Healthcare Analytics Foundations and Frameworks
What does it look like when data is used for good? In this new era of healthcare, we’re all relying on data and statistics to dictate best practices. Healthcare professionals must be prepared to sort through big data and utilize it to improve patient outcomes. Through real-world case studies, Healthcare Analytics Foundations and Frameworks provides you with the necessary skills to evaluate machine learning interventions and their impact on patient care, alleviate high healthcare costs by using data collected to improve care quality and outcomes and apply predictive analytics across the care continuum for diseases.
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No Mandatory Login Times
4-6 Hours of Work per Module
Your Subject Matter Expert:
Dr. Vikas Kumar
“If we can increase our usage of machine learning and artificial intelligence, we can provide a degree of relief to overtaxed physicians, nurses and staff and decrease the likelihood of burn out. It has the potential to really improve the efficiency in which healthcare is performed in the future.”
Module 1: Introduction to Healthcare Analytics
Define the role of healthcare analytics and how it relates to healthcare informatics. Describe the types and formats of healthcare data and list the healthcare data standards and codesets used to categorize and aggregate data in EHRs. List the value-based programs and understand how they can be used to measure healthcare performance.
Module 2: Statistical Foundations for Healthcare Analytics
Define basic statistical terms and techniques used in healthcare analytics and discuss the various metrics used to measure healthcare performance. Describe statistical tests used for comparing groups of items and outline methods for studying relationships between variables.
Module 3: Machine Learning in Healthcare
Outline the steps and processes in the machine learning pipeline. List machine learning algorithms and provide high-level descriptions of how they work. Define text mining and detail how it can be used to analyze unstructured data in EHRs. Discuss how advanced machine learning and deep learning techniques can be used to analyze imaging and clinical data.
Module 4: Applications of Healthcare Analytics
Analyze the ethical implications and limitations of healthcare analytics by examining the ways that predictive analytics can be applied across the care continuum for a disease. Explain how machine learning can be combined with evidence-based medicine to affect outcomes in cancer care. Describe how statistics and machine learning have been used for readmission modeling research.
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Dr. Vikas Kumar
Dr. Vikas “Vik” Kumar is a physician-turned-Data Scientist from Niskayuna, New York. He earned his MD degree from the University of Pittsburgh, but he discovered his true calling of computers and data science shortly afterward. Dr. Kumar then earned his MS degree from the College of Computing at Georgia Institute of Technology and subsequently began his career as a healthcare data scientist. He has participated in many successful projects related to descriptive, predictive and population health analytics. He currently resides in Atlanta, Georgia, and works as a data scientist at Digital Envoy, a well-established company in the digital marketing industry.
In 2018 Dr. Kumar published Healthcare Analytics Made Simple (Packt), a self-contained, all-in-one primer on healthcare analytics, covering topics including healthcare system foundations, healthcare quality and incentive programs, computer programming, data science and machine learning. He has presented his research and work at many academic conferences and community meetings, including the KDD data science conference and recently at the Data Science ATL Healthcare Analytics Meetup. Dr. Kumar has also appeared in several peer-reviewed journals, including the Journal of the American Medical Informatics Association (JAMIA).
Dr. Kumar has a BS degree in Neuroscience from Brown University.