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Cited by 84Data Quality and ManagementElectronic Health Records SystemsBig Data Technologies and Applications

In Search of Big Medical Data Integration Solutions - A Comprehensive Survey

Open access
Dhayne, Houssein, Haque, Rafiqul, Kilany, Rima, Taher, Yehia·2019·IEEE Access
DOI 10.1109/access.2019.2927491
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Abstract

In recent years, the radical advancement of technologies has given rise to an abundance of software applications, social media, and smart devices such as smartphone, sensors, and so on. More extensive use of these applications and tools in various industrial domains has led to data deluge, which has fostered enormous challenges and opportunities. However, it is not only the volume of the data but also the speed, variety, and uncertainty, which are promoting a massive challenge for traditional technologies such as data warehouse. These diverse and unprecedented characteristics have engendered the notion of “Big Data.” The data-intensive industries have been experiencing a wide variety of challenges in terms of processing, managing, and analysis of data. For instance, the healthcare sector is confronting difficulties in respect of integration or fusion of diverse medical data stemming from multiple heterogeneous sources. Data integration is critically important within the healthcare sector because it enriches data, enhances its value, and more importantly paves a solid foundation for highly efficient and effective healthcare analytics such as predicting diseases or an outbreak. Several data integration technologies and tools have been developed over the last two decades. This paper aims at studying data integration technologies, tools, and applications within the healthcare domain. Furthermore, this paper discusses future research directions in the integration of Big healthcare data.

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  • Effective systems to integrate multiple data points and key health trends over time do not currently exist.

    “In the United States, patient data are stored digitally in Health Insurance and Portability and Accountability Act (HIPAA)–compliant electronic medical records, and while medical records are capable of trending individual variables for laboratory tests and vital signs, effective systems to integrate multiple data points and key health trends over time do not currently exist [28].”

    From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [28]

  • Medical records are capable of trending individual variables for laboratory tests and vital signs.

    “In the United States, patient data are stored digitally in Health Insurance and Portability and Accountability Act (HIPAA)–compliant electronic medical records, and while medical records are capable of trending individual variables for laboratory tests and vital signs, effective systems to integrate multiple data points and key health trends over time do not currently exist [28].”

    From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [28]

  • In the United States, patient data are stored digitally in HIPAA-compliant electronic medical records.

    “In the United States, patient data are stored digitally in Health Insurance and Portability and Accountability Act (HIPAA)–compliant electronic medical records, and while medical records are capable of trending individual variables for laboratory tests and vital signs, effective systems to integrate multiple data points and key health trends over time do not currently exist [28].”

    From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [28]

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Research works printed but not linked: 15

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Authors
Dhayne, H., Haque, R., Kilany, R., & Taher, Y.
Affiliations
Saint Joseph University (LB)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019
Volume
7
Pages
91265-91290
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2019.2927491
OpenAlex
W2958919462
Cited by
84
References
163
Citations by year
2026: 8 · 2025: 13 · 2024: 12 · 2023: 16 · 2022: 10 · 2021: 11 · 2020: 12 · 2019: 2
FWCI
6.27
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
Keywords
big data, data integration, health data, heterogeneous data sources, medical data analysis, data integration tools, disease prediction, epidemic forecasting

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