The Effectiveness of Big Data in Health Care: A Systematic Review

Gaitanou, Panorea; Garoufallou, Emmanouel; Balatsoukas, Panos

doi:10.1007/978-3-319-13674-5_14

Cited by 18 publications

(14 citation statements)

References 22 publications

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“…Developed sequentially ordered case by case rules were presented mathematically. To the best of our knowledge, no robust algorithmic approach has yet been reported to evaluate treatment duration with individual medications in multiple treatment scenario [22,27].…”

Section: Discussionmentioning

confidence: 99%

“…Representative examples include the UK Clinical Practice Research Database and Centricity TM EMR (CEMR) database of USA [27,28]. The extraction, quality control and management of such voluminous longitudinal data under individual study protocols is highly methodologically and computationally involved, and challenging from data mining and analytical viewpoints [22,29]. Data science generally considers that data preparation tasks consume about 80% of total project timeline leaving only 20% for ultimate analysis itself [30,31].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data Mining Approach to Estimate the Duration of Drug Therapy from Longitudinal Electronic Medical Records

Montvida¹,

Arandjelović²,

Reiner³

et al. 2017

TOBIOIJ

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Background:Electronic Medical Records (EMRs) from primary/ ambulatory care systems present a new and promising source of information for conducting clinical and translational research. Objectives:To address the methodological and computational challenges in order to extract reliable medication information from raw data which is often complex, incomplete and erroneous. To assess whether the use of specific chaining fields of medication information may additionally improve the data quality. Methods:Guided by a range of challenges associated with missing and internally inconsistent data, we introduce two methods for the robust extraction of patient-level medication data. First method relies on chaining fields to estimate duration of treatment ("chaining"), while second disregards chaining fields and relies on the chronology of records ("continuous"). Centricity EMR database was used to estimate treatment duration with both methods for two widely prescribed drugs among type 2 diabetes patients: insulin and glucagonlike peptide-1 receptor agonists. Results:At individual patient level the "chaining" approach could identify the treatment alterations longitudinally and produced more robust estimates of treatment duration for individual drugs, while the "continuous" method was unable to capture that dynamics. At population level, both methods produced similar estimates of average treatment duration, however, notable differences were observed at individual-patient level. Conclusion:The proposed algorithms explicitly identify and handle longitudinal erroneous or missing entries and estimate treatment duration with specific drug(s) of interest, which makes them a valuable tool for future EMR based clinical and pharmaco-epidemiological studies. To improve accuracy of real-world based studies, implementing chaining fields of medication information is recommended.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Mining Approach to Estimate the Duration of Drug Therapy from Longitudinal Electronic Medical Records

Montvida¹,

Arandjelović²,

Reiner³

et al. 2017

TOBIOIJ

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“…The health care industry manages a wide amount of data every day. These have been categorised as follows (Groves, Kayyali, Knott, & Van Kuiken, ; Gaitanou, Garoufallou, & Balatsoukas, ): clinical data (electronic health data including patient data, hospital data, diagnosis and treatments and genomics provided from clinical laboratories), patient and sentiment data (data collected from wearable sensors and patients’ behaviour data from online networking tools such as Twitter), administrative and cost data (cost of care data and operational/financial performance measures) and pharmaceutical and research and development (R&D) data (data describing drugs, vaccines and substances of serums derived from pharmaceutical companies). …”

Section: Literature Reviewmentioning

confidence: 99%

“…The health care industry manages a wide amount of data every day. These have been categorised as follows (Groves, Kayyali, Knott, & Van Kuiken, 2013;Gaitanou, Garoufallou, & Balatsoukas, 2014): • clinical data (electronic health data including patient data, hospital data, diagnosis and treatments and genomics provided from clinical laboratories),…”

Section: Literature Reviewmentioning

confidence: 99%

Big data analytics in health: an overview and bibliometric study of research activity

Galetsi

Katsaliaki

2019

Health Info Libraries J

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Objective The study presents an overview of the research activity in Big Data Analytics (BDA) in the field of health and demonstrates the existing knowledge through related examples. The objective is to inform health librarians about the nature and magnitude of the technological innovations in health information analysis tools, its influence, and where and how further material could be searched. Methods We performed a bibliometric and co‐citation analysis within a total of 804 papers published between 2000 and 2016 and retrieved from the Web of Science and Scopus databases. Using the NVivo text analysis software, we identified the stakeholders of BDA in health and innovative decision support systems in the field. Results Our findings show a tremendous increase in published papers after 2014. Most of them are relevant to neurology and medical oncology. The stakeholders are clinicians, researchers, patients, administrators, IT specialists, vendors and policymakers. New BDA tools in medicine are mostly developed for disease monitoring purposes while they utilise visualisation to identify disease patterns and statistical analysis of past data for making predictions. Conclusions Health analytics provide a unique opportunity for advancing health information research and medical decision making. It provides health information professionals with new tools in problem‐solving offering new perspectives in prognosis and diagnosis of diseases.

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“…Big data may also require non-traditional storage methods and analytical techniques (Elgendy and Elragal, 2014). Sources of big data in human healthcare include electronic medical records, genomics, imaging data, and data from social networks and sensors (Gaitanou et al, 2014).…”

Section: Rationalementioning

confidence: 99%

A scoping review of ‘big data’, ‘informatics’, and ‘bioinformatics’ in the animal health and veterinary medical literature

Ouyang

Sargeant

Thomas

et al. 2019

Anim. Health. Res. Rev.

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Research in big data, informatics, and bioinformatics has grown dramatically (Andreu-Perez J, et al., 2015, IEEE Journal of Biomedical and Health Informatics 19, 1193–1208). Advances in gene sequencing technologies, surveillance systems, and electronic medical records have increased the amount of health data available. Unconventional data sources such as social media, wearable sensors, and internet search engine activity have also contributed to the influx of health data. The purpose of this study was to describe how ‘big data’, ‘informatics’, and ‘bioinformatics’ have been used in the animal health and veterinary medical literature and to map and chart publications using these terms through time. A scoping review methodology was used. A literature search of the terms ‘big data’, ‘informatics’, and ‘bioinformatics’ was conducted in the context of animal health and veterinary medicine. Relevance screening on abstract and full-text was conducted sequentially. In order for articles to be relevant, they must have used the words ‘big data’, ‘informatics’, or ‘bioinformatics’ in the title or abstract and full-text and have dealt with one of the major animal species encountered in veterinary medicine. Data items collected for all relevant articles included species, geographic region, first author affiliation, and journal of publication. The study level, study type, and data sources were collected for primary studies. After relevance screening, 1093 were classified. While there was a steady increase in ‘bioinformatics’ articles between 1995 and the end of the study period, ‘informatics’ articles reached their peak in 2012, then declined. The first ‘big data’ publication in animal health and veterinary medicine was in 2012. While few articles used the term ‘big data’ (n = 14), recent growth in ‘big data’ articles was observed. All geographic regions produced publications in ‘informatics’ and ‘bioinformatics’ while only North America, Europe, Asia, and Australia/Oceania produced publications about ‘big data’. ‘Bioinformatics’ primary studies tended to use genetic data and tended to be conducted at the genetic level. In contrast, ‘informatics’ primary studies tended to use non-genetic data sources and conducted at an organismal level. The rapidly evolving definition of ‘big data’ may lead to avoidance of the term.

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The Effectiveness of Big Data in Health Care: A Systematic Review

Cited by 18 publications

References 22 publications

Data Mining Approach to Estimate the Duration of Drug Therapy from Longitudinal Electronic Medical Records

Data Mining Approach to Estimate the Duration of Drug Therapy from Longitudinal Electronic Medical Records

Big data analytics in health: an overview and bibliometric study of research activity

A scoping review of ‘big data’, ‘informatics’, and ‘bioinformatics’ in the animal health and veterinary medical literature

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