Actionable digital phenotyping: a framework for the delivery of just-in-time and longitudinal interventions in clinical healthcare
Designed to improve health, today numerous wearables and smartphone apps are used by millions across the world. Yet the wealth of data generated from the many sensors on these wearables and smartwatches has not yet transformed routine clinical care. One central reason for this gap between data and clinical insights is the lack of transparency and standards around data generated from mobile device that hinders interoperability and reproducibility. The clinical informatics community has offered solutions via the Fast Healthcare Interoperability Resources (FHIR) standard which facilities electronic health record interoperability but is less developed towards precision temporal contextually-tagged sensor measurements generated from today’s ubiquitous mobile devices. In this paper we explore the opportunities and challenges of various theoretical approaches towards FHIR compatible digital phenotyping, and offer a concrete example implementing one such framework as an Application Programming Interface (API) for the open-source mindLAMP platform. We aim to build a community with contributions from statisticians, clinicians, patients, family members, researchers, designers, engineers, and more.