A Data-Driven Framework for Identifying and Prioritizing RPA Opportunities in Healthcare Processes
A research paper proposes a four-module, data-driven framework for identifying and prioritizing Robotic Process Automation (RPA) opportunities in healthcare processes. The framework includes a Process Taxonomy of twenty recurring hospital processes across five value streams, a Prioritization module using an Analytic Hierarchy Process matrix with consistency check, a Tool-Tier Selection module recommending the least-cost technology (Python bot, n8n, or UiPath), and a Return-on-Investment module. Applied to a synthetic portfolio, 12 of 20 processes clear a threshold (details truncated).
The paper addresses the problem that 30-50% of RPA initiatives in U.S. hospitals underperform due to informal process selection. It introduces a structured framework to catalogue, prioritize, match to automation tier, and forecast financial return before committing resources. The framework is applied to a synthetic portfolio spanning all twenty processes and a reference data-flow architecture linking to hospital EHR/payer/ERP systems.
The framework uses Analytic Hierarchy Process (AHP) with an explicit consistency check to derive an Automation Suitability Index. Tool-tier selection matches process complexity, integration, and compliance profiles to the least-cost sufficient technology tier (Python bot, n8n, UiPath). ROI module quantifies labor savings, error-cost avoidance, payback, and NPV.
Healthcare RPA adoption is hampered by lack of repeatable selection methods. This framework could standardize opportunity assessment, reducing underperformance and enabling more systematic automation in hospital administrative processes.
Provides a repeatable method to prioritize RPA investments, potentially improving success rates and ROI in healthcare administrative automation.
Next signals include validation on real hospital data, integration with EHR/payer/ERP systems, and potential adoption by healthcare RPA consultancies or enterprise automation teams.