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Additional data, advanced analytics improve performance of machine learning referral app

Credit: Regenstrief Institute

Research scientists from Regenstrief Institute and Indiana University have further improved the performance of Uppstroms, a machine learning application that identifies patients who may need referrals to wraparound services, by incorporating additional personal and population-level data sources and advanced analytical approaches.

Research team affiliations include Regenstrief, IU Fairbanks School of Public Health at IUPUI, IU School of Medicine and Eskenazi Health.

Uppstroms has been in use at nine clinics associated with a safety net hospital in Indianapolis. The algorithm identifies with social risks such as or struggles with food or housing. This allows clinicians to offer these patients referrals to specialized services such as a dietician, behavioral health or a , with the goal of addressing the need before it turns into a crisis.

Evidence suggests that at least one in four adults, and possibly as many as one in two, have a need driven by social determinants of health.

“These wraparound services can enhance primary care delivery by addressing socioeconomic, behavioral and financial needs that cannot be addressed by primary care providers,” said Suranga Kasthurirathne, Ph.D., first author on the paper, Regenstrief research scientist and assistant professor of pediatrics at IU School of Medicine. “To make it more useful in the , we incorporated a wide spectrum of patient-level data and more granular population health data to improve the precision of the app, leading to fewer false positives.”

Innovations to prior approaches

Additional data added to the algorithm included patient-level social determinants of health, insurance, medication history and behavioral health history. These data came from Eskenazi Health’s electronic health record system and the Indiana Network for Patient Care, which is managed by the Indiana Health Information Exchange. Population-level social determinants of health measured at census-tract area, which

Health article

Bad sleep patterns could up the risk of heart disease in older adults

Getting different amounts of sleep each night or sleeping during the day instead of at night is called irregular sleep or having an irregular sleep schedule. People who work night shifts or whose work shifts change over time may be at higher risk for this condition.

Irregular sleep had already been linked to diseases such as obesity and diabetes. A recent five-year study funded by the National Institutes of Health also found a link in older adults between irregular sleep and heart disease, the leading cause of death in the U.S.

“Among minority populations, particularly African Americans, the link between sleep and heart disease was even stronger.”

That study discovered that participants between the ages of 45 and 84 whose sleep habits were the most irregular had more than double the risk of developing a heart problem, compared with those with regular sleep habits. Among minority populations, particularly African Americans, the link between sleep and heart disease was even stronger.

Heart disease is often caused by blockages in our arteries, which carry blood through our bodies to support our heart, other organs, and tissues. These blockages can lead to heart attacks, stroke, and death.

Luckily the study offered good news, too.

Researchers found that improving sleep patterns could help reduce the risk of heart disease. For example, going to bed around the same time each night and prioritizing sleep when possible have a positive effect. It’s also important to seek help from a health care provider if you have frequent problems sleeping or feel that excessive sleepiness is a barrier to daily activities.

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