Axes of a revolution: challenges and promises of big data in healthcare
Abstract
Health data are increasingly being generated at a massive scale, at various levels of phenotyping and from different types of resources. Concurrent with recent technological advances in both data-generation infrastructure and data-analysis methodologies, there have been many claims that these events will revolutionize healthcare, but such claims are still a matter of debate. Addressing the potential and challenges of big data in healthcare requires an understanding of the characteristics of the data. Here we characterize various properties of medical data, which we refer to as 'axes' of data, describe the considerations and tradeoffs taken when such data are generated, and the types of analyses that may achieve the tasks at hand. We then broadly describe the potential and challenges of using big data in healthcare resources, aiming to contribute to the ongoing discussion of the potential of big data resources to advance the understanding of health and disease.
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Integrating multi-source information, e.g. genomics and clinical imaging, would be critical for accurate patient representation learning as well as the associated downstream clinical tasks.
“Therefore, integrating multi-source information, e.g. genomics and clinical imaging, would be critical for accurate patient representation learning as well as the associated downstream clinical tasks [120].”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [120]
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Digital technology and COVID-19
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“This can be mitigated by training with diverse data from multiple sites without centralization of data 33 using methods such as transfer learning 34,35 or FL. FL is a method used to train AI models on disparate data sources, without the data being transported or exposed outside their original…”
MethodMedical 4.0 technologies for healthcare: Features, capabilities, and applications
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Language models are an effective representation learning technique for electronic health record data
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Utility of teleconsultation in accessing eye care in a developing country during COVID-19 pandemic
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- Authors
- Shilo, S., Rossman, H., & Segal, E.
- Affiliations
- Rambam Health Care Campus (IL)
- Weizmann Institute of Science (IL)
- Corresponding author
- Segal, Eran
- Venue
- Nature Medicine
- Published
- 2020-01
- Volume
- 26
- Issue
- 1
- Pages
- 29-38
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W3000686922
- Semantic Scholar
- 718b5a40dba91bfa0bdfb9ac9ca4381425d2ff95
- Cited by
- 370
- References
- 136
- Citations by year
- 2026: 37 · 2025: 58 · 2024: 68 · 2023: 52 · 2022: 56 · 2021: 69 · 2020: 30
- FWCI
- 9.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- big data in healthcare, health data sources, disease understanding, data-driven medicine
- MeSH terms
- Big Data, Delivery of Health Care, Humans, Medicine, Reference Standards, Cohort Studies, Disease Progression
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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