New Approaches to Preventing Financial Exploitation: A Focus on the Banks

Use of Telephone- and Internet-Based Support to Elicit and Address Financial Abuse and Mismanagement in Dementia: Experiences from the Care Ecosystem Study

BACKGROUND

Financial mismanagement and abuse in dementia have serious consequences for patients and their families. Vulnerability to these outcomes reflects both patient and contextual factors.

OBJECTIVE

Our study aimed to assess how multidisciplinary care coordination programs assist families in addressing psychosocial vulnerabilities and accessing needed resources.

METHODS

Our study was embedded in a clinical trial of the Care Ecosystem, a telephone- and internet-based supportive care intervention for patients with dementia and caregivers. This program is built around the role of the Care Team Navigator (CTN), an unlicensed dementia care guide who serves as the patient and caregiver's primary point of contact, screening for common problems and providing support. We conducted a qualitative analysis of case summaries from a subset of 19 patient/caregiver dyads identified as having increased risk for financial mismanagement and abuse, to examine how Care Ecosystem staff identified vulnerabilities and provided support to patients and families.

RESULTS

CTNs elicited patient and caregiver needs using templated conversations to address common financial and legal planning issues in dementia. Sources of financial vulnerability included changes in patients' behavior, caregiver burden, intrafamily tension, and confusion about resources to facilitate end-of-life planning. The Care Ecosystem staff's rapport with their dyads helped them address these issues by providing emotional support, information on how to access financial, medical, and legal resources, and improving intra-familial communication.

CONCLUSION

The Care Ecosystem offers a scalable way to address vulnerabilities to financial mismanagement and abuse in patients and caregivers through coordinated care by unlicensed care guides supported by a multidisciplinary team.

Big Data Analytical Approaches to the NACC Dataset: Aiding Preclinical Trial Enrichment

BACKGROUND

Clinical trials increasingly aim to retard disease progression during presymptomatic phases of Mild Cognitive Impairment (MCI) and thus recruiting study participants at high risk for developing MCI is critical for cost-effective prevention trials. However, accurately identifying those who are destined to develop MCI is difficult. Collecting biomarkers is often expensive.

METHODS

We used only noninvasive clinical variables collected in the National Alzheimer's Coordinating Center (NACC) Uniform Data Sets version 2.0 and applied machine learning techniques to build a low-cost and accurate Mild Cognitive Impairment (MCI) conversion prediction calculator. Cross-validation and bootstrap were used to select as few variables as possible accurately predicting MCI conversion within 4 years.

RESULTS

A total of 31,872 unique subjects, 748 clinical variables, and additional 128 derived variables in NACC data sets were used. About 15 noninvasive clinical variables are identified for predicting MCI/aMCI/naMCI converters, respectively. Over 75% Receiver Operating Characteristic Area Under the Curves (ROC AUC) was achieved. By bootstrap we created a simple spreadsheet calculator which estimates the probability of developing MCI within 4 years with a 95% confidence interval.

CONCLUSIONS

We achieved reasonably high prediction accuracy using only clinical variables. The approach used here could be useful for study enrichment in preclinical trials where enrolling participants at risk of cognitive decline is critical for proving study efficacy, and also for developing a shorter assessment battery.