The mediating effects of public genomic knowledge in precision medicine implementation: A structural equation model approach

Empowering Healthcare through Precision Medicine: Unveiling the Nexus of Social Factors and Trust

This study investigated the impact of social factors on the acceptance of precision medicine (PM) using a quantitative survey grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The findings revealed that social influence has a significantly positive effect on PM acceptance, while the influence of social media is found to be insignificant. Performance expectancy emerged as the most influential factor, demonstrating a significant relationship with PM acceptance. Trust plays a crucial moderating role, mitigating the impact of social factors on PM acceptance. While exploring the mediating effects of trust, we identified a significant mediation effect for social influence and performance expectancy on PM acceptance. However, the mediation effect of social media influence is insignificant. These findings highlight the importance of trust in shaping decisions regarding PM acceptance. These findings have significant implications for healthcare practitioners and policymakers aiming to promote the adoption of precision medicine in clinical practice.

Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice

The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).

Structured approaches to implementation of clinical genomics: A scoping review

PURPOSE

This study aimed to assess the extent to which structured approaches to implementation of clinical genomics, proposed or adapted, are informed by evidence.

METHODS

A systematic approach was used to identify peer-reviewed articles and gray literature to report on 4 research questions: 1. What structured approaches have been proposed to support implementation? 2. To what extent are the structured approaches informed by evidence? 3. How have structured approaches been deployed in the genomic setting? 4. What are the intended outcomes of the structured approaches?

RESULTS

A total of 30 unique structured approaches to implementation were reported across 23 peer-reviewed publications and 11 gray literature articles. Most approaches were process models, applied in the preadoption implementation phase, focusing on a "service" outcome. Key findings included a lack of implementation science theory informing the development/implementation of newly designed structured approaches in the genomic setting and a lack of measures to assess implementation effectiveness.

CONCLUSION

This scoping review identified a significant number of structured approaches developed to inform the implementation of genomic medicine into clinical practice, with limited use of implementation science to support the process. We recommend the use of existing implementation science theory and the expertise of implementation scientists to inform the design of genomic programs being implemented into clinical care.