A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes
Researchers propose a four-module, data-driven framework to identify, prioritize, and cost-justify RPA candidates among 20 hospital administrative processes, with 12 clearing the threshold.
The paper presents a framework combining a taxonomy of 20 recurring hospital processes across five value streams, an Analytic Hierarchy Process-based Automation Suitability Index, a tool-tier selector (Python bots, open-source orchestrators like n8n, or enterprise platforms like UiPath), and an ROI module computing labor savings, error-cost avoidance, payback, and net present value. On a synthetic portfolio, 12 of 20 processes pass the prioritization threshold; rankings remained robust to ±20% weight perturbation (Spearman correlation 0.83, top-5 set preserved 97.7% across 2,000 Monte Carlo trials). An Automation Risk Index flags four qualifying processes as Critical risk, and budget-constrained optimization shows diminishing marginal NPV as spend scales from $400K to $1.03M. The authors note it is a conceptual synthesis of the literature rather than an empirically calibrated instrument, discussing HIPAA governance and providing a supplementary Python implementation.
- Taxonomy of 20 recurring hospital processes across five value streams with an AHP-based Automation Suitability Index
- 12 of 20 processes clear the prioritization threshold; ranking robust in Monte Carlo sensitivity testing
- Tool-tier selection spans Python bots, n8n orchestrators, and enterprise platforms such as UiPath
- Conceptual literature synthesis only; authors call for empirical validation on primary hospital data
Full article262 words · extracted from arxiv.org · click to collapse
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.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.09137