Houston: In the United States, there are more than 37 million individuals DiabetesYet many of them do not receive prompt treatment, which can lead to costly or even fatal consequences.
In primary care settings, effective therapies are available, but doctors lack the resources to identify patients who are most at risk. Predicting primary careA clinical decision support system that uses deep learning to predict which patients are most likely to face complications is being developed by University of Houston researchers to prevent poor health outcomes before they happen.
The first tool to be developed in the new AI system is the Diabetes Complication Severity Index (DCSI) progression tool, which, in addition to a patient’s health history, considers how their social and environmental circumstances—employment status, living arrangements, education level, food security—may increase their risk for complications. Research shows that these social factors can affect disease progression.
This tool will provide clinicians with timely, actionable insights so they can intervene early, reduce the percentage of individuals with diabetes, and reduce the number of complications that affect each patient.
“Our long-term goal is to help physicians be more proactive and less reactive when treating diabetes. artificial intelligence and machine learning, we can more effectively connect at-risk individuals to interventions before they get sick,” said Dr. Winston Liau, the project’s principal investigator and chair of the Department of Health Systems and Population Health Sciences at the Tillman J. Fertitta Family College of Medicine.
For years, insurance companies and researchers alike have used the DCSI to measure patient complications at one point in time. Still, no tools exist to predict which individuals are most at risk for elevated DCSI scores.
The tool will be developed in collaboration with the Humana Integrated Health System Science Institute at the University of Houston, and will leverage Humana Inc.’s unique data sets. – Claims, Health records, and individual and community social risk factors. The tool will be tested in the PRIME registry, a national platform that includes millions of primary care patients nationwide.
“The challenge with current prediction tools is that they provide little explanation and no guidance for next steps, limiting confidence and implementation. The tool we are developing will inform clinicians why patients are at risk and how to reduce that risk. will suggest measures for,” said Ioannis Kakadiaris. Hugh Roy and Lily Kranz Professor of Computer Science and Health Systems and Population Health Sciences at Cullen University.
“Humana is excited to collaborate with our partners at the University of Houston and leverage their AI and predictive analytics expertise along with our extensive diabetes experience using DCSI and social critical solutions impacting health. This tool is one of the tools to put actionable information in their hands. “Represents the best opportunity. Primary care physicians are at the point of service where real change in health happens,” said Dr. said Todd Prewitt, corporate medical director, clinical strategy and analytics at Humana.
In addition to diabetes, the researchers believe the tool could help predict complications associated with other conditions, such as uncontrolled hypertension or worsening depression. This tool will be particularly relevant as the health care industry shifts to a value-based care model where doctors are rewarded for improving patients’ health rather than being paid for each visit, procedure or test, regardless of the outcome.
The Fertitta Family College of Medicine, founded in 2019 on the social mission of improving health and health care in underserved urban and rural communities in Texas, emphasizes primary care education and research.
“As primary care doctors, we need an efficient way to leverage the vast amount of information we receive to improve the quality of life of our patients. The number of complications a patient experiences is strongly correlated with death or hospitalization, so developing this AI tool is serious,” said Liaw.
