The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Health Information Management: Implications of Artificial Intelligence on Healthcare Data and Information Management
Open accessAbstract
Objective: This paper explores the implications of artificial intelligence (AI) on the management of healthcare data and information and how AI technologies will affect the responsibilities and work of health information management (HIM) professionals. Methods: A literature review was conducted of both peer-reviewed literature and published opinions on current and future use of AI technology to collect, store, and use healthcare data. The authors also sought insights from key HIM leaders via semi-structured interviews conducted both on the phone and by email. Results: The following HIM practices are impacted by AI technologies: 1) Automated medical coding and capturing AI-based information; 2) Healthcare data management and data governance; 3) Fbtient privacy and confidentiality; and 4) HIM workforce training and education. Discussion: HIM professionals must focus on improving the quality of coded data that is being used to develop AI applications. HIM professional’s ability to identify data patterns will be an important skill as automation advances, though additional skills in data analysis tools and techniques are needed. In addition, HIM professionals should consider how current patient privacy practices apply to AI application, development, and use. Conclusions: AI technology will continue to evolve as will the role of HIM professionals who are in a unique position to take on emerging roles with their depth of knowledge on the sources and origins of healthcare data. The challenge for HIM professionals is to identify leading practices for the management of healthcare data and information in an AI-enabled world.
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Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076
Artificial intelligence in cardiology
Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.521 · Cited by 1,202
Artificial Intelligence Transforms the Future of Health Care
Noorbakhsh-Sabet, N., Zand, R., Zhang, Y., & Abedi, V. (2019) · The American Journal of Medicine · doi:10.1016/j.amjmed.2019.01.017 · Cited by 626
Google DeepMind and healthcare in an age of algorithms
Powles, J., & Hodson, H. (2017) · Health and Technology · doi:10.1007/s12553-017-0179-1 · Cited by 511
Will artificial intelligence solve the human resource crisis in healthcare?
Meskó, B., Hetényi, G., & Győrffy, Z. (2018) · BMC Health Services Research · doi:10.1186/s12913-018-3359-4 · Cited by 360
Bertsimas, D., Dunn, J., Velmahos, G. C., & Kaafarani, H. M. A. (2018) · Annals of Surgery · doi:10.1097/sla.0000000000002956 · Cited by 321
Golas, S. B., Shibahara, T., Agboola, S., Otaki, H., Sato, J., Nakae, T., Hisamitsu, T., Kojima, G., Felsted, J., Kakarmath, S., et al. (2018) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0620-z · Cited by 287
Delahanty, R. J., Kaufman, D., & Jones, S. S. (2018) · Critical Care Medicine · doi:10.1097/ccm.0000000000003011 · Cited by 105
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Rajula, H. S. R., Verlato, G., Manchia, M., Antonucci, N., & Fanos, V. (2020) · Medicina · doi:10.3390/medicina56090455 · Cited by 606
Franco D’Souza, R., Mathew, M., Mishra, V., & Surapaneni, K. M. (2024) · Medical Education Online · doi:10.1080/10872981.2024.2330250 · Cited by 147
Hah, H., & Goldin, D. S. (2021) · Journal of Medical Internet Research · doi:10.2196/33540 · Cited by 81
Pedro, A. R., Dias, M. B., Laranjo, L., Cunha, A. S., & Cordeiro, J. V. (2023) · PLOS ONE · doi:10.1371/journal.pone.0290613 · Cited by 74
Metadata
- Authors
- Stanfill, M. H., & Marc, D. T.
- Venue
- Yearbook of Medical Informatics
- Publisher
- Georg Thieme Verlag KG
- Published
- 2019-08
- Volume
- 28
- Issue
- 01
- Pages
- 056-064
- ISSN
- 0943-4747 (ISSN-L)
- 2364-0502
- OpenAlex
- W2968508357
- Cited by
- 163
- References
- 10
- FWCI
- 10.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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