Unpacking the design, implementation and uptake of community-integrated health care services: a critical realist synthesis

Introduction

Community-integrated care initiatives are increasingly being used for social and health service delivery and show promising outcomes. Nevertheless, it is unclear what structures and underlining causal agents (generative mechanisms) are responsible for explaining how and why they work or not.

Methods and analysis

Critical realist synthesis, a theory-driven approach to reviewing and synthesising literature based on the critical realist philosophy of science, underpinned the study. Two lenses guided our evidence synthesis, the community health system and the patient-focused perspective of integrated care. The realist synthesis was conducted through the following steps: (1) concept mining and framework formulation, (2) searching for and scrutinising the evidence, (3) extracting and synthesising the evidence (4) developing the narratives from causal explanatory theories, and (5) disseminate, implement and evaluate.

Results

Three programme theories, each aligning with three groups of stakeholders, were unearthed. At the systems level, three bundles of mechanisms were identified, that is, (1) commitment and motivation, (2) willingness to address integrated health concerns and (3) shared vision and goals. At the provider level, five bundles of mechanisms critical to the successful implementation of integrated care initiatives were abstracted, that is, (1) shared vision and buy-in, (2) shared learning and empowerment, (3) perceived usefulness, (4) trust and perceived support and (5) perceived role recognition and appreciation. At the user level, five bundles of mechanisms were identified, that is, (1) motivation, (2) perceived interpersonal trust, (3) user-empowerment, (4) perceived accessibility to required services and (5) self-efficacy and self-determination.

Conclusion

We systematically captured mechanism-based explanatory models to inform practice communities on how and why community-integrated models work and under what health systems conditions.

PROSPERO registration number

CRD42020210442.

Intention to Adopt AI-Powered Online Service among Tourism and Hospitality Companies

This study examines the factors affecting the adoption of artificial intelligence (AI) powered online service in the tourism and hospitality sector through an extension of the technology acceptance model (TAM) by including self-efficacy, subjective norms, technological knowledge, and perceived cost in the analysis. Data was collected from 336 respondents in Malaysia’s tourism and hospitality industry using the questionnaire survey. The empirical results confirmed that perceived usefulness, ease of use, attitude, cost, and technology knowledge significantly affected behavioral intention. Self-efficacy, perceived ease of use, and perceived usefulness affected the attitude towards AI. Attitude mediated the relationship between perceived ease of use and behavioral intention as well as the relationship between perceived usefulness and behavioral intention. This study contributes to enhancing AI's understanding of the tourism and hospitality industry context. This study also improves TAM by proposing a comprehensive model with cognitive external and technology-specific factors.

Technology Acceptance in Healthcare: A Systematic Review

Understanding the factors affecting the use of healthcare technologies is a crucial topic that has been extensively studied, specifically during the last decade. These factors were studied using different technology acceptance models and theories. However, a systematic review that offers extensive understanding into what affects healthcare technologies and services and covers distinctive trends in large-scale research remains lacking. Therefore, this review aims to systematically review the articles published on technology acceptance in healthcare. From a yield of 1768 studies collected, 142 empirical studies have met the eligibility criteria and were extensively analyzed. The key findings confirmed that TAM and UTAUT are the most prevailing models in explaining what affects the acceptance of various healthcare technologies through different user groups, settings, and countries. Apart from the core constructs of TAM and UTAUT, the results showed that anxiety, computer self-efficacy, innovativeness, and trust are the most influential factors affecting various healthcare technologies. The results also revealed that Taiwan and the USA are leading the research of technology acceptance in healthcare, with a remarkable increase in studies focusing on telemedicine and electronic medical records solutions. This review is believed to enhance our understanding through a number of theoretical contributions and practical implications by unveiling the full potential of technology acceptance in healthcare and opening the door for further research opportunities.