iptas provides a digital assistant for psychotherapists, streamlining administrative tasks through AI-based documentation. The platform translates current research into concrete therapeutic recommendations, supporting clinical decision-making. This results in reduced administrative burden, allowing practitioners more time for patient care while ensuring evidence-based treatment quality.
Funding
Funding not disclosed
Founders
Product
Problem
Psychotherapists must keep up with a rapidly expanding body of research while fulfilling extensive documentation requirements, which reduces the time available for direct patient care. The resulting knowledge overload and administrative load often lead to suboptimal therapy selection and longer waiting times for patients.
Solution
iptas delivers a cloud‑based digital assistant that converts the latest evidence‑based guidelines into patient‑specific therapy recommendations. The platform captures session audio, applies speech‑to‑text and natural‑language processing to generate structured documentation and billing‑ready reports automatically. Integrated ecological momentary assessment (EMA) via a mobile app continuously feeds psychometric data into the recommendation engine, enabling real‑time treatment adjustments. All data are stored with end‑to‑end encryption and GDPR‑compliant controls, allowing secure use in private practices, clinics, and training settings. Clinicians access a web dashboard that visualizes progress, alerts to risk patterns, and provides downloadable reports for electronic health‑record integration. By automating routine tasks and surfacing actionable evidence, iptas frees therapists to focus on therapeutic interaction while improving treatment quality.
Target Audience
Primary users are licensed psychotherapists and mental‑health clinics seeking evidence‑based decision support and automated documentation, as well as training institutions that require structured supervision tools.
Features
- AI‑driven recommendation engine that maps up‑to‑date clinical guidelines to individual patient profiles using machine‑learning classifiers.
- Automated session transcription and report generation powered by speech‑to‑text and NLP pipelines, producing structured notes, treatment plans, and billing codes.
- GDPR‑compliant data architecture with end‑to‑end encryption, role‑based access, and audit logging for secure storage of audio, transcripts, and EMA data.
- Mobile EMA application that collects real‑time symptom ratings and physiological metrics, feeding continuous feedback into the therapy model.
- Phase‑based therapy navigation tool with expert‑curated checklists and customizable treatment pathways aligned to diagnostic categories.
- Open API and FHIR‑compatible integration layer for seamless export of reports and session data to existing EHR systems.
- Analytics dashboard offering longitudinal visualizations, risk alerts, and outcome metrics for clinicians and administrators.
- Extensible modular roadmap including avatar‑based supervision, advanced outcome prediction models, and multi‑modal data fusion.