The challenge we were solving

Portugal’s aging population is growing, and with it, the risk of frailty, chronic illness, and social isolation. These issues put pressure on the health system and reduce quality of life for older adults.

In response, SNS24 launched the Senior Proximity (Proximidade Sénior) pilot in 2018 with two regional healthcare units (ACES Porto Oriental and ACES Oeste Sul). The goal: to offer remote support to frail older adults at home – preventing health incidents, detecting needs early, and integrating care across health, social, and safety domains.

With promising results, the next step was to evaluate whether this model could be scaled nationally. FrailCare.AI focused on two key challenges:

  • Developing a personalized, optimized telecare model for elderly care.
  • Creating AI-powered methods to assess the program’s impact and cost-effectiveness.

Project partners

 

Project goals

  1. Support the development of an improved telecare model for older adults with frailty by applying a co-creation approach involving researchers, health professionals, and older citizens.
  2. Use artificial intelligence to personalize intervention pathways and increase service efficiency.
  3. Design a method to evaluate the impact and cost-effectiveness of the SNS24 Senior Proximity program.
  4. Contribute to decisions around the future national expansion of the service.

Our role

  • Support the integration of AI and health data to personalize telecare interventions.
  • Participate in the co-creation process with elderly users and professionals to design sustainable, inclusive digital tools.
  • Contribute to the development of evaluation methodologies for long-term scalability and national impact.

Lead researcher at VOH

Ana Rita Londral, PhD

Executive Director

Project outcomes and impact

  • Developed a framework to use AI for personalized telecare targeting elderly individuals at risk of frailty.
  • Designed a method to measure program effectiveness and cost-efficiency.
  • Conducted a review of frailty screening studies, identifying potential for machine learning techniques.
  • Identified key challenges and opportunities in applying AI to public health telecare.

Publications

  • Oliosi, E., Guede-Fernández, F., & Londral, A. (2022). Machine Learning Approaches for the Frailty Screening: A Narrative Review. International journal of environmental research and public health, 19(14), 8825. https://doi.org/10.3390/ijerph19148825