The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system.
Open accessAbstract
Big data in medicine--massive quantities of health care data accumulating from patients and populations and the advanced analytics that can give those data meaning--hold the prospect of becoming an engine for the knowledge generation that is necessary to address the extensive unmet information needs of patients, clinicians, administrators, researchers, and health policy makers. This article explores the ways in which big data can be harnessed to advance prediction, performance, discovery, and comparative effectiveness research to address the complexity of patients, populations, and organizations. Incorporating big data and next-generation analytics into clinical and population health research and practice will require not only new data sources but also new thinking, training, and tools. Adequately utilized, these reservoirs of data can be a practically inexhaustible source of knowledge to fuel a learning health care system.
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“This study demonstrates that deployment governance can be treated as a first class computational problem in clinical artificial intelligence systems, distinct from model development and validation [8-11].”
From Adaptive FHIR Native AI Governance for Clinical Decision Support: A Modular, Auditable Deployment Framework for Real World Clinical AI (2026) · cites this paper as [9]
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“Robotic-assisted surgeries provide unparalleled precision, stability, and skill, reducing the risk of complications and accelerating patient recovery times.”
From Ethical implications of AI and robotics in healthcare: A review (2023) · cites this paper as [15]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
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“Surgeons can perform minimally invasive procedures with greater accuracy.[15]”
From Ethical implications of AI and robotics in healthcare: A review (2023) · cites this paper as [15]
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“Current monitoring practices are often reactive, retrospective, or limited to aggregate performance metrics, offering limited support for real time governance or accountability [8, 9].”
From Adaptive FHIR Native AI Governance for Clinical Decision Support: A Modular, Auditable Deployment Framework for Real World Clinical AI (2026) · cites this paper as [9]
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“The framework is organized into three interacting layers: a data layer, a governance layer, and an execution layer. This modular separation is intended to decouple clinical data representation, deployment decision logic, and predictive model execution, enabling independent evaluation and reuse of each component [9-11].”
From Adaptive FHIR Native AI Governance for Clinical Decision Support: A Modular, Auditable Deployment Framework for Real World Clinical AI (2026) · cites this paper as [9]
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Citations
Cites
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Self-citationOpen Science and Data Sharing in Clinical Research
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Self-citation“Phenotyping” Hospital Value of Care for Patients with Heart Failure
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Self-citationHow Do We Know the Value of Our Research?
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Self-citationGrant Applications With a Result-Based Orientation
Krumholz, H. M. (2013) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.113.000562 · Cited by 3
Self-citation
Flagged first, then by citations
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Cited by
Sorted by citations
Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579
Scalable and accurate deep learning with electronic health records
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“It is widely suspected that use such efforts, if successful, could provide major benefits not only for patient safety and quality but also in reducing health care costs [4, 33, 26, 42].”
BackgroundScalable and accurate deep learning with electronic health records.
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · eScholarship (California Digital Library) · Cited by 2,242
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The use of Big Data Analytics in healthcare
Batko, K. M., & Ślęzak, A. (2022) · Journal Of Big Data · doi:10.1186/s40537-021-00553-4 · Cited by 600
Metadata
- Authors
- Krumholz, H. M.
- Affiliations
- Yale University (US)
- Corresponding author
- Krumholz, Harlan M.
- ORCID
- Krumholz, Harlan M.
- Venue
- Health Affairs
- Publisher
- Health Affairs Publishing, LLC
- Published
- 2014-07
- Volume
- 33
- Issue
- 7
- Pages
- 1163-1170
- ISSN
- 0278-2715 (ISSN-L)
- 1544-5208
- PMC
- PMC5459394
- OpenAlex
- W2027540165
- Cited by
- 589
- References
- 19
- Citations by year
- 2026: 18 · 2025: 31 · 2024: 41 · 2023: 30 · 2022: 41 · 2021: 52 · 2020: 68 · 2019: 61 · 2018: 126 · 2017: 47 · 2016: 36 · 2015: 32 · 2014: 5
- FWCI
- 74.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Health Affairs (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (U01HL105270)
- 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, learning health systems, health care analytics, comparative effectiveness research, prediction, clinical research, population health, health data sources, clinician training, health policy
- MeSH terms
- Data Interpretation, Statistical, Delivery of Health Care, Humans, Learning, Medical Informatics, Thinking, Decision Support Systems, Clinical, Data Mining, Datasets as Topic
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