software dataclinical data qualityepic ehrhealth informaticshl7 fhir
Complete evaluation framework
What to assess and how to score it
Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Technical proficiency
35% weight
Check fluency with HL7 v2 segments, FHIR resources, SQL against EHR reporting tables (Epic Clarity, Cerner HealtheIntent), plus terminology work in ICD-10, SNOMED CT and LOINC.
Evidence to listen for
Command of the languages, frameworks, and data tools the role actually uses
Understands correctness, performance, and failure modes, not just syntax
Has opinions on testing and can justify them
Reads and reasons about code they did not write
Five-point scoring guide
1
Poor
Cannot work independently; fundamentals are missing.
2
Needs Improvement
Weak fundamentals; output needs heavy review.
3
Satisfactory
Competent for the role; needs guidance on complex or unfamiliar work.
4
Very Good
Strong practitioner; handles hard problems with little guidance.
5
Excellent
Names specific interface engines, writes joins against Clarity or Caboodle from memory, and maps local codes to SNOMED or LOINC accurately.
02
Evaluation factor
Systems and trade-offs
25% weight
Probe how they weighed interface engine routing versus API pulls, batch versus real-time feeds, and where they drew the line on PHI de-identification and access roles.
Evidence to listen for
Reasons about scale, latency, cost, and failure before writing code
Names the trade-off they chose and what they gave up
Understands the data lifecycle end to end
Anticipates what breaks at ten times the volume
Five-point scoring guide
1
Poor
No thinking beyond the immediate task; no awareness of scale or failure.
2
Needs Improvement
Limited architectural awareness; struggles with design decisions.
3
Satisfactory
Works within an existing design; makes sound local decisions.
4
Very Good
Designs for scale and maintainability; articulates trade-offs clearly.
5
Excellent
Explains latency, downtime and HIPAA minimum-necessary trade-offs behind a specific integration, including what broke and what they would build differently.
03
Evaluation factor
Evidence and rigour
25% weight
Assess how they validate clinical data: reconciliation counts against source systems, duplicate MRN detection, registry or quality measure numerators, and audit trails supporting eCQM or MIPS submissions.
Evidence to listen for
Validates results rather than trusting output
Knows how their work is measured and what a bad result looks like
Can describe a time their own analysis or model was wrong and how they caught it
Careful about data quality, leakage, and silent failure
Five-point scoring guide
1
Poor
Ships unvalidated work; no notion of how correctness is checked.
2
Needs Improvement
Validates superficially; misses obvious quality or leakage issues.
3
Satisfactory
Reasonable checks in place; rigour drops under time pressure.
4
Very Good
Validates thoroughly; can name a real error they caught in their own work.
5
Excellent
Cites measured error rates or reconciliation findings, describes validation queries used, and distinguishes a documentation artefact from a genuine clinical signal.
04
Evaluation factor
Collaboration and communication
15% weight
Look for evidence they sat with nurses, coders and revenue cycle staff, ran build sessions or go-live support, and translated clinician complaints into workable specifications.
Evidence to listen for
Explains technical work to non-technical stakeholders
Gives and takes code or peer review constructively
Documents enough that the work survives their absence
Aligns with team process rather than working around it
Five-point scoring guide
1
Poor
Cannot work in a team; resistant to feedback.
2
Needs Improvement
Communication issues create rework; lone-wolf tendencies.
3
Satisfactory
Adequate team member; documentation and review participation are light.
4
Very Good
Communicates well; reliable reviewer and collaborator.
5
Excellent
Describes named clinician stakeholders, workflow observation they did, and a spec or dashboard changed because of what frontline staff told them.
Put this rubric to work
Score every candidate against the same standard
Add these weighted factors to Hirevire and let AI evaluate recorded answers against your rubric.
