Healthcare AI for Automation or Allocation? A Transaction Cost Economics Framework
- URL: http://arxiv.org/abs/2604.16465v1
- Date: Wed, 08 Apr 2026 18:22:46 GMT
- Title: Healthcare AI for Automation or Allocation? A Transaction Cost Economics Framework
- Abstract summary: Using task statements and frequency weights from the O*NET occupational database, we characterised healthcare work at task granularity.<n>We coded each unique task using a constrained large language model into one dominant transaction-cost category.<n> clinician roles exhibited substantially higher transaction-cost intensity than non-clinician roles, driven primarily by greater burdens of information search and decision-related coordination.
- Score: 0.43995631888847014
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- FMRP-LEAN: A HIPAA-Compliant AI-Augmented LIMS Architecture for End-to-End Clinical Assay Workflow Optimization [0.0]
This paper presents FMRP-LEAN, a HIPAA-compliant, AI-augmented Laboratory Information Management System (LIMS) architecture that formalizes bio-specimen lifecycle management through a finite-state workflow model with explicit transition guards and dwell-time observability.
arXiv Detail & Related papers (2026-07-22T17:11:52Z) - Communication-Efficient Digital-Twin Coordination for Heterogeneous LLM Embodied Agents over Computing Power Networks [48.90302419915548]
Embodied agent teams powered by heterogeneous large language models (LLMs) are being widely deployed in smart factories, warehouses, and service robotics.<n>We propose LDT-Coord, a networked coordination framework built upon a lightweight digital twin (DT)<n>Specifically, each agent independently selects its intended action and reports both the action decision and a structured temporal constraint over shared resources to the DT server.
arXiv Detail & Related papers (2026-07-10T12:10:31Z) - HRIBench: Benchmarking Interaction-Centric Human-Robot Collaboration [62.19372189311434]
We introduce HRIBench, a diagnostic benchmark for intent-aware human-robot collaboration based on executable scenarios.<n>HRIBench represents collaborative tasks as structured scenario scripts that explicitly model agent roles, temporal dependencies, coordination constraints, and human behavior distributions.<n>Results show that current foundation robot policies struggle substantially in collaborative settings despite strong manipulation ability.
arXiv Detail & Related papers (2026-07-05T09:15:35Z) - Modeling and Interpreting Teamwork Dynamics in Cancer Care Outcome Prediction [18.543220644724766]
Cancer care requires a longitudinal approach in which treatments are planned and delivered according to the needs of each individual patient.<n>Planning and delivery are both team-based processes that depend on coordinated efforts among multiple healthcare professionals.<n>We apply machine learning methods to identify predictive signals of patient survival embedded in these collaborative structures.
arXiv Detail & Related papers (2026-06-03T06:28:59Z) - When Does Hierarchy Help? Benchmarking Agent Coordination in Event-Driven Industrial Scheduling [7.912877527497326]
DESBench is a benchmark for evaluating agent coordination in hierarchical event-driven scheduling.<n>We focus on four representative coordination paradigms: centralized, hierarchical, heterarchical, and holonic.
arXiv Detail & Related papers (2026-05-13T08:33:28Z) - HULK: Large-scale Hierarchical Coordination under Continual and Uncertain Temporal Tasks [5.337512368549053]
This work tackles the large-scale coordination problem under continual and uncertain temporal tasks.<n>The proposed hierarchical framework, HULK, consists of two interleaved layers: the rolling assignment of currently known tasks to subteams within a certain horizon, and the dynamic coordination within a subteam given the detected subtasks during online execution.<n>The method is validated rigorously over large-scale heterogeneous systems under various temporal tasks and environment uncertainties.
arXiv Detail & Related papers (2026-05-09T06:10:40Z) - Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs [52.26652574704317]
Large language models (LLMs) are increasingly deployed in teams, yet existing coordination approaches often occupy two extremes.<n>We introduce Language Agent Teams for Task Evolution (LATTE), a framework for coordinating LLM teams inspired by distributed systems.
arXiv Detail & Related papers (2026-05-07T14:19:17Z) - The Headless Firm: How AI Reshapes Enterprise Boundaries [5.856292656853395]
We argue that agentic AI induces a structural change in how coordination costs scale.<n>We formalize this claim as a coordination cost model with two falsifiable empirical predictions.
arXiv Detail & Related papers (2026-02-24T22:13:14Z) - Completion $\neq$ Collaboration: Scaling Collaborative Effort with Agents [48.95020665909723]
We argue for a shift from building and assessing task completion agents to developing collaborative agents.<n>We introduce collaborative effort scaling, a framework that captures how an agent's utility grows with increasing user involvement.
arXiv Detail & Related papers (2025-10-29T17:47:18Z) - A Multi-Objective Genetic Algorithm for Healthcare Workforce Scheduling [0.764671395172401]
We propose a Multi-objective Genetic Algorithm (MOO-GA) that models the hospital unit workforce scheduling problem as a multi-objective optimization task.<n>By defining objective functions for cost, patient care coverage, and staff satisfaction, the GA navigates the vast search space to identify a set of high-quality, non-dominated solutions.
arXiv Detail & Related papers (2025-08-28T16:16:10Z) - Shapley-Coop: Credit Assignment for Emergent Cooperation in Self-Interested LLM Agents [19.76624432791744]
Large Language Models (LLMs) show strong collaborative performance in multi-agent systems with predefined roles and roles.<n>central challenge in achieving coordination lies in credit assignment.<n>Shagley-Coop integrates Shapley Chain-of-Thought with structured protocols for effective price matching.
arXiv Detail & Related papers (2025-06-09T03:24:01Z) - Controlled Data Rebalancing in Multi-Task Learning for Real-World Image Super-Resolution [51.79973519845773]
Real-world image super-resolution (Real-SR) is a challenging problem due to the complex degradation patterns in low-resolution images.<n>We propose an improved paradigm that frames Real-SR as a data-heterogeneous multi-task learning problem.
arXiv Detail & Related papers (2025-06-05T21:40:21Z) - A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making [38.2229221645303]
Large Language Models (LLMs) promise to streamline this process by synthesizing vast medical knowledge and multi-modal health data.
Our MDAgents address this need by dynamically assigning collaboration structures to LLMs based on task complexity.
This framework improves diagnostic accuracy and supports adaptive responses in complex, real-world medical scenarios.
arXiv Detail & Related papers (2024-10-31T22:58:08Z) - Heterogeneous Graph Reinforcement Learning for Dependency-aware Multi-task Allocation in Spatial Crowdsourcing [33.915222518617085]
This paper formally investigates the problem of Dependency-aware Multi-task Allocation (DMA)
It presents a well-designed framework to solve it, known as Heterogeneous Graph Reinforcement Learning-based Task Allocation (HGRL-TA)
Experiment results demonstrate the effectiveness and generality of the proposed HGRL-TA in solving the DMA problem, leading to average profits that is 21.78% higher than those achieved using the metaheuristic methods.
arXiv Detail & Related papers (2024-10-20T17:00:45Z) - A Task-Interdependency Model of Complex Collaboration Towards
Human-Centered Crowd Work [0.5439020425818999]
We present a model centered on interdependencies, a phenomenon well understood to be at the core of collaboration.
We use it to explain challenges to scaling complex collaborative work, underscore the importance of expert workers, and explore the relationship between coordination intensity and occupational wages.
arXiv Detail & Related papers (2023-08-31T22:37:47Z) - Adaptive Value Decomposition with Greedy Marginal Contribution
Computation for Cooperative Multi-Agent Reinforcement Learning [48.41925886860991]
Real-world cooperation often requires intensive coordination among agents simultaneously.
Traditional methods that learn the value function as a monotonic mixing of per-agent utilities cannot solve the tasks with non-monotonic returns.
We propose a novel explicit credit assignment method to address the non-monotonic problem.
arXiv Detail & Related papers (2023-02-14T07:23:59Z) - The Medkit-Learn(ing) Environment: Medical Decision Modelling through
Simulation [81.72197368690031]
We present a new benchmarking suite designed specifically for medical sequential decision making.
The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
arXiv Detail & Related papers (2021-06-08T10:38:09Z) - Reinforcement Learning with Efficient Active Feature Acquisition [59.91808801541007]
In real-life, information acquisition might correspond to performing a medical test on a patient.
We propose a model-based reinforcement learning framework that learns an active feature acquisition policy.
Key to the success is a novel sequential variational auto-encoder that learns high-quality representations from partially observed states.
arXiv Detail & Related papers (2020-11-02T08:46:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.