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:BACKGROUND:High utilizers (HUs) are a small group of patients who impose a disproportionately high burden on the healthcare system due to their elevated resource use. Identification of persistent HUs is pertinent as interventions have not been effective due to regression to the mean in majority of patients. This study will use cost and utilization metrics to segment a hospital-based patient population into HU groups. METHODS:The index visit for each adult patient to an Academic Medical Centre in Singapore during 2006 to 2012 was identified. Cost, length of stay (LOS) and number of specialist outpatient clinic (SOC) visits within 1 year following the index visit were extracted and aggregated. Patients were HUs if they exceeded the 90th percentile of any metric, and Non-HU otherwise. Seven different HU groups and a Non-HU group were constructed. The groups were described in terms of cost and utilization patterns, socio-demographic information, multi-morbidity scores and medical history. Logistic regression compared the groups' persistence as a HU in any group into the subsequent year, adjusting for socio-demographic information and diagnosis history. RESULTS:A total of 388,162 patients above the age of 21 were included in the study. Cost-LOS-SOC HUs had the highest multi-morbidity and persistence into the second year. Common conditions among Cost-LOS and Cost-LOS-SOC HUs were cardiovascular disease, acute cerebrovascular disease and pneumonia, while most LOS and LOS-SOC HUs were diagnosed with at least one mental health condition. Regression analyses revealed that HUs across all groups were more likely to persist compared to Non-HUs, with stronger relationships seen in groups with high SOC utilization. Similar trends remained after further adjustment. CONCLUSION:HUs of healthcare services are a diverse group and can be further segmented into different subgroups based on cost and utilization patterns. Segmentation by these metrics revealed differences in socio-demographic characteristics, disease profile and persistence. Most HUs did not persist in their high utilization, and high SOC users should be prioritized for further longitudinal analyses. Segmentation will enable policy makers to better identify the diverse needs of patients, detect gaps in current care and focus their efforts in delivering care relevant and tailored to each segment.

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.

Similar Datasets

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