Lirio | Jun 13, 2022 | Blog, Closing Gaps in Care, Consumer Acquisition & Engagement
Your patients face a host of both external and internal barriers that keep them from scheduling and showing up for mammogram appointments — barriers that can change unexpectedly. To help healthcare organizations increase women’s health screenings, Lirio offers a Precision Nudging™ solution that moves patients to schedule and attend mammogram appointments.
Ashley West and Amy Bucher, Ph.D. | Jun 13, 2022 | Blog, Closing Gaps in Care, Consumer Acquisition & Engagement, Consumer Satisfaction & Loyalty
From lack of transportation to fear of what the visit might indicate in terms of their health outcomes, the reasons why well visits aren’t being used as the effective tool for disease prevention and management they could be are widespread. Discover how digital tools, such as Lirio’s Precision Nudging solution, work in service to primary care to improve well visit adoption.
Lirio | Dec 2, 2021 | Blog, Closing Gaps in Care
No matter what your patients’ reasons are for not getting vaccinated, reaching them with the right message at the right time will require a tailored communication approach. Discover best practices for overcoming barriers to vaccine adoption with personalized communication.
Patrick Hunt | Oct 5, 2021 | Artificial Intelligence, Blog, Consumer Satisfaction & Loyalty, Digital Health
Health systems now have the chance to use telehealth as a means to support holistic care delivery. Considering every aspect – physical, psychological, emotional, and spiritual – helps inform a complete understanding of a patient. This results in better health outcomes, improved access, and greater health equity. Learn how to better engage patients through telehealth and embrace holistic care delivery.
Christopher Symons, Ph.D. | Sep 3, 2021 | Artificial Intelligence, Behavioral Science, Blog
Lirio’s Behavioral Reinforcement Learning Lab (BReLL) recently published a paper describing a new approach, the Limited Data Estimator, for comparing reinforcement learning policies using limited historical data.