Qualitative Framework for Evaluating Clinical Data Science Systems: Beyond Technical Validation
Year: 2026
Type: article
Abstract: IntroductionThe proliferation of data science and artificial intelligence (AI) in healthcare has led to the rapid deployment of predictive models and clinical decision-support tools [1-3].Although advanced technical performance metrics are now available, real-world failures of healthcare AI systems often do not arise from algorithmic weaknesses alone [4,5].Instead, such failures are commonly driven by limitations in how these systems are evaluated and implemented, particularly with respect to system safety, clinical workflow integration, and human-AI interactions [6][7][8].Increasing evidence suggests that failures in clinical AI result not only from algorithm design, but also from inadequate evaluation, governance, and implementation processes [4,9].This communication presents a conceptual framework for evaluating clinical data science systems in healthcare.It draws on established evaluation frameworks in health infor-matics as well as the author's direct experience implementing and evaluating clinical data science systems in healthcare settings. II.
Source: Healthcare Informatics Research
Author: Samita M. Heslin
Institution: Stony Brook University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $300
OpenAlex ID: W7160544751
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