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A Framework for Digital Data Quality Assessment in Digital Biomarker Research

Authors:
Hui Zhang
Regan Giesting
Leah Miller
Guangchen Ruan
Neel Patel
Ju Ji
Tianran Zhang
Yi Lin Yang

Keywords: digital health technology, connected clinical trial, sensor data, data quality assessment, data visualization, digital biomarker.

Abstract:
Digital Health Technology (DHT) utilizes a combination of computing platforms, connectivity, software, and sensors for healthcare-related uses. Today, these technologies collect complex digital data from participants in clinical investigations, including wearable sensor signals and electronic Patient-Reported Outcomes (ePRO)s. These collected data are used to develop digital biomarkers (dBMs), which can act as health outcomes indicators for diagnosing and monitoring disease state and life quality. One essential step towards realizing the full potential of these complex digital data is to define the fundamental principles and methods to demonstrate sufficient data quality and fidelity needed for the research. This paper aims to develop a digital data quality assessment framework across the complete data life cycle in dBM research, including data quality metrics and methods to derive, visualize, and report digital data quality. Aggregating and reporting digital data quality is often challenging and error-prone. We developed a data quality assessment and reporting tool that defines data compliance criteria and views automatically generated quality reports at different levels in a consumable fashion. Combining all these methods helps to establish our digital data quality assessment framework to facilitate dBM research.

Pages: 1 to 10

Copyright: Copyright (c) IARIA, 2023

Publication date: April 24, 2023

Published in: conference

ISSN: 2519-8386

ISBN: 978-1-68558-041-4

Location: Venice, Italy

Dates: from April 24, 2023 to April 28, 2023