JMIR Publications

ABSTRACT

Background:

Older adults with cognitive deficits face difficulties recalling daily obstacles and lack self-awareness, amplifying the challenges for homecare clinicians to obtain reliable information on functional decline and homecare needs. The result may be suboptimal service delivery. Telemonitoring of ADL has emerged as a tool to optimize ADL homecare needs evaluation. Utilizing ambient sensors, telemonitoring of ADL gathers information about an individual's ADL behaviors within the home, such as preparing meals and sleeping. However, there is a significant gap in the comprehension of how ADL telemonitoring data can be integrated into clinical reasoning to better target homecare services.

Objective:

The current paper aimed to describe 1) how ADL telemonitoring data is used by clinicians in the process of maintaining care recipients with cognitive deficits at home as well as 2) the impact of ADL telemonitoring on homecare service delivery.

Methods:

We used an embedded mixed-methods multiple-case study design in which our cases of interest were three health institutions located in the greater Montreal region and offering public homecare services. An ADL telemonitoring system, named NEARS-SAPA, was deployed within those three health institutions for 4 years. Within each case were embedded sub-cases (care recipient, informal caregiver, clinician(s)). For the objectives of the present paper, we used the data collected during 45-60 min interviews with clinicians only. Quantitative metadata were also collected on each service provided to care recipients before and after the implementation of NEARS-SAPA to triangulate the qualitative data.

Results:

We analyzed 27 sub-cases, comprising 23 clinicians, that completed a total of 57 post-implementation interviews concerning 147 telemonitoring reports. Data analysis showed a 4-step decision-making process used by clinicians 1) Extraction of relevant telemonitoring data, 2) Comparison of telemonitoring data with other sources of information, 3) Risk assessment of the care recipient’s ADL performance and ability to remain at home, and 4) Maintenance or modification of the intervention plan. Quantitative data reporting the number of services received allowed to triangulate qualitative data pertaining to step 4. Overall, the results suggest a stabilization in monthly services following the introduction of the ADL telemonitoring system, particularly in cases where services were increasing prior to its implementation. This is consistent with qualitative data indicating that, in light of the telemonitoring data, most HSCP decided to maintain the current intervention plan rather than increasing or reducing services.

Conclusions:

Results suggest that ADL telemonitoring contributed to service optimization on a case-to-case basis. ADL telemonitoring may have an important role in reassuring clinicians about their risk management and the appropriateness of services delivery, especially when questions remain as to the relevance of services. Future studies may further explore the benefits of ADL telemonitoring for public healthcare systems with larger-scale implementation studies.