Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study.

Aditya Vaidyam,John Halamka,John Torous

doi:10.2196/30557

Abstract

BackgroundThere is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, and interactive care, but technical and organizational challenges, such as the lack of standards and easy-to-use tools, preclude the effective use of PGHD generated from consumer devices, such as smartphones and wearables.ObjectiveThis study outlines how we used mobile apps and semantic web standards such as HTTP 2.0, Representational State Transfer, JSON (JavaScript Object Notation), JSON Schema, Transport Layer Security (version 1.3), Advanced Encryption Standard-256, OpenAPI, HTML5, and Vega, in conjunction with patient and provider feedback to completely update a previous version of mindLAMP.MethodsThe Learn, Assess, Manage, and Prevent (LAMP) platform addresses the abovementioned challenges in enhancing clinical insight by supporting research, data analysis, and implementation efforts around PGHD as an open-source solution with freely accessible and shared code.ResultsWith a simplified programming interface and novel data representation that captures additional metadata, the LAMP platform enables interoperability with existing Fast Healthcare Interoperability Resources–based health care systems as well as consumer wearables and services such as Apple HealthKit and Google Fit. The companion Cortex data analysis and machine learning toolkit offer robust support for artificial intelligence, behavioral feature extraction, interactive visualizations, and high-performance data processing through parallelization and vectorization techniques.ConclusionsThe LAMP platform incorporates feedback from patients and clinicians alongside a standards-based approach to address these needs and functions across a wide range of use cases through its customizable and flexible components. These range from simple survey-based research to international consortiums capturing multimodal data to simple delivery of mindfulness exercises through personalized, just-in-time adaptive interventions.

Highlights

The medical field today is transitioning toward integrating patient-generated health data (PGHD) into clinical care to increase shared decision-making, coordination of care, patient safety, and clinical outcomes [1]
JMIR Mhealth Uhealth 2022 | vol 10 | iss. 1 | e30557 | p. 1 daily step count captured from a smartphone, medication surveys administered on the smartphone, and sleep quality data measured via a wearable sensor
Given the ability of smartphones and wearables to collect a myriad of continuous multimodal data relevant to care, such as heart rate, sleep, steps, and more, tools and systems to harness and use this vast amount of automatically generated PGHD are a health care priority

Summary

Introduction

Background The medical field today is transitioning toward integrating patient-generated health data (PGHD) into clinical care to increase shared decision-making, coordination of care, patient safety, and clinical outcomes [1]. Examples include a longitudinal view of symptoms of patient status between clinic visits captured via an app or information related to daily adherence to treatment plans [1] This may include a https://mhealth.jmir.org/2022/1/e30557 XSLFO RenderX. One challenge that remains toward this goal is the lack of technical infrastructure and organizational capability to handle the intake of accurate and valid PGHD from patient-owned consumer devices Such standards and tools supporting the effective and compatible integration of PGHD are needed to enhance clinical insight and support research and data analysis before PGHD can impact routine care. There is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, and interactive care, but technical and organizational challenges, such as the lack of standards and easy-to-use tools, preclude the effective use of PGHD generated from consumer devices, such as smartphones and wearables

Methods

Results

Discussion

Conclusion