Making Therapy More Data-Driven: A Practical Foundation
Presented by Alex Bendersky
12-Month Subscription
Unlimited access to:
- Thousands of CE Courses
- Patient Education
- Home Exercise Program
- And more
Nonfinancial: Alex Bendersky has no competing nonfinancial interests or relationships with regard to the content presented in this course.
In healthcare and musculoskeletal care, leveraging data from diverse sources, including health, utilization, and outcome data, is crucial for advancing practices. Modernizing the profession necessitates challenging established practices, and integrating data science is an essential step toward optimization, efficiency, and progress in clinical care, marking the profession's transition into the modern age.
Learning Objectives
- Recognize the current shortcomings of data practices and digital transformation efforts in healthcare
- Identify how digital transformation experiences from other industries like e-commerce, finance, manufacturing, and transportation can help guide healthcare practice
- Identify the core elements of a data strategy suitable to clinical environments and practical first steps
- Interpret core applicable concepts in data engineering, data science, machine learning, and AI
- Define ethical principles and legal requirements around patient privacy, safety, and human–AI collaboration in healthcare
Meet your instructor
Alex Bendersky
Dr. Bendersky is an innovative clinical and technology leader in musculoskeletal and digital health. With over two decades of experience, Dr. Bendersky currently serves as the director of digital health at Sparta Science, spearheading advanced platforms, language models, and AI to optimize human performance. An advocate of…
Chapters & learning objectives
1. Current State of Data Usage
This chapter includes an introduction to the language and ontology of data science, including simple parameters in legacy data and the emergence of advanced data science.
2. Core Elements of a Clinical Data Strategy
This chapter discusses safety in collecting and handling of healthcare data, including advanced data science and processing parameters.
3. Data Privacy, Security, and Ethics
This chapter provides examples of how integration of data science can enhance and improve the process of care delivery.
4. Data in Action
Creating data-centered clinical care is a multifaceted process with diverse forms and interactions. Small operational changes can unlock opportunities to enhance clinical care.
