Healthcare AI for Automation or Allocation? A Transaction Cost Economics Framework
For healthcare administrators and AI developers, this provides a framework to identify which tasks are most amenable to automation versus allocation, revealing that coordination costs, not just clinical complexity, determine AI applicability.
This paper operationalizes transaction-cost economics at the task level across healthcare occupations, finding that clinician roles have substantially higher transaction-cost intensity than non-clinician roles, driven by information search and decision-related coordination. This suggests AI opportunities are unevenly distributed based on coordination structure rather than technical complexity.
Healthcare productivity is shaped not only by clinical complexity but by the costs of coordinating work under uncertainty. Transaction-cost economics offers a theory of these coordination frictions, yet has rarely been operationalised at task level across health occupations. Using task statements and frequency weights from the O*NET occupational database, we characterised healthcare work at task granularity and coded each unique task using a constrained large language model into one dominant transaction-cost category (information search, decision and bargaining, monitoring and enforcement, or adaptation and coordination) together with an overall transaction-cost intensity score. Aggregating to the occupation level, clinician roles exhibited substantially higher transaction-cost intensity than non-clinician roles, driven primarily by greater burdens of information search and decision-related coordination, while dispersion of transaction costs within occupations did not differ. These findings demonstrate systematic heterogeneity in the nature of coordination work across healthcare roles and suggest that the opportunities for digital and AI interventions are unevenly distributed, shaped less by technical task complexity than by underlying coordination structure.