cs.AIApr 8, 2026

Healthcare AI for Automation or Allocation? A Transaction Cost Economics Framework

Authors: Ari Ercole

Organizations: Department of Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, UK

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Abstract

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.

Figures & tables

 Non-clinicianClinician
Weighted mean TCI2.7863.352
SEARCH_INFO share0.1800.267
BARGAIN_DECIDE share0.0130.073
MONITOR_ENFORCE share0.4040.352
ADAPT_COORDINATE share0.4030.308
Table 1: Headline role-level summaries (frequency-weighted). Values are computed from task-level labels and intensities using O*NET task frequency weights.
VariablepppFDRp_{\mathrm{FDR}}UUδ\deltax~clin\tilde{x}_{\mathrm{clin}}x~non\tilde{x}_{\mathrm{non}}
TCI2×10−62\times 10^{-6}1.5×10−51.5\times 10^{-5}1248.00.7273.4982.793
share_BARGAIN_DECIDE9.66×10−49.66\times 10^{-4}2.90×10−32.90\times 10^{-3}1075.50.4890.0590.000
share_SEARCH_INFO6.60×10−36.60\times 10^{-3}1.32×10−21.32\times 10^{-2}1025.50.4190.2660.180
share_MONITOR_ENFORCE5.47×10−25.47\times 10^{-2}6.56×10−26.56\times 10^{-2}508.0-0.2970.2860.385
share_ADAPT_COORDINATE5.14×10−25.14\times 10^{-2}6.56×10−26.56\times 10^{-2}505.0-0.3010.3400.445
TCI_sd5.41×10−15.41\times 10^{-1}5.41×10−15.41\times 10^{-1}791.00.0950.6580.628
Table 2: Occupation-level differences between clinician and non-clinician occupations. Mann–Whitney UU tests (two-sided) with Benjamini–Hochberg false discovery rate (FDR) correction; Cliff’s δ\delta reported as effect size. Positive δ\delta indicates higher values for clinician occupations.
Figure 1: Transaction‑cost friction across healthcare occupations. Occupations are plotted by mean transaction‑cost intensity and within‑occupation dispersion across tasks, capturing coordination burden (search, decision, monitoring, adaptation) rather than task complexity; clinician and non‑clinician roles occupy distinct coordination regimes, implying uneven digital opportunity.
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