Healthcare claims SQL screen

Practice claims data without pretending you have production experience.

Use this when a data analyst interview mentions healthcare, insurance, claims, providers, diagnosis codes, procedure codes, billed amount, paid amount, or rejected claims. The goal is a grounded answer shape, not a memorized healthcare tutorial.

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Prompt

You have a healthcare claims table and need to show you can analyze it for a data analyst interview. What tiny schema would you practice with, and what questions prove you understand the data grain?

Answer shape

  • Say the table grain first: one row per submitted claim, or one row per claim line if the dataset is line-item level.
  • Name the core identifiers: claim, patient, provider, service date, diagnosis, procedure, status, billed amount, and paid amount.
  • Practice grouped metrics before complex modeling: rejection rate, paid amount, duplicate claims, and payment lag.
  • Be truthful about experience. Practicing on sample claims data is not the same as handling production healthcare data.

Build the tiny dataset.

Create a small fake table with these fields:

  • claim_id, patient_id, provider_id
  • service_date, paid_date, diagnosis_code, procedure_code
  • claim_status, billed_amount, paid_amount

Then create 20 to 50 rows with a few rejected claims, repeated patients, repeated providers, missing paid dates, and one duplicate-looking claim.

Six practice questions

  • Claims per month by status.
  • Rejection rate by provider.
  • Average paid amount by procedure code.
  • Duplicate claims for the same patient, date, provider, and procedure.
  • Days between service date and paid date.
  • Top diagnosis codes by paid amount.

What to say out loud

"I am treating this as one row per claim. Before trusting any rejection-rate metric, I would check whether the table is claim-level or claim-line-level, whether status can change over time, and whether paid date is missing because the claim is still open or because the data is incomplete."

Common miss

Do not say "I know healthcare datasets" if you only practiced SQL on fake claims rows. A stronger answer is: "I have not handled production claims data, but I understand the shape I would expect and the checks I would run before trusting the result."

Move from a claims query to interview judgment.

The Product Analytics packet adds SQL follow-ups, metric debugging, product cases, and recommendation practice so your answer does not stop at a query that merely runs.

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This is the public $59 self-guided packet path. If a coaching or mock-interview session already gave you access, use that access instead of buying the same packet again.