Researchers receive IHPI's annual award for work informing federal disaster response planning for vulnerable populations, improving clinical prediction models used in hospitals nationwide
During its annual Member Forum on April 17, the University of Michigan Institute for Healthcare Policy & Innovation (IHPI) announced Sue Anne Bell, Ph.D., FNP-BC, FAAN, and Karandeep Singh, M.D., M.M.Sc., as the 2022 recipients of its annual Policy Impact Award.
The award recognizes IHPI researchers who have had a significant level of direct engagement with policy audiences, and a clear illustration of translating research to inform health policy or practice in the past year.
Sue Anne Bell, assistant professor of nursing and a nationally recognized scholar of disasters and health, was honored for her leadership in advising disaster preparedness and response efforts, particularly for vulnerable populations, at the federal level. Since 2017, she has served as a health scientist advisor on the Federal Emergency Management Agency (FEMA)’s National Advisory Council. She also currently serves as vice chair of the council’s Advancing Equity sub-committee, leading conversations on building equitable processes and outcomes for people affected by disasters. Most recently, she joined the National Advisory Council for Seniors and Disasters at the U.S. Department of Health and Human Services (HHS) in the Agency for Strategic Preparedness and Response. In November 2021, Bell testified before the Senate Special Committee on Aging at a hearing titled “Inclusive Disaster Management: Improving Preparedness, Response, and Recovery." Her testimony and published research informed legislation establishing programs and services to assist individuals with disabilities and older adults with disaster preparedness.
Karandeep Singh, assistant professor of learning health sciences and urology and Associate Chief Medical Information Officer for Artificial Intelligence at Michigan Medicine, was recognized for his sustained efforts to evaluate and improve clinical prediction models that leverage electronic health record data to help medical staff across the country recognize atypical symptoms faster and make more accurate diagnoses. His research has highlighted several problems with proprietary prediction models such as inadequate reporting by vendors, a disregard for clinical context, and a lack of regulation. To address these issues, Singh continually shares his findings and recommendations with leaders in the health information technology industry, government regulators, and policymakers. Notably, his constructive feedback helped Epic Systems, one of the largest providers of health information technology to U.S. hospitals, overhaul its sepsis prediction model to improve its accuracy and make its alerts more meaningful to clinicians trying to stave off the deadly condition. In 2022, Singh's work was cited in The White House Office of Science and Technology's Blueprint for an Artificial Intelligence Bill of Rights to justify the need to protect people from unsafe or ineffective systems.
As recipients of the Policy Impact Award, Bell and Singh will each receive $2,000 to support their ongoing research and will have their work showcased in a future IHPI policy product.
More about Bell's research & engagement:
- Commentary: Hurricane Ian: Older adults have many reasons for not evacuating – here’s why it’s important to check on aging neighbors
- Bell testifies before Senate Aging Committee about inclusive disaster management
- Video: IHPI experts discuss impact of climate change on health and health systems
- IHPI Brief: The health impacts of weather and climate-related disasters on older adults in the U.S.
- Research examines impact of hurricanes on hospitalizations, medical providers
More about Singh's research & engagement:
- Epic sepsis model’s ability to predict depends on hospital factors
- Epic overhauls popular sepsis algorithm criticized for faulty alarms (STAT News)
- An AI model predicting acute kidney injury works, but not without some tweaking
- New risk prediction model for opioid misuse after surgery surpasses accuracy of previous models
- Study of 24 U.S. hospitals shows onset of COVID-19 led to spike in sepsis alerts