Journal of advanced nursing · 2026
AI-Enabled Person-Centered Care for Unmet Home- and Community-Based Service Needs Among Chinese Older Adults: Latent Profiles and Risk Prediction.
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Yu Yushan, Zhu Dehua, Fujitaka Mimi, Zhang Jun, Zhang Wei-Hong, Petrovic Mirko, Chen Chun, Cheng Liangliang
Аннотация
AIM: To identify latent profiles of unmet home- and community-based service needs among Chinese older adults and to develop an interpretable machine learning model to support person-centered care.
DESIGN: Cross-sectional, multisite study.
METHODS: We recruited 487 community-dwelling adults aged 60 years and older from multiple provinces. Nineteen home- and community-based service items across health services, social services and family caregiver support domains were assessed. A descriptive Jaccard network summarized co-occurrence across services, latent class analysis identified subgroups with distinct patterns of unmet need, and TabNet classified participants into the combined higher-unmet-need group using eight selected demographic and health variables.
RESULTS: Pairwise co-occurrence among unmet service needs was widespread. Three latent class profiles were identified: Generally Met (69.6%), Healthcare-focused Unmet Needs (24.9%) and Extensive Unmet Needs (5.5%). TabNet showed moderate discrimination in the internal cross-validation analysis (AUC 0.767, 95% CI 0.720-0.808).
CONCLUSIONS: Latent profiles of unmet service needs were distinct. The interpretable eight-variable TabNet model may support first-stage community nursing screening by prioritizing older adults for subsequent professional assessment. External validation is required before routine implementation.
IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The model may help nursing teams prioritize older adults for follow-up assessment; referral and care coordination should be determined through professional assessment and shared decision-making.
IMPACT: The study addressed unmet service needs among older adults and may inform resource allocation and person-centered care planning in community-based care.
REPORTING METHOD: TRIPOD+AI statement.
PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
DESIGN: Cross-sectional, multisite study.
METHODS: We recruited 487 community-dwelling adults aged 60 years and older from multiple provinces. Nineteen home- and community-based service items across health services, social services and family caregiver support domains were assessed. A descriptive Jaccard network summarized co-occurrence across services, latent class analysis identified subgroups with distinct patterns of unmet need, and TabNet classified participants into the combined higher-unmet-need group using eight selected demographic and health variables.
RESULTS: Pairwise co-occurrence among unmet service needs was widespread. Three latent class profiles were identified: Generally Met (69.6%), Healthcare-focused Unmet Needs (24.9%) and Extensive Unmet Needs (5.5%). TabNet showed moderate discrimination in the internal cross-validation analysis (AUC 0.767, 95% CI 0.720-0.808).
CONCLUSIONS: Latent profiles of unmet service needs were distinct. The interpretable eight-variable TabNet model may support first-stage community nursing screening by prioritizing older adults for subsequent professional assessment. External validation is required before routine implementation.
IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The model may help nursing teams prioritize older adults for follow-up assessment; referral and care coordination should be determined through professional assessment and shared decision-making.
IMPACT: The study addressed unmet service needs among older adults and may inform resource allocation and person-centered care planning in community-based care.
REPORTING METHOD: TRIPOD+AI statement.
PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.