Dataset Information

A Database as a Service for the Healthcare System to Store Physiological Signal Data.

ABSTRACT: Wearable devices that measure physiological signals to help develop self-health management habits have become increasingly popular in recent years. These records are conducive for follow-up health and medical care. In this study, based on the characteristics of the observed physiological signal records- 1) a large number of users, 2) a large amount of data, 3) low information variability, 4) data privacy authorization, and 5) data access by designated users-we wish to resolve physiological signal record-relevant issues utilizing the advantages of the Database as a Service (DaaS) model. Storing a large amount of data using file patterns can reduce database load, allowing users to access data efficiently; the privacy control settings allow users to store data securely. The results of the experiment show that the proposed system has better database access performance than a traditional relational database, with a small difference in database volume, thus proving that the proposed system can improve data storage performance.

SUBMITTER: Chang HT

PROVIDER: S-EPMC5199088 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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A Database as a Service for the Healthcare System to Store Physiological Signal Data.

Chang Hsien-Tsung HT Lin Tsai-Huei TH

PloS one 20161229 12

Wearable devices that measure physiological signals to help develop self-health management habits have become increasingly popular in recent years. These records are conducive for follow-up health and medical care. In this study, based on the characteristics of the observed physiological signal records- 1) a large number of users, 2) a large amount of data, 3) low information variability, 4) data privacy authorization, and 5) data access by designated users-we wish to resolve physiological signa ...[more]

PMID: 28033415

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Project description:BackgroundThe Danish Helicopter Emergency Medical Service (HEMS) is part of the Danish pre-hospital response offering advanced patient care on scene and during rapid transport to definitive care. Monitoring HEMS performance and the quality of critical care has high national as well as international priority underlining the need for research in this field. The data quality of the Danish HEMS database is unknown. Furthermore, a set of quality indicators (QI) developed by an international collaboration group (EQUIPE) potentially for use in physician-staffed EMS, has recently been presented. The aim of the current study was to present the design and data quality of the Danish helicopter database, and to evaluate the coverage of available variables in the database according to the QIs proposed.MethodThe study included all helicopter dispatches between October 1st 2014 and April 30th 2018. The database layout and data entering procedure, as well as the key variables and data completeness were described. Furthermore, missing data and misclassifications were addressed. Lastly, the 26 QIs proposed by the EQUIPE-collaboration were evaluated for coverage in the HEMS database.ResultsA total of 13,392 missions were included in the study. The database includes a broad spectrum of mission- and patient-specific data related to the pre-hospital pathway of acutely ill or injured patients in a national coverage. Missing data for the majority of variables is less than 6.5%. The percentage of completed report forms has increased over time and reached 99.9% in 2018. Misclassification were observed for 294 patients in the study period corresponding to 3,7%. Less than half of the QIs proposed by the EQUIPE-collaboration group were directly available from the database.ConclusionsHelicopter Emergency Medical Services in Denmark are a new and sparsely investigated health care provider. The database contains nearly all missions dispatched by the five regional Emergency Medical Dispatch Centres. Generally, the data quality is considered high with great potential for future research. Potential quality indicators as proposed by the EQUIPE-collaboration group could inspire the configuration and design of the next version of Hemsfile creating an even more solid basis for research and quality improvement.

Project description:IntroductionGeneral practitioners (GPs) play a crucial role in the early management and treatment of the comorbidities and complications experienced by people with disability. However, GPs experience multiple constraints, including limited time and disability-related expertise. Knowledge gaps around the health needs of people with disability as well as the frequency and extent of their engagement with GPs mean evidence to inform practice is limited. Using a linked dataset, this project aims to enhance the knowledge of the GP workforce by describing the health needs of people with disability.Methods and analysisThis project is a retrospective cohort study using general practice health records from the eastern Melbourne region in Victoria, Australia. The research uses Eastern Melbourne Primary Health Network (EMPHN)-owned de-identified primary care data from Outcome Health's POpulation Level Analysis and Reporting Tool (POLAR). The EMPHN POLAR GP health records have been linked with National Disability Insurance Scheme (NDIS) data. Data analysis will involve comparisons across disability groups and the rest of the population to explore utilisation (eg, frequency of visits), clinical and preventative care (eg, cancer screening, blood pressure readings) and health needs (eg, health conditions, medications). Initial analyses will focus on NDIS participants as a whole and NDIS participants whose condition is either an acquired brain injury, stroke, spinal cord injury, multiple sclerosis or cerebral palsy, as classified by the NDIS.Ethics and disseminationEthics approval was obtained from the Eastern Health Human Research Ethics Committee (E20/001/58261), and approval for the general collection, storage and transfer of data was from the Royal Australian College of General Practitioners National Research Ethics and Evaluation Committee (protocol ID: 17-088). Dissemination mechanisms will include the engagement of stakeholders through reference groups and steering committees, as well as the production of research translation resources in parallel with peer-reviewed publications and conference presentations.

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Dataset Information

A Database as a Service for the Healthcare System to Store Physiological Signal Data.

Publications

A Database as a Service for the Healthcare System to Store Physiological Signal Data.

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