- Patients and Addresses (Introduction)
- Tracking AI Usage in Healthcare UPDATE
- Tracking AI Usage in Healthcare (part 3 of 3)
- Tracking AI Usage in Healthcare (part 2 of 3)
- Tracking AI Usage in Healthcare (part 1 of 3)
Patients and Addresses (Introduction)
Reference ONC-AHIMA Project Team. (2022). Project US@ ONC-AHIMA Companion Guide. Retrieved from https://oncprojectracking.healthit.gov/wiki/pages/viewpage.action?pageId=180486153 Patient associated with Address. ORM Summary: Addresses have types, and each address is associated with at most one type. However, it is possible for multiple addresses to be associated with the same type. ORM Verbalizer: Patient associated with Address.It is possible that…
Tracking AI Usage in Healthcare UPDATE
Overview of Metrics for Tracking AI Models Full Diagrams: ORM, Barker, ERD at bottom of article. Monitoring AI performance across diverse training locations and varied health populations is an increasingly critical aspect of ensuring the robustness and inclusivity of AI models. Training location can significantly influence the quality of datasets used, as geographical, cultural, and…
Tracking AI Usage in Healthcare (part 3 of 3)
UW Health and Epic co-hosted a Roundtable on Artificial Intelligence in Healthcare on June 5, 2024, in Washington, DC. It aimed to explore AI’s role in healthcare delivery and patient/provider experience with thought leaders from various sectors. The discussions centered around: Addressing workforce shortages: AI can enhance clinical and administrative efficiency and support human interactions…
Tracking AI Usage in Healthcare (part 2 of 3)
Tracking population geography when training AI models for healthcare is highly beneficial as it allows the AI to understand and adapt to regional health trends and disparities. This geographical data ensures the AI models are tailored to provide accurate predictions and recommendations, reflecting the specific health needs and conditions of different populations. The relationship between…
Tracking AI Usage in Healthcare (part 1 of 3)
Tracking which AI models are used to support decisions in healthcare when patients are involved is crucial for ensuring accountability and transparency in medical practices. It enables healthcare providers to evaluate the effectiveness and accuracy of AI recommendations, ensuring that patient care is optimized and tailored to individual needs. An AI Agent can assist in…
