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Improving Health Care with Advanced Analytics: Practical Considerations
Artificial intelligence (AI) is becoming ubiquitous in health care, largely through machine learning and predictive analytics applications. Recent applications of AI to common health care scenarios, such as screening and diagnosing, have fueled optimism about the use of advanced analytics to improve...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Ubiquity Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6437691/ https://www.ncbi.nlm.nih.gov/pubmed/30937325 http://dx.doi.org/10.5334/egems.276 |
Sumario: | Artificial intelligence (AI) is becoming ubiquitous in health care, largely through machine learning and predictive analytics applications. Recent applications of AI to common health care scenarios, such as screening and diagnosing, have fueled optimism about the use of advanced analytics to improve care. Careful and objective considerations need to be made before implementing an advanced analytics solution. Critical evaluation before, during, and after its implementation will ensure safe care, good outcomes, and the elimination of waste. In this commentary we offer basic practical considerations for developing, implementing, and evaluating such solutions based on many years of experience. |
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