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. 2019 Aug 12:5:25.
doi: 10.21037/mhealth.2019.07.04. eCollection 2019.

Actionable digital phenotyping: a framework for the delivery of just-in-time and longitudinal interventions in clinical healthcare

Aditya Vaidyam  1 , John Halamka  2 , John Torous  1
Affiliations

Actionable digital phenotyping: a framework for the delivery of just-in-time and longitudinal interventions in clinical healthcare

Aditya Vaidyam et al. Mhealth. .

Abstract

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.

Keywords: Application Programming Interface (API); Fast Healthcare Interoperability Resources (FHIR); digital phenotyping; mHealth; psychiatry.

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Conflict of interest statement

Conflicts of Interest: The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
The pyramid represents paired sets of clinical goals, shown on the left, and engineering goals, shown on the right; each set of goals builds upon the set of goals below it.
Figure 2
Figure 2
The protocol mapped visually onto the user interface of the mindLAMP app.
Figure 3
Figure 3
Transmission and interchange of data as shown through a standard browser window.
Figure 4
Figure 4
Multiple automation applets layered atop one-another with inputs and outputs chained together; the final applet represents the deployment of an intervention after statistical analysis is performed on data both internal and external to the protocol, after it is harmonized.
Figure 5
Figure 5
An example of reactive data providing clinical decision support.
See this image and copyright information in PMC

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