Urban Health Plan Significantly Reduces Patient No-Show Rate Leveraging eClinicalWorks EHR and healow Prediction AI Model

One of the largest New York FQHC systems successfully implements healow no-show prediction AI model to improve patient care and access

Urban Health Plan (UHP), one of the largest federally qualified community health center (FQHCs) systems in New York State, has improved patient care, reduced missed appointments, and ultimately increased revenue outcomes thanks to the eClinicalWorks EHR and healow no-show prediction AI model. With eClinicalWorks, the largest ambulatory cloud EHR, UHP looks to further improve the efficiency and operations of its 26 centers in the Bronx, Corona, Queens, and Central Harlem.

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“We are excited to see the impact the healow no-show prediction AI model is having on patient care at Urban Health Plan”

Urban Health Plan is comprised of twelve clinical sites, twelve school-based health centers, two mental health facilities, and ten administrative and program sites with roughly 984 associates and providers. UHP offers a wide range of services and provides primary care in major clinical areas such as adult medicine, dentistry, mental health, pediatrics, OB/GYN, and more. By identifying which patients are at higher risk of missing appointments, UHP can proactively reach out to those patients with targeted reminders and rescheduling options.

Missed appointments are a significant challenge for healthcare providers, leading to lost revenue, decreased patient satisfaction, and negative health outcomes. Using AI and machine learning, healow can help identify patients with high no-show probability with 90% accuracy. With the healow no-show prediction AI model, UHP has reported:

• A significant reduction in no-shows, which has helped UHP achieve the highest number of appointments in practice history with roughly 42,000 patient visits in March 2023.
• A 154% increase in completed visits for patients identified with a high no-show probability.

“As an FQHC, the healow no-show prediction AI model has positively impacted our practice in numerous ways. We have increased our patient volume with a record number of monthly visits, which translates into more revenue for additional resources and services for our patient population,” said Alison Connelly-Flores, chief medical information officer of Urban Health Plan. “And when patients receive timely care, they see better health outcomes. We look forward to measuring the health outcomes in relation to this project.”

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With the no-show prediction model, the UHP team streamlined the identification and outreach process. eClinicalMessenger managed outreach efforts through more than a million yearly voice messages, secure text messages, and email reminders. UHP also expanded intervention strategies depending on the patients’ circumstances by initiating healow TeleVisits or rescheduling appointments via healow Open Access.

“We are excited to see the impact the healow no-show prediction AI model is having on patient care at Urban Health Plan,” said Girish Navani, CEO and cofounder of eClinicalWorks. “By leveraging the power of data and machine learning, we can help providers like Urban Health Plan deliver more effective care to their patients and reduce the burden of missed appointments. This ultimately helps reduce the cost of healthcare and aid better patient outcomes.”

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