A pilot study on the epidemiology of hyperuricemia in Chinese adult population based on big data from Electronic Medical Records 2014 to 2018

To define huge datasets, the term of big data is used. The considered “4 V” datasets imply volume, variety, velocity and value for many areas especially in medical images, electronic medical records (EMR) and biometrics data. To process and manage such datasets at storage, analysis and visualization states are challenging processes. Recent improvements in communication and transmission technologies provide efficient solutions. Big data solutions should be multithreaded and data access approaches should be tailored to big amounts of semi-structured/unstructured data. Software programming frameworks with a distributed file system (DFS) that owns more units compared with the disk blocks in an operating system to multithread computing task are utilized to cope with these difficulties. Huge datasets in data storage and analysis of healthcare industry need new solutions because old fashioned and traditional analytic tools become useless.

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Clinical profile of pterygium in patients seeking eye care in India: electronic medical records-driven big data analytics report III

International Ophthalmology ◽

10.1007/s10792-020-01326-3 ◽

2020 ◽

Vol 40 (6) ◽

pp. 1553-1563 ◽

Cited By ~ 5

Author(s):

Anthony Vipin Das ◽

Sahitya Podila ◽

Gumpili Sai Prashanthi ◽

Sayan Basu

Keyword(s):

Big Data ◽

Electronic Medical Records ◽

Medical Records ◽

Data Analytics ◽

Big Data Analytics ◽

Clinical Profile ◽

Eye Care

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Hybrid blockchain–based privacy-preserving electronic medical records sharing scheme across medical information control system

Measurement and Control ◽

10.1177/0020294020926636 ◽

2020 ◽

Vol 53 (7-8) ◽

pp. 1286-1299

Author(s):

Yu Cao ◽

Yi Sun ◽

Jiangsong Min

Keyword(s):

Big Data ◽

Control System ◽

Control Systems ◽

Electronic Medical Records ◽

Medical Records ◽

Medical Information ◽

Information Control ◽

Personal Privacy ◽

Sharing Scheme ◽

Medical Big Data

With the development of big data and medical information control system, electronic medical records sharing across organizations for better medical treatment and advancement has attracted much attention both from academic and industrial areas. However, the source of big data, personal privacy concern, inherent trust issues across organizations and complicated regulation hinder the great progress of healthcare intelligence. Blockchain, as a novel technique, has been used widely to resolve the privacy and security issues in electronic medical records sharing process. In this paper, we propose a hybrid blockchain–based electronic medical records sharing scheme to address the privacy and trust issues across the medical information control systems, rendering the electronic medical records sharing process secure, effective, relatively transparent, immutable, traceable and auditable. Considering the above confidential issues, we use different sharing methods for different parts of medical big data. We share privacy-sensitive couples on the consortium blockchain, while sharing the non-sensitive parts on the public blockchain. In this way, authorized medical information control systems within the consortium can access the data on it for precise medical diagnosis. Institutions such as universities and research institutes can get access to the non-sensitive parts of medical big data for scientific research on symptoms to evolve medical technologies. A working prototype is implemented to demonstrate how the hybrid blockchain facilitates the pharmaceutical operations in a healthcare information control ecosystem. A blockchain benchmark tool Hyperledger Caliper is used to evaluate the performance of hybrid blockchain–based electronic medical records sharing scheme on throughput and average latency which proves to be practicable and excellent.

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Allergic eye disease in children and adolescents seeking eye care in India: Electronic medical records driven big data analytics report II

The Ocular Surface ◽

10.1016/j.jtos.2019.08.011 ◽

2019 ◽

Vol 17 (4) ◽

pp. 683-689 ◽

Cited By ~ 11

Author(s):

Anthony Vipin Das ◽

Pragnya R. Donthineni ◽

Gumpili Sai Prashanthi ◽

Sayan Basu

Keyword(s):

Big Data ◽

Children And Adolescents ◽

Electronic Medical Records ◽

Medical Records ◽

Data Analytics ◽

Big Data Analytics ◽

Eye Disease ◽

Eye Care

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Perspectives on Electronic Prescribing and Terminologies

American Society of Clinical Oncology Educational Book ◽

10.14694/edbook_am.2013.33.e370 ◽

2013 ◽

pp. e370-e373

Author(s):

David Liebovitz

Keyword(s):

Patient Safety ◽

Electronic Medical Records ◽

Medical Records ◽

International Classification Of Diseases ◽

Population Based ◽

Electronic Prescribing ◽

Medication History ◽

Diagnosis Codes ◽

Classification Of Diseases ◽

Potential Benefits

Electronic medical records provide potential benefits and also drawbacks. Potential benefits include increased patient safety and efficiency. Potential drawbacks include newly introduced errors and diminished workflow efficiency. In the patient safety context, medication errors account for significant patient harm. Electronic prescribing (e-prescribing) offers the promise of automated drug interaction and dosage verification. In addition, the process of enabling e-prescriptions also provides access to an often unrecognized benefit, that of viewing the dispensed medication history. This information is often critical to understanding patient symptoms. Obtaining significant value from electronic medical records requires use of standardized terminology for both targeted decision support and population-based management. Further, generating documentation for a billable encounter requires usage of proper codes. The emergence of International Classification of Diseases (ICD)-10 holds promise in facilitating identification of a more precise patient code while also presenting drawbacks given its complexity. This article will focus on elements of e-prescribing and use of structured chart content, including diagnosis codes as they relate to physician office practices.

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Toward a Literature-Driven Definition of Big Data in Healthcare

BioMed Research International ◽

10.1155/2015/639021 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 74

Author(s):

Emilie Baro ◽

Samuel Degoul ◽

Régis Beuscart ◽

Emmanuel Chazard

Keyword(s):

Big Data ◽

Electronic Medical Records ◽

Medical Records ◽

Data Reuse ◽

Secondary Use ◽

Meaningful Information ◽

Fields Of Study ◽

Study Characteristics ◽

Definition Of ◽

Privacy Issues

Objective.The aim of this study was to provide a definition of big data in healthcare.Methods.A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals(n)and the number of variables(p)for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed.Results.A total of 196 papers were included. Big data can be defined as datasets withLog⁡(n*p)≥7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues.Conclusion.Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data.

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