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HCA 610 Assignment Course Reflection and Self-Evaluation

HCA 610 Assignment Course Reflection and Self-Evaluation

 

For this assignment, you are required to write a reflection and self-evaluation of your understanding of health care business analyses. Your assignment should include a discussion of the following:

1) A summary of your understanding of health care business analyses: In your own words, why is it important? What does it entail?

2) What was the most significant concept you learned during the course? How did the course assignments assist in your

HCA 610 Assignment Course Reflection and Self-Evaluation

HCA 610 Assignment Course Reflection and Self-Evaluation

understanding of this concept?

3) What questions do you still have about health care business analyses? Was there anything else you were expecting to learn that the course did not cover?

4) How would you rate your overall understanding of health care business analyses? What steps do you plan on taking to increase your knowledge and understanding?

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The application of analytics in healthcare requires the transformation of data into usable information that can be relayed back to end-users. The adoption of EHRs and other electronic data mechanisms makes the application of analytical tools more tractable by providing the basic electronic data upon which to act. This coincides with the rise of the “data scientist,” a term sometimes applied to those who use analytics and can serve as a one-stop shop for data management, analysis, and interpretation of electronic data. In healthcare, this is particularly important for translating electronic bits into meaningful data.

These data scientists often need to draw from a dizzyingly broad spectrum of analytical methodologies. Well-established techniques, such as biostatistics and epidemiologic analysis, Monte Carlo and discrete-event simulation, and causal modeling are being joined by methods previously uncommon in healthcare. These newer methods include data mining, Bayesian statistics, optimization modeling, social network analysis, and agent-based simulation, just to name a few.

Analysis is dependent upon the context in which it is being performed. Clinical care and performance improvement can require very different data perspectives and use the data in unique ways. Clinical analytics involves improving the care of patients. This type of data is very different than process-oriented data and may include genetic data as well as clinical records, which are often narrative and may be more difficult to analyze on a large scale. Performance data, on the other hand, may be subject to the issues described above, namely availability and quality. Considering that EHRs were not designed with system performance in mind, figuring out how to capture these data with high quality at a low cost is a daunting, yet fundamentally important, task.

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