Evidence-Based Policy, Guidelines, and Practical Recommendations  NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics

Tertiary care settings must integrate genomic data to identify diseases with genetic predispositions. This will enable the organization for early identification of the disease and develop personalized interventions. The rationale for integrating genomics grounds from the COVID period, where identifying the disease’s origin, transmission, and evolution helped combat the disease. Moreover, genomics is crucial in preventing several communicable and non-communicable diseases (Khoury et al., 2021). 

Guidelines for Implementation

Initially, the organization must assemble a team of geneticists, data analytics, healthcare providers, and IT experts to undertake the implementation process. Secondly, tertiary care settings must evaluate the current technological infrastructure and perform a needs assessment to identify areas of enhancement for incorporating genomics. Another critical action that tertiary care settings must follow is prioritizing data security and patient privacy. This complies with data protection policies like the Health Insurance Portability and Accountability Act (HIPAA).

It is crucial to maintain public trust in the healthcare system and encourage active participation (Theodos & Sittig, 2020). Then, tertiary care settings must develop training programs for healthcare professionals to build their competencies in bioinformatics and address their challenges for effective results. Lastly, the organization must keep comprehensive records of policy implementation, training, and education and the results achieved. This report will help internal and external stakeholders analyze the project’s success and make necessary adjustments. 

Practical Recommendations

Practical recommendations for the project implementation include educating stakeholders about the new practices. The educational approach must be tailored to individual stakeholder’s needs (Turner et al., 2021), such as healthcare professionals will need in-depth training related to data analytics tools. Similarly, IT specialists may need specific training on healthcare issues and domains. Other than this, there must be a clear and accessible communication pathway, such as workshops, seminars, and online resources. Moreover, project leaders and the team must have clear goals and objectives to pursue and bring successful results. Another recommendation is for monitoring data, where specific standardized key performance indicators (KPIs) must be set, such as disease prevalence, early identification, and patient engagement. These KPIs and pre-implementation metrics will help perform a comparative analysis to evaluate successful outcomes and modify the plan.

Example of Implementation

Breast cancer is one of the most widespread cancers around the world, which requires early detection to prevent and improve patient outcomes. Bioinformatics for genetic screening has helped prevent the disease. Jürgens et al. (2022) performed a pilot study in an Estonian biobank and evaluated several genetic variants for preventing breast cancer in clinical settings. Since breast cancer guidelines are more focused on identifying personal and family history as essential factors, this study presents the hypothesis of prioritizing genetic factors.

According to the authors, early recognition of genetic predisposition will help in early prevention of the disease. The study showed the results that among 200,000 participants, 180 females were re-contacted due to the identification of breast cancer high-risk genes. These include BRCA1, BRCA2, TP53, STK11, and CDH1. Meanwhile, some moderate-risk genes were also identified, which are ATM, PALB2, CHEK2, NBN, and NF1. This study concluded the effectiveness of the genotype-first approach in clinical settings as this approach assists in developing personalized breast cancer screening and prevention programs to maximize the prevention and early detection of the disease.