Strategies to Access Patient Clinical Data from Distributed Databases

Almeida, João Rafael; Fajarda, Olga; Pereira, Arnaldo; Oliveira, José Luís

doi:10.5220/0007576104660473

Cited by 14 publications

(7 citation statements)

References 14 publications

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“…The analysis of the literature identified five types of digital health DQ outcomes: (1) clinical, (2) business process, (3) clinician, (4) research-related, and (5) organizational outcomes ( Multimedia Appendix 8 [ 15 , 16 , 20 , 31 , 33 , 39 , 40 , 42 , 51 , 52 , 55 , 57 , 58 , 61 , 63 , 64 , 84 , 90 , 105 , 113 , 166 , 175 - 178 ]). Using NVivo’s built-in cross-tab query coupled with subject matter expert analysis, it was identified that different DQ dimensions were related to DQ outcomes in different ways ( Table 2 ).…”

Section: Resultsmentioning

confidence: 99%

Digital Health Data Quality Issues: Systematic Review

Syed¹,

Eden²,

Makasi³

et al. 2023

J Med Internet Res

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Background The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact. Objective The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ? Methods Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework. Results The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes. Conclusions The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.

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Section: Resultsmentioning

confidence: 99%

Digital Health Data Quality Issues: Systematic Review

Syed¹,

Eden²,

Makasi³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…In order to obtain its goals, it initially gathered data from its environment and consequently developed knowledge and/or makes use of relevant data or knowledge of its neighboring agents. Data, knowledge, and previous experience fuel a reasoning module, usually implemented with machine learning methods [ 2 , 3 ] that performs decision making. This concept allows for the wide application of agents in even complicated problems.…”

Section: Methodsmentioning

confidence: 99%

An Efficient Agent Based Data Management Method of NoSQL Environments for Health Care Applications

Kotsilieris¹

2021

Healthcare

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Background: As medical knowledge is continuously expanding and diversely located, Health Information Technology (HIT) applications are proposed as a good prospect for improving not only the efficiency and the effectiveness but also the quality of healthcare services delivery. The technologies expected to shape such innovative HIT architectures include: Mobile agents (Mas) and NoSQL technologies. Mobile agents provide an inherent way of tackling distributed problems of accessing heterogeneous and spatially diverse data sources. NoSQL technology gains ground for the development of scalable applications with non-static and open data schema from complex and diverse sources. Methods and Design: This paper conducts a twofold study: It attempts a literature review of the applications based on the mobile agent (MA) and NoSQL technologies for healthcare support services. Subsequently, a pilot system evaluates the NoSQL technology against the relational one within a distributed environment based on mobile agents for information retrieval. Its objective is to study the feasibility of developing systems that will employ ontological data representation and task implementation through mobile agents towards flexible and transparent health data monitoring. Results and Discussion: The articles studied focus on applying mobile agents for patient support and healthcare services provision thus as to make a positive contribution to the treatment of chronic diseases. In addition, attention is put on the design of platform neutral techniques for clinical data gathering and dissemination over NoSQL. The experimental environment was based on the Apache Jena Fuseki NoSQL server and the JAVA Agent DEvelopment Framework -JADE agent platform. The results reveal that the NoSQL implementation outperforms the standard relational one.

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“…In Europe, a project inspired by the core principles of OHDSI was the European Medical Information Framework project (EMIF) [2] . One of its goals was to enhance access to patient-level data from distinct health institutions across Europe, while researchers could carry out distributed observational studies [11]. In one of the project's tracks, relevant cohort studies across Europe focusing on Alzheimer's Disease (AD) were connected.…”

Section: Introductionmentioning

confidence: 99%

Harmonising Alzheimer's Disease Cohorts using a Common ETL Tool

Almeida

Oliveira

2022

Preprint

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Background: Many scientific studies have sought to obtain better understanding of specific medical conditions. Concerning Alzheimer’s Disease, there is a lack of reliable diagnostics and this can be related to the availability of only small-scale ongoing biomarker studies and longitudinal cohorts including these subjects. Aiming to generate more substantial clinical evidence, researchers have started to perform multiple cohort analyses. While this is currently possible by harmonising these cohorts into a common data model, the migration pipelines are usually implemented using programming languages. Therefore, cohort owners may have difficulties in contributing during the validation stage of these pipelines.Results: To reduce the dependency on technical teams’ support when validating the data transformations, we propose the use of an ETL tool with visual features. BIcenter is a collaborative web platform designed to implement ETL tasks through the browser. These pipelines are constructed using drag-and-drop features and intuitive forms to customise the ETL steps. This tool is an open-source project and is accessible at https://bioinformatics-ua.github.io/BIcenter/.Conclusions: Our methodology produces interoperable cohorts for multicentric disease-specific studies. Therefore, we validated this using Alzheimer’s Disease cohorts from several countries, combining at the end 6,669 subjects and 172 medical attributes. The harmonised cohorts now enable multi-cohort querying and analysis, helping in the execution of new studies.

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Strategies to Access Patient Clinical Data from Distributed Databases

Cited by 14 publications

References 14 publications

Digital Health Data Quality Issues: Systematic Review

Digital Health Data Quality Issues: Systematic Review

An Efficient Agent Based Data Management Method of NoSQL Environments for Health Care Applications

Harmonising Alzheimer's Disease Cohorts using a Common ETL Tool

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