Full-Time
Forward Deployed Engineer
Heidi Health
501-1,000 employees
Medical document management for GP-patient communication
No salary listed
London, UK
Hybrid
Three days in the office per week required.
 Software Engineering (1) |
|---|
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- At least 4 years of experience in forward deploy, implementation engineering, or a customer-facing technical role.
- Ability to write code using Python, JavaScript, SQL, or another suitable language.
- Strong working knowledge of integration protocols including REST APIs, SAML, OpenID Connect, and SCIM.
- Ability to own ambiguous, high-stakes technical problems end-to-end without a playbook.
- Ability to explain complex integration failures to both technical executives and clinical stakeholders.
- Own end-to-end technical onboarding for enterprise customers, including EMR integrations, single sign-on, SCIM provisioning, data pipelines, and security configuration.
- Work directly with clinical and operational stakeholders to design and build custom workflows that fit how care is delivered, including writing code.
- Serve as the escalation point for complex technical issues by diagnosing problems across the stack, coordinating with engineering, and closing the loop with the customer.
- Build tooling, scripts, and automation that make deployments faster, more reliable, and easier to scale.
- Translate field observations into clear, actionable input for Product and Engineering.
- Document processes, train users, and build internal knowledge so customers can operate independently.
- Experience in SaaS, healthtech, or enterprise software is preferred.
- Experience working embedded with enterprise customers or health systems is a strong plus.
- Experience with FHIR and HL7 is ideally preferred.
Heidi Health builds a digital platform for managing medical documents and streamlining communication between general practitioners (GPs) and patients in Australia. It keeps referrals, prescriptions, test results, and follow-ups organized in one place and makes it easy to schedule appointments and message a GP. The product works through a web and mobile interface that users access via subscriptions or usage fees, giving teams and individuals quick, tap-away access to essential information. The company differentiates itself by focusing on the GP-patient workflow and convenient document management within the Australian market, delivered with a user-friendly experience and a culture of fast delivery and warmth. Heidi Health aims to reduce the need for physical waiting rooms, improve preventive and continuous care, and provide a clear, affordable way for practices and patients to manage medical information.
Company Size
501-1,000
Company Stage
Series B
Total Funding
$91.3M
Headquarters
Melbourne, Australia
Founded
2019
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Simplify's Take
What believers are saying
- On June 26, 2026, Heidi raised $16.6 million from Headline and Anthology.
- In July 2026, Canadian pilots saved 13 minutes per visit across 111,770 visits.
- The NHS Midlands framework now covers 70,000 clinicians across 15 trusts and 1,239 GP practices.
What critics are saying
- Heidi Evidence began blocking UK NHS emails in May 2026, shrinking point-of-care adoption.
- NHS and Ontario procurement scrutiny now forces slower sales cycles and heavier compliance spending.
- If clinicians stop switching workflows, Heidi becomes a feature, not a company.
What makes Heidi Health unique
- Heidi Health embeds directly into clinical workflows, minimizing implementation friction for doctors.
- Its product suite spans scribing, evidence, and communications, not just note-taking.
- Regulatory discipline and enterprise security certifications build trust with hospitals and governments.
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Benefits
Hybrid Work Options
Paid Holidays
Paid Sick Leave
Professional Development Budget
Company Equity
Growth & Insights and Company News
6 month growth
↑ 4%1 year growth
↑ 4%2 year growth
↑ 3%AI scribe hallucinations in clinical notes: how to prevent them. July 16, 2026 A physician finishes a 14-patient day, opens the EHR, and reviews the AI medical scribe's output. The note looks clean. But buried in the assessment is a medication the patient never mentioned, and a diagnosis code that does not match what was discussed in the room. This is not a rare software glitch. It is a structural problem with how most AI scribes are built, and it has a name: hallucination. Healthcare AI hallucinations occur when a language model generates confident-sounding clinical content that has no basis in the actual conversation. The note looks complete. The language sounds clinical. The information is wrong. For a physician reviewing 20 notes at the end of a shift, that error is easy to miss and costly to ignore. What are AI scribe hallucinations in clinical notes? AI scribe hallucinations in clinical notes happen when the model fills gaps in the conversation with fabricated but plausible-sounding content. The trigger is usually ambiguity: a patient speaks quickly, a name is mispronounced, a symptom is mentioned in passing, or the acoustic environment introduces noise. Instead of flagging the uncertainty, the model infers, and sometimes incorrectly so. * Medication confabulation: The model generates a drug name similar to what was said but phonetically distinct (e.g., "hydroxyzine" instead of "hydralazine") * Diagnosis carries forward errors: Past medical history items bleed into the current assessment * Plan fabrication: The AI adds follow-up instructions or referrals not discussed during the visit * Demographic drift: Age, sex, or pronoun substitution when patient identity is not anchored in the transcript These are not edge cases. They represent the predictable failure modes of general-purpose language models deployed in high-stakes clinical environments without adequate medical tuning. Why AI scribe errors in clinical notes are a patient safety problem. The consequences of AI scribe errors in clinical notes extend beyond chart accuracy. A hallucinogenic medication can cascade into a prescription error. A fabricated referral can delay appropriate care. A misattributed diagnosis code can trigger a billing denial or payer audit. A 2023 study published in JAMA Internal Medicine found that large language models generated incorrect medical information in a meaningful percentage of clinical Q&A tasks, particularly around drug dosages, contraindications, and diagnostic criteria, exactly where clinical note hallucinations cause the most harm. A signed note is a legal document. If a hallucinated finding is signed and submitted to a payer, the physician has certified content that they did not generate. A physician seeing 20 patients per day who misses one hallucinated line per note accumulates documentation risk at scale, across a full patient panel, over months. Which AI scribes are most vulnerable to healthcare AI hallucinations? Not all AI scribes carry equal hallucination risk. The architecture matters. General-purpose large language models, trained on broad internet text and applied to clinical transcription, are most prone to healthcare AI hallucinations. They have no reliable mechanism for "I don't know." When audio is unclear, they fill the gap with statistically probable medical language. That language may be accurate. It may not be. Tools built on medically tuned models carry lower hallucination risk because their outputs are constrained to clinically grounded patterns rather than general language prediction. The distinction matters when evaluating AI medical scribe accuracy in real settings. A scribe who performs well in a clean studio environment may hallucinate frequently in a busy outpatient clinic, due to ambient noise, patient crosstalk, non-native speaker accents, and rapid dictation. Several Tier 1 competitors, Freed AI, Heidi Health, and Nabla, have published minimal clinical accuracy data. The market relies largely on user testimonials rather than independent validation. DeepScribe holds a KLAS score of 98.8/100, the highest ambient AI score recorded by KLAS Research, because it invested in third-party accuracy validation. That score carries weight with enterprise buyers for a reason. Notiro's medically-tuned AI scribe is built to handle multi-problem visit complexity and real exam room acoustic conditions, the two environments where general-purpose models are most likely to hallucinate. How to prevent AI scribe hallucinations: A practical framework. Preventing AI scribe hallucinations in clinical notes requires action at three levels: tool selection, workflow design, and physician review practice. 1. Choose a medically-tuned model over a general-purpose one. The first prevention step happens before the scribe enters an exam room. A tool trained on clinical language, one that understands the difference between a symptom, a finding, and a plan item, is structurally less likely to confabulate than a general-purpose model applied to medical transcription. Ask vendors directly: is the underlying model general-purpose or medically-tuned? Ask what clinical datasets it was trained on. Ask whether the tool has been independently evaluated for AI medical scribe accuracy. Vague answers should be treated as a warning sign. 2. Require uncertainty flags in the output. A well-designed AI medical scribe should distinguish between what it heard clearly, what it inferred, and what it could not capture. Notes that present all content with equal confidence provide no signal about where review attention is needed. Look for tools that surface low-confidence segments or leave explicit placeholders rather than silently filling gaps with fabricated text. 3. Audit high-hallucination sections first. The Assessment and Plan sections carry the highest risk of hallucinations; they require the model to synthesize and generate conclusions rather than transcribe. The Subjective section is the second-highest risk when the patient speaks quickly or uses non-standard terminology. Reviewing these sections first reduces cognitive load and focuses attention where errors are most likely to appear. Use this quick review checklist before signing an AI-generated clinical note: 4. Standardize the pre-visit context. One underappreciated hallucination driver is an information-sparse session start. When the ambient scribe begins recording without a patient history baseline, the model fills context gaps from training data rather than the actual patient record. Patient Intake AI, available in Notiro but absent from every other Tier 1 AI scribe competitor, addresses this directly. When the patient's presenting complaint, medication list, and history are captured before the visit starts, the scribe begins with a richer context. The note has less to infer and more to transcribe. Hallucination risk drops accordingly. 5. Build a post-visit review protocol. No AI scribe is error-free, and the review step is not optional. A structured protocol under 90 seconds, scan Assessment/Plan, check medication names, confirm diagnosis codes against what was discussed, catches the majority of hallucinated content before the chart is signed. The Mass General Brigham AI scribe study documented a 21.2% drop in physician burnout scores after 84 days of AI scribe use, conducted with a structured physician review workflow in place. The tool reduced documentation time. The protocol protected accuracy. How AI medical scribe accuracy affects billing, not just safety. Clinical note: hallucinations create a billing problem alongside the safety problem. A hallucinated finding in the Assessment section can generate an incorrect ICD-10 code suggestion, and an ICD-10 code that does not match the documented encounter is a claim waiting to be rejected. According to CMS, ICD-10 has over 70,000 codes, and CPT has over 10,000. Manual post-visit code selection under time pressure is already a systematic source of undercoding errors. An AI scribe that introduces inaccuracies into the note content it pulls from compounds. This problem. Notiro's ICD-10 and CPT coding pulls codes from the visit audio and the generated note. When the note is accurate, the code suggestions are accurate. When hallucinations corrupt the note, the downstream coding is corrupted too. Clinical note accuracy is not a documentation concern alone; it is a revenue cycle concern. How notiro helps prevent clinical note hallucinations before they reach the chart. Healthcare AI hallucinations in clinical notes are a predictable consequence of deploying general-purpose language models in environments that demand clinical precision. The physician who uses a tool that fills ambiguity with fabricated text is accumulating liability one signed chart at a time. Prevention requires the right tool architecture, a structured review protocol, and a pre-visit context baseline that reduces the model's inference burden. The hallucination problem does not disappear with better AI. It is managed with better AI and a disciplined clinical workflow. AI scribe hallucinations corrupt more than the note; they corrupt the codes, the billing, and the legal record the physician signs. Notiro's medically-tuned ambient scribe is built on clinical language, not general-purpose text prediction, and its Patient Intake AI gives the model a verified context baseline before recording starts. Start your free trial at notiro, no IT setup, no enterprise contract. Available now. Frequently asked questions. Find quick answers to the most common questions about Notiro - how it works, what it documents, and how it fits into your clinical workflow.
NHSE region signs AI scribe deal covering 15 trusts. Published 19 hours ago The NHS has launched its largest-ever regional deployment of ambient voice technology, covering 70,000 clinicians across 15 trusts and 1,239 GP practices. NHS England's Midlands team ran a competitive procurement process, selecting Australian vendor Heidi Health as sole supplier for the framework, which spans emergency departments, outpatient services, and primary care. The framework emerged from pilots at the Dudley Group Foundation Trust, which, according to Heidi, reduced emergency care documentation time by 80 per cent and cut a six-month rheumatology letter backlog to 14 days. Neighbouring providers expressed interest in replicating the business case and rollout, prompting NHSE's Midlands team to coordinate a single regional procurement route. Five trusts - Dudley Group, Sandwell and West Birmingham Hospitals Trust, the Royal Wolverhampton Trust, Walsall Healthcare Trust, and University Hospitals of North Midlands Trust - have begun deployment. Heidi declined to name the remaining 10 trusts expected to follow. The framework was opt-in, meaning it covers only those trusts that chose to participate and is closed to new joiners. 0 comments. There are no comments to display.
Conversion: the quiet gap where healthcare businesses bleed money. * July 9, 2026 Part six of a series on diagnosing growth instead of guessing at it. In the Healthcare Growth Equation (Growth = Demand x Trust x Conversion x Measurement), conversion is the variable where money leaks out speedily and invisibly. You've earned the demand. You've built the trust. Someone is interested, ready, even saying yes. And then, in the gap between decision and action, you lose them - and you almost never find out why. This article shows you how to bridge that gap. Splicemarketing'll explore what conversion really means, why friction is almost always the culprit, and how to create a smoother conversion process. Conversion isn't your win-loss rate. First, a definition, because the word gets used loosely. When I talk about conversion, I don't mean your sales conversion rate - deals won versus deals lost. I mean the moment between someone deciding they want what you offer and actually acting on it. For a clinic, it's the gap between "I should book that" and a confirmed appointment. For a healthtech product, it's the gap between sign-up and the moment usage becomes a daily habit rather than another abandoned tool. That gap is where most healthcare businesses bleed, and it almost always comes down to one thing: friction. If the path to action makes someone think harder, work longer, or take one extra step, you've lost them. You've experienced this yourself. Every time you've started an online form that felt more demanding than your tax return and just closed the tab, that was a business losing you due to friction in the conversion process. Friction is the enemy, and it hides in plain sight. The reason conversion problems are so expensive is that friction is invisible to the business creating it. You designed the booking form, so it doesn't feel like friction to you. In fact, gathering all that data may actually be helpful to your internal processes, making the extra boxes seem trivial. But to the person on the other side, every extra question, every system glitch, every moment of "Wait, how do I do this?" is a reason to give up - and they leave without telling you. The clearest example I know comes from clinical software. MedicalDirector runs systems used across the majority of Australian general practice. When AI scribes entered the market - genuinely useful tools saving GPs real time - the friction point was always the same. The GP would use the scribe during the consultation, then, once the patient left, they had to manually copy and paste the scribe's notes into MedicalDirector. One extra step sits between the technology and the workflow. So MedicalDirector built Smart Scribe, an integration connecting AI partners like Heidi Health directly into the patient record. The note goes where it needs to go, inside the system the GP already uses. No extra step. The market didn't fail because of that friction - it reorganised itself around removing it. The lesson scales down to the simplest clinic. Splicemarketing worked with a high-end practice that had full pricing published openly on its website. Splicemarketing understood the instinct to be upfront about pricing, but Splicemarketing also saw potential patients taking fright before they understood the value and leaving without booking. So Splicemarketing offered something of value to download that showcased the offerings and carried the pricing inside it, giving context for the cost. It was gated content, which meant patients gave their email address to download it, allowing the clinic to nurture them over time. Same information, completely different conversion outcome - because the friction was removed at the exact point people were dropping off. The two friction traps. Integration or process friction. This is when acting requires too much effort up front. In healthtech, that's months of implementation, IT sign-off, custom development; nobody has time to implement something even if it would ultimately save them time. In a clinic, it's the booking that requires a phone call during business hours, or a form that asks for too much information at the "getting to know you" stage. Workflow or expectation mismatch. When the thing works, but only if the person changes how they already behave to access it. Every step that asks someone to deviate from their existing habit is a step where adoption dies. The businesses that break through do the opposite of adding steps - they remove them, and they meet people where they already are. Heidi Health went directly to individual GPs and made it free to start: no IT approval, no implementation project. A clinician could try it in a single consultation, get value immediately, and tell a colleague. That was a conversion decision, not a marketing tactic. It reduced the effort required to say yes, turning one interested user at a time into momentum the business could scale. Conversion is hiding retention. Conversion isn't only about the first action. The same friction logic governs patient acquisition and retention alike. The nurture sequence that brings someone back next month, the follow-up after a first consultation, the recapture of a lead who went cold - these are all conversion, applied to the relationship rather than the first sale. In healthcare, lifetime value and repeat and referral behaviour often matter far more than first-visit conversion. A business obsessing over the first booking while ignoring what happens after it is only reading half the variable. The full picture runs from the first click all the way through to the patient who comes back, and refers a friend, because every step of that journey was made easy. How to tell if conversion is your weakest variable. Some honest signals that conversion is your binding constraint: If demand is bringing people to the door and trust is making them willing, but they still aren't acting, conversion is where your growth is leaking. What fixing conversion looks like. Conversion is the most mechanical of the four variables, which is good news - it responds quickly to deliberate work. Find the single change that removes the most friction for the least effort, and start there. In practice that means: * Shortening forms to only the essential fields * Testing the booking flow on mobile as ruthlessly as on desktop * Adding tap-to-call buttons and sticky enquiry buttons for mobile users * Regularly reviewing the patient journey to find and remove friction points * Building a real lead-handling and follow-up process, including SMS recapture for people who didn't book the first time. * Mapping the whole conversion journey end to end, from first interest through to retention, so no step is left to chance. It's worth the effort. Splicemarketing helped one company improve its conversion rate by 20% using a mix of these strategies. The bottom line. Conversion is where the work of demand and trust either pays off or disintegrates. The money you lose here is the hardest to see, because a patient who gives up doesn't fill in a form to tell you they've gone. Removing friction - at the booking, in the follow-up, across the whole journey from first click to returning patient - is often the fastest, highest-leverage growth available to a healthcare business. If you're generating interest that isn't converting into booked and returning patients, there's almost certainly friction you can't see from the inside. Book a free consultation with the Splice Marketing team.
Heidi, an AI medical scribe platform, has integrated its documentation technology into Accuro EMR, Canada's largest single-platform electronic medical record system serving over 25,000 clinicians nationwide. The integration allows healthcare providers to complete patient visit documentation using AI whilst remaining within their existing Accuro workflows. Heidi's AI scribe can access selected EMR chart data to generate contextualised clinical notes, reducing administrative burden and clicks. Heidi is currently used in approximately 2.7 million consultations weekly across 190 countries. Since launching in Canada, the platform has returned over 8.2 million hours to Canadian clinicians. The integration was developed through continuous feedback and testing across different clinical settings to ensure practical workflows that support rather than disrupt existing practices.
Tandem CDS vs Heidi Evidence: how two scribe companies' decision-support tools compare on evidence and regulation. Two of the best-known names in clinical documentation, Tandem and Heidi, have both moved beyond the scribe and into clinical decision support. Tandem launched clinical decision support inside Ask Tandem; Heidi launched Heidi Evidence, an evidence layer it built after acquiring the UK company AutoMedica. On paper they answer the same question - help clinicians find and apply trusted medical knowledge - but they take different routes on regulation, sourcing and, most consequentially for UK clinicians, availability. Regulatory posture. Tandem has been explicit about climbing the regulatory ladder. Its AI scribe, coding assistant and clinical decision support are all CE marked under EU MDR as Class IIa medical devices, externally assessed by a notified body. For a decision-support product, that is a meaningful, independently checked baseline. Heidi's position is more layered. Its AI scribe is registered with the MHRA as a Class I device for summarisation functionality. Heidi Evidence, however, is newer, and independent coverage has noted that its UK regulatory status is still to be confirmed. So while both companies hold device registrations for their scribes, Tandem currently carries the firmer regulatory footing specifically for its decision-support product. Sourcing and evidence integrity. Both lean hard on cited answers, and both make a point of source transparency. Heidi Evidence is built in partnership with named clinical sources - HealthPathways, EMGuidance, MIMS, Vidal, NICE and BMJ Group - and is positioned as ad-free, providing concise summaries with transparent citations and verbatim excerpts. Heidi has framed this explicitly against ad-supported consumer AI, arguing that the integrity of clinical evidence should be non-commercial. It also lets clinicians upload their own sources, and is built in part on Anthropic's Claude models. Tandem's CDS draws on national guidelines plus locally uploaded protocols, and is designed to use the context of the documented visit. Its evidence story is anchored less in named content partnerships and more in the regulatory assurance of its Class IIa assessment. Both are new enough that real-world clinical validation is still emerging; neither should be treated as having a long published track record in decision support yet. The decisive factor for UK clinicians: availability. This is where the comparison stops being academic. Heidi Evidence reportedly began blocking UK NHS email addresses at enrolment in May 2026 - part of a wider pattern in which several clinical AI tools have stepped back from UK NHS access amid regulatory uncertainty. Tandem's CDS, by contrast, is available to selected customers across Europe and the UK. The result is a paradox worth naming: Heidi Evidence is built on UK-relevant sources like NICE and BMJ Group, yet reportedly excludes UK NHS clinicians at sign-up, while Tandem - a Swedish company - has pursued the higher regulatory class and stayed in the UK market. | Dimension | Tandem CDS | Heidi Evidence | | Product type | In-consultation decision support | Evidence layer (standalone or with scribe) | | Regulatory class | CE marked EU MDR Class IIa (notified-body assessed) | Scribe is MHRA Class I; Evidence regulatory status reportedly TBC | | Sourcing | National guidelines + local uploaded protocols | NICE, BMJ, MIMS, Vidal, HealthPathways, EMGuidance; user uploads | | Commercial model | Enterprise / EHR-integrated | Free for individual clinicians; ad-free | | UK NHS availability | Available (selected UK customers) | Reportedly blocking UK NHS emails at enrolment (May 2026) | | Clinical validation | Emerging | Emerging | Where iatroX sits. For a UK clinician, the practical question is not only "which decision-support tool is best?" but "which one can I actually use, grounded in UK practice?" iatroX is built around that gap: it is a UK-registered, MHRA-listed Class I clinical AI, free to use, grounded in NICE, CKS, SIGN and the SmPC. iatroX is deliberately a different layer from Tandem's in-consultation CDS - it informs the clinician's own judgement rather than automating or directing the decision, which is why it sits at Class I rather than Tandem's Class IIa. But unlike Heidi Evidence, it remains available to UK NHS clinicians, and unlike US-centric tools, it is built for UK guidelines from the ground up. The point is not that iatroX out-regulates anyone - Tandem's Class IIa is a higher bar - but that the UK knowledge layer is one a clinician can open today. And Class I being self-declared does not mean ungoverned: iatroX's registration is backed by a full clinical risk-management file and safety case, independently reviewed. Frequently asked questions. Is Heidi Evidence available to UK NHS clinicians? According to independent reporting, Heidi Evidence began blocking UK NHS email addresses at enrolment in May 2026. UK clinicians should check current availability directly, as the position may change. How does Tandem's CDS differ from Heidi Evidence on regulation? Tandem's clinical decision support is CE marked under EU MDR as a Class IIa device, externally assessed by a notified body. Heidi's scribe is MHRA Class I, but the regulatory status of Heidi Evidence specifically has been reported as still to be confirmed. Are these tools clinically validated? Both are recent, and real-world clinical validation for their decision-support products is still emerging. Neither has a long published evidence base in decision support yet, so claims should be read with that in mind. What is the UK-available alternative? iatroX is a free, UK-registered (UKCA-marked Class I) clinical AI grounded in NICE, CKS, SIGN and the SmPC, and remains available to UK NHS clinicians as a knowledge and reference layer.