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
Clinical competence
35% weight
Check licensure (LPC, LCSW, LMFT) and modality depth: CBT, DBT skills coaching, motivational interviewing, plus how they adapt protocols when an AI triage tool or symptom tracker feeds them client data.
Evidence to listen for
Command of the procedures, anatomy, and equipment the role requires
Holds current registration or certification
Knows normal from abnormal and what to do about each
Recognises when a case is outside their scope
Five-point scoring guide
1
Poor
Unsafe knowledge gaps; registration missing or lapsed.
2
Needs Improvement
Knowledge gaps that would affect patient care.
3
Satisfactory
Competent for standard cases; needs support on complex ones.
4
Very Good
Strong clinical knowledge; safe and reliable across the usual range.
5
Excellent
Names active state licensure, cites specific modalities used per presentation, and describes clinically integrating AI-generated summaries or PHQ-9 trends into treatment planning.
02
Evaluation factor
Patient safety and protocol
30% weight
Probe suicide and self-harm risk workflows: C-SSRS or Columbia screening, safety planning, escalation when a chatbot flags crisis language, and HIPAA handling inside telehealth and AI note platforms.
Evidence to listen for
Follows identification, infection control, and documentation protocol without prompting
Can describe an error or near miss and what they did
Escalates deterioration early
Treats protocol as protection rather than bureaucracy
Five-point scoring guide
1
Poor
Casual about protocol; would not report an error.
2
Needs Improvement
Inconsistent protocol adherence; slow to escalate.
3
Satisfactory
Follows protocol reliably; documentation sometimes thin.
4
Very Good
Protocol is instinctive; escalates early and reports honestly.
5
Excellent
Walks through a real escalation from AI flag to human contact, cites duty-to-warn thresholds, and never treats an algorithmic risk score as final.
03
Evaluation factor
Patient communication
20% weight
Assess rapport built over video and asynchronous chat: how they disclose AI involvement, obtain consent for session recording or transcription, and repair trust when a client distrusts the technology.
Evidence to listen for
Explains a procedure to an anxious or confused patient
Handles distress, pain, or refusal without losing control of the interaction
Respects privacy and dignity in practice, not just in principle
Works with families and carers
Five-point scoring guide
1
Poor
Dismissive of patients; no bedside awareness.
2
Needs Improvement
Task-focused; struggles with distressed patients.
3
Satisfactory
Adequate rapport; less confident in difficult interactions.
4
Very Good
Calm, clear, and respectful with anxious or difficult patients.
5
Excellent
Explains AI use in plain client-facing language, secures informed consent explicitly, and gives an example of restoring a client's trust after tooling discomfort.
04
Evaluation factor
Working in a clinical team
15% weight
Look for coordination with prescribers, care managers, and product or clinical engineering teams; ask about supervision hours, case consultation, and reporting model errors or unsafe AI outputs.
Evidence to listen for
Hands over cleanly and completely
Challenges a colleague when patient safety requires it
Takes direction from clinicians without deferring blindly
Handles shift work and pressure without becoming difficult to work with
Five-point scoring guide
1
Poor
Poor handover; cannot work in a clinical team.
2
Needs Improvement
Handover gaps; avoids raising concerns about colleagues.
3
Satisfactory
Reliable team member; handover adequate.
4
Very Good
Clean handovers and willing to speak up on safety.
5
Excellent
Describes routine consultation, warm handoffs to psychiatry, and a concrete instance of flagging a flawed AI output to the product or clinical safety team.
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Score every candidate against the same standard
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