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Many health-care professionals leave clinical practice temporarily or permanently. Interventions designed to facilitate the return of health-care professionals fail to consider returners’ psychosocial needs despite their importance for patient care. This study aims to evaluate the efficacy of a psychoeducational intervention in improving personal skills and well-being among UK-based health-care professionals returning to clinical practice.

In total, 20 health-care professionals took part in the one-day intervention and completed measures of demographics, self-efficacy, positive attitudes towards work and perceived job resources before and after the intervention. A baseline comparison group of 18 health-care professionals was also recruited.

Significant associations were detected between return-to-work stage and study group. Following the intervention, participants reported improvements in self-efficacy and, generally, perceived more job resources, whereas positive attitudes towards work decreased. While none of these changes were significant, the intervention was deemed acceptable by participants. This study provides modest but promising evidence for the role of psychoeducation as a tool in supporting the psychosocial needs of returning health-care professionals.

Additional research is needed to clarify the reliability of intervention effects, its effectiveness compared to alternative interventions, and the impact across different subgroups of returning health-care professionals.

Return-to-practice interventions should address the psychosocial needs of health-care professionals in terms of their personal skills and well-being. Psychoeducation can increase self-efficacy and perceptions of job resources among returning health-care professionals.

This study sheds light on a relatively understudied, but fundamental area – the psychosocial challenges of health-care professionals returning to clinical practice – and further justifies the need for tailored interventions.

This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service.

An adaptive optimal service resource management strategy was developed considering a value co-creation model in health information service with a focus on collaborative and interactive with users. The deep reinforcement learning algorithm was embedded in the Internet of Things (IoT)-based health information service system (I-HISS) to allocate service resources by controlling service provision and service adaptation based on user engagement behaviour. The simulation experiments were conducted to evaluate the significance of the proposed algorithm under different user reactions to the health information service.

The results indicate that the proposed service resource management strategy, considering user co-creation in the service delivery, process improved both the service provider’s business revenue and users' individual benefits.

The findings may facilitate the design and implementation of health information services that can achieve a high user service experience with low service operation costs.

This study is amongst the first to propose a service resource management model in I-HISS, considering the value co-creation of the user in the service-dominant logic. The novel artificial intelligence algorithm is developed using the deep reinforcement learning method to learn the adaptive service resource management strategy. The results emphasise user engagement in the health information service process.