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Heidi Health (AI Clinical Scribe)

From UBC Wiki
Source: AI Graphic Preisler, 2024, pg.6.

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Introduction

Heidi Health is an Australian-developed generative artificial intelligence (GenAI) platform that functions as an ambient clinical AI assistant and medical scribe. Designed to reduce administrative burden in healthcare, Heidi automatically records, transcribes, summarizes, and organizes clinical conversations, allowing clinicians to devote more attention to patients rather than documentation.

Originally launched as an AI-powered clinical documentation tool, Heidi has evolved into a broader clinical assistant that supports note generation, clinical decision support, coding assistance, workflow automation, and evidence retrieval. The platform is used by physicians, nurses, allied health professionals, dentists, veterinarians, mental health professionals, and medical trainees across a wide range of clinical settings.

Heidi is part of a growing class of healthcare technologies known as ambient clinical intelligence, which uses artificial intelligence to unobtrusively capture and interpret clinical encounters while reducing the documentation burden associated with electronic health records (EHRs). The company emphasizes clinician oversight, privacy, security, and regulatory compliance as central components of its design.

Background

Healthcare documentation has become a major contributor to clinician workload and professional burnout. Studies have shown that physicians frequently spend hours each day completing electronic health record documentation, often outside scheduled clinic hours. Generative AI systems such as Heidi seek to automate many of these administrative tasks while maintaining clinician control over the final medical record.

Heidi combines several artificial intelligence technologies:

  • Automatic speech recognition (ASR) to transcribe clinical conversations.
  • Large language models (LLMs) to summarize encounters and generate clinical documentation.
  • Natural language processing (NLP) to identify diagnoses, medications, investigations, and follow-up plans.
  • Evidence retrieval tools that provide research-supported clinical answers accompanied by transparent citations.
  • Workflow automation for clinical note templates, coding support, task management, and follow-up activities.

Unlike conventional AI scribes that focus primarily on documentation, Heidi positions itself as an "AI care partner" supporting clinicians throughout the clinical day—from patient consultations to documentation, clinical reasoning, and post-visit workflows. The platform includes an Evidence assistant that retrieves and summarizes medical literature while adapting responses to patient-specific clinical contexts.

Heidi is available in several subscription tiers, including a free version, Evidence Plus, Clinician, and enterprise offerings. Enterprise deployments include electronic health record integration, centralized administration, team management, and organizational support. The company reports that its platform complies with major international privacy and security standards including GDPR, HIPAA, SOC 2, and regional healthcare privacy requirements, while employing encryption and configurable data-retention policies.

Presentation

  • Guest: Dr Jessica Morley — Associate Research Scientist, Yale Digital Ethics Center; formerly UK Department of Health and Social Care and the Bennett Institute, University of Oxford. Morley argues we systematically overestimate what these tools can do and underestimate the harm. She makes the case for "skeptical optimism," explains why bioethics principles built for one-to-one care break down against many-to-many AI harms, and reframes ambient scribes as inference engines rather than transcription services — with real consequences for coding, billing and patient records. Then she gets practical: the guardrails, prompts and habits patients (and clinicians) can use today.

Use in Canada

Heidi has expanded into the Canadian healthcare market through its Canadian platform, which is tailored to Canadian clinicians and healthcare organizations. The company markets Heidi as an AI assistant that supports the full continuum of clinical work, including consultation documentation, evidence-based clinical decision support, coding assistance, and administrative workflow management.

Canadian users include family physicians, specialists, nurses, psychologists, allied health professionals, dentists, veterinarians, and learners. Heidi offers bilingual English and French interfaces and provides documentation describing its approach to Canadian privacy legislation and provincial health information requirements.

As in other jurisdictions, Heidi's use in Canada reflects increasing interest in ambient AI technologies as healthcare organizations seek solutions to clinician burnout, workforce shortages, and increasing documentation demands. The platform supports integration with electronic health record systems and emphasizes that clinicians retain responsibility for reviewing, editing, and approving all AI-generated documentation before it becomes part of the permanent medical record.

The introduction of ambient clinical AI into Canadian healthcare has also generated discussion regarding patient consent, protection of personal health information, algorithmic transparency, and the need for independent evaluation of AI-generated clinical documentation. These issues are part of broader debates surrounding the adoption of generative AI within Canadian healthcare systems and the evolving regulatory landscape for artificial intelligence in medicine.

References

  • "...Artificial intelligence (AI) scribes are increasingly deployed in U.S. health systems to document patient–physician encounters, with anticipated gains in administrative efficiency counterbalanced by emerging privacy risks. These systems capture audio of clinical encounters and generate transcripts and structured clinical notes, yet vendor practices differ substantially in how long each data type is retained and whether it is used for AI training. Federal and state privacy frameworks govern the collection, storage, and disclosure of such data, shaping institutional obligations and constraints. Effective implementation therefore depends on clear governance of data flows, appropriately calibrated retention and deletion policies, and transparent consent processes that align technical design with legal requirements and ethical principles."

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