Clinical Reporting & Data Export

Reliable data can improve preparation, but it must be framed accurately. Rohy AI's reporting features are intended to surface patient-authorized longitudinal context between sessions, not to function as a diagnostic or emergency-monitoring system.

1. What Data Can Be Reviewed or Exported?

Providers with appropriate access can review or export the following categories for connected patients, subject to the permissions those patients have granted:

  • Assessment History: PHQ-9 and GAD-7 results, where available.
  • Trend Summaries: Weekly or monthly mood patterns derived from patient-authorized activity.
  • Theme Summaries: Recurring themes or language markers identified by AI systems.
  • Behavioral Context: Check-ins or reflections that the patient has chosen to make visible.

2. Interpreting the AI Snapshot

Use Rohy AI outputs as supplemental context rather than clinical conclusions.

In practice:

  1. Review the trend line: Does the patient's own reporting point toward improvement, strain, or inconsistency?
  2. Review the peaks and dips: Ask what was happening around those dates rather than treating the graph as self-explanatory.
  3. Cross-check with the patient: Validate whether the summary matches the patient's lived experience before acting on it.

3. Security and Compliance Limits

Rohy AI uses platform security controls, but it is fully HIPAA compliant. If your workflow requires HIPAA-compliant infrastructure, contractual healthcare assurances, or regulatory sign-off, do not assume Rohy AI is suitable without separate written confirmation.

Generating Reports

Providers may generate summary reports for internal review or workflow preparation. Any use inside an EHR, payer workflow, or regulated clinical environment remains the provider's responsibility.

Reports may include:

  • Summary score graphs
  • Key recurring themes
  • Session-prep highlights

4. Enterprise Evaluation

Organizations with advanced workflow requirements should contact Rohy AI before assuming product fit for regulated or high-risk environments.

Sources

  1. Simon GE, et al. Ability of the Patient Health Questionnaire-9 to predict subsequent depression diagnosis. JAMA. 2013.
  2. Naslund CH, et al. Digital technologies for monitoring and improving psychiatric outcomes. World Psychiatry. 2017.
  3. Chan S, et al. Mobile health app for depression and anxiety: a review and analysis of the evidence. JMIR Mental Health. 2016.

Related: Patient Onboarding Toolkit · Clinical Implementation Guide · Psychometric Analysis of 19 Models