The Unique Nature of Healthcare Data
Healthcare data presents challenges unlike any other industry. Patient records span years or decades, containing diagnoses, treatments, lab results, and outcomes that must be analyzed while respecting strict privacy regulations. The data is inherently messy, with multiple coding systems, free-text clinical notes, and complex temporal relationships between events.
When interviewing at hospitals, health tech companies, or pharmaceutical firms, you will face questions that test not just your SQL skills but your understanding of how healthcare organizations think about data.
Patient Cohort Analysis
One of the most common interview topics in healthcare SQL involves identifying patient populations that meet specific criteria. These cohort queries form the foundation of clinical research and population health management.
A typical question might ask you to find all patients who were diagnosed with diabetes within one year of being prescribed a specific medication. This requires understanding how diagnosis codes work, how to link prescriptions to patients over time, and how to handle the inherent ambiguity in medical data.
The complexity increases when you must exclude patients who meet certain criteria. Finding patients with condition A who never had condition B, while accounting for the fact that condition B might appear in multiple coding systems, tests both your SQL skills and your domain awareness.
These queries typically require:
- Joins across multiple clinical tables (encounters, diagnoses, procedures, medications)
- Date arithmetic to establish temporal relationships
- Understanding of medical coding systems like ICD-10 and CPT
- Careful handling of NULL values where data may be missing
Clinical Event Sequences
Healthcare outcomes often depend on the sequence of clinical events. Did the patient receive treatment A before or after treatment B? How many days elapsed between diagnosis and intervention? These temporal questions are central to healthcare analytics.
Interview questions in this area might ask you to calculate the average time from initial diagnosis to treatment initiation, identify patients who had an adverse event within 30 days of a procedure, or find cases where a follow-up appointment did not occur within the recommended timeframe.
Window functions become essential for these analyses. LAG and LEAD help you examine the sequence of events for each patient. DATEDIFF calculations determine intervals between clinical milestones. Partitioning by patient ensures that each individual's timeline is analyzed correctly.
Handling Multiple Coding Systems
Healthcare uses several coding systems simultaneously. Diagnoses might be recorded using ICD-10 codes, internal problem list codes, or SNOMED-CT. Procedures use CPT codes, HCPCS codes, or internal identifiers. Medications might be identified by NDC numbers, RxNorm codes, or brand names.
Interviewers want to see that you understand this complexity. A question might ask you to find all patients with cardiovascular disease, knowing that this category spans dozens of ICD-10 codes and might also be recorded in problem lists using different terminology.
Your solution should demonstrate awareness that medical concepts rarely map one-to-one with codes. You might need to use pattern matching with LIKE or regular expressions, join to reference tables that group codes into clinical categories, or handle mappings between different coding systems.
Regulatory and Privacy Considerations
While you will not be writing HIPAA-compliant code in an interview, demonstrating awareness of privacy requirements shows domain maturity. Healthcare organizations cannot freely query patient data without appropriate safeguards.
Interview discussions might touch on how queries should be structured to support audit logging, why certain fields require special handling, or how de-identification requirements affect analytical approaches. Showing that you think about these issues, even briefly, distinguishes you from candidates who treat healthcare data like any other dataset.
Quality Metrics and Outcomes
Healthcare organizations increasingly tie reimbursement to quality metrics. These measures require precise SQL calculations that follow specific definitions. Readmission rates must be calculated using exact timeframes and exclusion criteria. Mortality rates must account for risk adjustment. Infection rates must identify qualifying events using specific diagnosis codes.
Interview questions might present you with a metric definition and ask you to implement it in SQL. The challenge lies in translating detailed specifications into correct queries while handling edge cases appropriately.
Practice with Realistic Healthcare Scenarios
Understanding these patterns requires practice with realistic healthcare data structures. Generic SQL practice problems rarely capture the complexity of clinical data relationships.
SQLSandboxes offers case studies that simulate healthcare analytics scenarios, including patient cohort analysis and clinical event sequences. The SQL Editor provides practice with healthcare-style schemas.
For more on preparing for domain-specific interviews, see our guide on Why Domain-Specific SQL Practice Matters and our article on SQL in Finance and Fintech.
Key Takeaways
When preparing for healthcare SQL interviews, focus on these areas:
- Cohort identification using complex inclusion and exclusion criteria
- Temporal analysis of clinical event sequences
- Familiarity with medical coding systems and their hierarchies
- Window functions for patient-level timeline analysis
- Precise implementation of quality metric definitions
Healthcare organizations value candidates who combine technical SQL skills with an understanding of how medical data is structured and used. Demonstrating this dual competency will set you apart in interviews.