Healthcare Insights AI Lab

The Healthcare Insights AI Lab was launched in 2017 under the direction of Professor Gordon Gao and is staffed by CHIDS-affiliated faculty scientists and graduate students, who work in close collaboration with Inovalon (NYSE: INOV) and the New Jersey Institute of Technology under the direction of Professor Yi Chen to envision, create and grow impactful AI-based projects.   The Lab is a pipeline to the ideas, advanced machine learning computational methods and people across the University of Maryland network. CHIDS has extensive connections that can be leveraged for specific projects and aims, notably in Computer Science, Engineering, Medicine, Pharmacy, Nursing, Behavioral and Social Sciences and Public Health Schools. The University of Maryland is Maryland’s flagship university and one of the world’s premier research institutes, and the Information Systems group, in particular, is ranked in the top 10 worldwide by Business Week and U.S. News and World Report; access to these resources deliver substantial value.

Interested in working on an AI project with the team? Please reach out to us at chids@rhsmith.umd.edu.

Why an AI Lab?

The Rise of Artificial Intelligence

A new technological revolution is coming, which many predict will be even more powerful than the PC, the Internet, or the mobile phone. Powered by big data and machine learning, modern artificial intelligence (“AI”) has shown great promise to tackle many grand challenges. Google AlphaGo repeatedly beat the human world champion in Go, Tesla’s Autopilot can identify road signs as it whizzes down the highway, and Facebook can automatically link friends by recognizing faces in millions of photos, just to name a few examples of AI’s emerging accomplishments.  A core breakthrough fueling this new wave of AI is the Deep Learning algorithm, which can leverage Big Data to enable more intelligent analyses.

Leveraging the Power of AI for Healthcare Business

Deep Learning based AI is a particularly good fit for health system data, as the volume and diversity of this data have surged in recent years. Many of these health system data sources are unstructured or semi-structured. While traditional methods for analysis are struggling to provide meaningful insights, Deep Learning based AI offers tremendous potential. Major healthcare players are actively exploring a range of AI applications. A 2017 survey by HIMSS finds that over half of the hospitals in the US plan to adopt AI in the next five years.

The entirety of the healthcare ecosystem is undergoing a massive transformation driven by the shift from volume-based to value-based care delivery and reimbursement. Data, its aggregation, analysis, and targeted application is a fundamental capability required to enable the desired outcomes and economics within this new healthcare market. There are near term practical AI applications that are quite feasible to achieve, which also build the foundation to enable future aspirational and transformational applications. These applications range from extracting meaning from textual data, predicting high-risk high-cost events and plan members, motivating member engagement and behaviors, and optimizing population management decisions through precision care, and much more; critical topics the entire healthcare field.