Dataset Information

Common data elements for epilepsy mobile health systems.

ABSTRACT: OBJECTIVE:Common data elements (CDEs) are currently unavailable for mobile health (mHealth) in epilepsy devices and related applications. As a result, despite expansive growth of new digital services for people with epilepsy, information collected is often not interoperable or directly comparable. We aim to correct this problem through development of industry-wide standards for mHealth epilepsy data. METHODS:Using a group of stakeholders from industry, academia, and patient advocacy organizations, we offer a consensus statement for the elements that may facilitate communication among different systems. RESULTS:A consensus statement is presented for epilepsy mHealth CDEs. SIGNIFICANCE:Although it is not exclusive, we believe that the use of a minimal common information denominator, specifically these CDEs, will promote innovation, accelerate scientific discovery, and enhance clinical usage across applications and devices in the epilepsy mHealth space. As a consequence, people with epilepsy will have greater flexibility and ultimately more powerful tools to improve their lives.

SUBMITTER: Goldenholz DM

PROVIDER: S-EPMC5934312 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Common data elements for epilepsy mobile health systems.

Goldenholz Daniel M DM Moss Robert R Jost David A DA Crone Nathan E NE Krauss Gregory G Picard Rosalind R Caborni Chiara C Cavazos Jose E JE Hixson John J Loddenkemper Tobias T Salazar Tracy Dixon TD Lubbers Laura L Harte-Hargrove Lauren C LC Whittemore Vicky V Duun-Henriksen Jonas J Dolan Eric E Kasturia Nitish N Oberemk Mark M Cook Mark J MJ Lehmkuhle Mark M Sperling Michael R MR Shafer Patricia O PO

Epilepsia 20180331 5

<h4>Objective</h4>Common data elements (CDEs) are currently unavailable for mobile health (mHealth) in epilepsy devices and related applications. As a result, despite expansive growth of new digital services for people with epilepsy, information collected is often not interoperable or directly comparable. We aim to correct this problem through development of industry-wide standards for mHealth epilepsy data.<h4>Methods</h4>Using a group of stakeholders from industry, academia, and patient advoca ...[more]

PMID: 29604050

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Project description:BACKGROUND:Information and communication technologies have long become prominent components of health systems. Rapid advances in digital technologies and data science over the last few years are predicted to have a vast impact on health care services, configuring a paradigm shift into what is now commonly referred to as digital health. Forecasted to curb rising health costs as well as to improve health system efficiency and safety, digital health success heavily relies on trust from professional end users, administrators, and patients. Yet, what counts as the building blocks of trust in digital health systems has so far remained underexplored. OBJECTIVE:The objective of this study was to analyze what relevant stakeholders consider as enablers and impediments of trust in digital health. METHODS:We performed a scoping review to map out trust in digital health. To identify relevant digital health studies, we searched 5 electronic databases. Using keywords and Medical Subject Headings, we targeted all relevant studies and set no boundaries for publication year to allow a broad range of studies to be identified. The studies were screened by 2 reviewers after which a predefined data extraction strategy was employed and relevant themes documented. RESULTS:Overall, 278 qualitative, quantitative, mixed-methods, and intervention studies in English, published between 1998 and 2017 and conducted in 40 countries were included in this review. Patients and health care professionals were the two most prominent stakeholders of trust in digital health; a third-health administrators-was substantially less prominent. Our analysis identified cross-cutting personal, institutional, and technological elements of trust that broadly cluster into 16 enablers (altruism, fair data access, ease of use, self-efficacy, sociodemographic factors, recommendation by other users, usefulness, customizable design features, interoperability, privacy, initial face-to-face contact, guidelines for standardized use, stakeholder engagement, improved communication, decreased workloads, and service provider reputation) and 10 impediments (excessive costs, limited accessibility, sociodemographic factors, fear of data exploitation, insufficient training, defective technology, poor information quality, inadequate publicity, time-consuming, and service provider reputation) to trust in digital health. CONCLUSIONS:Trust in digital health technologies and services depends on the interplay of a complex set of enablers and impediments. This study is a contribution to ongoing efforts to understand what determines trust in digital health according to different stakeholders. Therefore, it offers valuable points of reference for the implementation of innovative digital health services. Building on insights from this study, actionable metrics can be developed to assess the trustworthiness of digital technologies in health care.

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Common data elements for epilepsy mobile health systems.

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Common data elements for epilepsy mobile health systems.

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