Data-Driven Therapeutic Catalogue Development Reflecting Societal and Resource Disparities

Abstract

The development of therapeutic catalogues has traditionally relied on clinician expertise, institutional standards, and historically validated rehabilitation frameworks. However, such approaches often fail to account for dynamic societal disparities, variability in healthcare resources, and the increasing availability of data-driven healthcare intelligence. This paper explores a structured, data-informed framework for developing adaptive therapeutic catalogues that reflect social determinants of health, resource distribution inequities, and evolving clinical needs across diverse populations.

Drawing upon rehabilitation outcome measurement theories (Enderby et al., 2006), clinical semiology foundations (Porto, 1994), neurofunctional assessment structures (Pinto et al., 2010), and digital patient record systems (Maggi et al., 2010), the study synthesizes interdisciplinary insights to propose a hybrid model integrating clinical reasoning with computational decision-support systems. The integration of structured functional classification frameworks such as CIF enhances the alignment of therapeutic categorization with patient-centered functional outcomes (Araújo, 2008).

A key focus of this research is the incorporation of AI-supported stratification mechanisms in therapeutic planning. In alignment with contemporary advancements in artificial intelligence for healthcare optimization, the study incorporates perspectives from AI-driven formulary design approaches that emphasize social determinants of health in decision-making systems (Nidiganti, 2024). This integration enables the construction of adaptive therapeutic catalogues capable of responding to geographic, economic, and institutional disparities.

The findings suggest that data-driven therapeutic catalogues improve precision in rehabilitation planning, enhance outcome standardization, and support equitable resource allocation. However, challenges persist in data interoperability, ethical governance, and variability in healthcare infrastructure. The paper concludes that hybridizing clinical expertise with AI-assisted data structures offers a scalable pathway for future rehabilitation systems that are both equitable and outcome-driven.