An Ontology for Digital Medicine Outcomes: Development of the Digital Medicine Outcomes Value Set (DOVeS).

Abstract

Background: Over the last 10-15 years, US health care and the practice of medicine itself have been transformed by a proliferation of digital medicine and digital therapeutic products (collectively, digital health tools [DHTs]). While a number of DHT classifications have been proposed to help organize these tools for discovery, retrieval, and comparison by health care organizations seeking to potentially implement them, none have specifically addressed that organizations considering their implementation approach the DHT discovery process with one or more specific outcomes in mind. An outcomes-based DHT ontology could therefore be valuable not only for health systems seeking to evaluate tools that influence certain outcomes, but also for regulators and vendors seeking to ascertain potential substantial equivalence to predicate devices.

Objective: This study aimed to develop, with inputs from industry, health care providers, payers, regulatory bodies, and patients through the Accelerated Digital Clinical Ecosystem (ADviCE) consortium, an ontology specific to DHT outcomes, the Digital medicine Outcomes Value Set (DOVeS), and to make this ontology publicly available and free to use.

Methods: From a starting point of a 4-generation-deep hierarchical taxonomy developed by ADviCE, we developed DOVeS using the Web Ontology Language through the open-source ontology editor Protégé, and data from 185 vendors who had submitted structured product information to ADviCE. We used a custom, decentralized, collaborative ontology engineering methodology, and were guided by Open Biological and Biomedical Ontologies (OBO) Foundry principles. We incorporated the Mondo Disease Ontology (MONDO) and the Ontology of Adverse Events. After development, DOVeS was field-tested between December 2022 and May 2023 with 40 additional independent vendors previously unfamiliar with ADviCE or DOVeS. As a proof of concept, we subsequently developed a prototype DHT Application Finder leveraging DOVeS to enable a user to query for DHT products based on specific outcomes of interest.

Results: In its current state, DOVeS contains 42,320 and 9481 native axioms and distinct classes, respectively. These numbers are enhanced when taking into account the axioms and classes contributed by MONDO and the Ontology of Adverse Events.

Conclusions: DOVeS is publicly available on BioPortal and GitHub, and has a Creative Commons license CC-BY-SA that is intended to encourage stakeholders to modify, adapt, build upon, and distribute it. While no ontology is complete, DOVeS will benefit from a strong and engaged user base to help it grow and evolve in a way that best serves DHT stakeholders and the patients they serve.