Effectiveness of cardiac rehabilitation programs on medication adherence in patients with cardiovascular disease: A systematic review and meta-analysis

The Caring Life Course Theory: Opening new frontiers in care-A cardiac rehabilitation example

AIM(S)

To operationalize the Caring Life Course Theory (CLCT) as a framework for improving cardiac rehabilitation (CR) engagement and informing ways to address disparities in rural, low socio-economic areas.

METHODS

A secondary analysis of data collected from 15 CR programmes to identify CR patterns through the CLCT lens using a mixed-methods approach. All analytical processes were conducted in NVivo, coding qualitative data through thematic analysis based on CLCT constructs. Relationships among these constructs were quantitatively assessed using Jaccard coefficients and hierarchical clustering via dendrogram analysis to identify related clusters.

RESULTS

A strong interconnectedness among constructs: 'care from others', 'capability', 'care network' and 'care provision' (coefficient = 1) highlights their entangled crucial role in CR. However, significant conceptual disparities between 'care biography' and 'fundamental care' (coefficient = 0.4) and between 'self-care' and 'care biography' (coefficient = 0.384615) indicate a need for more aligned and personalized care approaches within CR.

CONCLUSION

The CLCT provides a comprehensive theoretical and practical framework to address disparities in CR, facilitating a personalized approach to enhance engagement in rural and underserved regions.

IMPLICATIONS

Integrating CLCT into CR programme designs could effectively address participation challenges, demonstrating the theory's utility in developing targeted, accessible care interventions/solutions.

IMPACT

Explored the challenge of low CR engagement in rural, low socio-economic settings. Uncovered care provision, transitions and individual care biographies' relevance for CR engagement. Demonstrated the potential of CLCT to inform/transform CR services for underserved populations, impacting practices and outcomes.

REPORTING METHOD

EQUATOR-MMR-RHS.

PATIENT CONTRIBUTION

A consumer co-researcher contributed to all study phases.

Healthcare Systems and Artificial Intelligence: Focus on Challenges and the International Regulatory Framework

BACKGROUND

Nowadays, healthcare systems are coping with the challenge of countering the exponential growth of healthcare costs worldwide, to support sustainability and to guarantee access to treatment for all patients.

METHODS

Artificial Intelligence (AI) is the technology able to perform human cognitive functions through the creation of algorithms. The value of AI in healthcare and its ability to address healthcare delivery issues has been a subject of discussion within the scientific community for several years.

RESULTS

The aim of this work is to provide an overview of the primary uses of AI in the healthcare system, to discuss its desirable future uses while shedding light on the major issues related to implications within international regulatory processes. In this manuscript, it will be described the main applications of AI in various aspects of health care, from clinical studies to ethical implications, focusing on the international regulatory framework in countries in which AI is used, to discuss and compare strengthens and weaknesses.

CONCLUSIONS

The challenges in regulatory processes to facilitate the integration of AI in healthcare are significant. However, overcoming them is essential to ensure that AI-based technologies are adopted safely and effectively.