AI Unlocks Hidden Care Needs in Long COVID Patients

Hospitals across the U.S. vary in equipment, staffing, capabilities, and patient populations. While profiles for common conditions may seem universal, individual nuances require attention due to differing patient needs and hospital situations (1^✔ ✔Trusted Source
A latent transfer learning method for estimating hospital-specific post-acute healthcare demands following SARS-CoV-2 infection

Go to source). New research suggests that artificial intelligence could enhance overall care by analyzing data from various hospitals to create more accurate patient groups, reflecting local populations. AI can help identify specific care needs, such as the required departments and care teams. Led by researchers at the Perelman School of Medicine at the University of Pennsylvania, the study, published in Cell Patterns, examined electronic health records of long-COVID patients. It identified four distinct sub-populations—such as those with asthma or mental health conditions—and their specific needs.

“Existing studies pool data from multiple hospitals but fail to consider differences in patient populations, and that limits the ability to apply findings to local decision-making,” said Yong Chen, PhD, a professor of Biostatistics and the senior author of the study. “Our work moves toward providing actionable insights that can be tailored to individual institutions and can further the goal of offering more adaptive, personalized care.”

Machine Learning Reveals Long-COVID Patient Clusters

The study team used a machine learning artificial intelligence technique called “latent transfer learning”, to examine de-identified data on long-COVID patients pulled from eight different pediatric hospitals. Through this, they were able to call out four sub-populations of patients who had pre-existing health conditions. These four included:

Mental health conditions, including anxiety, depression, neurodevelopmental disorders, and attention deficit hyperactivity disorder

Atopic/allergic chronic conditions, such as asthma or allergies

Non-complex chronic conditions, like vision issues or insomnia

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Complex chronic conditions, including those with heart or neuromuscular disorders

With those sub-populations identified, the system was also able to track what care patients required across the hospital, pointing toward updated profiles that would allow hospitals to better address increases in different patient types.

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“Without identifying these distinct subpopulations, clinicians and hospitals would likely provide a one-size-fits-all approach to follow-up care and treatment,” said the study’s lead author, Qiong Wu, PhD, a former post-doctoral researcher in Chen’s lab who now is an assistant professor of biostatistics at the University of Pittsburgh School of Public Health. “While this unified approach might work for some patients, it may be insufficient for high-risk subgroups that require more specialized care. For example, our study found that patients with complex chronic conditions experience the most significant increases in inpatient and emergency visits.”

The latent transfer learning system directly pulled out the effects these populations had on hospitals, pointing to exactly where resources should be allocated.

If the machine learning system had been in place around March 2020, Wu believes that it might have provided some key insight to mitigate some of the effects of the pandemic, including focusing resources and care on the groups most likely in need.

“This would have allowed each hospital to better anticipate needs for ICU beds, ventilators, or specialized staff—helping to balance resources between COVID-19 care and other essential services,” Wu said. “Furthermore, in the early stages of the pandemic, collaborative learning across hospitals would have been particularly valuable, addressing data scarcity issues while tailoring insights to each hospital’s unique needs.”

“Chronic conditions like diabetes, heart disease, and asthma often exhibit significant variation across hospitals because of the differences in available resources, patient demographics, and regional health burdens,” Wu said.

The researchers believe the system they developed could be implemented at many hospitals and health systems, only requiring “relatively straightforward” data-sharing infrastructure, according to Wu. Even hospitals not able to actively incorporate machine learning could benefit, , through shared information.

“By utilizing the shared findings from network hospitals, it would allow them to gain valuable insights,” Wu said.

Reference:

1. A latent transfer learning method for estimating hospital-specific post-acute healthcare demands following SARS-CoV-2 infection - (https://www.cell.com/patterns/fulltext/S2666-3899(24)00238-1)

Source-Eurekalert