To evaluate medication and diagnostic equipment availability for cardiovascular and cerebrovascular diseases (CCVDs) in primary healthcare institutions and identify gaps between urban and rural settings. A mixed-methods study was conducted in Henan Province from July 1 to October 30, 2024. For the quantitative component, a stratified random sample of 2,000 primary healthcare institutions was selected; 1,895 valid electronic questionnaires were analyzed. For the qualitative component, 24 staff from 8 purposively selected institutions participated in online semi-structured interviews. Categorical variables were compared using chi-square tests, while continuous variables with non-normal distribution were analyzed using the Mann-Whitney U test. Among 1,895 institutions (461 urban, 1,434 rural), the mean number of available CCVD drug categories was significantly lower in rural than in urban settings across most therapeutic classes (P < 0.05). Only six medications (valsartan, amlodipine, epinephrine, atorvastatin, Compound Danshen Dripping Pills, and Suxiao Jiuxin Pills) reached an availability rate exceeding 80%. More than 30% of institutions reported unmet demand for nitroglycerin, amiodarone, digoxin, intravenous antihypertensives, and norepinephrine. Although over 80% of institutions could perform basic diagnostics (ECG, complete blood count, fasting glucose, lipid profile, serum creatinine, and urinalysis), qualitative interviews revealed substantial shortages of up-to-date equipment. Pronounced urban–rural disparities in medication availability persist. Despite basic testing capacity, diagnostic equipment is often obsolete and insufficient. Policy measures—including supply chain improvements, prescribing regulation reforms, and collaborative hospital networks—are urgently needed to strengthen CCVD care in primary settings.

Background: Primary care training is a key strategy for strengthening healthcare services in China. This study aimed to systematically assess the training needs and service capacity improvement demands among primary healthcare workers in Henan Province, and to examine regional and role-specific variations, providing evidence for optimizing primary care workforce development. Methods: A cross-sectional online survey was conducted between April 11 and April 22, 2024, among 136,040 primary healthcare workers registered on the “Huayi Mobile Medical Education” training platform across 18 cities in Henan Province. A self-designed questionnaire assessed demographic characteristics, training needs, and service capacity enhancement. Descriptive statistics and chi-square tests were used to explore differences across regions and professional roles. Multivariable logistic regression models were constructed to examine associations between professional role and high training demand for selected core competencies, adjusting for institution type, professional title, and job position. Results: Overall, primary healthcare workers reported high demand (>75%) for training in the diagnosis and treatment of common diseases, rational drug use, and emergency response skills. Significant regional differences were observed, with certain areas showing higher demand for training in Traditional Chinese Medicine techniques, infectious disease control, or rehabilitation services. Training needs also varied by professional role: clinical staff emphasized clinical knowledge and Traditional Chinese Medicine, public health workers prioritized essential public health services, and nursing and technical personnel focused on first aid and emergency response. Conclusions: Training needs among primary healthcare workers in Henan Province are urgent and diverse, with clear regional and role-specific variations. These findings highlight the need for a competency-oriented, context-specific training system that improves relevance and practical applicability, thereby enhancing primary care service capacity and supporting the efficient allocation of health resources.

Objectives: To elicit stated preferences and willingness-to-pay (WTP) for artificial intelligence (AI)-enabled blended care in type 2 diabetes mellitus (T2DM), and to examine preference heterogeneity by digital experience and socioeconomic status (SES). Design: Cross-sectional discrete choice experiment (DCE). Setting: 12 community health centres in Jiaozuo and Puyang, Henan Province, China. Data were collected between June and August 2025. Participants: 423 adults diagnosed with T2DM for at least 6 months, recruited using consecutive convenience sampling from routine follow-up appointments. Of 769 participants who completed the survey, 346 were excluded following prespecified data quality criteria (retention rate: 55.0%). Outcome measures: Outcome measures included preference weights and WTP (in Chinese Yuan, ¥) for five DCE attributes: monthly subscription fee, recommendation source, feedback modality, in-person follow-up frequency and expert oversight, estimated using mixed logit models. Simulated uptake probabilities for tailored service packages across four user profiles were computed. Results: Among 423 participants, 80.6% had never used AI tools. Price was the dominant driver of choice (62.7% relative attribute importance). Profound preference heterogeneity emerged across subgroups: rural residents (n=78) were highly price-sensitive but preferred physician endorsement (WTP ¥17.58 (US$2.55), 95% CI ¥5.98 to ¥29.17); female participants (n=224) valued guideline recommendations (WTP ¥18.45 (US$2.67), 95% CI ¥7.81 to ¥29.40); and diabetes app users (n=34) were the least price-sensitive but showed a negative preference for AI instant feedback, instead preferring human dietitian feedback. Expert oversight carried a consistent negative WTP across all profiles. Targeting tailored service bundles to intended subgroups increased uptake by 8–16 percentage points compared with non-targeted bundles. Conclusions: A ‘digital experience paradox’ exists whereby digitally experienced users view human interaction as a premium service, while underserved groups rely on specific trust markers such as physician endorsement. To avoid widening the digital divide, AI-enabled blended diabetes care must move beyond standardised models towards configurable, equity-driven service pathways.