Metrics: Measuring, Comparing, and Privileging Your PA and NP Workforce
Data is just data if not analyzed and measured. By enrolling in Bryant University’s course in Metrics, you’ll learn how to analyze data to improve patient outcomes, productivity, and more.
With the advent of electronic health records (EHR), the amount of electronic data has exploded. Combine your clinical and analytical knowledge to guide decision-making in your healthcare organization.
What You’ll Learn
This is a real-world practical course taught by expert Dr. Atin Jindal, a pioneer in the field of metrics, analysis, and artificial intelligence.
By attending this course, you’ll be able to:
- Select key performance indicators that are important to your organization.
- Understand patterns in the data.
- Interpret data and present it to decision-makers.
- Gain insights into a specific research question.
- Understand the impact the EMR system has on the healthcare system.
- Track provider performance and productivity using EMR systems.
- Deliver a real-world project to your organization.
This course consists of eight weeks of guided learning, each comprising multimedia, interactive modules, and curated readings. You should expect to spend approximately six to seven hours per week working through the materials and assignments.
Who Should Attend
- Physician assistants or nurse practitioners who are currently in supervisory roles or seeking to enter a supervisor position.
- Individuals who seek to earn Continuing Medical Education credits.
Earn Continuing Medical Education Credits
This course is approved by the American Academy of Physician Associates (AAPA) for 24 Category 1 CME credits.
Learn from Instructor Dr. Atin Jindal, a Clinical Informatician
An academic hospitalist and clinical informatician, Atin Jindal works at The Miriam Hospital with Lifespan and Brown University. Based in Providence, Rhode Island, he teaches medical students and residents in internal medicine rotations at the bedside. His interests include artificial intelligence and natural language processing in the clinical context, which he has applied to improve documentation, reduce COPD readmissions and improve sepsis care.