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-clinician
Clinician
Weighted mean TCI
2.786
3.352
SEARCH_INFO share
0.180
0.267
BARGAIN_DECIDE share
0.013
0.073
MONITOR_ENFORCE share
0.404
0.352
ADAPT_COORDINATE share
0.403
0.308
Table 1: Headline role-level summaries (frequency-weighted). Values are computed from task-level labels and intensities using O*NET task frequency weights.
Variable
p
pFDR
U
δ
x~clin
x~non
TCI
2×10−6
1.5×10−5
1248.0
0.727
3.498
2.793
share_BARGAIN_DECIDE
9.66×10−4
2.90×10−3
1075.5
0.489
0.059
0.000
share_SEARCH_INFO
6.60×10−3
1.32×10−2
1025.5
0.419
0.266
0.180
share_MONITOR_ENFORCE
5.47×10−2
6.56×10−2
508.0
-0.297
0.286
0.385
share_ADAPT_COORDINATE
5.14×10−2
6.56×10−2
505.0
-0.301
0.340
0.445
TCI_sd
5.41×10−1
5.41×10−1
791.0
0.095
0.658
0.628
Table 2: Occupation-level differences between clinician and non-clinician occupations. Mann–Whitney U tests (two-sided) with Benjamini–Hochberg false discovery rate (FDR) correction; Cliff’s δ reported as effect size. Positive δ 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.
Using a stylised coordination problem drawn from inpatient capacity management, three archetypal forms of AI deployment are described: effort-reducing technologies, observability-oriented systems, and interventions that alter underlying incentive structures. Effort reduction and observability may improve performance within existing patterns of behaviour but do not, in general, change which actions are individually rational. As a result, such interventions are typically absorbed into existing equilibria. By contrast, interventions that modify how local actions map to downstream consequences by redistributing or bounding local risk can change stable system behaviour. These mechanism-level interventions differ not in technical sophistication but in their interaction with institutional incentives. The analysis suggests that expectations of system-level gains from AI should be conditioned on whether a deployment changes incentives rather than optimising tasks or information flows alone. For healthcare organisations and policymakers, this has practical implications for procurement, governance, and evaluation of digital technologies.
Ari Ercole
Cambridge Centre for AI in Medicine, University of Cambridge, UK · Magdalene College, University of Cambridge, UK
Organizations devote substantial resources to coordination, yet which tasks actually require it for correctness remains unclear. The problem is acute in multi-agent AI systems, where coordination cost is directly measurable and can exceed the cost of the work itself. Distributed systems theory provides a precise criterion: coordination is required when a task specification is non-monotonic, meaning that as histories grow, new information can invalidate prior conclusions. Here we show that Thompson's classic taxonomy of interdependence maps to that criterion, yielding a decision rule for when coordination is required for correctness. We formalize the correspondence in a bridge theorem, apply the rule to 65 workflows from the American Productivity & Quality Center (APQC) and, with a calibrated large language model (LLM), 13,417 Occupational Information Network (ONET) tasks, and illustrate it in multi-agent AI simulations. Under our decompositions, 74% of workflows and 42% of ONET tasks are monotonic, implying that up to 24-57% of coordination spending is unnecessary for correctness.
Harang Ju
Carey Business School, Johns Hopkins University, Baltimore, MD 21202
Successful diffusion of AI in the workforce hinges on the economic value that AI brings to human endeavors. Bringing AI into the workforce is more than deploying a powerful new technology -- it is launching a new form of collaboration. Each human worker is now endowed with a team of AI agents; work can be delegated to these agents, and the role of the human shifts towards managing and monitoring. How can we maximize the economic value from collaboration with AI in the workforce? How can we make it a "true" collaboration that empowers human workers rather than replacing them? We take an approach that combines the fields of theoretical computer science and economics, highlighting the potential of algorithmic tools grounded in economic principles to improve the effectiveness of human-AI collective work. We consider two tiers of tools: (1) tools for better coordination, via algorithmic management of interdependencies; (2) tools for better cooperation, via contractual incentive alignment. We show how a principled approach based on algorithmic and economic research enhances both coordination and cooperation, charting a pathway for future research to inform AI markets.
Nicole Immorlica, Inbal Talgam-Cohen
Yale University · Microsoft Research · Tel Aviv University