Skill Guide

SQL & Database Querying (EHR, claims, and claims data warehouses)

The proficiency in using SQL to extract, transform, and analyze structured data from Electronic Health Record (EHR) systems, claims adjudication databases, and integrated claims data warehouses to support clinical, financial, and operational decision-making.

This skill is the core engine for deriving actionable intelligence from fragmented healthcare data silos, directly enabling cost containment, quality improvement, and regulatory compliance. It transforms raw administrative and clinical data into strategic assets that drive value-based care models and operational efficiency.

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9.0 Avg Demand

20% Avg AI Risk

How to Learn SQL & Database Querying (EHR, claims, and claims data warehouses)

Focus on foundational healthcare data models: master the structure of standard EHR tables (e.g., Patient, Encounter, Diagnosis) and claims tables (e.g., ClaimHeader, ClaimDetail, Procedure). Learn core SQL syntax (SELECT, FROM, WHERE, JOIN) with an emphasis on joining these tables on keys like MemberID, ClaimID, and ProcedureCode. Understand basic data integrity and null handling in messy healthcare datasets.

Apply SQL to common healthcare business problems: writing queries to calculate readmission rates, total cost of care per member, or procedure code frequency. Move to more complex operations like CTEs (Common Table Expressions) for multi-step analysis, window functions for running totals or rankings, and aggregations with ROLLUP/CUBE for hierarchical reporting. Common mistakes include incorrect join logic leading to duplicate rows and misunderstanding the granularity of claims vs. clinical data.

Architect and optimize queries against massive, complex claims data warehouses. Design and implement efficient data models for specific use cases (e.g., risk adjustment, episode grouping). Master query performance tuning (indexing, partitioning, execution plans) on platforms like Teradata, Amazon Redshift, or Google BigQuery. Mentor analysts on best practices, enforce data governance, and align query logic with precise clinical and financial definitions from ICD-10, CPT, and HCPCS code sets.

Practice Projects

Beginner

Project

Demographic and Utilization Profile

Scenario

A health plan needs a basic member profile: age, gender, and count of outpatient visits in the last year for a sample of members.

How to Execute

1. Write a query to select member demographics from the MemberDim table. 2. Join to the EncounterFact table on MemberKey, filtering by EncounterDate. 3. Use COUNT(DISTINCT EncounterKey) to get visit count, grouped by member. 4. Handle NULLs in the encounter count with COALESCE.

Intermediate

Project

Provider Performance Scorecard

Scenario

Calculate a 30-day hospital readmission rate for a set of Diagnosis Related Groups (DRGs) to benchmark provider performance.

How to Execute

1. Use a CTE to identify the initial inpatient admission (DischargeDate, DRGCode) from the InpatientClaim table. 2. Write a second CTE to find any subsequent inpatient admission within 30 days for the same member, joining on MemberID and using date logic. 3. Compute the readmission rate: (Count of Readmissions / Count of Index Admissions) * 100. 4. Group results by provider (FacilityID) and DRG for the scorecard.

Advanced

Project

Risk Adjustment Factor (RAF) Score Replication

Scenario

Validate the CMS-HCC risk adjustment model output by replicating the core logic to calculate a member's RAF score from raw claims and enrollment data.

How to Execute

1. Map historical diagnosis codes (ICD-10-CM) from claim headers to HCC categories using a CMS-provided Grouper software or a validated mapping table. 2. Implement SQL logic to apply the model's disease interaction factors (e.g., diabetes with CHF). 3. Build a dynamic SQL or stored procedure to apply the correct model coefficients (payment year, demographic factors) to the hierarchical category flags. 4. Compare the output against the plan's official RAF scores, investigating and documenting discrepancies.

Tools & Frameworks

Software & Platforms

SQL Server Management Studio (SSMS) / Azure Data StudioTeradata SQL Assistant / BTEQAmazon Redshift / Google BigQuerydbt (data build tool)

SSMS and Teradata tools are staples for on-premises and legacy data warehouse environments. Cloud platforms (Redshift, BigQuery) are the modern standard for scalable analytics. dbt is used to build modular, documented, and tested SQL-based transformation layers within a data warehouse.

Healthcare Data Standards & Code Sets

ICD-10-CM/PCSCPT® / HCPCS Level IIDRG Grouper (MS-DRG, APR-DRG)CMS-HCC Model Documentation

Not coding languages, but essential domain knowledge. These code sets and methodologies define the 'language' of the data you are querying. The HCC model documentation is the specification for replicating risk adjustment logic in SQL.

Interview Questions

Answer Strategy

Demonstrate an understanding of data reconciliation across disparate systems. Strategy: Use a LEFT JOIN from the lab table to the claims table on patient and date parameters, then filter for NULLs in the claims table. Sample Answer: 'I would use a LEFT OUTER JOIN from the LabResultFact table to the ProcedureClaimFact table on MemberKey and where the ClaimServiceDate is between the LabOrderDate and LabOrderDate + 60. The WHERE clause would filter for rows where the ProcedureClaimKey IS NULL, isolating the unmatched records.'

Answer Strategy

Test for impact, technical depth, and rigor. The core competency is the ability to translate business needs into technical solutions and validate them. Sample Answer: 'I built a query to calculate the PMPM (Per Member Per Month) cost trend for chronic condition cohorts. The challenge was correctly attributing costs to condition episodes defined by specific claim sequences. I used a CTE to flag episodes, then another to allocate costs, and validated the output against the actuarial team's sample audit. The analysis showed a 15% cost overrun in the diabetes cohort for one provider group, leading to a targeted intervention.'