Medical Speech & Transcription

This task turns docs’ words to text—think “statins” tagged in a dictation or “cough” marked in a note (e.g., “biopsy” transcribed, “slur” flagged)—to train AI to hear medicine right. Our team annotates these voices, streamlining care with clear records.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are essential in orchestrating the annotation and structuring of data for Medical Speech & Transcription within healthcare AI workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to label datasets that enhance AI’s ability to transcribe medical speech accurately.

Training and Onboarding

PMs design and implement training programs to ensure workers master terminology tagging, speech pattern annotation, and transcription accuracy. For example, they might train teams to transcribe “hypertension” from a recording or tag “hesitation” in a patient chat, guided by sample audio and clinical standards. Onboarding includes hands-on tasks like annotating doctor dictations, feedback loops, and calibration sessions to align outputs with AI transcription goals. PMs also establish workflows, such as multi-pass reviews for medical jargon.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., annotating 15,000 audio clips) and set metrics like word accuracy, term precision, or context consistency. They track progress via dashboards, address transcription errors, and refine methods based on worker insights or evolving healthcare needs.

Collaboration with AI Teams

PMs connect annotators with machine learning engineers, translating technical requirements (e.g., high recall for rare terms) into actionable annotation tasks. They also manage timelines, ensuring labeled datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The transcribers, taggers, or speech analysts perform the detailed work of labeling and structuring audio datasets for AI training. Their efforts are auditory and medical, requiring precision and clinical knowledge.

Labeling and Tagging

For speech data, we might tag terms as “dosage” or “symptom.” In complex tasks, they label specifics like “rapid speech” or “murmur.”

Contextual Analysis

Our team decodes audio, transcribing “X-ray ordered” with tone or tagging “pain” in a groan, ensuring AI catches every medical cue.

Flagging Violations

Workers review datasets, flagging misheard words (e.g., “dose” as “doze”) or noisy audio (e.g., background chatter), maintaining dataset quality and reliability.

Edge Case Resolution

We tackle complex cases—like accents or overlapping voices—often requiring slow playback or escalation to medical audio experts.

We can quickly adapt to and operate within our clients’ healthcare platforms, such as proprietary dictation tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of clips per shift, depending on the complexity of the speech and annotations.

Data Volumes Needed to Improve AI

The volume of annotated speech data required to enhance AI systems varies based on the diversity of voices and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional transcription model might require 5,000–20,000 annotated clips per category (e.g., 20,000 doctor notes). For varied or technical speech, this could rise to ensure coverage.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 clips per issue (e.g., missed terms) are often needed. For instance, refining a model might demand 5,000 new annotations.

Scale for Robustness

Large-scale applications (e.g., nationwide telemedicine) require datasets in the hundreds of thousands to handle edge cases, rare jargon, or new accents. An annotation effort might start with 100,000 clips, expanding by 25,000 annually as systems scale.

Active Learning

Advanced systems use active learning, where AI flags tricky clips for further annotation. This reduces total volume but requires ongoing effort—perhaps 500–2,000 clips weekly—to sustain quality.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and medical precision across datasets.

Multilingual & Multicultural Medical Speech & Transcription

We can assist you with medical speech and transcription across diverse linguistic and cultural landscapes.

Our team is equipped to annotate and analyze speech data from global healthcare settings, ensuring accurate, contextually relevant datasets tailored to your specific AI objectives.

We work in the following languages: