Biomedical informatics advancing the national health agenda: the AMIA 2015 year-in-review in clinical and consumer informatics
Open accessAbstract
The field of biomedical informatics experienced a productive 2015 in terms of research. In order to highlight the accomplishments of that research, elicit trends, and identify shortcomings at a macro level, a 19-person team conducted an extensive review of the literature in clinical and consumer informatics. The result of this process included a year-in-review presentation at the American Medical Informatics Association Annual Symposium and a written report (see supplemental data). Key findings are detailed in the report and summarized here. This article organizes the clinical and consumer health informatics research from 2015 under 3 themes: the electronic health record (EHR), the learning health system (LHS), and consumer engagement. Key findings include the following: (1) There are significant advances in establishing policies for EHR feature implementation, but increased interoperability is necessary for these to gain traction. (2) Decision support systems improve practice behaviors, but evidence of their impact on clinical outcomes is still lacking. (3) Progress in natural language processing (NLP) suggests that we are approaching but have not yet achieved truly interactive NLP systems. (4) Prediction models are becoming more robust but remain hampered by the lack of interoperable clinical data records. (5) Consumers can and will use mobile applications for improved engagement, yet EHR integration remains elusive.
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ML methods enable the development of AI applications that facilitate the discovery of previously unrecognized patterns in the data without the need to specify decision rules for each specific task or to account for complex interactions among input features. Therefore, ML has become the preferred framework for building AI utilities.
“In summary, ML methods enable the development of AI applications that facilitate the discovery of previously unrecognized patterns in the data without the need to specify decision rules for each specific task or to account for complex interactions among input features. Therefore, ML has become the preferred framework for building AI utilities.34”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [34]
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RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials
Marshall, I. J., Kuiper, J., & Wallace, B. C. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv044 · Cited by 317 · see citing works
Rusanov, A., Weiskopf, N. G., Wang, S., & Weng, C. (2014) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-14-51 · Cited by 137 · see citing works
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Research works printed but not linked: 2
- [1]1. Masys D. AMIA Informatics Years in Review. http://faculty.washington.edu/dmasys/YearInReview/. Accessed 13 July 2016. [Google Scholar]
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Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817 · see citing works
Self-citationYang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717 · see citing works
Artificial intelligence in diabetes management: Advancements, opportunities, and challenges
Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101213 · Cited by 287 · see citing works
“34 Based on the present background, we review the recent advancements in the application of AI in the clinical practice of diabetes management and then discuss the opportunities and challenges of AI applications.”
Alderson, P. O., Donlin, M. J., & Morrison, L. A. (2021) · doi:10.1101/2021.05.13.21255493 · Cited by 3 · see citing works
- Authors
- Roberts, K., Boland, M. R., Pruinelli, L., Dcruz, J., Berry, A., Georgsson, M., Hazen, R., Sarmiento, R. F., Backonja, U., Yu, K.-H., Jiang, Y., & Brennan, P. F.
- Affiliations
- United States National Library of Medicine (US)
- Columbia University (US)
- University of Minnesota (US)
- Centers for Disease Control and Prevention (US)
- University of Washington (US)
- Blekinge Institute of Technology (SE)
- Stanford Medicine (US)
- University of Michigan (US)
- School of Nursing, University of Wisconsin-Madison
- Corresponding author
- Roberts, Kirk
- ORCID
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-08-07
- Volume
- 24
- Issue
- e1
- Pages
- e185-e190
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC6080724
- OpenAlex
- W2490973085
- Cited by
- 25
- References
- 2
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 3 · 2022: 1 · 2021: 6 · 2020: 1 · 2019: 2 · 2018: 2 · 2017: 3 · 2016: 3
- FWCI
- 0.68
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- University of Washington Tacoma Digital Commons (University of Washington Tacoma) (submittedVersion)
- Funders
- US National Library of Medicine (1K99LM012104)
- University of Washington
- intramural research program (T15LM00707, T15LM007442)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- clinical informatics, consumer health informatics, electronic health records, learning health systems, customer engagement, clinical decision support, natural language processing, clinical prediction model, EHR interoperability, mobile health apps
- MeSH terms
- Consumer Health Informatics, Humans, Medical Informatics, Patient Participation, Societies, Medical, United States, Public Health Informatics, Meaningful Use
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