OBJECTIVE This study aimed to construct a digital-intelligent empowered full-course pharmaceutical care model for pediatric asthma and validate its practical efficacy, so as to provide a referable paradigm for pharmaceutical services targeting children with chronic diseases.

METHODS An innovative framework based on Six Sigma management methodology was adopted to ensure the scientific validity and sustainability of the functional design of full-course pharmaceutical care for pediatric asthma. Digital and intelligent technologies were utilized to realize core service functions, including intelligent prescription auditing and artificial intelligence mini-program-based dynamic follow-up. On this basis, a novel intelligent pharmaceutical care model for pediatric asthma was constructed and implemented, with enrolled children assigned to the observation group. To verify the efficacy of the model, evaluation indicators at each follow-up stage were compared between the observation group and the control group, which received traditional pharmaceutical care for pediatric asthma.

RESULTS A total of 412 eligible children were enrolled in the study, including 202 cases in the control group and 210 cases in the observation group. All primary outcome indicators exhibited distinct temporal dynamic changes. At the first follow-up, no significant intergroup differences were observed in the follow-up retention rate, medication adherence score, overall asthma control rate, or three pulmonary function parameters. However, the observation group showed a significantly higher mastery score of controller inhalers than the control group(P<0.01). At the second follow-up, the observation group achieved a significantly higher follow-up retention rate(P<0.05), as well as superior overall asthma control rate and better pulmonary function indicators(P<0.01). At the third follow-up, the medication adherence score of the observation group was significantly higher than that of the control group(P<0.01). During the entire follow-up period, the observation group presented a significantly lower incidence of adverse drug reactions and a significantly higher rate of standardized adverse reaction management(P<0.01). In the service satisfaction evaluation, no significant difference was found in the privacy dimension between the two groups, while the observation group obtained significantly higher scores in the other seven evaluation dimensions(P<0.01).

CONCLUSION With the extension of follow-up time, the digital-intelligent full-course intelligent pharmaceutical care model gradually presents comprehensive advantages over traditional pharmaceutical care in terms of follow-up retention, medication adherence, asthma control level, pulmonary function, inhaler technique proficiency, adverse reaction monitoring and disposal, and medical service satisfaction. This model has promising prospects for clinical popularization and application in pediatric chronic disease management.