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Artificial intelligence‐led decision support services to reduce caregiver load related to persons with cognitive impairment
Author(s) -
Dorai Chitra,
Stern Edith
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.046194
Subject(s) - usability , dementia , cognition , set (abstract data type) , caregiver burden , service (business) , population , psychology , caregiver stress , medicine , gerontology , nursing , disease , psychiatry , computer science , business , environmental health , pathology , human–computer interaction , marketing , programming language
Background The number of people in the US living with cognitive impairment (including Alzheimer’s Disease and other dementia) is over 5.8M today, and is expected to increase to nearly 14M by 2050 [c.f. alz.org]. The rapidly growing population of informal caregivers is subject to high stress due to patient behavioral symptoms, increased demands on caregiver time, and lack of knowledge about new care giving tasks. Many of these tasks are complex and time consuming such as determining and coordinating services/resources for the patients, seeking information and talking with the clinical team about patient conditions and care plans, and arranging for someone to care for their charge. Method We hypothesize that the power of artificial intelligence (AI) technology can be harnessed to meaningfully support caregivers in time consuming, complex tasks encountered in care giving and thereby reduce their load before, during and after diagnosis. We have developed a set of ARTful (accountable, responsible, and transparent) AI services to support informal caregivers of patients with mild cognitive impairment who are being diagnosed and treated at a hospital Memory Clinic. Barriers to adoption of AI technology services include usability (as defined in the System Usability Scale), acceptability (fear of AI) and experience (digitally unprepared) on the part of patient/caregiver pairs. Result In this paper, we present case studies covering service onboarding, caregiver exploration and patterns of use of the AI technology, and initial assessment of its impact on adherence to care plans and on caregiver burden. Preliminary findings indicate that well‐thought out AI‐led solutions can reduce caregiver load and increase care plan adherence. A double‐blind study is planned to quantitatively assess impact as measured by scores on the Oberst Caregiver Burden Scale. Conclusion A demonstration of the effectiveness of this technology in reducing caregiver load will lead to a scalable, cost‐effective solution that raises the standard of current practices in neurodegenerative care, and leads to improved health outcomes.