Patient Health Questionnaire Scoring and Documentation
Understand the requirements for accurate PHQ scoring and see how our AI medical scribe turns these results into a structured clinical draft.
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Clinicians tracking mood
Best for providers who need to document PHQ scores and the resulting clinical interpretation in a note.
Score-to-note transition
Get a clear breakdown of how to move from a raw questionnaire score to a finalized EHR entry.
AI-assisted drafting
See how Aduvera captures the encounter context to draft the narrative surrounding the PHQ score.
See how Aduvera turns a recorded visit into a transcript-backed draft you can review before charting around patient health questionnaire scoring.
Precision for Behavioral Health Documentation
Move beyond raw numbers to high-fidelity clinical narratives.
Transcript-Backed Scoring Context
Review the exact patient statements that led to a specific score using per-segment citations.
Structured Behavioral Note Styles
Draft PHQ results into SOAP or APSO formats, ensuring the score is linked to the assessment and plan.
EHR-Ready Score Summaries
Generate a clean, copy-pasteable summary of the questionnaire results and clinician review for your EHR.
From Questionnaire to Final Note
Turn a scored assessment into a professional clinical record.
Record the Review
Record the encounter as you discuss the PHQ scores and symptoms with the patient.
Verify the Draft
Review the AI-generated note to ensure the score and the patient's reported severity are captured accurately.
Finalize and Paste
Confirm the transcript-backed citations and paste the structured note directly into your EHR.
Clinical Standards for PHQ Documentation
Accurate Patient Health Questionnaire scoring requires more than just a total sum; it necessitates documenting the severity of individual symptoms and the patient's functional impairment. A strong note should clearly state the total score, the corresponding severity category (e.g., mild, moderate, or severe), and the specific items that triggered clinical concern, such as sleep disturbance or suicidal ideation, to provide a longitudinal view of the patient's health.
Aduvera replaces the manual effort of translating these scores into a narrative by recording the clinical discussion and drafting the note in real-time. Instead of recalling the score from memory or manually typing a summary, clinicians can review a draft that links the PHQ result to the patient's own words, ensuring the final EHR entry is a high-fidelity reflection of the visit.
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Common Questions on PHQ Scoring & Documentation
Transcript-backed documentation, clinician review, and EHR-ready note output are built into every workflow.
Can I use my specific PHQ scoring patterns in Aduvera?
Yes, the AI drafts structured notes based on your encounter, allowing you to review and finalize the score within your preferred note style.
Does the AI automatically calculate the PHQ score?
The app records the encounter and generates notes from the discussion; it is designed for clinician review to ensure the final score is accurate.
How does the scribe handle the 'patient-reported' nature of the score?
Aduvera uses transcript-backed source context, so you can verify exactly how the patient described their symptoms relative to the score.
Can I draft a pre-visit brief based on previous PHQ scores?
Yes, the app supports pre-visit briefs alongside note generation to help you prepare for the encounter.
Reclaim your evenings from chart notes
Let Aduvera turn visit conversations into a cleaner first draft so you can review faster and finish documentation with less after-hours work.