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.