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

ACCESS DATA

Publications

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

Similar Datasets

Project description:ObjectiveCommon data elements (CDEs) are standardized questions and answer choices that allow aggregation, analysis, and comparison of observations from multiple sources. Clinical CDEs are foundational for learning health care systems, a data-driven approach to health care focused on continuous improvement of outcomes. We aimed to create clinical CDEs for pediatric epilepsy.MethodsA multiple stakeholder group (clinicians, researchers, parents, caregivers, advocates, and electronic health record [EHR] vendors) developed clinical CDEs for routine care of children with epilepsy. Initial drafts drew from clinical epilepsy note templates, CDEs created for clinical research, items in existing registries, consensus documents and guidelines, quality metrics, and outcomes needed for demonstration projects. The CDEs were refined through discussion and field testing. We describe the development process, rationale for CDE selection, findings from piloting, and the CDEs themselves. We also describe early implementation, including experience with EHR systems and compatibility with the International League Against Epilepsy classification of seizure types.ResultsCommon data elements were drafted in August 2017 and finalized in January 2020. Prioritized outcomes included seizure control, seizure freedom, American Academy of Neurology quality measures, presence of common comorbidities, and quality of life. The CDEs were piloted at 224 visits at 10 centers. The final CDEs included 36 questions in nine sections (number of questions): diagnosis (1), seizure frequency (9), quality of life (2), epilepsy history (6), etiology (8), comorbidities (2), treatment (2), process measures (5), and longitudinal history notes (1). Seizures are categorized as generalized tonic-clonic (regardless of onset), motor, nonmotor, and epileptic spasms. Focality is collected as epilepsy type rather than seizure type. Seizure frequency is measured in nine levels (all used during piloting). The CDEs were implemented in three vendor systems. Early clinical adoption included 1294 encounters at one center.SignificanceWe created, piloted, refined, finalized, and implemented a novel set of clinical CDEs for pediatric epilepsy.

Project description:BackgroundBacteria are prey for many viruses that hijack the bacterial cell in order to propagate, which can result in bacterial cell lysis and death. Bacteria have developed diverse strategies to counteract virus predation, one of which is the clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR associated (Cas) proteins immune defense system. Species within the bacterial family Vibrionaceae are marine organisms that encounter large numbers of phages. Our goal was to determine the significance of CRISPR-Cas systems as a mechanism of defense in this group by investigating their prevalence, phylogenetic distribution, and genome context.ResultsHerein, we describe all the CRISPR-Cas system types and their distribution within the family Vibrionaceae. In Vibrio cholerae genomes, we identified multiple variant type I-F systems, which were also present in 41 additional species. In a large number of Vibrio species, we identified a mini type I-F system comprised of tniQcas5cas7cas6f, which was always associated with Tn7-like transposons. The Tn7-like elements, in addition to the CRISPR-Cas system, also contained additional cargo genes such as restriction modification systems and type three secretion systems. A putative hybrid CRISPR-Cas system was identified containing type III-B genes followed by a type I-F cas6f and a type I-F CRISPR that was associated with a prophage in V. cholerae and V. metoecus strains. Our analysis identified CRISPR-Cas types I-C, I-E, I-F, II-B, III-A, III-B, III-D, and the rare type IV systems as well as cas loci architectural variants among 70 species. All systems described contained a CRISPR array that ranged in size from 3 to 179 spacers. The systems identified were present predominantly within mobile genetic elements (MGEs) such as genomic islands, plasmids, and transposon-like elements. Phylogenetic analysis of Cas proteins indicated that the CRISPR-Cas systems were acquired by horizontal gene transfer.ConclusionsOur data show that CRISPR-Cas systems are phylogenetically widespread but sporadic in occurrence, actively evolving, and present on MGEs within Vibrionaceae.

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.

Dataset Information

Common data elements for epilepsy mobile health systems.

Publications

Common data elements for epilepsy mobile health systems.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets