Applying Advanced Analytics in Clinical Care
AHRQ hosted a webinar to discuss how advanced analytics can be applied into clinical care. Specifically, the panel featured findings from research projects that applied machine learning and natural language processing techniques to effectively analyze unstructured text information and process data from multiple sources for identification of patients who would benefit from treatments or interventions.
At the conclusion of this webinar, participants should be able to:
- Review how machine learning algorithms in conjunction with natural language processing can be used to identify patients at high risk for death.
- Evaluate the benefits of using EHR-integrated machine learning algorithms to identify patients with epilepsy who could benefit from surgery.
- Describe how data mining and machine learning can help forecast adverse outcomes among surgical patients.
- Discuss different advanced data analytic techniques for improving the quality, safety, effectiveness, and efficiency of care.
- Identify how to best integrate advanced data analytics into clinical practice.
Chun-Ju (Janey) Hsiao, Ph.D.
Eligible providers were able to earn up to 1.5 CE/CME contact hours for participating in the live webinar.
If you have any questions, please send an email to DigitalHealthcareResearch@ahrq.hhs.gov.