arXiv AI
A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes
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Hugging Face Trending Papers
Sep 8
A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes
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.
arXiv AI
Aug 26
FLARE: A Systematic, Uncertainty-Aware Framework for Evidence-Based Adoption of Artificial Intelligence in Healthcare
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 AI
Aug 11
From Single Chatbots to Governed Agent Ecosystems: An Agentic AI Pattern Catalogue and Orchestration Framework for Mission-Critical Hospital Information Management Systems
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 AI
Aug 7
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
arXiv:2608. 06112v1 Announce Type: new Abstract: Hospitals are rapidly adopting artificial intelligence for triage, imaging, scheduling etc.
Hugging Face Trending Papers
Aug 6
From Siloed Algorithms to Compliance-First Agentic Platforms: A Multi-Layered Architecture for Hospital AI Systems
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 AI
Sep 15
KnowBench: Effort Reduction as a Unified, Deployment-Grounded Benchmark for Clinical AI
KnowBench is a new benchmark for clinical AI that measures Effort Reduction (ER), the proportion of system-generated clinical work product accepted by clinicians after expert and safety review. The metric is applied uniformly across various administrative tasks—visit notes, billing codes, orders, EHR summarization, patient summaries, and decision support—using the clinician’s review-and-attestation as ground truth. An initial deployment of Knowtex’s models achieved an aggregate ER of 97.99% across more than one million encounters in six months, with specialty-specific ER ranging from 96.8% to 98.9%.