JMIR Publications

ABSTRACT

Background:

Telecare uses technology to help people live more independently at home. When an adverse event (such as someone falling or a bath overflowing) happens the technology reactively senses this and alerts a call centre to respond. If the technology can detect a person's current (and past) states and behaviours, with machine leaning we can more proactively identify potential risks before an adverse event occurs, and intervene. Despite social care organisations being data rich, very little predictive analytics are currently routinely applied in this setting.

Objective:

The aim of this study was to understand how specific telecare data (monitoring falls, identifying people at risk of falling and providing services in response) are collected, managed and used in the largest health and social care region in Scotland. The objectives were to: (i) map the community alarm data flow to understand what data was being collected, by what services, and where it was stored, linked and managed; and (ii) identify the current barriers and opportunities around staff and organisations using or applying predictive analytics routinely within telecare service provision.

Methods:

This qualitative study involved interviews with health & social care professionals working in Glasgow City Council (GCC) and a telecare service provider (Tunstall©). Interviews explored experiences of the systems and data access, processes for collecting and using the data, and how it might be better used to target services in a more proactive way. Data underwent thematic framework analysis. Descriptions of the data flow were used to develop a visual representation of the sociotechnical system.

Results:

14 participants working at operational and managerial levels took part. A complex sociotechnical telecare system was identified, involving multiple staff roles, with data exchanged across 11 teams, using 17 systems, with four distinct data sources. Key challenges related to (i) sub-optimal systems and equipment, (ii) data recording inefficiencies and use, (iii) specific patient population barriers and IT literacy, and (iv) limited resources and support. Opportunities for more predictive telecare included establishing a more structured and integrated approach to data management, scope for improved data organisation and retrieval, better cross-platform integration and data sharing, and the use of tools/models to support insightful data analysis tailored to the users

Conclusions:

Scottish telecare data services require improved infrastructure to be managed in ways that support more predictive telecare services. This includes more structured and linked datasets and greater integration between the services and systems to allow service providers to have integrated, up to date, real time connected data in order to build accurate and meaningful models.