How is data improving clinical outcomes? with Patrick McGill
The Data Standard
Episode Summary
With more statistically rigorous analytic methods to further automate insight identification, data science and machine learning can help health systems align effective measures with specific improvement goals more accurately and faster than typical data analysis. Health systems can decrease the risk, cost, and time associated with outcomes improvement and accelerate the process by using data science to help determine which measures will help them meet their goals for their specific populations. As the industry continues to work toward outcomes improvement, leading organizations will rely on data science and machine learning to test hypotheses, identify opportunities faster and more accurately, and ensure that their improvement measures support their overall goals.
Meet The Host
Catherine Tao
Data scientist at The Data Standard
Catherine Tao is a tech enthusiast looking for new methods for building connections with businesses around the world. Her extensive knowledge of data science allowed her to develop new solutions and implement them into existing ecosystems. She is currently working as a Data scientist and Exclusive Podcast Producer at The Data Standard.
Meet The Guest
Patrick McGill
EVP, Chief Analytics Officer at Community Health Network
After starting my career as a Family Medicine Physician, I currently serve as the Chief Analytics Officer for Community Health Network overseeing all Data & Analytic functions, Information Technology, Clinical Informatics, Enterprise Services and Business Process Management (Performance Improvement and Enterprise Portfolio Management). I have special focus on Digital Transformation, Clinical Variation, Population Health & Value Based Care with EMR optimization and Clinical Workflow redesign.
