Research on the Home and Community-Based Elderly Care Service Ecosystem Driven by Digital-Intelligence Empowerment

INTRODUCTION: Fragmented demand records, invisible provider capacity, and weak closure verification reduce the responsiveness of home and community-based elderly care. OBJECTIVES: This paper specifies and evaluates a lightweight digital-intelligence workflow system rather than a predictive medical-diagnosis tool. The system links multi-channel demand intake, rule-based priority scoring, provider eligibility filtering, work-order state tracking, delivery verification, exception logging, and feedback closure. The architecture retains a core-provider layer, a beneficiary layer, and a four-module support system, but implements them through minimum data fields, explicit state transitions, and auditable scoring rules. METHODS: An eight-week AB/BA pilot was conducted at two community centers with 42 adults aged 65–82 and 386 valid service requests. RESULTS: Compared with the routine workflow, the supported workflow reduced mean demand-to-confirmation time from 58.4 to 36.9 min and staff coordination time from 8.9 to 6.2 min per request. On-time completion increased from 79.2% to 89.1%, while matching accuracy increased from 83.8% to 91.6%. CONCLUSION: The configuration uses ordinary tablets, office computers, spreadsheet-compatible records, role-based access, and human fallback channels, providing an implementable coordination mechanism for community elderly care.