Proposes a four-module data-driven framework that catalogues, prioritizes, tiers, and forecasts ROI for hospital RPA candidates to reduce the 30-50% underperformance rate.
The framework, introduced by Gomez and Castillo in September 2026, addresses the 30–50% underperformance of hospital Robotic Process Automation (RPA) initiatives by providing a repeatable, auditable pipeline that moves beyond informal process selection. It consists of four integrated modules: a Process Taxonomy cataloging twenty recurring hospital processes; a Multi-Criteria Prioritization model using an Analytic Hierarchy Process (AHP) to generate an Automation Suitability Index (ASI); a Tool-Tier Selection module that matches processes to the least-cost technology (Python, n8n, or Enterprise RPA); and a Return-on-Investment (ROI) quantification model for financial forecasting.
Applied to a synthetic portfolio, 12 of 20 processes cleared the prioritization threshold, with a priority ranking robust to weight perturbations. The framework recommends specific automation tiers based on criteria such as legacy system dependency, API availability, and PHI compliance, while forecasting payback periods ranging from 1.8 to 13.0 months and multi-year net present values to support governance committee decisions. A supplementary Python implementation is provided for reproducibility, though the framework remains a conceptual synthesis requiring empirical validation in specific hospital settings.
The paper addresses a persistent problem in healthcare automation: many robot process automation (RPA) projects fail to deliver their expected benefits, with the brief citing a 30–50% underperformance rate. It argues that much of this gap is not due to RPA technology itself, but to weak opportunity selection and opaque business cases. The contribution is a four-module, data-driven framework for hospitals that systematically identifies which administrative or back-office processes are worth automating, ranks them by attractiveness, groups them into implementation tiers, and estimates expected return on investment before deployment.
The framework is organized around a pipeline from process inventory to financial forecasting. First, it catalogues candidate RPA opportunities from hospital operations. Second, it applies quantitative prioritization logic to distinguish high-value, high-feasibility candidates from poor fits. Third, it tiers the resulting opportunities—likely by risk, value, implementation complexity, or readiness—so that organizations can sequence pilots and scale more deliberately. Fourth, it forecasts ROI in a way that makes the expected payoff and uncertainty more explicit. The central insight is that RPA portfolio decisions should be treated as a data-analytic and portfolio-management problem rather than a collection of isolated automation requests.
This matters because healthcare organizations face high administrative workload, staffing constraints, and pressure to improve efficiency without expanding headcount. A disciplined framework can help hospital leaders allocate limited automation budgets more effectively, reduce the chance of investing in low-yield or fragile automations, and build a defensible business case for each candidate process. For technically literate readers, the work is relevant not only to RPA adoption but also to process mining, operational analytics, and decision-support systems for digital transformation in regulated, high-cost environments.