Heidi
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About
Heidi is an AI-powered medical scribe designed to assist clinicians with note-taking, insurance-pleading, results-finding, and other administrative tasks. It offers features like transcription, customization, and output generation, allowing clinicians to create notes, letters, and summaries quickly. It provides various pricing plans, from a free version to enterprise solutions for organizations. Heidi prioritizes data privacy and security, adhering to standards like HIPAA, GDPR, and the APP. The tool is designed to reduce clinicians' administrative burden, freeing them to focus more on patient care. It emphasizes user-friendly interfaces and secure systems.
Platform
Features
• teams
• memory
• transcribe
• custom template editor
• context
• ask heidi
• output
• customize
FAQs
Is audio recording stored?
No, Heidi does not store audio recordings of patient consultations. The system uses ambient listening technology to transcribe conversations in real time, but the audio itself is not retained.
How do you deal with regional conditions and terminology?
Heidi utilizes a custom model that is specifically engineered to handle regional dialects and medical terminology variations. This model achieves market-leading word error rates, ensuring accurate transcription regardless of regional differences in medical language. Our clinical governance team continuously monitors and improves the system's performance with local speech patterns and terminology.
How do you appropriately represent minorities including indigenous and first nations people?
Heidi's clinical governance team continuously monitors and assesses the system's performance across diverse populations, including minority groups. This ongoing evaluation helps minimize bias and ensures fair representation in the documentation process.
How do you handle and store patient consent?
Patient consent is a crucial aspect of using Heidi. Users can configure prompts to seek patient consent before scribing each encounter, and this consent is documented within the system. Heidi provides flexibility in how consent is obtained, allowing clinicians to integrate consent-seeking into their existing workflows, whether through intake forms, verbal agreements, or visual cues in the consultation room which we provide in our resource centre.
Where is your data processed? How does the tool ensure compliance with state and territory laws regarding the recording of conversations? Can you show me a flowchart of data flow for a patient interaction?
Heidi processes data using a combination of localized and, when necessary for performance, offshore services. Compliance with state and territory laws is ensured through pseudonymization, non-retention policies, and the use of compliant local storage solutions. While we don't have a specific flowchart available, Heidi's data flow is designed to prioritize patient privacy and comply with relevant regulations at every step of the process.
How long is data retained on Heidi? Can data retention policies within the AI scribe system be customized, and do users have control over storage and deletion?
Heidi offers fully customizable data retention options, allowing users and organizations to set retention periods anywhere between 1 day and "never delete." By default, accounts are set to "never delete" to ensure transcripts, which may be valuable for documentation or evidence of consultations, are not unintentionally lost. However, users can easily adjust these settings within their preferences, and organizations can configure them globally. Consultations are recorded locally during interactions and securely transmitted for transcription and processing, with robust encryption ensuring privacy throughout. Heidi processes the recordings to create temporary draft records that doctors can use to generate medical notes. The draft records are accessed by doctors within Heidi to review and edit, with options to format them using internationally recognized templates like SOAP or custom layouts. Once finalized and saved into the patient’s official medical record, the temporary records can be deleted from Heidi. These temporary records are akin to shorthand notes and are not intended to form part of the official medical record. Importantly, only the doctor has access to these temporary records, and once deleted, they cannot be recovered by Heidi or any other party.
Can you run a version of Heidi without third-party processing?
For enterprise customers, Heidi can be configured to run within siloed AWS and Azure environments, minimizing third-party processing. However, this configuration may affect performance and some product functionality. Heidi's standard version uses third-party processors like Kinde or Stripe to provide optimal service while maintaining a strict compliance framework to protect patient privacy.
You say you're compliant but prove it. How should you evaluate other vendors' compliance claims? Is there published data on the clinical utility, validity, and safety of the AI scribe?
Heidi takes compliance seriously, having invested in certifications like ISO27001, SOC2 Type 2, and meeting regulations such as HIPAA, GDPR, and the APP. When assessing other vendors, check for these internationally recognized third-party certifications, consult their Trust Centers, and request detailed compliance documentation. For clinical utility, validity, and safety, we’re engaged in ongoing research with several institutions. If you’re interested in exploring studies on Heidi’s impact, reach out—we’re always open to supporting further research on our AI scribe’s benefits in clinical settings.
Does my session data get used for model training?
We don't use any of your sensitive health information for model training. We only use your data for the purpose it was collected- for a full list of uses please refer to our privacy policy.
My patients are concerned about the secondary uses of their data. Will it be sold? Will it be used for training?
No, absolutely not! We don’t sell patient data—ever. Our only focus is on helping clinicians ease their administrative headaches
How do you mitigate against technical errors in Heidi such as written mistakes in the output?
To mitigate technical errors, Heidi employs advanced language models and continuously monitors performance. However, clinicians must review and edit all AI-generated documentation before finalizing, as they remain responsible for the accuracy of medical records.
Can you provide studies or references that demonstrate the effectiveness and safety of the tool in a clinical setting?
While we have conducted numerous case studies demonstrating Heidi's effectiveness, we are currently engaged in formal research at several institutions. We welcome clinicians and researchers interested in studying Heidi's impact on clinical workflows and patient care to contact us for collaboration opportunities at support@heidihealth.com.
What features are included to minimize mishearing, incorrect categorization, or omission of critical clinical information? How does the AI scribe handle accents, dialects, and medical terminology specific to local practice? Has the tool been trained to accurately recognize and transcribe local speech patterns and terms?
Heidi uses a custom model specifically engineered to handle medical terminology and regional dialects, achieving industry-leading word error rates, ensuring accurate transcription of regional accents and medical terms. The system also employs context-aware processing to minimize incorrect categorization. In addition, LLMs systematically correct for mishearings in the transcript to render high-quality notes. In our rating systems we record less than 1 negative rating for every 1000 notes that Heidi creates.
Does the tool facilitate easy review and correction of notes by the practitioner before they are entered into the patient health record?
Yes, Heidi is designed with a user-friendly interface that allows practitioners to easily review, edit, and approve all AI-generated notes before they are finalized. This step is crucial in maintaining the accuracy and integrity of patient health records.
How does the tool account for clinical information that is not explicitly spoken during the consultation?
While Heidi primarily transcribes spoken information, it's designed to capture context and interpret clinical narratives including via the context tab or where clinicians dictate physical and observation findings directly to Heidi before or after visits. As Heidi is a listening tool, clinicians need to add any unspoken observations or assessments during their review of the AI-generated notes.
Pricing Plans
Pro
USD799.00 / monthly• Everything in Free, plus:
• Custom templates
• Unlimited Pro actions
• Unlimited AI documents
• Priority customer support
Pro
USD99.00 / annually• Everything in Free, plus:
• Custom templates
• Unlimited Pro actions
• Unlimited AI documents
• Priority customer support
Together
USD1199.00 / monthly• Everything in Pro, plus:
• Template creation
• Team template sharing
• Team MFA
• Priority customer support
Together
USD99.00 / annually• Everything in Pro, plus:
• Template creation
• Team template sharing
• Team MFA
• Priority customer support
Enterprise
USD1.23 / custom• Everything in Together, plus:
• Tailored to your system
• Multi-team management
• Coding workflows
• SSO & user directories
Job Opportunities
MLOps Engineer
AI-powered medical scribe for clinicians, reducing administrative burden and improving patient care. HIPAA, GDPR, and APP compliant.
Benefits:
Flexible work with a 50% hybrid environment
Additional paid day off for your birthday and wellness days
Full gym access in our Melbourne location and Sydney TBC.
A generous personal development budget of $500 per annum
Learn from some of the best engineers and creatives, joining a diverse team
Experience Requirements:
Strong experience with cloud infrastructure (AWS required, GCP beneficial)
Hands-on experience with Kubernetes, preferably EKS or GKE
Understanding of ML/AI deployment challenges and solutions
Experience with implementing security controls and compliance requirements
Knowledge of monitoring and observability practices for ML systems
Other Requirements:
Problem-solving mindset with a focus on security and scalability
Strong communication skills to work with cross-functional teams
Ability to balance technical requirements with business needs
Experience in fast-paced startup environments
Dedication to maintaining high standards in a regulated environment
Responsibilities:
Design and implement secure infrastructure for hosting LLM services and speech-to-text solutions across AWS and GCP environments
Lead the deployment and management of containerized ML services using Kubernetes (EKS/GKE)
Implement and maintain monitoring systems for ML model performance and ML infrastructure health
Establish and enforce security best practices and compliance requirements for healthcare AI systems
Collaborate with ML engineers to support deployment of ML models and services and optimise ML pipeline efficiency and reliability
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Senior DevOps Engineer
AI-powered medical scribe for clinicians, reducing administrative burden and improving patient care. HIPAA, GDPR, and APP compliant.
Benefits:
Flexible work with a 50% hybrid environment
Additional paid day off for your birthday and wellness days
Full gym access in our Melbourne location and Sydney TBC..
A generous personal development budget of $500 per annum
Learn from some of the best engineers and creatives, joining a diverse team
Experience Requirements:
Strong experience with cloud infrastructure (Azure required, AWS and GCP beneficial)
Hands-on experience with Kubernetes and container orchestration
Deep experience with observability tools, particularly Datadog
Proven track record in implementing SRE practices
Strong experience with Infrastructure as Code (Terraform)
Other Requirements:
Problem-solving mindset with a focus on reliability and scalability
Strong communication skills to work with cross-functional teams
Ability to balance technical requirements with business needs
Experience in fast-paced startup environments
Dedication to maintaining high standards in a regulated environment
Responsibilities:
Design and implement secure Azure infrastructure while working with existing AWS environments
Lead the implementation and management of containerized services using Kubernetes
Establish comprehensive observability and monitoring using Datadog for both infrastructure and applications
Design and implement robust SRE practices including SLOs, error budgets, and automated remediation
Lead incident response and post-mortem processes to drive continuous improvement
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Senior Software Engineer (Backend)
AI-powered medical scribe for clinicians, reducing administrative burden and improving patient care. HIPAA, GDPR, and APP compliant.
Benefits:
Flexible work with a 50% hybrid environment
Additional paid day off for your birthday and wellness days
Full gym access in our Melbourne location and Sydney TBC..
A generous personal development budget of $500 per annum
Learn from some of the best engineers and creatives, joining a diverse team
Experience Requirements:
4+ years of backend development experience
Strong back-end development experience in Python
Solid experience with MongoDB and Redis for data management
Experience building & managing message queue systems at scale
Deep understanding of API design principles, including security, versioning, and performance
Other Requirements:
Strong sense of ownership & strong problem-solving skills
Data modeling, design patterns, understanding trade-offs
Data & application security knowledge and skills
Experience in integrating with third-party systems
Competent with source code control and CI/CD pipeline technologies
Responsibilities:
Autonomously deliver beautiful & robust code with considerations on code structure design and scalability
Implement and optimize data storage solutions
Design, build, and maintain high-performance backend systems using Python
Architect and manage cloud infrastructure, ensuring scalability and reliability
Develop and manage message queue systems for real-time data processing
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