Phase 1: Invitation to take part in a survey
To disability support coordinators
My name is 高見恭平. I am a master’s student in the Harada Laboratory at Okayama University’s Graduate School of Integrated Health Systems Science.
This information sheet explains the study “An exploratory mixed-methods study of the effectiveness of AI use in the work of disability support coordinators.” If you understand the study and agree to take part, we invite you to participate.
There is no disadvantage if you choose not to participate. If anything is unclear, please contact us using the form.
How to take part
You can take part using the online survey form.
When you submit the form, details such as your name and email address are not collected automatically unless you enter them.
1. Study protocol
(1) Background and objectives
In recent years, expectations for information and communication technology and AI in welfare services have grown. Workforce shortages are part of the reason: estimates suggested that about 27,000 additional care managers would be needed by 2025. In services for older adults, AI adoption has advanced since around 2016 and has helped make care management more efficient.
In disability welfare, the number of disability support coordinators has been rising and stands at about 27,000. However, staffing has not kept up with rising demand. Nationally, 15.6% of adult service users and 30% of child service users use self-prepared plans without a support coordinator. As in services for older adults, workforce shortages are a challenge. Research on AI and the development of tools for disability welfare have lagged, and support technology remains underdeveloped.
Tools for practice need to be based on the needs of disability support coordinators. Few previous studies have focused on this group, and AI use in the profession is still at an early stage. To introduce AI-based technical support, we first need to understand the needs of coordinators as the people who would use it.
This study will survey all designated consultation support providers in Okayama Prefecture to identify possible uses of AI and barriers to adopting it.
(2) Methods
We will survey approximately 600 disability support coordinators at 206 designated providers in Okayama Prefecture. The survey asks about awareness of AI tools, willingness to use them, challenges in day-to-day work, and what features and support coordinators would want. Most questions use a five-point scale, with open-ended fields asking why respondents chose their answers.
We will calculate descriptive statistics, run chi-square tests by characteristics including years in the profession and provider size, and carry out exploratory analyses. We will use content analysis for the open-ended responses.
(3) Expected contribution to health and welfare
Effective use of AI could improve the quality and efficiency of consultation support. In disability welfare, people’s needs vary widely and are highly individual. Analysis and decision support from AI could help coordinators develop care plans promptly and accurately, reduce their workload, and leave more time for working directly with people and supporting their decisions.
AI could also make care planning more efficient, support judgment, offer new perspectives, and improve the consistency and quality of consultation support. It could analyse large amounts of data to suggest care options and areas for improvement, and draw on past cases and predictions to help identify risks and avoid omissions. We expect that this could support more appropriate and continuous care and make it easier to explain options and reach agreement with service users.
We also expect that AI support could help coordinators provide more advanced care with less administrative burden and, ultimately, contribute to people’s independence and quality of life.