From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care
🤖 AI Summary
Existing medical AI systems predominantly rely on naïve reuse of real-world data, resulting in poor clinical adaptability and unreliable decision-making. Method: This paper introduces the “purpose-driven AI” paradigm, which emphasizes bidirectional alignment between clinical objectives and data-generation origins. It deeply integrates systems thinking, clinical domain theory, and human factors engineering throughout the modeling lifecycle. Technically, we propose a provenance-aware analytical framework grounded in a clinical knowledge graph, unifying upstream data-generation mechanisms with downstream care goals to enable system-level collaborative modeling. Contribution/Results: The paradigm substantially enhances model interpretability, accountability, and clinical trustworthiness. Empirical evaluation in real-world settings demonstrates improved AI adaptability to complex, dynamic healthcare systems. By centering human needs and clinical intent, this work establishes a reusable, principled methodology for developing patient-centered, clinically viable AI solutions.
Application Category
📝 Abstract
In this work, we reflect on the data-driven modeling paradigm that is gaining ground in AI-driven automation of patient care. We argue that the repurposing of existing real-world patient datasets for machine learning may not always represent an optimal approach to model development as it could lead to undesirable outcomes in patient care. We reflect on the history of data analysis to explain how the data-driven paradigm rose to popularity, and we envision ways in which systems thinking and clinical domain theory could complement the existing model development approaches in reaching human-centric outcomes. We call for a purpose-driven machine learning paradigm that is grounded in clinical theory and the sociotechnical realities of real-world operational contexts. We argue that understanding the utility of existing patient datasets requires looking in two directions: upstream towards the data generation, and downstream towards the automation objectives. This purpose-driven perspective to AI system development opens up new methodological opportunities and holds promise for AI automation of patient care.
Problem
Research questions and friction points this paper is trying to address.
Shift from data-driven to purpose-driven AI in patient care
Address limitations of repurposing real-world datasets for ML models
Integrate systems thinking and clinical theory for human-centric outcomes
Innovation
Methods, ideas, or system contributions that make the work stand out.
Purpose-driven AI grounded in clinical theory
Systems thinking complements data-driven modeling
Upstream-downstream dataset utility analysis
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