Disrupting Healthcare Silos: Addressing Data Volume, Velocity and Variety With a Cloud-Native Healthcare Data Ingestion Service

2020 ◽  
Vol 24 (11) ◽  
pp. 3182-3188
Author(s):  
Rohit Ranchal ◽  
Paul Bastide ◽  
Xu Wang ◽  
Aris Gkoulalas-Divanis ◽  
Maneesh Mehra ◽  
...  
Keyword(s):  
Healthcare Data ◽  
Volume Velocity ◽  
Data Volume ◽  
Data Ingestion

scholarly journals DataMed – an open source discovery index for finding biomedical datasets

2018 ◽  
Vol 25 (3) ◽  
pp. 300-308 ◽  
Author(s):  
Xiaoling Chen ◽  
Anupama E Gururaj ◽  
Burak Ozyurt ◽  
Ruiling Liu ◽  
Ergin Soysal ◽  
...  
Keyword(s):  
Open Source ◽  
Search Engine ◽  
Language Processing ◽  
Data Reuse ◽  
Biomedical Domain ◽  
Biomedical Data ◽  
Data Discovery ◽  
Healthcare Data ◽  
Core Elements ◽  
Data Ingestion

Abstract Objective Finding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain. Materials and Methods DataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health–funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium. It consists of 2 main components: (1) a data ingestion pipeline that collects and transforms original metadata information to a unified metadata model, called DatA Tag Suite (DATS), and (2) a search engine that finds relevant datasets based on user-entered queries. In addition to describing its architecture and techniques, we evaluated individual components within DataMed, including the accuracy of the ingestion pipeline, the prevalence of the DATS model across repositories, and the overall performance of the dataset retrieval engine. Results and Conclusion Our manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services. Currently, we have made the DataMed system publically available as an open source package for the biomedical community.

scholarly journals Big Data in Healthcare

2020 ◽  
Vol 8 (6) ◽  
pp. 2127-2131
Keyword(s):  
Big Data ◽  
Healthcare Providers ◽  
Information Storage ◽  
Healthcare Industry ◽  
Positive Health ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Data Volume ◽  
Effective Decision ◽  
Effective Decision Making

To improve quality in healthcare, it is very much important to store, manage and retrieve as well as use the data & information properly as it has great potential to help leaders in effective decision making. Managing data in healthcare is not easy task as it has many associated risk. As per new trends in healthcare industry, it is observed that the data volume generation in healthcare is growing rapidly. Big Data is substantial, less organized and mixed in nature. In addition to that, big data is considered one of the best tools to reduce the associated and functional cost of healthcare providers worldwide. While income should not be only a main or prime indicator, it is equally important for healthcare providers to gather the most valuable present tools and techniques and setup to force or inculcate big data effectively otherwise it can harm or risk organization to lose money in business as well as profit. Objective: This paper is focusing on the special factors of Big Data in healthcare. The main aim of this study is to find the roles of big data in healthcare and know how big data is helping in data transaction in healthcare industry. Methods: More than 30(n=30) published papers have been reviewed and suitable papers (n=18) have been included to make the conclusion. Information was condensed utilizing distinct measurable assessment & techniques. Findings: According to investigation of published articles, it has established that the role of big data is very much unique and important as well as it is helping healthcare providers to improve the patient safety and quality by providing smooth health information storage and exchange with high privacy and security. Conclusion: Big Data in healthcare is a new concept introduced in healthcare data analytics and management which is basically focusing in improving the drug and disease discovery, personal healthcare record, electronic health record, effective decision in diagnosis and treatment by healthcare practitioners and at most helps in getting desired and positive health outcome. The data is one of the crucial factors in healthcare and it is high time for healthcare providers to look into those matters in enormous way.

Public Reporting of Cardiovascular Data: Benefits, Pitfalls, and Vision for the Future

2016 ◽  
Vol 10 (1) ◽  
pp. 36
Author(s):  
Gregory J Dehmer ◽  
Keyword(s):  
Clinical Data ◽  
Clinical Status ◽  
Public Reporting ◽  
Professional Organizations ◽  
Healthcare Data ◽  
Outcomes Of Care ◽  
The Future ◽  
Policy Statements ◽  
Quality And Outcomes ◽  
Reporting Programs

Public reporting of healthcare data is not a new concept. This initiative continues to proliferate as consumers and other stakeholders seek information on the quality and outcomes of care. Furthermore, mandates for the development of additional public reporting efforts are included in several new healthcare legislations such as the Affordable Care Act. Many current reporting programs rely heavily on administrative data as a surrogate for true clinical data, but this approach has well-defined limitations. Clinical data are traditionally more difficult and costly to collect, but more accurately reflect the clinical status of the patient, thus enhancing validity of the quality metrics and the reporting program. Several professional organizations have published policy statements articulating the main principles that should establish the foundation for public reporting programs in the future.

Survey of Cloud Computing in Healthcare Sector

2017 ◽  
Vol 7 (7) ◽  
pp. 337
Author(s):  
S. Karthiga Devi ◽  
B. Arputhamary
Keyword(s):  
Cloud Computing ◽  
Healthcare Providers ◽  
High Volume ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Cloud Infrastructure ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Number Of Patients

Today the volume of healthcare data generated increased rapidly because of the number of patients in each hospital increasing.  These data are most important for decision making and delivering the best care for patients. Healthcare providers are now faced with collecting, managing, storing and securing huge amounts of sensitive protected health information. As a result, an increasing number of healthcare organizations are turning to cloud based services. Cloud computing offers a viable, secure alternative to premise based healthcare solutions. The infrastructure of Cloud is characterized by a high volume storage and a high throughput. The privacy and security are the two most important concerns in cloud-based healthcare services. Healthcare organization should have electronic medical records in order to use the cloud infrastructure. This paper surveys the challenges of cloud in healthcare and benefits of cloud techniques in health care industries.

ANALISIS PENGARUH TINGKAT VOLUME DAN JENIS KENDARAAN TERHADAP KONSENTRASI PARTICULATE MATTER (PM10) (STUDI KASUS: JL. SUTAN SYAHRIR, JL. AHMAD YANI DAN JL. KOM. YOS. SUDARSO KOTA PONTIANAK)

2014 ◽  
Vol 2 (1) ◽  
Author(s):  
Ridwan Maulana
Keyword(s):  
Particulate Matter ◽  
Line Source ◽  
Data Volume

ABSTRAK Perkembangan Kota Pontianak yang semakin pesat, ditambah dengan perkembangan penduduk yang semakin meningkat, telah membuat sistem transportasi jalan raya mengalami tingkat kompleksitas yang tinggi , salah satu dampak yang ditimbulkan adalah pencemaran udara perkotaan. Particulate Matter (PM10) merupakan salah satu bentuk zat pencemar yang disebabkan oleh sektor transportasi tersebutserta dapat menyebabkan gangguan kesehatan khususnya pada sistem pernapasan. Oleh sebab itu penelitian ini dilakukan untuk mengetahui tingkat konsentrasi partikulat udara (Particulate Matter (PM10)) khususnya di Jalan Sutan Syahrir, Jalan Ahmad Yani dan Jalan Kom. Yos. Sudarso Jeruju Kota Pontianak. Ketiga lokasi penelitian tersebut dipilih untuk mewakili peruntukkan tata guna lahan yang berbeda yaitu Jalan Sutan Syahrir berlokasi di pinggiran kota, Jalan Jend. Ahmad Yani berlokasi di tengah kota, dan Jalan Kom. Yos. Sudarso Jeruju yang berlokasi di kawasan industri. Data yang digunakan merupakan data sekunder yang didapat dari BLHD Provinsi Kalbar yaitu data volume kendaraan yang melintas pada ketiga jalan tersebut. Jenis-jenis kendaraan dibagi menjadi 4 golongan yaitu golongan 1 (sepeda motor), golongan 2 (sedan, angkot, pickup), golongan 3 (bis mikro, bis), golongan 4 (truck 2 as 4 roda, truck 2 as 6 roda, truck 3 as, truk 4 as, trailer).Metode penelitian yang digunakan terbagi menjadi 2 bagian, yaitu perhitungan (perhitungan beban laju emisi transportasi dan konsentrasi Particulate Matter (PM10) dengan rumus dispersi Gaussian untuk Line Source serta analisis korelasi data untuk memperoleh hubungan antara jumlah kendaraan dengan konsentrasi Particulate Matter (PM10) menggunakan aplikasi SPSS 16. Dari hasil analisis, bahwa jenis kendaraan golongan 1 memiliki kontribusi yang paling besar terhadap konsentrasi Particulate Matter (PM10) yaitu dengan konsentrasi terbesar yaitu 901425,466 dimana nilai konsentrasi tersebut melebihi Ambang Batas Baku Mutu Udara Ambien Nasional yaitu 150 , hal ini dikarenakan sepeda motor memiliki jumlah yang paling banyak apabila dibandingkan dengan kendaraan lain di ketiga jalan tersebut. Kendaraan golongan 2 memiliki jumlah terbanyak kedua diikuti dengan golongan 4 dan 3. Maka dapat disimpulkan bahwa jumlah kendaraan total memang mempengaruhi konsentrasi Particulate Matter (PM10) pada Jalan Sutan Syahrir, Jalan Jend. Ahmad Yani dan Jalan Kom. Yos Sudarso dilihat dari hasil korelasinya yang mendekati nilai 1 (positif kuat) yaitu 0,963 dengan menggunakan aplikasi SPSS 16. Kata Kunci :Particulate Matter (PM10), Golongan Kendaraan, Korelasi.

Healthcare Data Warehousing

2017 ◽  
Vol 4 (4) ◽  
pp. 1
Author(s):  
ARUNACHALAM S. ◽  
PAGE TOM ◽  
THORSTEINSSON G. ◽  
◽  
◽  
...  
Keyword(s):  
Data Warehousing ◽  
Healthcare Data

Perancangan Sistem Monitoring Volume Bahan Bakar Pada Prototype Sephull Bubble Vessel

2019 ◽  
Vol 4 (1) ◽  
pp. 29
Author(s):  
Mochammad Nasir ◽  
Mochammad Ali Mudhoffar ◽  
Nurhadi
Keyword(s):  
Data Volume

Sephull Bubble Vessel adalah kapal dengan pelumasan udara yaitu kapal dengan injeksi udara di bagian bawahnya, disain kapal ini untuk mendapatkan sebuah kapal dengan kemampuan berlayar dengan kecepatan tinggi dengan konsumsi bahan bakar yang minimal. Untuk mengetahui Efesiensi bahan bakar ini, dilakukan perbandingan konsumsi bahan bakar pada saat kapal beroperasi dengan menggunakan sistem pelumasan udara dengan tanpa menggunakan sistem pelumasan udara. Pada saat ini masih menggunakan cara manual dengan mengukur sisa bensin setiap selesai dilakukan uji coba pada kedua kondisi tersebut. Dalam kesempatan ini akan dirancang Sistem Monitoring Volume Bahan Bakar pada Prototype Sephull Bubble Vessel, dengan sistem ini maka untuk mengetahui efesiensi penggunaan bahan bakar bisa diketahui dengan mudah. Perancangan sitem ini menggunakan sensor Universal Fuel Sender, output dari sensor tersebut akan diolah oleh Mikrokontroller AT-Mega 8535 dan volume bahan bakar akan ditampilkan melalui tampilan LCD 16x2. Volume bahan bakar ini juga dapat diMonitoring melalui komputer dengan menggunakan program LabView sehingga data volume bahan bakar dapat disimpan dalam sebuah file komputer.Keywords : Universal Fuel Sender; AT-Mega 8535; LabView

Software platform for automated preparation and analytical processing of heterogeneous datasets

2020 ◽  
pp. 15-20 ◽  
Author(s):  
E. D. Avedyan ◽  
I. V. Voronkov
Keyword(s):  
Data Processing ◽  
Analytical Data ◽  
Volume Growth ◽  
Software Platform ◽  
External Data ◽  
Heterogeneous Datasets ◽  
Data Volume ◽  
Processing Resources ◽  
Modular Platform ◽  
Network Technologies

Summary: the article proposes new software platform for automating the processes of preprocessing and marking up datasets with the aim of further solving analytical problems such as image classification and processing textual and parametric information using neural network technologies. The software platform uses modern technologies and combines a large number of methods in the form of a modular platform, which can be supplemented as the tasks of analytical data processing become more complicated. The need to develop such a software platform is dictated primarily by the fact that, given the current level of data volume growth, the actual transition to deep data analytics remains unattainable without such software platforms, since confidentiality, access to information and the use of external data processing resources are required.

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