scholarly journals A review of big data and medical research

2020 ◽  
Vol 8 ◽  
pp. 205031212093483 ◽  
Author(s):  
Mary Mallappallil ◽  
Jacob Sabu ◽  
Angelika Gruessner ◽  
Moro Salifu
Keyword(s):  
Big Data ◽  
Study Design ◽  
Large Scale ◽  
Health Data ◽  
Biological Data ◽  
Easy Access ◽  
Real World Data ◽  
Privacy And Security ◽  
Biometric Data ◽  
Electronic Health

Universally, the volume of data has increased, with the collection rate doubling every 40 months, since the 1980s. “Big data” is a term that was introduced in the 1990s to include data sets too large to be used with common software. Medicine is a major field predicted to increase the use of big data in 2025. Big data in medicine may be used by commercial, academic, government, and public sectors. It includes biologic, biometric, and electronic health data. Examples of biologic data include biobanks; biometric data may have individual wellness data from devices; electronic health data include the medical record; and other data demographics and images. Big data has also contributed to the changes in the research methodology. Changes in the clinical research paradigm has been fueled by large-scale biological data harvesting (biobanks), which is developed, analyzed, and managed by cheaper computing technology (big data), supported by greater flexibility in study design (real-world data) and the relationships between industry, government regulators, and academics. Cultural changes along with easy access to information via the Internet facilitate ease of participation by more people. Current needs demand quick answers which may be supplied by big data, biobanks, and changes in flexibility in study design. Big data can reveal health patterns, and promises to provide solutions that have previously been out of society’s grasp; however, the murkiness of international laws, questions of data ownership, public ignorance, and privacy and security concerns are slowing down the progress that could otherwise be achieved by the use of big data. The goal of this descriptive review is to create awareness of the ramifications for big data and to encourage readers that this trend is positive and will likely lead to better clinical solutions, but, caution must be exercised to reduce harm.

scholarly journals Privacy, security, and the public health researcher in the era of electronic health record research

2016 ◽  
Vol 8 (3) ◽  
Author(s):  
Neal D Goldstein ◽  
Anand D Sarwate
Keyword(s):  
Public Health ◽  
Data Privacy ◽  
Large Scale ◽  
Health Data ◽  
Shared Responsibility ◽  
Privacy And Security ◽  
The Public ◽  
Health Researcher ◽  
Public Health Researcher ◽  
Electronic Health

Health data derived from electronic health records are increasingly utilized in large-scale population health analyses. Going hand in hand with this increase in data is an increasing number of data breaches. Ensuring privacy and security of these data is a shared responsibility between the public health researcher, collaborators, and their institutions. In this article, we review the requirements of data privacy and security and discuss epidemiologic implications of emerging technologies from the computer science community that can be used for health data. In order to ensure that our needs as researchers are captured in these technologies, we must engage in the dialogue surrounding the development of these tools.

Patient Privacy and Security in E-Health

2019 ◽  
pp. 562-575
Author(s):  
Güney Gürsel
Keyword(s):  
Education Research ◽  
Descriptive Study ◽  
Health Data ◽  
Easy Access ◽  
Major Contributor ◽  
Patient Privacy ◽  
Privacy And Security ◽  
Digital Era ◽  
Healthcare It ◽  
Access To Data

In the digital era, undoubtedly, e-health is a major contributor for decision support, education, research and management activities in healthcare. It provides tremendous benefits by easy store and access to data. This easiness brings a big problem together with the benefits. Users have easy access to vast amount of sensitive health data about patients. This may give way to misuse and abuse. That is why the concepts of privacy and security becomes very popular and point of major concern. This chapter is a descriptive study aimed to give principles of these concepts and invoke awareness about.

scholarly journals Beyond simple charts: Design of visualizations for big health data

2016 ◽  
Vol 8 (3) ◽  
Author(s):  
Oluwakemi Ola ◽  
Kamran Sedig
Keyword(s):  
Big Data ◽  
Disease Surveillance ◽  
Large Scale ◽  
Health Management ◽  
High Volume ◽  
Health Data ◽  
Making Sense ◽  
Interactive Visualizations ◽  
Scatter Plots ◽  
At Risk Populations

Health data is often big data due to its high volume, low veracity, great variety, and high velocity. Big health data has the potential to improve productivity, eliminate waste, and support a broad range of tasks related to disease surveillance, patient care, research, and population health management. Interactive visualizations have the potential to amplify big data’s utilization. Visualizations can be used to support a variety of tasks, such as tracking the geographic distribution of diseases, analyzing the prevalence of disease, triaging medical records, predicting outbreaks, and discovering at-risk populations. Currently, many health visualization tools use simple charts, such as bar charts and scatter plots, that only represent few facets of data. These tools, while beneficial for simple perceptual and cognitive tasks, are ineffective when dealing with more complex sensemaking tasks that involve exploration of various facets and elements of big data simultaneously. There is need for sophisticated and elaborate visualizations that encode many facets of data and support human-data interaction with big data and more complex tasks. When not approached systematically, design of such visualizations is labor-intensive, and the resulting designs may not facilitate big-data-driven tasks. Conceptual frameworks that guide the design of visualizations for big data can make the design process more manageable and result in more effective visualizations. In this paper, we demonstrate how a framework-based approach can help designers create novel, elaborate, non-trivial visualizations for big health data. We present four visualizations that are components of a larger tool for making sense of large-scale public health data. 

Patient Privacy and Security in E-Health

2017 ◽  
pp. 553-566
Author(s):  
Güney Gürsel
Keyword(s):  
Education Research ◽  
Descriptive Study ◽  
Health Data ◽  
Easy Access ◽  
Major Contributor ◽  
Patient Privacy ◽  
Privacy And Security ◽  
Digital Era ◽  
Healthcare It ◽  
Access To Data

In the digital era, undoubtedly, e-health is a major contributor for decision support, education, research and management activities in healthcare. It provides tremendous benefits by easy store and access to data. This easiness brings a big problem together with the benefits. Users have easy access to vast amount of sensitive health data about patients. This may give way to misuse and abuse. That is why the concepts of privacy and security becomes very popular and point of major concern. This chapter is a descriptive study aimed to give principles of these concepts and invoke awareness about.

scholarly journals “Big Data” and the Electronic Health Record

2014 ◽  
Vol 23 (01) ◽  
pp. 97-104 ◽  
Author(s):  
M. K. Ross ◽  
Wei Wei ◽  
L. Ohno-Machado
Keyword(s):  
Big Data ◽  
Electronic Health Record ◽  
Language Processing ◽  
Clinical Data ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Health Record ◽  
Privacy And Security ◽  
Pubmed Database ◽  
Electronic Health

Summary Objectives: Implementation of Electronic Health Record (EHR) systems continues to expand. The massive number of patient encounters results in high amounts of stored data. Transforming clinical data into knowledge to improve patient care has been the goal of biomedical informatics professionals for many decades, and this work is now increasingly recognized outside our field. In reviewing the literature for the past three years, we focus on “big data” in the context of EHR systems and we report on some examples of how secondary use of data has been put into practice. Methods: We searched PubMed database for articles from January 1, 2011 to November 1, 2013. We initiated the search with keywords related to “big data” and EHR. We identified relevant articles and additional keywords from the retrieved articles were added. Based on the new keywords, more articles were retrieved and we manually narrowed down the set utilizing predefined inclusion and exclusion criteria. Results: Our final review includes articles categorized into the themes of data mining (pharmacovigilance, phenotyping, natural language processing), data application and integration (clinical decision support, personal monitoring, social media), and privacy and security. Conclusion: The increasing adoption of EHR systems worldwide makes it possible to capture large amounts of clinical data. There is an increasing number of articles addressing the theme of “big data”, and the concepts associated with these articles vary. The next step is to transform healthcare big data into actionable knowledge.

scholarly journals Research on Supply Chain Financial Risks and Preventive Measures in the Era of Big Data

2020 ◽  
Vol 214 ◽  
pp. 03032
Author(s):  
Weiting Sun ◽  
Puxue Shen
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Financial Institutions ◽  
Financial Risk ◽  
Large Scale ◽  
Theory And Practice ◽  
Privacy And Security ◽  
Internet Applications ◽  
Supply Chain Finance ◽  
Big Data Technology

The emergence of supply chain finance has reduced the financing costs of SMEs. Due to the development of a diversified supply chain financial subject platform, there is a lack of risk control in terms of theory and practice. Big data is generated in Internet applications and combined with information technology to form big data technology. It can provide financial institutions with large-scale data analysis methods and can effectively improve the efficiency and ability of financial institutions to serve supply chain members. However, big data has some problems, such as higher processing cost, lower authenticity, and difficulty in effectively protecting the privacy and security of users. There are many problems with this new development model. This article focuses on the risk problems faced by supply chain finance. It discusses the use of big data technology to effectively solve the supply chain financial risk problems, and gives some measures that can be effectively solved for how to effectively avoid these risk factors. By effectively solving the financial risk problem in the era of big data, it provides guarantee for the benign development of enterprises, and provides a certain reference for researchers engaged in related fields and workers in this field.

scholarly journals Progress Towards and Challenges in Biological Big Data

2018 ◽  
Vol 1 (4) ◽  
pp. 35-38
Author(s):  
Manisha Sritharan ◽  
Farhat A. Avin
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Modern Technology ◽  
Biological Data ◽  
Biomedical Data ◽  
Data Generation ◽  
Huge Amount ◽  
Efficient Manner ◽  
Meaningful Information ◽  
Research Pattern

Biological big data represents a vast amount of data in bioinformatics and this could lead to the transformation of the research pattern into large scale. In medical research, a large amount of data can be generated from tools including genomic sequencing machines. The availability of advanced tools and modern technology has become the main reason for the expansion of biological data in a huge amount. Such immense data should be utilized in an efficient manner in order to distribute this valuable information. Besides that, storing and dealing with those big data has become a great challenge as the data generation are tremendously increasing over years. As well, the blast of data in healthcare systems and biomedical research appeal for an immediate solution as health care requires a compact integration of biomedical data. Thus, researchers should make use of this available big data for analysis rather than keep creating new data as they could provide meaningful information with the use of current advanced bioinformatics tools.

Patient Privacy and Security in E-Health

2017 ◽  
pp. 1469-1482
Author(s):  
Güney Gürsel
Keyword(s):  
Education Research ◽  
Descriptive Study ◽  
Health Data ◽  
Easy Access ◽  
Major Contributor ◽  
Patient Privacy ◽  
Privacy And Security ◽  
Digital Era ◽  
Healthcare It ◽  
Access To Data

In the digital era, undoubtedly, e-health is a major contributor for decision support, education, research and management activities in healthcare. It provides tremendous benefits by easy store and access to data. This easiness brings a big problem together with the benefits. Users have easy access to vast amount of sensitive health data about patients. This may give way to misuse and abuse. That is why the concepts of privacy and security becomes very popular and point of major concern. This chapter is a descriptive study aimed to give principles of these concepts and invoke awareness about.

scholarly journals Measuring COVID-19 and Influenza in the Real World via Person-Generated Health Data

2020 ◽  
Author(s):  
Nikki Marinsek ◽  
Allison Shapiro ◽  
Ieuan Clay ◽  
Ben Bradshaw ◽  
Ernesto Ramirez ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Demographic Data ◽  
Wearable Sensors ◽  
Health Data ◽  
Step Count ◽  
Shortness Of Breath ◽  
Real World Data ◽  
Participatory Surveillance ◽  
Over Time

BackgroundSince the beginning of the COVID-19 pandemic, data from smartphones and connected sensors have been used to better understand presentation and management outside the clinic walls. However, reports on the validity of such data are still sparse, especially when it comes to symptom progression and relevance of wearable sensors.ObjectiveTo understand the relevance of Person-Generated Health Data (PGHD) as a means for early detection, monitoring, and management of COVID-19 in everyday life. This type of data include quantifying prevalence and progression of symptoms from self-reports as well as changes in activity and physiological parameters continuously measured from wearable sensors, and contextualizing findings for COVID-19 patients with those from cohorts of flu patients.Design, Setting, and ParticipantsRetrospective digital cohort study of individuals with a self-reported positive SARS-CoV-2 or influenza test followed over the period 2019-12-02 to 2020-04-27. Three cohorts were derived: Patients who self-reported being diagnosed with flu prior to the SARS-CoV-2 pandemic (N=6270, of which 1226 also contributed sensor PGHD); Patients who reported being diagnosed with flu during the SARS-CoV-2 pandemic (N=426, of which 85 also shared sensor PGHD); and patients who reported being diagnosed with COVID-19 (N=230, of which sensor PGHD was available for 41). The cohorts were derived from a large-scale digital participatory surveillance study designed to track Influenza-like Illness (ILI) incidence and burden over time.ExposuresSelf-reported demographic data, comorbidities, and symptoms experienced during a diagnosed ILI episode, including SARS-CoV-2. Physiological and behavioral parameters measured daily from commercial wearable sensors, including Resting Heart Rate (RHR), total step count, and nightly sleep hours.Main Outcomes and MeasuresWe investigated the percentage of individuals experiencing symptoms of a given type (e.g. shortness of breath) across demographic groups and over time. We examined illness duration, and care seeking behavior, and how RHR, step count, and nightly sleep hours deviated from expected behavior on healthy days over the course of the infection episode.ResultsSelf-reported symptoms of COVID-19 present differently from flu. COVID-19 cases tended to last longer than flu (median of 12 vs. 9 days), are uniquely characterized by chest pain/pressure, shortness of breath, and anosmia. The fraction of elevated RHR measurements collected daily from commercial wearable devices rise significantly in the 2 days surrounding ILI symptoms onset, but does not appear to do so in a way specific to COVID-19. Steps lost due to COVID-19 persists for longer than for flu.Conclusion and RelevancePGHD can be a valid source of longitudinal real world data to detect and monitor COVID-19-related symptoms and behaviors at population scale. PGHD may provide continuous, near real-time feedback to intervention effectiveness that otherwise requires waiting for symptoms to develop into contacts with the healthcare system. It has also the potential to increase pre-test probability of other downstream diagnostics. To effectively leverage PGHD for participatory surveillance it is crucial to invest in the creation of trusted, long-term communication channels with individuals through which data can be efficiently collected, consented, and contextualized, while protecting the privacy of individuals and ultimately facilitating the transition in and out of care.

scholarly journals Biometric Monitoring Devices: Modern Solutions to Protecting Athletes’ Data Privacy

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tristan A. Dietrick
Keyword(s):  
Data Privacy ◽  
Personal Data ◽  
Collegiate Athletes ◽  
Health Data ◽  
Easy Access ◽  
Biometric Data ◽  
Sports Industry ◽  
Privacy Law ◽  
Increased Risk ◽  
Biometric Information

Smartwatches like Fitbits provide users with easy access to quantifiable health data. In the sports industry, tracking this biometric information may be particularly beneficial to athletes, whose livelihoods revolve around their health and fitness. Nonetheless, under the current regime, professional and collegiate athletes’ biometric health data are inadequately protected. Data privacy law is still in its infancy, but in the meantime, athletes must consider that motivations to sell or misuse players’ biometric information may outpace legal developments. This Paper will analyze the promise and risk of collecting professional and collegiate athletes’ health and biometric data, particularly through fitness wearables. It will provide a closer look at wearables in professional sports and consider the increased risk posed to college athletes. Finally, this Paper will consider possible solutions to maximize the benefits of newfound technology while simultaneously minimizing risks to players’ health information, privacy, and personal data ownership.

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