Exploring the Barriers and Opportunities for a More Predictive Data-Driven Telecare Service: Qualitative Study in...

Area of Science:

• Gerontology
• Health Informatics
• Public Health

Background:

• Telecare services utilize technology for independent living, reactively responding to adverse events.
• Proactive risk identification via machine learning on telecare data can prevent adverse events.
• Social care organizations possess rich data but underutilize predictive analytics.

Purpose of the Study:

• To understand telecare data collection, management, and usage in Scotland.
• To map community alarm data flow and identify data management practices.
• To identify barriers and opportunities for predictive analytics in telecare.

Main Methods:

• Qualitative study involving interviews with health and social care professionals.
• Explored experiences with telecare systems, data access, and usage.
• Thematic framework analysis of interview data and sociotechnical system mapping.

Main Results:

• A complex sociotechnical telecare system with fragmented data exchange was identified.
• Key challenges include suboptimal systems, data inefficiencies, patient barriers, and limited resources.
• Opportunities lie in structured data management, integration, and user-tailored analytics tools.

Conclusions:

• Scottish telecare data services need improved infrastructure for predictive analytics.
• Structured, linked datasets and system integration are crucial for real-time data.
• Enhanced data management will enable the development of accurate predictive models for telecare.