FLARE is a systematic, uncertainty‑aware framework that evaluates the financial and operational implications of adopting AI in healthcare. It integrates fuzzy logic, time‑driven activity‑based costing, and return‑on‑investment analysis to estimate costs of clinical service delivery, AI development and operation, and the economic impact of workflow integration. A case study on AI‑assisted large vessel occlusion detection in the CT stroke pathway demonstrated that FLARE can quantify conventional pathway costs, AI‑related costs, and AI‑enabled savings, identifying a break‑even threshold of about 3,992 patients per year and a positive first‑year ROI at typical stroke volumes of 5,000 patients.
arXiv:2609.09137v1 Announce Type: new Abstract: Robotic Process Automation (RPA) is widely used to reduce administrative burden in United States hospitals, yet an estimated 30-50% of RPA initiatives...
The paper presents a four‑module, data‑driven framework to identify and prioritize robotic process automation (RPA) opportunities in U.S. hospitals. It includes a process taxonomy, an automation suitability index, a tool‑tier selection recommendation, and a return‑on‑investment analysis, all applied to a synthetic portfolio of twenty hospital processes. The authors demonstrate the framework’s robustness through Monte Carlo simulations and discuss governance and future validation steps.
AI Morbidity and Mortality (AI M&M) is a structured, blameless framework designed to review clinical AI failures. It combines standardized case intake, evidence preservation, investigator reconstruction, tool‑in‑loop attribution, and corrective‑action tracking, classifying each event across four linked dimensions: Trigger, Mechanism, Clinical Pathway, and Corrective Action. The authors demonstrate the framework with five outpatient medication and clinical decision‑support cases, achieving full agreement among reviewers on all classification axes.
The scoping review examines how artificial intelligence (AI) is applied across all stages of medication management in rural healthcare settings, from prescribing to post-administration monitoring. It identifies four main themes: the types of AI used, the medication phases impacted, the effectiveness in reducing errors, and rural-specific challenges such as infrastructure and alert fatigue. Studies show machine‑learning surveillance can cut prescribing and transcription errors by 34% to 80%, yet barriers like governance gaps, funding limits, and clinician resistance remain.
arXiv:2607. 12527v1 Announce Type: new Abstract: Musculoskeletal diseases are among the leading causes of disability worldwide and create the greatest global need for rehabilitation.
READY or Not: Reliable Enterprise Agent Deployment introduces a framework for qualifying AI agents for enterprise workflows. It measures reliability and operating cost under various oversight policies, selects the minimum‑cost policy that meets a specified reliability target, and statistically qualifies it on held‑out cases. In a clinical audit study, READY revealed that two agents with nearly identical autonomous accuracy required markedly different levels of human review to achieve the same reliability target.
arXiv:2608. 06112v1 Announce Type: new Abstract: Hospitals are rapidly adopting artificial intelligence for triage, imaging, scheduling etc.
arXiv:2608. 07627v1 Announce Type: new Abstract: Hospitals are racing to embed AI, while coping with the surge in adaptation of the technology in other industries, into the triage management, documentation, scheduling, and revenue-cycle workflows, yet most deployments remain as fragmented pilots that stall at the edge of production, exposing patients and institutions to operational fragility, ungoverned risk, and mounting technical debt.
arXiv:2608. 13618v1 Announce Type: new Abstract: A single clinical algorithm can deliver unequal accuracy across patient groups, and concern about such disparity has grown as artificial intelligence (AI) spreads through clinical decision-making.
Hospitals are rapidly adopting artificial intelligence for triage, imaging, scheduling etc. , yet most deployments remain isolated point solutions locked inside departmental silos, resulting in duplicated effort, hidden risks, and unrealized enterprise value.
arXiv:2608.28708v1 Announce Type: cross Abstract: Prospective silent trials provide an important bridge between retrospective validation of artificial intelligence (AI) models and their use in clinic...
arXiv:2603. 28825v2 Announce Type: replace-cross Abstract: 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.