Rosner Benjamin, Horridge Matthew, Austria Guillen, Lee Tiffany, Auerbach Andrew
Division of Clinical Informatics and Digital Transformation, University of California San Francisco, San Francisco, CA, United States.
Division of Hospital Medicine, University of California San Francisco, San Francisco, CA, United States.
JMIR Med Inform. 2025 Feb 6;13:e67589. doi: 10.2196/67589.
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.
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.
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.
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.
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.
在过去10至15年中，数字医学和数字治疗产品（统称为数字健康工具[DHTs]）的激增改变了美国的医疗保健和医学实践本身。虽然已经提出了一些DHT分类方法，以帮助医疗保健组织整理这些工具，以便于发现、检索和比较，这些组织可能会考虑实施这些工具，但没有一种分类方法专门针对那些在考虑实施方法时将DHT发现过程与一个或多个特定结果相结合的组织。因此，基于结果的DHT本体不仅对寻求评估影响某些结果的工具的卫生系统有价值，而且对寻求确定与谓词设备潜在实质等同性的监管机构和供应商也有价值。
本研究旨在通过加速数字临床生态系统（ADviCE）联盟，在行业、医疗保健提供者、支付方、监管机构和患者的投入下，开发一种特定于DHT结果的本体，即数字医学结果价值集（DOVeS），并使该本体公开可用且免费使用。
从ADviCE开发的四代深度层次分类法出发，我们通过开源本体编辑器Protégé使用网络本体语言，并利用向ADviCE提交结构化产品信息的185家供应商的数据，开发了DOVeS。我们使用了一种定制的、分散的、协作的本体工程方法，并以开放生物医学本体（OBO）铸造原则为指导。我们纳入了蒙多疾病本体（MONDO）和不良事件本体。开发完成后，DOVeS在2022年12月至2023年5月期间对另外40家之前不熟悉ADviCE或DOVeS的独立供应商进行了实地测试。作为概念验证，我们随后开发了一个利用DOVeS的DHT应用查找器原型，以使用户能够根据感兴趣的特定结果查询DHT产品。
在当前状态下，DOVeS分别包含42320条和9481条原生公理和不同的类。考虑到MONDO和不良事件本体贡献的公理和类，这些数字会有所增加。
DOVeS可在BioPortal和GitHub上公开获取，并拥有知识共享许可协议CC-BY-SA，旨在鼓励利益相关者对其进行修改、调整、扩展和分发。虽然没有本体是完整的，但DOVeS将受益于强大且积极参与的用户群体，以帮助其以最能服务DHT利益相关者及其所服务患者的方式发展和演变。
