Identifying solutions to meet unmet needs of family caregivers using human-centered design

Background

Given the rapidly aging society, shrinking workforce, and reducing dependency ratio, there is an increasing challenge for family members to provide care for older adults. While a broad understanding of caregiver burden and its consequences have been studied across various contexts, there is a need to better understand this challenge among family caregivers in Asian societies.

Methods

This study is a cross-sectional observational study. A total of 20 dyads of community-based older adults, who required assistance with at least one activities of daily living, and family caregivers in Thailand participated in the study. We used the first three stages out of five stages of human-centered design: empathize, define, and ideate.

Results

On average caregivers were 59.2 years old, with 43% still employed. Of the older adult participants, 10 were interviewed, the others had moderate-to-severe cognitive impairment. Based on the analysis, six caregiver personas (i.e. semi-fictional characters) are identified. Caregiver personas of “The 2-Jober” and “My Life Purpose” has the highest caregiver burden score whereas “The Spouse” has the lowest. Based on the specific needs of the caregiver persona “My Life Purpose”, the team brainstormed more than 80 potential solutions which were classified into three categories of solutions that satisfied the metrics of desirability, feasibility and viability: distributed medical care system, technology-charged care network, and community gathering for rest and recuperation.

Conclusions

These solutions are culturally sensitive given that they are built around established behavioral patterns. This is an illustration of a method of innovation that can be applied to bring a culturally specific understanding, and to develop products and services to enable further independent aging.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-02790-5.

A new digital classification schema to detect treatment variances and enable value-based payment reforms

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Background: Value based payment reforms which improve the outcome to cost ratio (paying for outcomes not for services) require detailed information on both elements. Although the digitalization of medicine via the EHR may facilitate data collection, the imprecise ICD9 and ICD10 schemas hinder analysis (eg: all breast cancers are ICD9 174.9). A more precise classification schema that accounts for biologic variances to allow analysis (and reduction) of treatment variances is needed. Methods: We have developed a digital classification (COTA Nodal Address) based on known clinically relevant prognostic variables. (eg: breast cancer variables include stage, tumor size, hormone status, her2 neu status, etc.). The digital classification also accounts for therapy status (neoadjuvant vs adjuvant, etc.) and line (1st or 2nd treatment, etc.). Results: Using breast cancer as an example, there are > 2975 unique CNA phenotypes that encompass prognostic and treatment status variables. However, in a series of 1204 patients in a community setting only 114 CNAs have been utilized, suggesting biologic disequilibrium. Two phenotypes accounted for the top 25% of cases, 6 for the top 50% and 16 for the top 75%. Early stage, small tumor, hormone receptor positive were most common. The CNA classification uncovered treatment variances among patients with similar phenotypes. In an ongoing episodes of care payment program, young breast cancer patients with early stage, good performance status, hormone positive, her2 neu negative, very small tumors (with similar extended attributes) received varying adjuvant treatment strategies (hormone alone 46% , non-anthracycline 35%, anthracycline chemotherapy 4%, observation 1%). Conclusions: Unlike ICD9/10 systems, a digital classification schema that precisely segregates cancers into unique cohorts based on complete biologic characteristics can rapidly identify behavioral variances at scale that drive differences in outcomes and costs. Using the breast cancer model, we have noted demographic disequilibrium in cancer presentations (potentially a factor of both biology and screening practices) and major variances in treatment strategies (with impact on outcomes/ costs).