Machine Learning & AI: Data-Driven Insight in Healthcare

Topic: Healthcare Analytics

Intended Audience: Healthcare Analytics Managers, Chief Analytics Officers, VP Cost Containment, Head of Technology Innovation, VP Clinical Informatics, VP Medical Management, Managed Care Organizations, or other heath care organizations

Length: 43 minutes

Healthcare analytics is a rapidly growing industry. In the U.S., it is expected that this industry will grow to nearly 20 billion by 2020. Despite the adoption of sophisticated analytics into many health care areas, many sectors in health care remain relatively unexposed to the advantages and return on investment analytical techniques and tools can provide.

Value can be extracted from each of the main sources of health care data, including insurance claims and clinical data (EHR, biomarker), using machine learning techniques.

For example, claims data can be mined to:

• Identify sources of fraud, waste, and abuse
• Prioritize claim review
• Rank provider care
• Identify those at risk of becoming addicted to medications such as opioids.

Clinical data can be modeled to predict disease events, progression, and severity as well as uncover biomarkers and other factors that are tightly linked to the disease itself.

This webinar will highlight the key types of health care data, the sophistication of current analytics in many health care areas, and techniques to generate health care insights with high ROI, refined over more than 20 years of experience developing and deploying successful data analysis solutions.

Attendees will learn:

• Where some divisions of health care land on the 10-levels of analytics and why
• Use of visualization in claims data to score risk or predictions, such as consumer risk for opioid addiction, relative ranking of providers, prioritizing claim-review cases, and more
• Determining the best methods to motivate payers
• Discover and choose biomarkers with the best attributes to predict disease