Competency F
Define
My understanding of competency F is to be able to utilize information technology to conduct data analysis, support clinical decision making, and EHR program implementation. A health informaticist major responsibility is collecting and analyzing data to find treads and patterns to get valuable insights is crucial to make the right clinical decision proactively. Fridsma (Warner, 1995, as cited in Fridsma, 2016) stated that health informatics performs an interdisciplinary expertise that can be broken up into 5 things: “signal process, database design, decision making, modeling and simulation, and human machine interface”. In addition, Thye (Hubner, et al., 2019, as cited in Thye, 2021) summarized core competencies that related to the science of data and computing including “clinical decision, consumer health information, data analytics, data protections and security, information and knowledge management in patient care, care process and IT integration ICT application and architecture.”
Discuss
The courses that I took helped me to achieve the competency F outcomes. INFM 207 DAM-Digital Assets Management provided me an opportunity to learn how to use metadata to organize digital data assets in a way that makes it easier to be identified and available to users. This course also taught me the concept of metadata, taxonomies, and how to use them to design a digital asset management system. The class engaged me to identify the uses, evaluate, and develop a healthcare program. INFM 201 Informatics: Technology Fundamentals taught me the concepts of algorithm development, coding, common program language, web framework, and data/file sharing between systems. The course also engaged me to use best practice to develop a user-centered design framework which would specifically meet stakeholders’ needs. INFM 203 Big Data Analytics and Management taught me fundamental concepts of big data technology, management, and data analysis. The course also engaged me to use Virtual to practice Hadoop labs and use Jupiter Notebook for running Python and data visualization. The course also provided me an opportunity to use big data technologies to analyze data, then communicated and visualized result effectively.
Evidence
Evidence #1
INFM 207 DAM- Digital Assets Management
In this project, I designed a DAM system that will effectively help wound care centers to manage their procedure videos and photographs. I used metadata design to ensure their wound care assets can be accessible for doctors and nurses, as well as the students who are pursuing wound care certificates at local nursing schools as well. Also, my DAM system allows for the storage of years of wound care procedure videos and photos with detailed metadata, well-structured indexing, and search tools.
Evidence #2
INFM 201 Informatics: Technology Fundamentals
In this final project, I concluded that MatrixCare system was not effectively managing risks, because after caregivers are putting patient data in the system, most of the data was not used to support clinical decision making and improve risk management. My project was to improve workflow and dataflow by adding a risk management module in the MatrixCare system. My goal was to using data meaningfully to improve the quality, efficiency, and safety of residents in nursing homes.
Evidence #3
INFM203 Big Data Analytics and Management
In this Big Data project, I focused on health-related datasets based on which I tried to use data analytic tools to determine which race has the highest cardiovascular disease mortality in Florida. I used Microsoft Excel to isolate the important parts of the data and use Excel commands to obtain and plot the results. I selected a dataset from healthdata.gov titled “Rates and Trends in Hypertension-related Cardiovascular Disease Mortality Amount US Adults (35+) by Country, Age Group, Race/Ethnicity, and Sex – 2000-2019”. The process of analyzing big data taught me that it was very important to start with what do I exactly want to find out from the data. I also learned that some datasets are too extensive to analyze without a strong analytic tool such as Hadoop and Jupyter instead of Microsoft Excel.
Summary
In summary, health informatics is the specialty that facilitates better clinical decision making to improve patient care output by using the latest technology to capture, communicate, analyze, and visualize data. Tabassum anticipated that big data analytics will impact health care significantly in way of providing care, including dentistry and dental health care, patient privacy, data security, data governance, effective analytics leaning cost reducing, and prediction in diagnosis in order to prevent disease and treatment (Tabassum, 2018). I am interested in learning data analytics tools such as Python, SQL, and Excel. I believe that I achieved the competency F by finishing the data analysis projects and developing a lifelong learning skills.
Reference
Fridsma, D. B. (2016). The scope of health informatics and the advanced health informatics certification. Journal of the American Medical Informatics Association, 23(4), 855-856. https://doi.org/10.1093/jamia/ocw099
Thye, J. (2021, May 17). Understanding Health Informatics Core Competencies. HIMSS. Tabassum. (2018, December 26). https://www.himss.org/resources/health-informatics#Part1
Tabassum. (2018, December 26). Big Data Analytics in Health Informatics. International Journal of Development Research, 8(12), 24445–24448.