医疗、护理等
OBJECTIVES:With the rapid development of smart nursing, artificial intelligence (AI) systems for the dynamic assessment of venous thromboembolism (VTE) risk have gradually been introduced into clinical practice. As the direct users of such systems, nurses' acceptance substantially affects their effectiveness in clinical implementation. However, it remains unclear whether there is population heterogeneity in nurses' acceptance of AI-based dynamic VTE risk assessment. This study aims to identify latent profiles of nurses' acceptance of AI-based dynamic VTE risk assessment using latent profile analysis (LPA) and to explore the associated factors, thereby providing empirical evidence for development of targeted and stratified implementation and training strategies. METHODS:Convenience sampling was used to survey nurses from tertiary hospitals in southern, central, and northern Anhui Province between April and June 2025. Data were collected using a general information questionnaire, the Clinical Nurses' Behavior Scale for the Prevention of VTE in Hospitalized Patients, the Nurses' Professional Attitude Scale, and the Questionnaire on Nurses' Acceptance of AI-Based Dynamic VTE Risk Assessment. LPA was performed to identify latent profiles of nurses' acceptance, and multinomial Logistic regression analysis was used to examine the factors associated with profile membership. RESULTS:A total of 2 323 valid questionnaires were collected. Nurses' acceptance of AI-based dynamic VTE risk assessment was classified into 3 latent profiles: the low-acceptance-concerned resistance group [388 nurses (16.8%)], the moderate-acceptance-cautious observation group [1 177 nurses (50.5%)], and the high-acceptance-active adoption group [758 nurses (32.7%)]. Years of clinical experience, frequency of night shifts, scores on the knowledge dimension of the Clinical Nurses' Behavioral Scale for the Prevention of VTE in Hospitalized Patients, and scores on the Nurses' Professional Attitude Scale were factors associated with the latent profiles of nurses' acceptance of AI-based dynamic VTE risk assessment (all P<0.05). After adjustment for covariates, with the low-acceptance-concerned resistance group as the reference, higher scores on the Nurses' Professional Attitude Scale were associated with significantly greater likelihoods of membership in both the moderate-acceptance-cautious observation group and the high-acceptance-active adoption group, with odds ratios (OR) of 1.023 and 1.074, respectively (both P<0.001). Nurses who rarely or never work night shifts and those working 2-3 night shifts per month were more likely to be classified into the moderate-acceptance-cautious observation group (OR=1.773, OR=2.243, P<0.05) and the high-acceptance-active adoption group (OR=3.964, OR=4.300, P<0.001). Nurses with ≤5 years of clinical experience (OR=4.942, P<0.001) and those with 10 to 19 years of clinical experience (OR=3.361, P=0.006) were more likely to belong to the high-acceptance-active adoption group. Nurses with higher scores on the knowledge dimension of the Clinical Nurses' Behavior Scale for the Prevention of VTE in Hospitalized Patients were also more likely to belong to the high-acceptance-active adoption group (OR=1.042, P=0.009). CONCLUSIONS:There is substantial population heterogeneity in nurses' acceptance of AI-based dynamic VTE risk assessment. Hospital administrators should pay particular attention to nurses in the low-acceptance-concerned resistance and moderate-acceptance-cautious observation groups and implement targeted measures to improve their acceptance of this new technology.
The World Federation of Hemophilia (WFH) released its new Annual Global Survey (AGS) in October 2022. The report sets goals for diagnosis and treatment of hereditary bleeding disorders, including a 25% increase in the diagnosis rate of hemophilia and a 14% increase in the diagnosis rate of von Willebrand disease (vWD) patient by 2025. It also aims to provide appropriate care and treatment particularly by increasing the rate of prophylactic treatment for severe hemophilia patients under the age of 18 by 25%, and continuing to provide humanitarian aid to at least 20 000 patients annually through the WFH. The AGS 2022 estimates the annual average prevalence of hemophilia worldwide, with hemophilia A (HA) at 17.1 per 100 000 males, including severe HA at 6.0 per 100 000 males; and hemophilia B (HB) at 3.8 per 100 000 males, including severe HB at 1.1 per 100 000 males. Based on the world population (7.9 billion, with 4 billion males), it is estimated that the global prevalence of hemophilia is around 10.6 per 100 000, with a total of 830 895 individuals with hemophilia worldwide, of which approximately 282 266 are severe cases. The prevalence rate of hemophilia in China is between 2.73 and 3.09 per 100 000, which is lower than that of the global average. It is indicated in the report that there are 11 700 female hemophilia patients, accounting for 5% of the total number of hemophilia patients; and there are 54 066 female vWD patients, accounting for 56% of the total vWD population, and 34 370 cases of other bleeding disorders in females， highlighting that vWD is the most common bleeding disorder among women. In China, there are a total of 27 689 patients with HA, with the age distribution as follows: 4% aged 0-4 years, 21% aged 5-13 years, 12% aged 14-18 years, 43% aged 19-44 years, 16% aged 45 and above, and 4% with an uncertain age. Compared with low-income countries, high-income countries have a higher diagnosis rate for mild hemophilia and fewer patients with uncertain diagnosis. Patients with hemophilia in low-income countries are at a significant risk of early death. The use of emicizumab for prophylactic treatment in economically developed regions is significantly higher than that in China. There is still a considerable gap between China and European and American countries in the diagnosis and treatment of hemophilia and other bleeding disorders. It is necessary for clinical professionals in China to enhance their understanding of hemophilia and other bleeding disorders and to continuously improve diagnosis and treatment capabilities.
