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New AI partnership gives mental health clinicians up to 5 hours back every week. By [email protected] Contributor, Innowell * New integration between Innowell and Heidi helps mental health services support more clients and spend more time on care - without expanding their workforce. * Integration combines Innowell's clinical intelligence with Heidi's AI Scribe to reduce administration by up to 5 hours per clinician each week.[1] * The launch comes as 1 in 5 Australians experience a mental health condition each year, including almost 2 in 5 (38.8%) of 16-24 year-olds.[2] * The technology is designed to help clinicians spend less time on paperwork and more time delivering care. SYDNEY, AUSTRALIA, 29 July 2026 - Australian digital mental health platform Innowell has announced a new integration with AI Care Partner Heidi, helping mental health services reduce administrative burden, support clinical decision-making, and increase capacity without expanding their workforce. By combining Innowell's clinical intelligence with Heidi's AI ambient scribe technology, the integration can save clinicians up to 5 hours each week,[1] time that can instead be spent with clients. The partnership launches at a time of significant pressure on Australia's mental health system, with demand for mental health support rising,[2] while workforce shortages continue to impact care.[3]Around 9 in 10 psychiatrists say workforce shortages are negatively impacting care, and around 7 in 10 (70%) report symptoms of burnout.[3] Despite annual mental health spending of approximately $14.5 billion (2023-24), 7% of the total government expenditure,[4] the system remains constrained by workforce and capacity limitations. "Mental health conditions can have a profound impact on individuals and their families, while also placing significant strain on clinicians and the broader health system. As demand rises and care becomes more complex, integrated tools can support clinicians to reduce fragmentation and help them focus on timely, person-centred care," said Associate Professor Frank Iorfino, University of Sydney Brain and Mind Centre. "When clinicians spend less time on administration, they can spend more time delivering care. Technology should support better conversations with clients, not create more work." Technology is playing an increasing role in supporting clients through measurement-based care, where clinicians regularly capture patient-reported data to monitor symptoms, identify risks earlier and track progress over time. This provides a more complete picture of a person's mental health and supports clinicians with richer information to inform care. "Measurement-based care gives clinicians the information they need to understand what is changing for a person over time, not just what is discussed in a single appointment. Combined with AI-powered documentation, clinicians can now access richer insight while spending less time on administration, helping them respond earlier and deliver more connected care," said Bianca Bowron-Cuthill, CEO of Innowell. Innowell's clinical intelligence captures and tracks patient-reported outcomes across the care journey, including assessments, risk signals and progress over time. Built on research from the University of Sydney's Brain and Mind Centre and more than 50 peer-reviewed studies, Innowell supports earlier intervention. Data published in a real-world study demonstrated a median 1.9-day documented clinical response time, when Innowell's automated high suicidality is flagged.[5] In 2026 Innowell has helped to detect 166 suicidal thought and behaviour escalations, ensuring that people get access to the right care and support faster.[1] "Every minute a clinician spends catching up on notes is time that could be dedicated to patients Heidi is designed to reduce the burden of clinical documentation so clinicians can stay focused on the conversation in front of them. Through this integration with Innowell, clinicians can spend less time on administration and more time delivering informed care," said Dr Simon Kos, Chief Medical Officer, Heidi. Together, the integration helps mental health services improve clinician wellbeing, increase capacity and deliver more responsive, connected care without requiring additional workforce resources. For media enquiries, please contact: About Innowell Innowell is the clinical intelligence layer for mental health services - turning every assessment, session and check-in into evidence that guides the right care, at the right intensity, at the right time. Built on 15 years of research with the University of Sydney's Brain and Mind Centre and more than 50 peer-reviewed studies, Innowell gives mental health services the measurement infrastructure to understand outcomes, identify risk earlier and deliver more connected care - the clinical intelligence behind better mental health care. To find out more, visit innowell.org. About Heidi Heidi is building an AI Care Partner to expand clinical capacity by supporting every stage of care delivery. In addition to its AI scribe, Heidi has also introduced Evidence, giving clinicians access to trusted medical research at the point of care. Heidi supports more than 2.7 million patient interactions each week in 110 languages from 190 countries. Founded in Melbourne, Australia, Heidi has raised $96.6M USD from global investors including Point72 Private Investments, Blackbird, Headline, Phoenix Court's growth fund Latitude, Possible Ventures and Archangel. Heidi aligns with leading international healthcare and privacy frameworks, including NHS requirements, GDPR, HIPAA, and the Australian Privacy Principles, and maintains enterprise-grade security certifications including ISO 27001, SOC 2 Type II, Cyber Essentials Plus, and ISO 42001. References [1] Innowell. Data on File. July 2026. [4] Australian Institute of Health and Welfare. Mental Health. https://www.aihw.gov.au/mental-health. Accessed July 2026. [5] Chong et al. 2024,. Understanding Engagement with Digital Mental Health Technology in Mental Health Services. Multicenter Observational Study. Journal of Medical Internet Research. 2025;27:e67597 doi: 10.2196/67597 [email protected] Contributor, Innowell

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.