SenseCare
Counselling Use Case
Background
A community social care agency employs 1,500 regular volunteers, serving 86,000 seniors, and actively engages more than 7,600 at-risk seniors via a spectrum of integrated eldercare services.
Objectives
Enable home monitoring devices and video communications to provide early warning of mental health problems during teleconsultations while paying special attention to the fact that the elderly are often less able to take care of themselves.
Use non-intrusive tele-counselling and remote monitoring to evaluate daily changes in the patients’ emotional state of mind.
Integrate emotion recognition AI into the therapist-patient loop for detection, awareness, and treatment for mental health issues.
Solutions
SenseSafe’s algorithm analyses subtle changes in facial expressions, tone of voice and speech patterns via multiple devices.
This provides social workers with real-time information concerning psychological changes, cognitive appraisals, and subjective feelings.
The deep learning algorithm has high accuracy when it comes to identifying signs of depression, anxiety, grief, and pain.
Remote patient monitoring of patient emotions can fill important information gaps between video consultant and the caregiver.
This allows for transmission, evaluation and communication of critical patient information to the team, helping to facilitate review of the patient’s mental health counselling plan.
Emotion sensing together with psychometric analytics can provide early warning of critical issues.
If the algorithm detects potential suicidal tendencies or extreme pain, the device automatically alerts the caregiver enabling fast intervention.
We also integrate SenseCare emotion sensing technology into mobile app-based tools which can provide support via a 24/7 helpline and automated talk therapy.
Remote mental healthcare can be less intimidating, and less expensive compared to in-person consultation with psychiatrists.
This is especially important since or the healing process tends to be lengthy and time consuming.
Benefits
Most of the patients concerned are more than 60 years old.
In general, they feel comfortable engaging in 3-way video calls with a doctor and a care give.
Feedback from more than 500 patients reveal that in most cases patients did not think that using the video application was especially complicated.
Non-intrusive emotion detection of depressed patients performing routine activities identified the elements triggering mood changes and that prompted self-healing remedies.
Once patients became aware of their emotional state, they were able to better regulate their emotions in stressful or challenging situations.
Many elderly people find virtual counselling to be convenient. Most would need help to go to a hospital in person.
There was concern about risking exposure to the COVID virus if they appeared in person.
Identifying the mental health status of patients made it possible for a combination of cognitive and behavioural therapies to change negative thought patterns so that patients could manage their symptoms and enjoy a more productive and less stressful life.
Emotion AI and automated talk therapy reduced costs and allowed more people access to mental health care.
Caregivers were happy to get emotion data on their patients daily.
Previously, this information had been difficult to obtain.
Remote monitoring and counselling provided timely access and a 24/7 watch on serious cases of depression.
That enabled earlier interventions heading off downstream clinical events.
