JMIR Publications

ABSTRACT

Background:

Telecare and telehealth are important care at home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care.

Objective:

This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings.

Methods:

The PRISMA-ScR checklist was adhered to alongside Arksey and O’Malley’s methodological framework. English language papers published in Medline, EMBASE and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion/exclusion criteria.

Results:

86 papers were included in this review. The types of analytics featuring in this review can be categorised as anomaly detection (n=22), diagnosis (n=32), prediction (n=22) and activity recognition (n=10). The most common health conditions represented were Parkinson’s disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools and barriers; opportunities that exist, such as including Patient Reported Outcomes (PROs), for future predictive analytics in telecare and telehealth.

Conclusions:

All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into more routinely collected care data processes. Datasets used must be of suitable size and diversity, ensuring models are generalisable to a wider population and can be appropriately trained, validated and tested.