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Data Quality Impact Analysis MicroSim

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About This MicroSim

This MicroSim makes the cost of poor data quality concrete by running a graph query against a small patient-provider-prescription-diagnosis graph and letting you corrupt the data. Toggling issues changes the answer: duplicate patient records over-count, missing TREATED_BY edges under-count, inconsistent diagnosis codes drop matches, and null prescription dates remove records — each pushing the query result away from the correct answer on clean data.

How to Use

Pick a query from the dropdown (for example, "Patients of Dr. 0") and note that on clean data the result matches the correct answer. Then toggle the data-quality issue checkboxes and watch the result change and the accuracy flag flip to a red "wrong by" warning — adding a duplicate inflates the count, while removing an edge or a value deflates it. Use "Reset to clean data" to return to the accurate baseline. The lesson: analytics are only as trustworthy as the graph they run on.

Iframe Embed Code

You can add this MicroSim to any web page by adding this to your HTML:

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<iframe src="https://dmccreary.github.io/modeling-healthcare-data/sims/data-quality-impact-analysis-microsim/main.html"
        height="450px"
        width="100%"
        scrolling="no"></iframe>

Lesson Plan

Grade Level

9-12 (High School Geometry)

Duration

10-15 minutes

Prerequisites

TODO: List prerequisites.

Activities

  1. Exploration (5 min): TODO
  2. Guided Practice (5 min): TODO
  3. Assessment (5 min): TODO

Assessment

TODO: List assessment criteria.

References

  1. TODO: Add references.