We will work across the University of Nebraska system to connect clinicians, engineers, and data and artificial intelligence scientists to create solutions that intelligently simplify care for clinicians and patients.
Our goals are to make health care more efficient and effective for clinicians, and to make patients safer and more satisfied with their care.
Our Core Focus Areas
Good Data
Good Design
Human and Computer Cognition
Health care suffers because computers don’t talk to one another. We will promoted data liquidity through the creation of good data. Good data is computable/machine readable and maintains its original semantic meaning (i.e., interoperability). To date, neither the government nor standards organizations have been able to solve this issue; current electronic health record systems only compound the problem. The center will be a world leader in good data. We will address foundational issues related to good data to provide secure, high quality, reusable data for biomedical and AI researchers, clinicians and patients.
Our expertise in human factors design allows us to build, test and validate health care solutions that are more efficient and effective for clinicians and more satisfying for clinicians and patients. Core to good design is the Nebraska Clinical Encounter Framework. This framework allows us to deconstruct clinical care into core components then work with clinicians to create solutions, optimizing dataflow, workflow, and reducing cognitive load. Making clinicians more efficient and effective will reduce clinician burnout and ultimately reduce by health care costs (efficiency) while also improving quality (effectiveness).
The third key to making clinicians more efficient and effective is developing artificial intelligence solutions that work. It starts with good data that can be transformed into information, knowledge and decisions. Good design that understands the wants and needs of clinicians and then applying the AI solutions that facilitate, not replace human cognition. This core involves data interpretation, modeling, fusion, integration, warehousing, application development and deployment powered by sound software engineering; both big data and small data research areas ranging from machine learning to simulation.
