Behaviour assessment tools in long-term care homes in Canada: a survey

Understanding health information exchange processes within Canadian long-term care: A scoping review

BACKGROUND

Providing supportive care to long-term care residents with complex medical conditions generates substantial amounts of health information. This information must be documented, shared and acted upon by the various care providers within the circle of care.

OBJECTIVES

The purpose of this scoping review is to describe the current digital health information exchange (HIE) processes used within Canadian long-term care facilities (LTCFs).

METHODS

The scoping review followed Arksey and O'Malley's approach to the methodology. Electronic databases (e.g. CINAHL, MEDLINE and SCOPUS) were searched between 2010 and 2020 using terms including 'health information exchange', 'communication' and 'health information technology'. Articles were included if they were Canadian-based and relevant to our definition of health information exchange.

RESULTS

The search yielded 2091 citations for title and abstract screening; 78 citations were selected for independent full-text review, 42 of those met study criteria. The findings revealed gaps between the expectations of HIE for quality health care and the realities of HIE processes that impact the provision of care in long-term care.

CONCLUSIONS

We conclude that increased provider engagement and effective use of HIE processes is recommended to improve the safety and quality of health care in the long-term care sector.

IMPLICATIONS FOR PRACTICE

HIE implementation should be preceded a review of various aspects of workflow to identify information gaps and inefficiencies that can be addressed by digitization.

Wearable multimodal sensors for the detection of behavioral and psychological symptoms of dementia using personalized machine learning models

Introduction

Behavioral and psychological symptoms of dementia (BPSD) signal distress or unmet needs and present a risk to people with dementia and their caregivers. Variability in the expression of these symptoms is a barrier to the performance of digital biomarkers. The aim of this study was to use wearable multimodal sensors to develop personalized machine learning models capable of detecting individual patterns of BPSD.

Methods

Older adults with dementia and BPSD (n = 17) on a dementia care unit wore a wristband during waking hours for up to 8 weeks. The wristband captured motion (accelerometer) and physiological indicators (blood volume pulse, electrodermal activity, and skin temperature). Agitation or aggression events were tracked, and research staff reviewed videos to precisely annotate the sensor data. Personalized machine learning models were developed using 1‐minute intervals and classifying the presence of behavioral symptoms, and behavioral symptoms by type (motor agitation, verbal aggression, or physical aggression).

Results

Behavioral events were rare, representing 3.4% of the total data. Personalized models classified behavioral symptoms with a median area under the receiver operating curve (AUC) of 0.87 (range 0.64–0.95). The relative importance of the different sensor features to the predictive models varied both by individual and behavior type.

Discussion

Patterns of sensor data associated with BPSD are highly individualized, and future studies of the digital phenotyping of these behaviors would benefit from personalization.