NegotiAge: Development and pilot testing of an artificial intelligence-based family caregiver negotiation program.

BACKGROUND: Family caregivers of people with Alzheimer's disease experience conflicts as they navigate health care but lack training to resolve these disputes. We sought to develop and pilot test an artificial-intelligence negotiation training program, NegotiAge, for family caregivers. METHODS: We convened negotiation experts, a geriatrician, a social worker, and community-based family caregivers. Content matter experts created short videos to teach negotiation skills. Caregivers generated dialogue surrounding conflicts. Computer scientists utilized the dialogue with the Interactive Arbitration Guide Online (IAGO) platform to develop avatar-based agents (e.g., sibling, older adult, physician) for caregivers to practice negotiating. Pilot testing was conducted with family caregivers to assess usability (USE) and satisfaction (open-ended questions with thematic analysis). RESULTS: Development: With NegotiAge, caregivers progress through didactic material, then receive scenarios to negotiate (e.g., physician recommends gastric tube, sibling disagrees with home support, older adult refusing support). Caregivers negotiate in real-time with avatars who are designed to act like humans, including emotional tactics and irrational behaviors. Caregivers send/receive offers, using tactics until either mutual agreement or time expires. Immediate feedback is generated for the user to improve skills training. Pilot testing: Family caregivers (n = 12) completed the program and survey. USE questionnaire (Likert scale 1-7) subset scores revealed: (1) Useful-Mean 5.69 (SD 0.76); (2) Ease-Mean 5.24 (SD 0.96); (3) Learn-Mean 5.69 (SD 0.74); (4) Satisfy-Mean 5.62 (SD 1.10). Items that received over 80% agreements were: It helps me be more effective; It helps me be more productive; It is useful; It gives me more control over the activities in my life; It makes the things I want to accomplish easier to get done. Participants were highly satisfied and found NegotiAge fun to use (91.7%), with 100% who would recommend it to a friend. CONCLUSION: NegotiAge is an Artificial-Intelligent Caregiver Negotiation Program, that is usable and feasible for family caregivers to become familiar with negotiating conflicts commonly seen in health care.

Transforming a Negotiation Framework to Resolve Conflicts among Older Adults and Family Caregivers.

BACKGROUND: Family caregivers of older people with Alzheimer's dementia (PWD) often need to advocate and resolve health-related conflicts (e.g., determining treatment necessity, billing errors, and home health extensions). As they deal with these health system conflicts, family caregivers experience unnecessary frustration, anxiety, and stress. The goal of this research was to apply a negotiation framework to resolve real-world family caregiver-older adult conflicts. METHODS: We convened an interdisciplinary team of national community-based family caregivers, social workers, geriatricians, and negotiation experts (n = 9; Illinois, Florida, New York, and California) to examine the applicability of negotiation and conflict management frameworks to three older adult-caregiver conflicts (i.e., caregiver-older adult, caregiver-provider, and caregiver-caregiver). The panel of caregivers provided scenarios and dialogue describing conflicts they experienced in these three settings. A qualitative analysis was then performed grouping the responses into a framework matrix. RESULTS: Upon presenting the three conflicts to the caregivers, 96 responses (caregiver-senior), 75 responses (caregiver-caregiver), and 80 responses (caregiver-provider) were generated. A thematic analysis showed that the statements and responses fit the interest-rights-power (IRP) negotiation framework. DISCUSSION: The interests-rights-power (IRP) framework, used in business negotiations, provided insight into how caregivers experienced conflict with older adults, providers, and other caregivers. Future research is needed to examine applying the IRP framework in the training of caregivers of older people with Alzheimer's dementia.

Aspects of cognition that impact aging-in-place and long-term care planning.

BACKGROUND: Older adults frequently defer decisions about their aging-in-place/long-term care (AIP-LTC) needs. As a result, when older adults experience worsening Alzheimer's disease, family members/friends become surrogate decision makers. We sought to understand what aspects of cognition impact older adult AIP-LTC planning. METHODS: As part of the PlanYourLifespan (PYL)-LitCog study, we longitudinally examined AIP-LTC decision-making among a cohort (LitCog) of community-based older adults (65 years and older) recruited from hospital-associated primary care clinics in Chicago, Illinois, with extensive cognitive testing. PlanYourLifespan.org (PYL) is an evidence-based online intervention that facilitates AIP-LTC planning. Subjects underwent baseline testing, received the PYL online intervention, and then were surveyed at 1, 6, and 12 months about AIP-LTC decision-making. Cross-sectional logistic regression analysis was conducted examining cognitive variables that impacted AIP-LTC decision-making. RESULTS: Of the 293 older adults interviewed (mean age 73.0 years, 40.4% non-White), subjects were more likely to have made AIP-LTC decisions if they had adequate inductive reasoning (ETS letter sets total-OR = 1.14 (95% CI = 1.03-1.27; p < 0.05)) and adequate working memory (size judgment span total-OR = 1.76 (95% CI = 1.13-2.73; p < 0.05)). There were no differences in decision-making observed in verbal abilities, long-term memory, or processing speed. All analyses were adjusted for participant gender, race, age, and decision-making response at baseline. CONCLUSION: Inductive reasoning and working memory are critical to AIP-LTC decision-making. Screening routinely for these specific cognitive domains is important in targeting and helping older adults prepare in time for their future AIP-LTC needs.