- Kevin Robell MA, ATC
- Scott Mullet MA, AT, CEFE
Essential - I, II, IV, VCourse Description:
The amount of clinical data that athletic trainers collect and manage is rapidly growing in both size and complexity. Surviving this new normal presents significant challenges to practitioners, requiring constant growth and innovation to advance clinical practice metrics, optimize patient outcomes and demonstrate practitioner value.
Analytics is an established health IT framework designed to facilitate the conversion of clinical data to meaningful action. This course will expose attendees to essential understanding of this infrastructure and important steps involved when implementing a “culture of analytics” within an athletic training practice or organization.
Educational Needs/Practice Gaps:
As we have been immersed in the Big Data economy, clinical athletic trainers have become inundated with massive volumes of data yet are drastically under-informed from said data. Health Care in general, more specifically the profession of Athletic Training is “lagging” behind the adoption and implementation of data analytics strategies and infrastructure that can ultimately transform patient care and improve practitioner value. It is imperative that ATs become skilled in basic data analytics in order to extract insight from organizational and clinical data.
This course will expose participants to current concepts and knowledge within Clinical Data Analytics as well as demonstrate ways to leverage clinical data to enhance organization metrics.Clinical Bottom Line:
As the healthcare industry moves rapidly towards “value-based” care models, it is imperative that athletic trainers learn to harness the vast amount of clinical data they naturally hold or risk falling behind other health care disciplines that are making this investment. As organizational and patient-centered goals evolve, this “big data economy” is forcing industries to adapt to a changing technological landscape at the risk of becoming obsolete.
- Define data analytics and recognize its role in the area of health information technology.
- Describe the types of analytics in the understanding of clinical data.
- Identify important steps when implementing any data analytics project.
- Recognize attributes of data analytics technologies and classify their role in the clinical data lifecycle.
- Explain common terminology used in data analytics.