Chevron Left
Zurück zu Analytical Solutions to Common Healthcare Problems

Bewertung und Feedback des Lernenden für Analytical Solutions to Common Healthcare Problems von University of California, Davis

4.4
Sterne
18 Bewertungen
5 Bewertungen

Über den Kurs

In this course, we’re going to go over analytical solutions to common healthcare problems. I will review these business problems and you’ll build out various data structures to organize your data. We’ll then explore ways to group data and categorize medical codes into analytical categories. You will then be able to extract, transform, and load data into data structures required for solving medical problems and be able to also harmonize data from multiple sources. Finally, you will create a data dictionary to communicate the source and value of data. Creating these artifacts of data processes is a key skill when working with healthcare data....

Top-Bewertungen

RT
5. Jan. 2020

Very good, although I would suggest the Health Informatics as a starting course

SC
17. Jan. 2020

Excellent material and a great introduction to data analytics!

Filtern nach:

1 - 5 von 5 Bewertungen für Analytical Solutions to Common Healthcare Problems

von Debopam R

30. Okt. 2020

I had a lot of hope with this course but was sadly disappointed. The course content is all over the place and does not follow a structure or a flow. It talks about groupers in week 2, and talks on ETL in week 3, again brings risk stratification and some modelling concept in week 4 but linkages are missing.

My suggestion think how an analytics practitioner should follow a methodology and take him through the steps. It should start with SEMMA concept and take a practitioner through the steps like

S : what data is needed , concept and concept mapping, understand flow, input and output

E: explore : descriptive and visualizations

M: Modify: bring here concepts of grouper algorithms, risk adjustment

M : Talk here on predictive models, regressions, supervised and unsupervised learnings and risk stratification strategies

A: Talk about how you analyze output, how you train your learning set

Show with use causes scenarios of handling Fraud, Risk Adjustments and Risk Stratification.

von Rosetta T

6. Jan. 2020

Very good, although I would suggest the Health Informatics as a starting course

von Silvio C

18. Jan. 2020

Excellent material and a great introduction to data analytics!

von Abiodun A

26. Juni 2020

Very informative. Would have preferred more practical examples on data analysis

von JOEL B

23. Mai 2020

Thank you ...