility of at-risk patients, and unclear trajectories for kidney injury progression. By integrating clinical context before algorithm development, we identified high-impact prediction targets that support actionable interventions for hospitalists, nephrologists, and intensivists, demonstrating how human-centered design can bridge technical model performance and real-world clinical utility.