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Competency F
Competency F
Conduct informatics analysis and visualization applied to different real-world field, such as health science and sports.
Conduct informatics analysis and visualization applied to different real-world field, such as health science and sports.
Introduction
Introduction
This competency is crucial as part of a strong foundation in informatics. Applying the knowledge and skills acquired from my informatics courses and within the health pathway supports data-driven decision-making. Analysis and visualization are significant in public health promotion and disease prevention. By analyzing data on health behaviors, effective prevention programs can be developed. As more data is collected, analyzed, and leveraged with machine learning and artificial intelligence (AI), we can identify trends, patterns, and solutions far more accurately and efficiently than people can (Machine learning and AI: The Future of Data Analysis, 2023). This enables allocating resources to most needy areas, such as funding for prevention programs, healthcare facilities, and emergencies.
Furthermore, by analyzing the impact of previous interventions, areas for improvement are identified, and evidence-based policies can be implemented. Data visualization is essential in informatics as it helps simplify complex data into understandable visual formats like graphs, maps, and charts. People prefer visual information, as it is processed faster by the brain than text (Pamplona, 2023). As a result, data visualization has become a crucial tool in informatics across all industries. The use of analysis and visualization techniques in informatics has expanded: businesses now rely on data to gain valuable insights, improve operations, and foster innovation. Business intelligence tools like Tableau create interactive dashboards and reports to understand complex data analysis better. Being able to interpret information and present factual data and solutions is paramount for a future career in informatics.
Evidence 1
Evidence 1
California Childhood Obesity Prevention Policy: Funding Sports Programs for Medicaid and CHIP recipients
I selected this paper for Competency F because informatics plays a crucial role in implementing and evaluating public health programs, in this case, the fight against childhood obesity. The rigorous analysis and interpretation of data provide the foundation for data-driven decision-making, identifying trends and patterns in obesity rates and related factors. This scientific approach reassures us that we are on the right track. It is crucial to evaluate the effectiveness of program interventions and, if they are unsuccessful, understand what alternate programs may work. Also, using data to determine which populations are most at risk and monitoring changes over time can support interventions in place. Public health programs need to identify areas with the greatest need. In my paper, I identified past programs that were not successful - nutrition-based programs- and identified those that were most at risk. Mapping tools can efficiently measure poverty rates and childhood obesity to determine the correlation. By harnessing the power of informatics, public health professionals can develop and implement more effective childhood obesity prevention and treatment programs. My childhood obesity prevention paper uses informatics skills and knowledge, along with data, to promote funding sports for low-income families.
Evidence 2
Evidence 2
Exploring Tableau for Data Visualization
During INFM 203, I formed a strong foundation regarding big data and its technologies. The focus was on understanding fundamental big data concepts and challenges, including the three Vs—volume, Velocity, and Variety. Big data workflows, software such as Hadoop and MapReduce, standard data formats, data warehousing, and data cleaning were also reviewed. As part of the class, we learned how to utilize Tableau, which is a data visualization and business intelligence tool. I was tasked with exploring how to create different visualizations from structured data files. My evidence here provides charts and graphs communicating complex information in a simple visualization. The final Tableau dashboard I created includes a sales analysis to identify top-selling products and sales trends by food distributors over a set time period, with a search functionality. While my evidence focuses on food sales, creating clear and informative visualizations based on accurate data increases effective communication and knowledge across all industries. The skills I learned can be applied to healthcare and are essential in interpreting data to uncover patterns, trends, and insights that drive decision-making.
Conclusion
Conclusion
My informatics coursework has given me a strong foundation in analysis, data visualization, and problem-solving. As data-driven decision-making transforms operations, using visualization tools will be critical to capture data trends effectively, and while the use of machine learning and AI expands, so will the desire for real-time data analysis (The future of Data Visualization: 2024 and beyond, 2023). I am confident in applying informatics analysis and visualization to address real-world challenges across various industries. This confidence is a testament to the adaptability of my informatics skills, which can be applied to a wide range of scenarios, making me ready for any challenge.
References
References
Machine learning and AI: The Future of Data Analysis. eHealth4everyone. (2023, May 31). https://ehealth4everyone.com/machine-learning-and-ai-the-future-of-data-analysis/
Medium. (2023, December 15). The future of Data Visualization: 2024 and beyond. Medium. https://medium.com/@mokkup/the-future-of-data-visualization-2024-and-beyond-3173a8e60494
Pamplona, F. (2023, September 22). The Power of Visuals. MedTech Intelligence. https://medtechintelligence.com/column/the-power-of-visuals/
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