A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes

• URL: http://arxiv.org/abs/2609.09137v1
• Date: Tue, 08 Sep 2026 17:55:28 GMT
• Title: A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes
• Abstract summary: An estimated 30-50% of Robotic Process Automation initiatives underperform in U.S. hospitals.<n>We propose a four- module, data-driven framework unifying these decisions.<n>The framework is a conceptual synthesis of the literature rather than an instrument calibrated on primary hospital data.
• Score: 0.808778527821476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robotic Process Automation (RPA) is widely used to reduce administrative burden in United States hospitals, yet an estimated 30-50% of RPA initiatives underperform because processes are selected informally, without a repeatable method to catalogue candidates, prioritize them, match each to an automation tier -- a Python bot, an open-source orchestrator such as n8n, or an enterprise platform such as UiPath -- and forecast financial return before committing resources. We propose a four-module, data-driven framework unifying these decisions: a Process Taxonomy of twenty recurring hospital processes across five value streams; a Prioritization module deriving an Automation Suitability Index from an Analytic Hierarchy Process matrix with an explicit consistency check; a Tool-Tier Selection module recommending the least-cost technology sufficient for a process complexity, integration, and compliance profile; and a Return-on-Investment module quantifying labor savings, error-cost avoidance, payback, and net present value. Applied to a synthetic portfolio spanning all twenty processes, plus a reference data-flow architecture linking it to hospital EHR/payer/ERP systems: 12 of 20 clear the prioritization threshold; the ranking is robust to +/-20% weight perturbation (Spearman correlation 0.83, top-5 set preserved 97.7%, 2,000 Monte Carlo trials); an Automation Risk Index flags four qualifying processes as Critical risk; a budget-constrained portfolio optimization shows diminishing marginal NPV as spend scales from $400K to $1.03M; and a second Monte Carlo analysis shows portfolio NPV stays positive at its 5th percentile. The framework is a conceptual synthesis of the literature rather than an instrument calibrated on primary hospital data; we discuss HIPAA governance and a research agenda for empirical validation. A supplementary Python implementation accompanies the paper.

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