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Start by clarifying the schema relationships and the definition of age_band and geography. Then, build a query that aggregates paid_amt for 2020 by age_band, ensuring all age_bands are included even if they have no claims in 2020, and exclude NULL paid_amt. Use a LEFT JOIN from a distinct list of age_bands to the aggregated claims, and filter for 2020 claims while preserving age_bands with zero spend.
Pro tip: Mention that you would validate the results by checking for unexpected NULLs or missing age_bands, and consider performance implications of joining large tables. Also, clarify whether 'across all states' means summing over all states or grouping by state as well—typically it means summing over all states, but confirm to avoid misinterpretation.
Ask about the structure of member, claim, and geography tables, and confirm that age_band is derived from member demographics. Ensure that 'across all states' means aggregating over all states without grouping by state.
Get a distinct list of age_band values from the member table (or a reference table) to ensure all bands appear in the output, even those with no claims in 2020.
Filter claims to the year 2020, exclude rows where paid_amt is NULL, and sum paid_amt grouped by age_band. Join member to claim to get age_band.
LEFT JOIN the distinct age_bands to the aggregated sums, replacing NULL sums with 0.00. Ensure the final output has one row per age_band with the total 2020 paid amount.
Check that all age_bands are present, NULLs are excluded, and the sum is formatted to two decimal places. Consider edge cases like age_bands with no members.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Wrapping the denominator in NULLIF felt almost too easy once I remembered it existed.
Build on the previous query by wrapping it in a CTE or subquery that computes the total 2020 paid amount across all age bands, then join or cross-join that total back to each age band's sum. Calculate the percentage as (band_sum / total_sum) * 100, using a CASE statement or NULLIF to avoid division by zero, and round to two decimal places. Ensure the output is between 0 and 100 by handling edge cases like negative amounts or totals of zero.
Pro tip: In healthcare analytics, paid amounts can be negative due to reversals or adjustments, so consider whether to use absolute values or net amounts; also, explicitly state that you're using NULLIF or CASE to handle division by zero, as this shows attention to data quality and production readiness.
Compute the sum of paid amounts for each age band for the year 2020, ensuring you filter correctly and group by age band.
Calculate the total paid amount across all age bands for 2020, either as a separate CTE or a window function like SUM() OVER ().
For each age band, divide its sum by the total sum and multiply by 100, using NULLIF or a CASE statement to return 0 when the total is zero.
Round the result to two decimal places using ROUND(..., 2) and ensure the output is between 0 and 100, possibly clamping if necessary.
Check that percentages sum to 100 (or close due to rounding) and that no division errors occur; present the final query with clear column aliases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data sources and definitions: identify the member and claims tables, confirm how to determine Georgia residency (e.g., state field in member address), and define the age bands based on age at the time of service or a fixed reference date. Then write a query that joins claims to members, filters for Georgia residents, groups by year and age band, and computes the totals and year-over-year changes. Finally, validate the results by checking for data quality issues and ensuring the age bands are mutually exclusive and cover the required ranges.
Pro tip: Pay attention to the age calculation: use the member's age as of the claim date or a consistent reference date (e.g., January 1 of the claim year) to avoid misclassification, especially for members who age into a new band during the year. Also, explicitly confirm whether 'paid totals' means allowed amount, paid amount, or member responsibility, as this can significantly affect the results.
Confirm the definitions of 'paid totals' (e.g., paid amount, allowed amount), the age bands (44-65 and 65+), and how to determine Georgia residency (e.g., member's state at the time of claim). Ask about any exclusions or data quality filters.
Locate the member and claims tables. Join claims to members on member ID, ensuring that only members with a Georgia address are included. Be careful to exclude non-GA members even if their claims could be linked.
Compute each member's age at the time of service (or a consistent reference date) and assign them to the appropriate age band (44-65 or 65+). Ensure the bands are mutually exclusive and cover the required ranges.
Filter claims for years 2019 and 2020. Group by year and age band, and sum the paid amounts to get the total paid for each combination.
For each age band, calculate the absolute change (2020 total minus 2019 total) and the percentage change ((2020-2019)/2019 * 100). Handle cases where the 2019 total is zero to avoid division errors.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.