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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Deployment governance can be treated as a first-class computational problem in clinical AI systems, distinct from model development and validation.
“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]
In “New Methods and Tools” (page unavailable), the paper says health care leaders should attend to implementation beyond developing tools. That supports treating implementation as a separate concern, but the paper does not describe deployment governance as a computational problem in clinical AI or distinguish it from model validation. (LLM, conf=0.72)
Experimental — accuracy not yet measuredRobotic-assisted surgeries provide precision, stability, and skill, reducing risk of complications and accelerating patient recovery times.
“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]
The ‘What The Future Could Hold’ section discusses using algorithms to predict recovery after surgery, not the effects of robotic-assisted surgery. Searches for robotic surgery, precision, complications, and faster recovery found no passage in our copy supporting the attributed claim; page numbers were not available. (LLM, conf=0.78)
Experimental — accuracy not yet measuredSurgeons can perform minimally invasive procedures with greater accuracy using robotic-assisted surgery.
“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]
The “What The Future Could Hold” section discusses algorithms for predicting recovery after surgery, not how surgeons perform procedures. Searches for robotic-assisted surgery, minimally invasive procedures, and surgical accuracy found no passage addressing the claim. (LLM, conf=0.77)
Experimental — accuracy not yet measuredCurrent monitoring practices are often reactive, retrospective, or limited to aggregate performance metrics, offering limited support for real-time governance or accountability.
“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]
In “The Stakes,” the paper says routine clinical data are largely wasted; in “What the Future Could Hold,” it proposes using data to anticipate problems. Neither section characterizes current monitoring practices as reactive, retrospective, or limited to aggregate metrics, or assesses their support for real-time governance and accountability. Searches for those terms and related monitoring language found no passage addressing that claim. (LLM, conf=0.72)
Experimental — accuracy not yet measuredThe framework is organized into three interacting layers-a data layer, a governance layer, and an execution layer-intended to decouple clinical data representation, deployment decision logic, and predictive model execution.
“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]
The New Methods and Tools section discusses analytic approaches, and What the Future Could Hold discusses interactive presentation of results. Neither describes a data, governance, and execution layer or the proposed decoupling of clinical data representation, deployment decisions, and model execution. Searches for those layer names and functions found no supporting passage in our copy; page numbers were not available. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Cites
The environment and disease: association or causation?
Hill, A. B. (2015) · Journal of the Royal Society of Medicine · doi:10.1177/0141076814562718 · Cited by 7,302 · see citing works
The Environment and Disease: Association or Causation?
Hill, A. B. (1965) · Proceedings of the Royal Society of Medicine · doi:10.1177/003591576505800503 · Cited by 6,974 · see citing works
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365 · see citing works
Platt, J. R. (1964) · Science · doi:10.1126/science.146.3642.347 · Cited by 2,907 · see citing works
Representation of the Elderly, Women, and Minorities in Heart Failure Clinical Trials
Heiat, A., Gross, C. P., & Krumholz, H. M. (2002) · Archives of Internal Medicine · doi:10.1001/archinte.162.15.1682 · Cited by 723 · see citing works
Self-citationOptum Labs: Building A Novel Node In The Learning Health Care System
Wallace, P. J., Shah, N. D., Dennen, T., Bleicher, P. A., & Crown, W. H. (2014) · Health Affairs · doi:10.1377/hlthaff.2014.0038 · Cited by 449 · see citing works
Morgan, T. M., Krumholz, H. M., Lifton, R. P., & Spertus, J. A. (2007) · JAMA · doi:10.1001/jama.297.14.1551 · Cited by 270 · see citing works
Self-citationSystems Biology and P4 Medicine: Past, Present, and Future
Hood, L. (2013) · Rambam Maimonides Medical Journal · doi:10.5041/rmmj.10112 · Cited by 267 · see citing works
Mapping Physician Networks with Self-Reported and Administrative Data
Barnett, M. L., Landon, B. E., O’Malley, A. J., Keating, N. L., & Christakis, N. A. (2011) · Health Services Research · doi:10.1111/j.1475-6773.2011.01262.x · Cited by 263 · see citing works
Web-scale pharmacovigilance: listening to signals from the crowd
White, R. W., Tatonetti, N. P., Shah, N. H., Altman, R. B., & Horvitz, E. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001482 · Cited by 200 · see citing works
Physician Patient-sharing Networks and the Cost and Intensity of Care in US Hospitals
Barnett, M. L., Christakis, N. A., O’Malley, A. J., Onnela, J.-P. “., Keating, N. L., & Landon, B. E. (2012) · Medical Care · doi:10.1097/mlr.0b013e31822dcef7 · Cited by 194 · see citing works
Advances in Machine Learning and Data Mining for Astronomy
Way, M. J., Scargle, J. D., Ali, K., & Srivastava, A. N. (2012) · Chapman & Hall/CRC eBooks · doi:10.1201/b11822 · Cited by 138 · see citing works
Machine learning and genome annotation: a match meant to be?
Yip, K. Y.-L., Cheng, C., & Gerstein, M. (2013) · Genome biology · doi:10.1186/gb-2013-14-5-205 · Cited by 100 · see citing works
A Historic Moment for Open Science: The Yale University Open Data Access Project and Medtronic
Krumholz, H. M., Ross, J. S., Gross, C. P., Emanuel, E. J., Hodshon, B., Ritchie, J. D., Low, J. B., & Lehman, R. (2013) · Annals of Internal Medicine · doi:10.7326/0003-4819-158-12-201306180-00009 · Cited by 86 · see citing works
Self-citationOpen Science and Data Sharing in Clinical Research
Krumholz, H. M. (2012) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.112.965848 · Cited by 41 · see citing works
Self-citationKrumholz, H. M. (2009) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.108.844035 · Cited by 30 · see citing works
Self-citationPatterns of Change in Nesiritide Use in Patients With Heart Failure
Partovian, C., Li, S., Xu, X. J., Lin, H., Strait, K. M., Hwa, J., & Krumholz, H. M. (2013) · JACC Heart Failure · doi:10.1016/j.jchf.2013.04.005 · Cited by 13 · see citing works
Self-citation“Phenotyping” Hospital Value of Care for Patients with Heart Failure
Xu, X. J., Li, S., Lin, H., Normand, S. T., Kim, N. V., Ott, L. S., Lagu, T. C., Duan, M., Kroch, E. A., & Krumholz, H. M. (2014) · Health Services Research · doi:10.1111/1475-6773.12197 · Cited by 11 · see citing works
Self-citationHow Do We Know the Value of Our Research?
Krumholz, H. M. (2013) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.113.000423 · Cited by 10 · see citing works
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 · see citing works
Self-citation
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Research works printed but not linked: 2
- [10]National Research Council Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease Washington (DC) National Academies Press 2011 22536618
- [18]National Research Council Frontiers in massive data analysis Washington (DC) National Academies Press 2013
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 · see citing works
Scalable 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) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · see citing works
“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 · see citing works
Electronic health records to facilitate clinical research
Cowie, M. R., Blomster, J. I., Curtis, L. H., Duclaux, S., Ford, I., Fritz, F., Goldman, S., Janmohamed, S., Kreuzer, J., Leenay, M., et al. (2017) · Clinical Research in Cardiology · doi:10.1007/s00392-016-1025-6 · Cited by 651 · see citing works
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 · see citing works
Taylor, R. A., Pare, J. R., Venkatesh, A. K., Mowafi, H. O., Melnick, E. R., Fleischman, W. M., & Hall, M. K. (2015) · Academic Emergency Medicine · doi:10.1111/acem.12876 · Cited by 496 · see citing works
Concurrence of big data analytics and healthcare: a systematic review
Mehta, N., & Pandit, A. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.03.013 · Cited by 494 · see citing works
Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects
Kent, D. M., Steyerberg, E. W., & Klaveren, D. V. (2018) · BMJ · doi:10.1136/bmj.k4245 · Cited by 487 · see citing works
Early prediction of circulatory failure in the intensive care unit using machine learning
Hyland, S. L., Faltys, M., Hüser, M., Lyu, X., Gumbsch, T., Esteban, C., Bock, C., Horn, M., Moor, M., Rieck, B., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0789-4 · Cited by 477 · see citing works
Big data analytics to improve cardiovascular care: promise and challenges
Rumsfeld, J. S., Joynt, K. E., & Maddox, T. M. (2016) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2016.42 · Cited by 398 · see citing works
Ethical implications of AI and robotics in healthcare: A review
Elendu, C., Amaechi, D. C., Elendu, T. C., Jingwa, K. A., Okoye, O. K., John Okah, M., Ladele, J. A., Farah, A. H., & Alimi, H. A. (2023) · Medicine · doi:10.1097/md.0000000000036671 · Cited by 370 · see citing works
“[15] 3.”
A framework for value-creating learning health systems
Menear, M., Blanchette, M., Demers‐Payette, O., & Roy, D. A. (2019) · Health Research Policy and Systems · doi:10.1186/s12961-019-0477-3 · Cited by 346 · see citing works
Andre, J. B., Bresnahan, B. W., Mossa‐Basha, M., Hoff, M. N., Smith, C. P., Anzai, Y., & Cohen, W. A. (2015) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2015.03.007 · Cited by 264 · see citing works
Ahmad, T., Lund, L. H., Rao, P., Ghosh, R., Warier, P., Vaccaro, B. J., Dahlström, U., O’Connor, C. M., Felker, G. M., & Desai, N. R. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.117.008081 · Cited by 248 · see citing works
Varma, N., Piccini, J. P., Snell, J. D., Fischer, A., Dalal, N., & Mittal, S. (2015) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2015.04.033 · Cited by 232 · see citing works
Big data in digital healthcare: lessons learnt and recommendations for general practice
Agrawal, R., & Prabakaran, S. (2020) · Heredity · doi:10.1038/s41437-020-0303-2 · Cited by 218 · see citing works
The state of Cancer Care in America, 2017: a report by the American Society of Clinical Oncology
The American Society of Clinical Oncology (2017) · Journal of Oncology Practice · doi:10.1200/jop.2016.020743 · Cited by 204 · see citing works
Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence
Bonkhoff, A. K., & Grefkes, C. (2021) · Brain · doi:10.1093/brain/awab439 · Cited by 195 · see citing works
Raine, R., Fitzpatrick, R. M., Barratt, H., Bevan, G., Black, N. A., Boaden, R., Bower, P., Campbell, M., Denis, J., Devers, K. J., et al. (2016) · Health Services and Delivery Research · doi:10.3310/hsdr04160 · Cited by 187 · see citing works
The Cardiovascular Health in Ambulatory Care Research Team (CANHEART)
Tu, J. V., Chu, A., Donovan, L. R., Ko, D. T., Booth, G. L., Tu, K., Maclagan, L. C., Guo, H., Austin, P. C., Hogg, W. E., et al. (2015) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.114.001416 · Cited by 187 · see citing works
Perlman, K., Benrimoh, D. A., Israel, S. A., Rollins, C. P. E., Brown, E. H., Tunteng, J.-F., You, R., You, E. L., Tanguay-Sela, M., Snook, E., et al. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.09.067 · Cited by 185 · see citing works
Data Integration Challenges for Machine Learning in Precision Medicine
Martínez-García, M., & Hernández-Lemus, E. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.784455 · Cited by 158 · see citing works
Educating the nurses of 2025: Technology trends of the next decade
Risling, T. (2017) · Nurse Education in Practice · doi:10.1016/j.nepr.2016.12.007 · Cited by 161 · see citing works
Bhavnani, S. P., Parakh, K., Atreja, A., Druz, R. S., Graham, G. N., Hayek, S. S., Krumholz, H. M., Maddox, T. M., Majmudar, M. D., Rumsfeld, J. S., et al. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.10.018 · Cited by 162 · see citing works
Self-citationReproductive management in dairy cows - the future
Crowe, M. A., Hostens, M., & OPSOMER, G. (2018) · Irish Veterinary Journal · doi:10.1186/s13620-017-0112-y · Cited by 158 · see citing works
Navarro, S. M., Wang, E. Y., Haeberle, H. S., Mont, M. A., Krebs, V. E., Patterson, B. M., & Ramkumar, P. N. (2018) · The Journal of Arthroplasty · doi:10.1016/j.arth.2018.08.028 · Cited by 157 · see citing works
Chiew, C. J., Liu, N., Wong, T. H., Sim, Y. E., & Abdullah, H. R. (2020) · Annals of Surgery · doi:10.1097/sla.0000000000003297 · Cited by 151 · see citing works
Big data analytics enhanced healthcare systems: a review
Shafqat, S., Kishwer, S., Rasool, R. U., Qadir, J., Amjad, T., & Ahmad, H. F. (2018) · The Journal of Supercomputing · doi:10.1007/s11227-017-2222-4 · Cited by 149 · see citing works
Population-Based Approaches to Mental Health: History, Strategies, and Evidence
Purtle, J., Nelson, K. L., Counts, N. Z., & Yudell, M. (2020) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040119-094247 · Cited by 157 · see citing works
Parnell, S. M., Elmqvist, T., McPhearson, T., Nagendra, H., Sörlin, S., Haase, D., Güneralp, B., Dahiya, B., Bai, X., Alberti, M., et al. (2018) · Cambridge University Press eBooks · doi:10.1017/9781316647554 · Cited by 142 · see citing works
An Analysis of the Learning Health System in Its First Decade in Practice: Scoping Review
Platt, J. E., Raj, M., & Wienroth, M. (2019) · Journal of Medical Internet Research · doi:10.2196/17026 · Cited by 132 · see citing works
Healthcare big data processing mechanisms: The role of cloud computing
Rajabion, L., Shaltooki, A. A., Taghikhah, M., Ghasemi, A., & Badfar, A. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.05.017 · Cited by 127 · see citing works
Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective
Zhang, S., Bamakan, S. M. H., Qu, Q., & Li, S. (2019) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2018.2864254 · Cited by 125 · see citing works
Biomedical Big Data: New Models of Control Over Access, Use and Governance
Vayena, E., & Blasimme, A. (2017) · Journal of Bioethical Inquiry · doi:10.1007/s11673-017-9809-6 · Cited by 120 · see citing works
A review of the literature on big data analytics in healthcare
Galetsi, P., & Katsaliaki, K. (2019) · Journal of the Operational Research Society · doi:10.1080/01605682.2019.1630328 · Cited by 121 · see citing works
Visvanathan, K., Levit, L. A., Raghavan, D., Hudis, C. A., Wong, S. L., Dueck, A. C., & Lyman, G. H. (2017) · Journal of Clinical Oncology · doi:10.1200/jco.2017.72.6414 · Cited by 112 · see citing works
Tursunbayeva, A., Franco, M., & Pagliari, C. (2017) · Government Information Quarterly · doi:10.1016/j.giq.2017.04.001 · Cited by 112 · see citing works
Big Data in Health Care: Applications and Challenges
Hong, L., Luo, M., Wang, R., Lu, P., Lu, W., & Lu, L. J. (2018) · Data and Information Management · doi:10.2478/dim-2018-0014 · Cited by 112 · see citing works
Exploring the Potential of Predictive Analytics and Big Data in Emergency Care
Janke, A. T., Overbeek, D. L., Kocher, K. E., & Levy, P. D. (2015) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2015.06.024 · Cited by 108 · see citing works
Hoeyer, K. (2023) · The MIT Press eBooks · doi:10.7551/mitpress/14926.001.0001 · Cited by 107 · see citing works
Data-intensive resourcing in healthcare
Hogle, L. F. (2016) · BioSocieties · doi:10.1057/s41292-016-0004-5 · Cited by 103 · see citing works
A systematic perspective on the applications of big data analytics in healthcare management
Kamble, S., Gunasekaran, A., Goswami, M., & Manda, J. (2018) · International Journal of Healthcare Management · doi:10.1080/20479700.2018.1531606 · Cited by 102 · see citing works
Chen, J. H., Alagappan, M., Goldstein, M. K., Asch, S. M., & Altman, R. B. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.03.006 · Cited by 101 · see citing works
Oikonomou, E. K., Siddique, M. M., & Antoniades, C. A. (2020) · Cardiovascular Research · doi:10.1093/cvr/cvaa021 · Cited by 100 · see citing works
Rizwan, A., Zoha, A., Zhang, R., Ahmad, W., Arshad, K., Ali, N. A. A., Alomainy, A., Imran, M. A., & Abbasi, Q. H. (2018) · IEEE Access · doi:10.1109/access.2018.2859340 · Cited by 98 · see citing works
Moss, T. J., Lake, D. E., Calland, J. F., Enfield, K. B., Delos, J. B., Fairchild, K. D., & Moorman, J. R. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000001738 · Cited by 98 · see citing works
Artificial intelligence in medicine and the disclosure of risks
Kiener, M. (2021) · AI & SOCIETY · doi:10.1007/s00146-020-01085-w · Cited by 97 · see citing works
Hidalgo-Mazzei, D., Mateu, A., Reinares, M., Matic, A., Vieta, E., & Colom, F. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.08.005 · Cited by 92 · see citing works
Nurmaini, S., Partan, R. U., Caesarendra, W., Dewi, T., Rahmatullah, M. N., Darmawahyuni, A., Bhayyu, V., & Firdaus, F. (2019) · Applied Sciences · doi:10.3390/app9142921 · Cited by 92 · see citing works
Interpretable Dynamic Treatment Regimes
Zhang, Y., Laber, E. B., Davidian, M., & Tsiatis, A. A. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1345743 · Cited by 95 · see citing works
EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases
Gossec, L., Kedra, J., Servy, H., Pandit, A., Stones, S. R., Berenbaum, F., Finckh, A., Baraliakos, X., Stamm, T., Gómez-Cabrero, D., et al. (2019) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2019-215694 · Cited by 91 · see citing works
Falsification Testing of Instrumental Variables Methods for Comparative Effectiveness Research
Pizer, S. D. (2015) · Health Services Research · doi:10.1111/1475-6773.12355 · Cited by 89 · see citing works
Jammal, A. A., Thompson, A. C., Mariottoni, E. B., Urata, C. N., Estrela, T., Berchuck, S. I., Tseng, H. C., Asrani, S., & Medeiros, F. A. (2020) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2020.05.019 · Cited by 83 · see citing works
Miller, P. E., Pawar, S., Vaccaro, B., McCullough, M., Rao, P., Ghosh, R., Warier, P., Desai, N. R., & Ahmad, T. (2019) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2019.01.018 · Cited by 81 · see citing works
Haase, D., Güneralp, B., Dahiya, B., Bai, X., & Elmqvist, T. (2018) · Cambridge University Press eBooks · doi:10.1017/9781316647554.003 · Cited by 82 · see citing works
The reuse of public datasets in the life sciences: potential risks and rewards
Sielemann, K., Hafner, A., & Pucker, B. (2020) · PeerJ · doi:10.7717/peerj.9954 · Cited by 79 · see citing works
The need for ‘skeptical’ accountants in the era of Big Data
McKinney, E. H., Yoos, C. J., & Snead, K. C. (2017) · Journal of Accounting Education · doi:10.1016/j.jaccedu.2016.12.007 · Cited by 80 · see citing works
Peng, S., Huang, J., Liu, X., Deng, J., Sun, C., Tang, J., Chen, H., Cao, W., Wang, W., Duan, X., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.994359 · Cited by 76 · see citing works
Kasthurirathne, S. N., Vest, J. R., Menachemi, N., Halverson, P. K., & Grannis, S. J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx130 · Cited by 71 · see citing works
Musa, S. M., Haruna, U. A., Manirambona, E., Eshun, G., Ahmad, D. M., Dada, D. A., Gololo, A. A., Musa, S. S., Abdulkadir, A. K., & Lucero‐Prisno, D. E. (2023) · Public health reviews · doi:10.3389/phrs.2023.1605821 · Cited by 69 · see citing works
Harnessing the Heart of Big Data
Scruggs, S. B., Watson, K. E., Su, A. I., Hermjakob, H., Yates, J. R., Lindsey, M. L., & Ping, P. (2015) · Circulation Research · doi:10.1161/circresaha.115.306013 · Cited by 66 · see citing works
Burlacu, A., Iftene, A., Jugrin, D., Popa, I. V., Lupu, P. M., Vlad, C.-E., & Covic, A. C. (2020) · BioMed Research International · doi:10.1155/2020/9867872 · Cited by 65 · see citing works
Charting a future for epidemiologic training
Brownson, R. C., Samet, J. M., Chavez, G. F., Davies, M., Galea, S., Hiatt, R. A., Hornung, C. A., Khoury, M. J., Koo, D., Mays, V. M., et al. (2015) · Annals of Epidemiology · doi:10.1016/j.annepidem.2015.03.002 · Cited by 63 · see citing works
Karnuta, J. M., Navarro, S. M., Haeberle, H. S., Billow, D. G., Krebs, V. E., & Ramkumar, P. N. (2019) · Journal of Orthopaedic Trauma · doi:10.1097/bot.0000000000001454 · Cited by 62 · see citing works
Application of Artificial Intelligence in Pharmaceutical and Biomedical Studies
Thakur, A., Mishra, A. P., Panda, B., Rodríguez, D. C. S., Gaurav, I., & Majhi, B. (2020) · Current Pharmaceutical Design · doi:10.2174/1381612826666200515131245 · Cited by 61 · see citing works
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Wang, Z., Xiong, H., Zhang, J., Yang, S., Boukhechba, M., Zhang, D., Barnes, L. E., & Dou, D. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3161046 · Cited by 62 · see citing works
Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU
Salas-Vega, S., Haimann, A., & Mossialos, E. A. (2015) · Health Systems & Reform · doi:10.1080/23288604.2015.1091538 · Cited by 60 · see citing works
Kim, M.-K., & Park, J.-H. (2017) · Information Development · doi:10.1177/0266666916652671 · Cited by 59 · see citing works
Chen, J. H., Goldstein, M. K., Asch, S. M., Mackey, L. W., & Altman, R. B. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw136 · Cited by 59 · see citing works
Naik, A. D., Hundt, N. E., Vaughan, E. M., Petersen, N. J., Zeno, D., Kunik, M. E., & Cully, J. A. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.8634 · Cited by 57 · see citing works
Zheng, L., Wang, Y., Hao, S., Shin, A. Y., Jin, B., Ngo, A. D., Jackson-Browne, M. S., Feller, D. J., Fu, T., Zhang, K., et al. (2016) · JMIR Medical Informatics · doi:10.2196/medinform.6328 · Cited by 57 · see citing works
Evolution of primary care databases in UK: a scientometric analysis of research output
Vezyridis, P., & Timmons, S. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-012785 · Cited by 54 · see citing works
Chronic Diseases and Health Monitoring Big Data: A Survey
Lin, R., Ye, Z., Wang, H. H., & Wu, B. (2018) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2018.2829704 · Cited by 56 · see citing works
Digital maturity: are we ready to use technology in the NHS?
Johnston, D. S. (2017) · Future Healthcare Journal · doi:10.7861/futurehosp.4-3-189 · Cited by 55 · see citing works
Furtner, D., Shinde, S. P., Singh, M., Wong, C. H., & Setia, S. (2021) · Pharmaceutical Medicine · doi:10.1007/s40290-021-00412-w · Cited by 54 · see citing works
Needle in a Haystack: Natural Language Processing to Identify Serious Illness
Udelsman, B. V., Chien, I., Ouchi, K., Brizzi, K. T., Tulsky, J. A., & Lindvall, C. J. (2018) · Journal of Palliative Medicine · doi:10.1089/jpm.2018.0294 · Cited by 54 · see citing works
A maturity grid assessment tool for learning networks
Lannon, C., Schuler, C. L., Seid, M., Provost, L., Fuller, S., Purcell, D., Forrest, C. B., & Margolis, P. A. (2020) · Learning Health Systems · doi:10.1002/lrh2.10232 · Cited by 53 · see citing works
Big Data, Health Informatics, and the Future of Cardiovascular Medicine
Kim, J. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.01.006 · Cited by 53 · see citing works
MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records
Xu, Z., So, D. R., & Dai, A. M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17260 · Cited by 52 · see citing works
Personalised Medicine, Individual Choice and the Common Good
van Beers, B. C., Dickenson, D., & Sterckx, S. (2018) · Cambridge University Press eBooks · doi:10.1017/9781108590600 · Cited by 52 · see citing works
Digital pills: a scoping review of the empirical literature and analysis of the ethical aspects
Martani, A., Geneviève, L. D., Poppe, C. J., Casonato, C., & Wangmo, T. (2020) · BMC Medical Ethics · doi:10.1186/s12910-019-0443-1 · Cited by 52 · see citing works
Chen, J. H., Podchiyska, T., & Altman, R. B. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv091 · Cited by 52 · see citing works
Jammal, A. A., Thompson, A. C., Mariottoni, E. B., Estrela, T., Shigueoka, L., Berchuck, S. I., & Medeiros, F. A. (2020) · Ophthalmology · doi:10.1016/j.ophtha.2020.06.027 · Cited by 51 · see citing works
Convergent evolution of health information management and health informatics
Gibson, C. J., Abrams, K. R., & Dixon, B. E. (2015) · Applied Clinical Informatics · doi:10.4338/aci-2014-09-ra-0077 · Cited by 51 · see citing works
Competencias digitales clave de los profesionales sanitarios
Delgado, J. A. M., Alonso, F. J. M., Monte‐Boquet, E., de Tomás, J. F. Á., & Díez, J. M. C. (2019) · Educación Médica · doi:10.1016/j.edumed.2019.02.010 · Cited by 51 · see citing works
Alberti, M., McPhearson, T., & Gonzalez, A. (2018) · Cambridge University Press eBooks · doi:10.1017/9781316647554.004 · Cited by 53 · see citing works
Big data analytics in health: an overview and bibliometric study of research activity
Galetsi, P., & Katsaliaki, K. (2019) · Health Information & Libraries Journal · doi:10.1111/hir.12286 · Cited by 53 · see citing works
Salazar-Reyna, R., Aleu, F. G., Gutiérrez, E. M. A. G., Díaz-Ramírez, J., Garza‐Reyes, J. A., & Kumar, A., V (2020) · Management Decision · doi:10.1108/md-01-2020-0035 · Cited by 53 · see citing works
Marshall, D. A., Burgos-Liz, L., Pasupathy, K. S., Padula, W. V., IJzerman, M. J., Wong, P. K., Higashi, M. K., Engbers, J. D. T., Wiebe, S., Crown, W. H., et al. (2015) · PharmacoEconomics · doi:10.1007/s40273-015-0330-7 · Cited by 49 · see citing works
It is time to learn from patients like mine
Gombar, S., Callahan, A., Califf, R. M., Harrington, R. A., & Shah, N. H. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0091-3 · Cited by 49 · see citing works
A framework for analysing learning health systems: Are we removing the most impactful barriers?
McLachlan, S., Dube, K., Johnson, O. A., Buchanan, D., POTTS, H. W. W., Gallagher, T., & Fenton, N. (2019) · Learning Health Systems · doi:10.1002/lrh2.10189 · Cited by 47 · see citing works
Identifying requisite learning health system competencies: a scoping review
McDonald, P. L., Phillips, J. R., Harwood, K. J., Maring, J., & van der Wees, P. J. (2022) · BMJ Open · doi:10.1136/bmjopen-2022-061124 · Cited by 47 · see citing works
Trinkley, K. E., Ho, P. M., Glasgow, R. E., & Huebschmann, A. G. (2022) · Academic Medicine · doi:10.1097/acm.0000000000004801 · Cited by 45 · see citing works
Ahmad, T., Desai, N. R., Wilson, F. P., Schulte, P. J., Dunning, A. M., Jacoby, D. L., Allen, L. A., Fiuzat, M., Rogers, J. G., Felker, G. M., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0145881 · Cited by 46 · see citing works
Chartash, D., Paek, H., Dziura, J. D., Ross, B. K., Nogee, D., Boccio, E., Hines, C., Schott, A. M., Jeffery, M. M., Patel, M. D., et al. (2019) · JMIR Medical Informatics · doi:10.2196/15794 · Cited by 44 · see citing works
Application of machine learning to the prediction of postoperative sepsis after appendectomy
Bunn, C., Kulshrestha, S., Boyda, J., Balasubramanian, N., Birch, S., Karabayir, İ., Baker, M. S., Luchette, F. A., Modave, F. P., & Akbilgiç, O. (2020) · Surgery · doi:10.1016/j.surg.2020.07.045 · Cited by 45 · see citing works
Big Data to Improve Public Health in Low- and Middle-Income Countries
Çınaroğlu, S. (2019) · Advances in public policy and administration (APPA) book series · doi:10.4018/978-1-5225-7591-7.ch005 · Cited by 43 · see citing works
A review of machine learning for big data analytics: bibliometric approach
El-Alfy, E.-S. M., & Mohammed, S. A. (2020) · Technology Analysis and Strategic Management · doi:10.1080/09537325.2020.1732912 · Cited by 43 · see citing works
Weissman, G. E., Ungar, L., Harhay, M. O., Courtright, K. R., & Halpern, S. D. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.12.001 · Cited by 42 · see citing works
Agaronnik, N. D., Lindvall, C. J., El-Jawahri, A. R., He, W. W., & Iezzoni, L. I. (2020) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2020.04.024 · Cited by 42 · see citing works
- 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 · 2013: 1
- 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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