From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care

TL;DR

This paper advocates shifting from purely data-driven AI models to purpose-driven systems that incorporate clinical theory and sociotechnical context for safer, more effective patient care automation.

Contribution

It introduces a purpose-driven AI paradigm grounded in clinical theory and systems thinking, emphasizing the importance of context in developing patient care automation systems.

Findings

Purpose-driven AI aligns better with clinical outcomes.

Systems thinking enhances model relevance and safety.

New methodological opportunities for AI in healthcare emerge.

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.