The heterogeneity of public preferences for the first healthcare visit: A discrete choice experiment in the context of Vietnam.

Primary healthcare is critical in addressing the main health problems of communities. In Vietnam, the increasing healthcare demands cause major challenges, especially overcrowding. This study identified public preferences regarding the selection of healthcare facilities for first visit. A discrete choice online survey was generated from five attributes including visit duration, travel time, personal connection with medical staff, doctors' experience, and health insurance. A Dz -efficient design constructed 36 choice sets, divided into three blocks of 12 choice sets. Each block formed one version of the questionnaire, which was randomly distributed to the participants. Heterogeneity in participant preferences was analysed by a latent class model with socio demographic characteristics and experiences of the last visit. 822 participants valued doctors' experience for both minor and severe symptoms. Preference heterogeneity for minor symptoms was quick service provision, highly experienced doctors, and payment through health insurance for the first (44.18%), second (32.17%), and third classes (23.66%), respectively. Regarding severe symptoms, they favoured all five attributes, quick health service, and reduced travel time for the first, second, and third classes, respectively (heterogeneities of 58.16%, 27.79%, and 14.05%, respectively). Predictions of choice from the worst to optimal healthcare facility scenario were 8.91%-61.91% and 10.16%-69.83% for minor and severe symptoms, respectively. Knowledge regarding public preference heterogeneity supports policymakers increase public acceptance in choosing primary healthcare facilities. Visit duration and doctors' experience should be considered a priority in decision making.

The annual economic burden incurred by heart failure patients in Vietnam: a retrospective analysis.

Introduction: Heart failure (HF) is a chronic condition associated with substantial mortality and hospitalisation, resulting in costly inpatient visits. The healthcare systems of several countries, including Vietnam, experience considerable difficulty in dealing with the enormous fiscal burden presented by HF. This study aims to analyse the direct medical costs associated with HF inpatient treatment from the hospital perspective. Materials and methods: This study retrospectively analysed the electronic medical records of patients diagnosed with HF from 2018 to 2021 at Military Hospital 175 in Vietnam. The sample consisted of 906 hospitalised patients (mean age: 71.2 ± 14.1 years). The financial impact of HF was assessed by examining the direct medical expenses incurred by the healthcare system, and the costs of pharmaceutical categories used in treatment were explored. Results: The cumulative economic burden of HF from 2018 to 2021 was US$1,068,870, with annual costs ranging from US$201,670 to US$443,831. Health insurance covered 72.7% of these costs. Medications and infusions, and medical supplies accounted for the largest expenses, at 29.8% and 22.1%, respectively. The medication HF group accounted for 13.01% of these expenses, of which the costliest medications included nitrates (2.57%), angiotensin II receptor blockers (0.51%), ivabradine (0.39%), diuretics (0.24%), and mineralocorticoid receptor antagonists (0.23%). Comorbidities and the length of hospital stay significantly influenced annual treatment costs. Conclusion: The study reveals that HF significantly impacts Vietnam's healthcare system and citizens, requiring a comprehensive understanding of its financial implications and efficient management of medical resources for those diagnosed. This study highlights the substantial economic burden of HF on Vietnam's healthcare system, with medication costs, particularly antithrombotic drugs, representing the largest expense. Most healthcare costs were covered by health insurance, and expenses were significantly influenced by comorbidity and length of hospital stay. These findings can inform healthcare policy, resource allocation and optimise management strategies in Vietnam.

Healthcare students' knowledge, attitudes, and perspectives toward artificial intelligence in the southern Vietnam.

The application of new technologies in medical education still lags behind the extraordinary advances of AI. This study examined the understanding, attitudes, and perspectives of Vietnamese medical students toward AI and its consequences, as well as their knowledge of existing AI operations in Vietnam. A cross-sectional online survey was administered to 1142 students enrolled in undergraduate medicine and pharmacy programs. Most of the participants had no understanding of AI in healthcare (1053 or 92.2 %). The majority believed that AI would benefit their careers (890 or 77.9 %) and that such innovation will be used to oversee public health and epidemic prevention on their behalf (882 or 77.2 %). The proportion of students with satisfactory knowledge significantly differed depending on gender (P < 0.001), major (P = 0.003), experience (P < 0.001), and income (P = 0.011). The percentage of respondents with positive attitudes significantly differed by year level (P = 0.008) and income (P = 0.003), and the proportion with favorable perspectives regarding AI varied considerably by age (P = 0.046) and major (P < 0.001). Most of the participants wanted to integrate AI into radiology and digital imaging training (P = 0.283), while the fifth-year students wished to learn about AI in medical genetics and genomics (P < 0.001, 4.0 ± 0.8). The male students had 1.898 times more adequate knowledge of AI than their female counterparts, and those who had attended webinars/lectures/courses on AI in healthcare had 4.864 times more adequate knowledge than those having no such experiences. The majority believed that the barrier to implementing AI in healthcare is the lack of financial resources (83.54 %) and appropriate training (81.00 %). Participants saw AI as a "partner" rather than a "competitor", but the majority of low knowledge was recorded. Future research should take into account the way to integrate AI into medical training programs for healthcare students.