Hi Pulse Readers - this week, we’re diving into:

• Whakarongorau Aotearoa's new AI mental health Welcome service,

• what a 1000-people RCT reveals about conversational AI compared with face-to-face group therapy,

• and how you can automate your daily to-do list with Heidi Tasks.

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2. Case Study – Your Fortnightly Practical

Video Source: Hendrix Health

Automate a To-do List Using Heidi Tasks

Case Presentation: The admin pile has been getting smaller for Dr Harry. Using Heidi, his volume of leftover work at the end of each day has noticeably dropped.

Today, he wrapped up a busy morning clinic. Across 15 patients, there were referrals to make, repeat bloods to order, results to chase, and a couple of patient callbacks he had promised to do himself. He had a mental list, a few sticky notes, and a vague sense that he had probably forgotten something.

Dr Harry wonders whether Heidi can help him track these action / to-do items consistently, without relying on memory or paper.

Approach: Capture follow-up actions automatically from each consultation with Heidi Tasks, then review and manage them from a single “Master” list across patients.

1. Open the Tasks panel during or after a session
Click the Tasks button at the bottom right of your Note to open the Tasks panel on the side. Heidi will have already identified to-do items from the transcript and listed them automatically. You can adjust how the panel behaves and turn auto-generation on or off in your settings.

2. Review what Heidi has captured, organised by category
Each task is auto-categorised into one of five categories. Order covers things like blood tests or imaging requests. Coordinate covers referrals to specialists or allied health. Communicate covers patient callbacks, results delivery, and reminders. Document covers letters, summaries, or reports that still need to be drafted. Action covers general tasks that don't fall neatly into the above categories. You can edit a task title, change its category, or add a manual task by clicking "new task" at the bottom of the list.

3. View the evidence or generate a document directly from a task
Click the three dots button on the right of any task. From there, you can view the task evidence (which links you to the exact point in the transcript where Heidi picked up the task), or generate a document based on the task in one click. Heidi will automatically mark the task as Done once the document is ready. This can be undone if needed. As always, as the clinician you remain responsible for reviewing the evidence and confirming clinical appropriateness before actioning any task or sending any document.

4. Manage outstanding to-dos through the “Master” Task List
Navigate to your “Master” Task List by clicking the Task button on the very left. This list shows every outstanding to-do across all patients and sessions in one place, so you can work through your tasks at the end of the day without scrolling through individual notes. Tick items off as you complete them, or delete tasks that no longer apply.

Note: Heidi Tasks is currently in a phased beta rollout. Enable it by turning on Heidi Labs in Preferences.

Outcomes: With Heidi Tasks, every referral, test order, callback, and outstanding letter is already captured and categorised by the end of his morning clinic. Dr Harry spends the last 20 minutes of his day working through the Master Task List, rather than re-reading every note to remind himself what he meant to do. The risk of a forgotten test result or an unsent referral is significantly reduced.

This is very helpful for a busy GP managing dozens of small to-dos across a full day of patients.

Disclaimer: Hendrix Health is the official New Zealand partner for Heidi Health.

3. The Pulse - Your Fortnightly Update

Whakarongorau Aotearoa Launches AI Welcome Service for Mental Health and Family Harm Helplines

Whakarongorau Aotearoa (New Zealand Telehealth Services) will launch a Microsoft Azure AI-powered Welcome service in May 2026 to support people contacting its helplines, while they wait to connect with a trained counsellor. The service will first deploy across SMS to the national 1737 Need to talk? helpline, and webchat for the Whakarongorau-run Women's Refuge service.

Image Source: geneticliteracyproject.org

Whakarongorau handles more than 7,000 interactions daily across health, mental health, and social services, reaching over 735,000 people in Aotearoa annually. Mental health demand is rising sharply: at-risk contacts have grown 153 percent since 2019, and the average session now takes around 50 percent longer.

When someone makes contact, the Welcome service identifies itself as AI and lets the user choose to engage or wait in silence. It gathers contact details and the reason for contact, checks in on how the person is feeling, and offers non-clinical support such as breathing techniques. By the time a kaimahi (staff member) joins, they have context to focus on care from the first message.

Key Features:

• Clear AI disclosure: Users are told upfront they are interacting with AI and can choose to engage or wait in silence.

• Non-clinical scope: The service does not provide clinical guidance, diagnosis, or medical advice, using empathetic language to hold the person until a counsellor is available.

• Context handover: Counsellors join the conversation with contact details, reason for contact, and emotional state already captured, reducing time spent on basics.

• Co-designed and tested: Developed with clinical oversight and frontline staff input, then tested with mental health service partners and people with lived experience.

Implications for the Health System and Clinicians: For New Zealand’s mental health and crisis support sector, this is a practical example of AI absorbing pre-clinical work that traditionally consumes counsellor time at the start of each interaction. The narrow, non-clinical scope to use AI to keep people engaged while they wait, is the key design choice.

One caution is that the service has not yet launched and no outcomes data is available. The real test will be whether it holds up safely across diverse users in distress, and whether wait-time experience and counsellor workload measurably improve once live.

Conversational AI in Mental Health: What a 995-Student RCT Reveals

A randomised clinical trial published in JAMA Network Open compared a conversational AI mental health platform with face-to-face group therapy and a waiting list control among 995 university students reporting psychological distress in Israel.

Participants were assigned to a 12-week AI platform (Kai, KAI.AI Inc), 12 weekly 90-minute group therapy sessions led by licensed psychologists, or a waiting list. The AI drew on multiple therapy and mindfulness techniques, with users encouraged to engage at least 3 times weekly. Those with active suicidality, current psychotherapy, or severe mental disorders were excluded.

Image Source: Journal of the American Medical Association (JAMA)

Key Findings:

• Greater anxiety reduction with AI: The AI group showed significantly greater anxiety reduction than both group therapy and the waiting list. Group therapy did not differ significantly from control group.

• Clinical transition rates: Among participants meeting baseline clinical anxiety thresholds, 57.9% in the AI group moved into the non-clinical range, compared with 14.4% in group therapy and 9.8% in the control group.

• Depression, well-being, and life satisfaction: All three outcomes favoured AI over the waiting list at 12 weeks and 3 months, with smaller and less consistent differences between AI and group therapy.

• No effect on PTSD: No significant differences emerged between any group at either time point, suggesting the platform did not address trauma-related symptoms.

• Therapy-seeking intention fell with AI: Intention to seek therapy declined from 29.8% to 23.5% in the AI group, remained stable in group therapy, and rose to 37.0% in the control group.

Implications for Healthcare Systems:
The results suggest conversational AI may have a role as an early-intervention tool for mild-to-moderate distress, particularly where access to face-to-face care is challenged. Here are some limitations: outcomes were self-reported, participants were university students in one country, attrition reached 35% at 3 months, and PTSD was unaffected. The drop in therapy-seeking intention warrants caution, as it may reflect genuine recovery or unmet need that goes unescalated.

Read the full trial in JAMA here.

4. Vitals – Quick Bytes

• Victoria Expands Virtual Hospital Pilot to 400 Patients

  The Victorian Government is expanding its first Virtual Hospital pilot, jointly led by The Royal Melbourne Hospital and Austin Health. For heart failure and post-cardiac surgery patients, the programme uses video, phone, and remote monitoring to deliver hospital-level specialist care at home and in regional hospitals through virtual wards and ward rounds; and a virtual foetal medicine service with the Royal Women's Hospital. There is also a planned neurology programme in partnership with Northeast Health Wangaratta. Since December 2025, more than 260 patients have been treated and over 1,000 hospital bed days saved, with the pilot now scaling to 400 patients by the end of June. The figures come from early government announcements rather than independent evaluation, with a formal review scheduled for June 2026. (premier.vic.gov.au, thermh.org.au)

• Frontier LLMs Still Stumble on Differential Diagnosis, Large Benchmarking Study Finds

  A cross-sectional study in JAMA Network Open tested 21 off-the-shelf large language models on 29 standardised clinical vignettes from the MSD Manual, scoring 16,254 responses in total. This included GPT-5, Claude 4.5 Opus, Gemini 3.0, and Grok 4. Using a new multidimensional benchmark called PrIME-LLM, the authors found that reasoning-optimised models scored higher than non-reasoning ones, and all models did best on final diagnosis and management. However, failure rates for differential diagnosis were over 80% across every model, while final diagnosis failure rates stayed below 40%. The authors conclude that current LLMs jump to single answers too quickly, and cannot yet be trusted for unsupervised, patient-facing decisions. The caveat is that the study tested base models only. Tools that retrieve from trusted sources (such as Heidi Evidence) were not tested, which may lift real-world performance.

We’d love to hear your thoughts, so join the conversation by leaving a comment below:

Leave a comment

Stay tuned for more insights in the next edition of The Pulse.

Have a great day & see you in two weeks!

Your Hendrix Health Team

Hi Pulse Readers - this week, we’re diving into:

• how air medical helicopter teams uses AI to speed up patient transfers,

• what we know about AI diabetes coaching programme from an RCT,

• and how you can add patient context in a scribe session to improve the output.

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2. Case Study – Your Fortnightly Practical

Video Source: Hendrix Health

Add Context to Support Continuity of Care

Case Presentation: Dr Harry has come a long way. He has built a dedicated ADHD assessment template, used Ask Heidi to generate ad-hoc referral letters and support documents, reviewed trusted sources and guidelines before complex consultations, and managed multilingual consults with patients and their whānau, all within Heidi.

Today, he is seeing a patient for a f/u appointment. The previous visit generated a detailed note covering initial presenting complaint, history, developmental background, and a management plan. At the end of f/u consultation, Dr Harry found himself manually entering details from the prior visit to complete today's note.

He wonders whether there is a way to bring that past clinical context into the current session so that Heidi’s output reflects the full clinical picture, not just what was discussed today.

Approach: Use Heidi's context window to load relevant information from previous visits, so that the generated note is more targeted and clinically complete from the outset.

1. Open the context tab before starting the session
At the top of a new Heidi session, the default tab is the note tab. To the very left of that is the context tab. Open this before transcribing the consultation.

2. Load the relevant history into Heidi and label it clearly
Copy and paste the relevant past notes from your PMS or EHR into Heidi’s context window. Add a clear label directly above the pasted text so that Heidi understands this information is from a previous encounter and not the current one.

For example: "History from last visit: [paste prior notes here]".

Another option is uploading individual files directly from your computer by clicking the paper clip icon at the bottom left corner.

Note: You can also include other relevant background information, such as letters returned from a specialist and radiology or pathology reports. This could also be ambulance notes in an ED context.

3. Transcribe the consultation as usual
Gain consent and transcribe the visit normally. The note that Heidi generates will take into account everything in the context window alongside what was captured in the transcript. The result is a note that reflects continuity of care rather than treating the visit as a single, isolated session. You will still need to review the final note for accuracy before copying it into your PMS or EHR.

4. Keep consent in mind before loading any context
For patients known to you who have previously consented, load the prior context before the session as outlined above. However, if a patient known to you withdraws consent at the start of a f/u appointment, delete the session entirely. This will also remove any context already uploaded, respecting their decision.

For new patients, gain consent before using Heidi at all. If consent is confirmed, you can add the background clinical information into the context window after the consultation instead.

Important: Do not use the context window if the patient declines the use of an AI scribe.

Outcomes: By using the context window, Dr Harry no longer needs to manually enter a patient's history or cross-reference multiple documents. His notes already reflect the patient's relevant history, are more consistent across visits, and require fewer edits before being copied into his PMS.

For patients like his ADHD follow-ups, where continuity across multiple long appointments matters, this makes a meaningful difference to both documentation quality and admin time after the consultation.

Disclaimer: Hendrix Health is the official New Zealand partner for Heidi Health.

3. The Pulse - Your Fortnightly Update

AI Stroke Detection and Care Coordination Now Live Across 2,000 US Hospitals

Viz.ai, an AI-enabled stroke detection and coordination tool, is now in use across 2,000 hospitals in the United States. The system analyses CT scans within seconds, and when a suspected large vessel occlusion is identified, sends an alert to the treating specialist’s smartphone. This removes the manual chain of phone calls, image sharing, and delayed handovers that traditionally adds critical minutes between a scan being taken and a decision being made.

Image Source: Viz.ai

At regional hospitals that transfer stroke patients to larger centres for treatment, the system automates care-team activation and streamlines transfer coordination. The treating specialist can view the imaging and begin coordinating care within minutes of the scan being completed.

The platform has also been extended into air-medical transport. A Life Flight module, deployed as a pilot at the University of California, San Diego, allows helicopter dispatch and flight crews to join the same secure communication thread as stroke and surgical teams. This gives flight crews access to patient details, medications, and imaging findings before landing at the referring hospital, reducing the need for repeated handovers on arrival.

Key Features:

• Faster transfers: At Adventist Health & Rideout, a regional stroke centre in California, the time from patient arrival to transfer out was reduced by 44%, bringing performance in line with American Heart Association benchmarks

• Earlier detection: Time from scan completion to identification of a vessel occlusion fell by 84%, with specialist notification dropping from 45 minutes to 7 minutes

• Low adoption burden: Life Flight teams were trained on the new module through a single 15 to 20 minute session covering workflow steps, device settings, and test activations, with no additional training required for the existing stroke team.

• Clinician satisfaction: Overall satisfaction with the communication process improved by approximately 60% across all roles, with 100% of respondents preferring the new workflow

Implications for the Health System and Clinicians: For health systems that transfer stroke patients between regional hospitals and specialist centres, this is a practical example of AI reducing delays by improving how teams communicate, rather than by making clinical decisions. The value is in removing bottlenecks that slow treatment when minutes matter most.

The caution is that the strongest results come from early, single-site evaluations. Whether these gains hold consistently across different hospital networks and patient populations will need longer-term evaluation at scale.

AI-Powered Diabetes Prevention: What We Learned

On 3 April 2026, a Johns Hopkins-led phase III randomised controlled trial was presented at the NIH Pragmatic Trials Collaboratory's Grand Rounds. Published in JAMA in late 2025, the trial evaluated whether referral to a fully automated, AI-powered Diabetes Prevention Program (DPP) was non-inferior to referral to a human coach-led DPP among 368 adults with pre-diabetes and overweight or obesity across two US clinical sites, followed for 12 months.

The AI-led DPP used a reinforcement learning algorithm delivered via a mobile app and Bluetooth-enabled scale. The human-led DPP comprised four CDC-recognised programmes with the highest designation for effectiveness, delivered remotely.

Image Source: Journal of the American Medical Association (JAMA)

Key Findings:

• Non-inferiority confirmed: 31.7% in the AI-led group and 31.9% in the human-led group achieved the primary composite outcome, which included weight loss, physical activity, and HbA1c targets at 12 months. A risk difference of -0.2%, well within the pre-specified non-inferiority margin of -15%.

• Higher initiation and completion with AI: 93.4% of those referred to the AI-led DPP actually began the programme compared with 82.7% in the human-led group, and 63.9% completed it compared with 50.3%.

• Different engagement patterns: Among those who achieved the outcome, 74% were programme completers in the AI group compared with 56% in the human-led group, suggesting the AI programme depends more on sustained app engagement to deliver results.

• No study-related adverse events, with 85.1% of participants retained at 12 months.

Implications for Healthcare Systems:
AI-based programmes can run on demand and are not constrained by the scheduling, staffing, and capacity limitations that restrict traditional DPPs. The important caveats are that participants were motivated volunteers with high educational attainment from only two sites, and the trial measured surrogate outcomes rather than diabetes incidence. The value of the AI-led DPP appears to be accessibility and reach rather than superiority over human coaching.

Read the full trial in JAMA here.

Watch the Grand Rounds session on AI for Diabetes Prevention here.

4. Vitals – Quick Bytes

• Western Australia Funds AI Pilot to Improve Hospital Workflow and Efficiency

  The Western Australian Government is investing A$700,000 in an AI pilot at Royal Perth Hospital to predict length of stay and streamline hospital workflows ahead of the 2026 winter season. The system will schedule tasks including medical imaging, laboratory tests, pharmacy medication packs, and discharge summaries, with the aim of improving bed availability and reducing delays. A new system-wide data platform will feed live dashboards for the State Health Operations Centre to track demand, capacity, and patient movement across the public health system. If successful, the pilot may be scaled across multiple hospitals and integrated with other digital health initiatives.

• US Survey Finds Public Openness to AI in Healthcare is Declining

  A national survey of 1,007 US adults commissioned by The Ohio State University Wexner Medical Center found that only 42% are open to AI being used in their healthcare, down from 52% in 2024. The belief that AI can improve healthcare efficiency also fell, from 64% to 55%. At the same time, 51% of respondents reported using AI to make a health decision without consulting a medical professional, and 62% use AI to understand symptoms before deciding whether to seek care. Researchers noted the decline is consistent with the natural technology hype cycle and expect trust to stabilise as the true use cases for AI in healthcare become clearer. (medicalxpress.com)

We’d love to hear your thoughts, so join the conversation by leaving a comment below:

Leave a comment

Stay tuned for more insights in the next edition of The Pulse.

Have a great day & see you in two weeks!

Your Hendrix Health Team

Hi Pulse Readers - this week, we’re diving into:

• expansion of AI-assisted hospital room technology across the US,

• what a large RCT tells us about AI in stroke care decision support,

• and how you can make the most out of Heidi’s functions when you’re speaking multiple languages during a consultation.

Subscribe now

2. Case Study – Your Fortnightly Practical

Video Source: Hendrix Health

Managing Multilingual Consults with Heidi

Case Presentation: Last time, Dr Harry used Heidi Evidence to review trusted sources and guide his approach to a clinical question. It is quick, reliable, and completely ad-free to get the answers he needs.

Today, he is seeing a patient whose mother speaks only Cantonese, while the daughter speaks English. While being fluent in both English and Cantonese, Dr Harry wants to communicate clearly with both patient and whānau, but without doubling his workload by manually translating explanations or writing separate documents.

He considers whether Heidi can support multilingual transcription and communication within the same session.

Approach: Changing Heidi’s input language settings to capture multilingual consults and using Ask Heidi’s translation capability generate patient-friendly explanations in multiple languages.

1. Set input languages before starting the consult
Adjust Heidi’s input settings to match the languages spoken in the room, for example English and Cantonese. This allows Heidi to process mixed-language conversations without breaking workflow or requiring separate documentation. Heidi can listen for up to 3 languages when transcribing. For example, English, Te Reo Māori, and Cantonese.

2. Conduct the consultation naturally
Allow the conversation to flow between you, the patient, and family members in their preferred languages. There is no need to force everything into English, as it reduces accuracy and rapport.

Heidi captures the consult and generates a structured note in English (or any output language you select) for clinical documentation.

3. Generate a patient-friendly explanation
After completing the consult, click the Ask Heidi bar at the bottom of your screen:

“Create a simple patient explanation of the diagnosis and management plan in English.”

This ensures clarity, especially for complex conditions like ADHD or mental health presentations.

4. Translate for the patient and whānau
Follow-up in Ask Heidi with: ”Translate this explanation into [language of your choice].”

Then, review the output before providing both versions of the patient explainer to the patient and their family.

Important: Do not rely on this workflow if you cannot verify the translation yourself. You remain responsible for accuracy and clinical safety in any case.

Outcomes: Dr Harry is now able to manage multilingual consults without slowing down or compromising communication quality. Instead of fragmented conversations and duplicated admin, he delivers clear, structured explanations in both languages within minutes. This improves patient understanding, supports whānau involvement, and reduces the risk of miscommunication.

Dr Harry maintains a single, efficient workflow while adapting to the diverse patient populations in Aotearoa New Zealand.

Disclaimer: Hendrix Health is the official New Zealand partner for Heidi Health.

3. The Pulse - Your Fortnightly Update

AI-Assisted Hospital Rooms Expand Across 25 Hospitals (WVU Health System)

WVU Health System has begun rolling out AI-assisted “intelligent hospital room” technology across all 25 hospitals in its network, covering more than 3,000 beds across West Virginia, Ohio, Pennsylvania, and Maryland. The rollout is delivered through a partnership with hellocare.ai, and is one of four new partnerships the AI company has announced with US health systems in 2026.

Image Source: Hellocare.ai

When a patient is admitted, bedside devices, video systems, and monitoring tools connect into a single virtual care platform that is integrated with the Epic EHR. The platform combines AI, secure video collaboration, real-time communication tools, and clinical workflow integration to support patient observation, fall prevention, remote consults, and communication between care teams, patients, and families.

Virtual care teams, including remote nurses, can then support bedside staff through virtual nursing workflows, while clinicians and specialists can also join care remotely through virtual rounding and telehealth.

Key Features:

• Virtual nursing support: Remote nurses support bedside teams through virtual nursing workflows and communication.

• AI-assisted patient monitoring: Continuous observation may help detect deterioration earlier and supports fall prevention.

• Integrated communication: Video, messaging, and EHR integration allow easier communication between care teams, patients, and families.

• Virtual rounding and telehealth: Specialists can review and contribute to care without being physically on-site.

• Large-scale rollout: Deployment across 25 hospitals and 3,000+ beds signals standardised infrastructure rather than pilot-stage experimentation

In some hospitals, additional features such as digital whiteboards, door signage, and patient infotainment systems are also being added.

Implications for the Health System and Clinicians:
This reflects a broader shift in how inpatient care is organised, separating physical presence from clinical contribution. Tasks that do not require hands-on care, such as monitoring, communication, and routine support activities, can now be delivered remotely.

For clinicians, the potential value is better communication, more support with monitoring, and less pressure on bedside teams. For health systems, the appeal is clear: improving patient safety and workflow efficiency without relying only on traditional staffing models that are vulnerable to shortages.

The rollout is large and the platform is clearly integrated into hospital workflows, but there is no published data on staff and patient outcomes. The real question is whether these benefits hold up in practice.

AI Stroke Decision Support: What a 21,000-Patient Cluster RCT Reveals

A large cluster-randomised clinical trial in The BMJ evaluated an AI-based clinical decision support system (CDSS) for acute ischaemic stroke across 77 hospitals in China, involving 21,603 patients admitted within seven days of symptom onset.

The system combined AI-assisted imaging analysis, classification of stroke cause, reminders for pending assessments, and evidence-based treatment recommendations during admission and at discharge.

Image Source: the BMJ

Key Findings:

• Fewer new vascular events at three months: The primary outcome, a composite of ischaemic stroke, haemorrhagic stroke, myocardial infarction, or vascular death, occurred in 2.9% of patients in CDSS hospitals versus 3.9% in usual care hospitals, a relative risk reduction of about 26%.

• Improved stroke care quality: The composite adherence score for evidence-based stroke care was higher in the intervention group, 91.4% versus 89.8%. The biggest gains were seen in secondary prevention and inpatient care measures, including dual antiplatelet therapy, anticoagulation for atrial fibrillation, dysphagia screening, and DVT prophylaxis, where baseline adherence was lower.

• Benefits persisted over 12 months: New vascular events remained significantly lower at six months, 3.4% versus 4.8%, and at 12 months, 4.0% versus 5.5%, driven primarily by reductions in recurrent ischaemic stroke.

• No clear difference in disability, mortality, or bleeding: Despite fewer recurrent vascular events, the trial did not find significant differences in disability, all-cause mortality, or moderate to severe bleeding, which suggests the system improved secondary prevention without increased safety risk.

Implications for Healthcare Systems:
AI-based decision support can improve stroke care quality and reduce recurrent vascular events when embedded directly into routine workflows, particularly in time-critical, protocol-driven pathways like stroke. A CDSS can help clinicians follow guidelines more consistently and reduce variation in care at scale. The caution is important: this study was conducted at the hospital level rather than individual patients, in a population where most strokes were relatively mild, and it did not assess cost-effectiveness or include thrombectomy decision-making. As such, further validation across different health systems and patient populations will be needed before widespread adoption.

Read the full study here.

4. Vitals – Quick Bytes

• New Zealand’s National Diabetes Roadmap Signals AI-Enabled Screening

  Health New Zealand has launched a National Diabetes Roadmap that includes exploring AI in diabetic retinopathy screening and piloting retinal photo screening in primary care settings. The strategy also aligns HbA1c thresholds with international standards to support earlier detection and intervention. Alongside workforce expansion and community-based care, this signals a shift toward more technology-enabled diabetes management at a national level. However, this remains policy direction rather than proven deployment. The takeaway is simple: AI is now part of the national plan, but impact will depend on whether these pilots translate into real-world reductions in complications.

• Studies on AI-Enabled Stethoscopes Suggest Better Detection, But Implementation Limits Impact

  Two recent studies examined whether AI-enabled stethoscopes can improve detection of cardiovascular disease in primary care. In a prospective study of 357 at-risk patients, AI-augmented digital auscultation substantially improved sensitivity for moderate-to-severe valvular heart disease compared with standard auscultation, detecting 12 previously undiagnosed cases versus 6 with routine care, though with lower specificity.

  Meanwhile, the larger TRICORDER cluster-randomised implementation trial across 205 UK primary care practices found no significant increase in population-level detection of heart failure, atrial fibrillation, or valvular heart disease after 12 months, largely because real-world uptake was inconsistent and use declined over time. Taken together, it is clear that the technology itself performs well. But to deliver real clinical impact also relies upon workflow integration, sustained clinician use, and access to follow-up testing and specialist care.

We’d love to hear your thoughts, so join the conversation by leaving a comment below:

Leave a comment

Stay tuned for more insights in the next edition of The Pulse.

Have a great day & see you in two weeks!

Your Hendrix Health Team

Hi Pulse Readers - this week, we’re diving into:

• how Patchwork Health’s AI rostering tool helps NHS hospitals reduce shift gaps and agency spending,

• AI-prediction of breast cancer risk after an all-clear mammogram,

• and how you can use Heidi Evidence to quickly review research and guidelines.

Subscribe now

2. Case Study – Your Fortnightly Practical

Video Source: Hendrix Health

Using Heidi to Find Evidence

Case Presentation: Dr Harry have been using Ask Heidi to turn his completed ADHD assessment notes into ad-hoc documents when he doesn’t have a template, and it’s been working well.

Today, he is about to see a new patient who describes a long history of inattention, disorganisation, and poor follow through dating back to school, but the presentation is not straightfoward. They are now struggling at work, sleeping poorly, feeling anxious, and drinking alcohol regularly.

Dr Harry is not sure whether this is uncomplicated adult ADHD or another condition presenting in a similar way. He does not want to rely on memory alone or waste time seraching across multiple sources while the patient is in the waiting room.

He wants a fast way to review quality evidence, sense-check the differential, and identify what else he should ask before making any decisions.

Approach: Use Heidi Evidence when the clinical picture is unclear and you want a faster, more efficient way to review trusted sources, including local guidelines and research studies.

1. Enter the clinical question in plain language
Start by typing the scenario into Heidi Evidence as you would naturally think about it in practice. For example:

“I’m about to see a 25M, long history of inattention and disorganisation now affecting work, some anxiety, poor sleep, regular EtOH use, ?ADHD ?something else, what should i think about patient is in waiting room”

You do not need to write a perfect academic search query. A clinical shorthand is usually enough for Heidi Evidence to return a useful starting point.

2. Review cite sources and key points from Heidi Evidence
Heidi Evidence can help explore alternative explanations and common overlaps, in this case anxiety disorder, depression, sleep disorders, substance use, autism spectrum, and medical contributors. This is especially useful in cases where the symptoms are non-specific and the differentials are not yet clear.

If you use Heidi Evidence to review evidence, for example by asking, “What is the latest research on psychedelics in mental health treatment?”, Heidi Evidence would often generate a summarised answer with key points at the top, followed by a more detailed response. Each point is linked to its source, so you can quickly see where the information is coming from and open the original article or guideline if you wish to.

3. Ask focused follow-up questions to guide the consult
Once the initial answer appears, you can narrow the query further by asking follow-up questions. You can also use it like Ask Heidi, or link the question to a consultation or patient.

You can also upload your own custom sources, so Heidi’s answers are tailored to your hospital or practice environment, rather than generic evidence and guidelines.

4. Use the evidence to support, not replace, clinical judgement
Heidi Evidence does not make diagnostic recommendations. Instead, it gives you a fast and efficient way to review evidence and guidelines related to the clinical context.

You still need to review the output critically and apply clinical judgement before making decisions on further management.

Outcomes: Using Heidi Evidence, Dr Harry can approach a more complex consultation in an evidence-informed way. He can quickly review research studies and local guidelines by asking questions. This helps him practise more safely, and make clinical decisions with greater confidence when the presentationis not straightforward.

Disclaimer: Hendrix Health is the official New Zealand partner for Heidi Health.

3. The Pulse - Your Fortnightly Update

AI Rostering Tool Rolls Out Across NHS Trusts to Cut Shift Gaps and Agency Spend

(a February update worth revisiting)

Patchwork Health, a doctor-led workforce management platform, has launched an AI-powered Preference-Based Rostering tool for NHS hospitals, designed to build clinician rotas that better match staff preferences while still meeting compliance and operational requirements. The tool is already being used at four NHS trusts across London, Yorkshire, and the South West of England.

Image Source: Patchwork Health

The system collects clinician shift preferences, alongside workforce rules, minimum staffing requirements, and service demand. Its AI algorithm then processes thousands of possible roster combinations to generate a flexible rota in minutes, distributing night shifts, weekend work, and longer shifts more fairly than traditional manual scheduling.

Once the rota is produced, managers spend less time making repeated manual changes after publication, while clinicians gain more control over their working patterns. This is intended to reduce rota-related frustration, cut shift gaps, and lower reliance on temporary agency staff, while keeping service delivery stable.

Key Features:

• Operational impact: In an early 10-week ward trial comparing the tool with a traditional roster-building process, Patchwork reported a 97% reduction in unfilled shifts.

• Cost reduction: Temporary staffing costs fell by 98%, from £18,000 to £400 over the same 10-week period.

• Preference-based design: The system is built to account for both positive and negative clinician shift preferences, with Patchwork’s modelling suggesting rosters can meet 98% of negative preferences at the point of creation.

• Established NHS footprint: Patchwork says it already works with over 200 healthcare organisations, is partnered with over half of NHS trusts and health boards across the UK, and has supported more than 120,000 healthcare professionals through its wider workforce platform.

Implications for the Health System and Clinicians:
This is one of the more practical healthcare AI rollout stories from recent weeks because it targets a real operational bottleneck. Rostering is a high-friction problem that affects clinician retention, burnout, staffing gaps, and agency spend.

The broader lesson is that some of the most useful AI in healthcare may come from workforce tools that make services easier to operate, not just diagnostic and clinical decision support. The obvious caution is that the strongest result so far come from early company-reported trials, so the real test will be whether these gains hold across full NHS rollouts at scale.

AI in Breast Screening: What the BRAIx Risk Score Means for Personalised Screening

A population cohort study in The Lancet Digital Health evaluated the BRAIx risk score, an AI-derived mammography-based tool that was repurposed from detection, to the prediction of both current and 4-year breast cancer risk.

The study used a training dataset of nearly 400,000 women, then tested the model in an independent cohort of over 96,000 women from BreastScreen Victoria, Australia with external validation in a Swedish screening population.

Image Source: The Lancet Digital Health

Key Findings:

• Strong intermediate-term risk prediction: The BRAIx risk score was a strong predictor of breast cancer diagnosed within 4 years after an all-clear screen, with consistent performance across both Australian and Swedish screening datasets.

• High-risk subgroup identified despite an initial all-clear: Among the top 2% of women with the highest BRAIx risk scores who were initially cleared at screening, 9.7% were diagnosed with breast cancer within 4 years, a risk level higher than that reported for BRCA1 and BRCA2 mutation carriers over the same timeframe.

• Outperformed traditional risk factors: The BRAIx risk score was a stronger predictor of 4-year breast cancer risk than age, family history, mammographic density, recall status, screening round, or country of birth. After accounting for BRAIx, mammographic density was no longer a significant predictor in the Australian test data.

• The score alone was as good as full risk models: Full multivariable models performed only marginally better than the BRAIx risk score alone, suggesting that traditional risk factors contribute little additional predictive value once the AI score is already accounted for.

Implications for Healthcare Systems:
For screening programmes, the practical implication is a shift toward more personalised, risk-based pathways, such as closer follow-up or supplemental imaging for women at highest intermediate-term risk, rather than a one-size-fits-all screening model. The caution, however, is important: this was still a risk-prediction study, not a prospective implementation trial, so any policy change will still need real-world validation, cost-effectiveness modelling, and careful attention to equity.

Read the full study here.

4. Vitals – Quick Bytes

• New Zealand explores AI Integration for Breast Screening

  Health New Zealand is exploring how AI can safely support BreastScreen Aotearoa, with current scoping extending beyond mammogram reading to breast density reporting, image quality support at acquisition, and breast cancer risk stratification. The move comes as screening demand rises, radiologist workforce shortages persist, the screening age extends to 74, and transition from largely paper-based, opt-in process to a digital-first, opt-out register through Te Puna. International evidence, including Sweden’s MASAI trial, suggests AI-supported screening may improve early cancer detection and reduce interval cancers. There is a clear opportunity for New Zealand, but the real challenge is implementation, ensuring any adoption is safe, equitable, and workable within the existing infrastructure. (healthcareitnews.com)

• UK Pilots AI and Robotic Bronchoscopy to Speed Lung Cancer Diagnosis

  The NHS has launched a pilot at Guy’s and St Thomas’ that combines Optellum’s AI risk stratification for low-dose CT lung scans with Intuitive’s Ion robotic bronchoscopy to identify and biopsy small, high-risk lung nodules, including lesions as small as 6 mm. The aim is to shorten diagnosis by replacing weeks of repeat scans and invasive procedures with a single 30-minute biopsy appointment as targeted lung screening expands across England. More than 300 robotic biopsy procedures have already been carried out, with 215 patients going on to receive cancer treatment. The main caution is that this remains a pilot, so impacts on waiting times, outcomes, cost-effectiveness, bias, and equity still need full evaluation before wider rollout.

We’d love to hear your thoughts, so join the conversation by leaving a comment below:

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Stay tuned for more insights in the next edition of The Pulse.

Have a great day & see you in two weeks!

Your Hendrix Health Team

Hi Pulse Readers - this week, we’re diving into:

• India’s AI-powered E-ICU model revolutionising critical care delivery,

• what 1.5 million Australian encounters reveal about AI virtual triage and ED demand shift,

• and how you can “ask Heidi to do anything” for you.

Subscribe now

2. Case Study – Your Fortnightly Practical

Image Source: Heidi Health

“Ask Heidi to Do Anything…”

Case Presentation: Last time, Dr Harry managed to build a dedicated ADHD assessment template to streamline his documentation. His consult notes became faster, more structured, and consistent across follow-ups.

Today, he met a young child whose presentation raised concerns, and he decided the child would benefit from a referral to a paediatrician for specialist input. However, he does not often write paediatric ADHD referral letters, and he has no pre-built template for this.

Dr Harry has already finished documenting the assessment with his ADHD template. But now he needs to draft a structured referral letter from scratch. He stays back an extra 30 minutes at the end of the day to do this.

He wonders whether Heidi can help with these kind of ad-hoc tasks – where no template exists and the structure is different from his standard notes.

Approach: Instead of writing from scratch or building a new template for a task you perform only occasionally, use Ask Heidi to repurpose the consult you have documented.

1. Finalise and review the consult note first
Complete and review your note as usual. Confirm accuracy of history, examination findings, medications, and impression.
Ask Heidi should work from an accurate, reviewed source document.

2. Ask Heidi to generate a specialist referral letter with a prompt
Simply type what you want to create into the Ask Heidi to do anything bar at the bottom of the screen, or tap the Ask Heidi button on mobile to open the text box and start typing.

Prompt example:

“Using this consult note, draft a referral letter to a paediatrician for ADHD assessment. Include relevant history, developmental details, family background, school concerns, previous interventions, current medications, risk assessment, and a clear clinical question. Write in formal New Zealand referral style.”

Note: A prompt is the instruction you give an artificial intelligence to tell it what to do. The clearer and more specific the instruction, the better the output.

3. Refine the output with follow up prompts in the same Ask Heidi chat box
Heidi analyses the existing consult note and context, and generates a structured referral letter in a new chat window. You may then tell Heidi to:

“Expand the developmental history and include psychosocial context.”

“Condense this to one page while retaining all clinically relevant information.”

This iterative prompting takes seconds, not another 20 minutes of rewriting. You will still need to review the final version for accuracy before copying and pasting it into your PMS or EHR referral system.

4. Apply the same workflow to other ad-hoc tasks
From the same consult note, you can also use Ask Heidi to:

• Draft a school support letter

• Generate a patient-friendly explanation of why referral is needed

• Summarise the case for a MDT discussion / conference

• Pull in medication information or add billing codes

Multiple potential outputs within seconds and no templates required.

Outcomes: By using Ask Heidi, Dr Harry no longer feels slowed down by work he does not have a template for or is less familiar with. Whether it is a paediatric referral, a school letter, or a one-off report, he can generate a refined output in minutes.

Instead of being disrupted by ad-hoc administrative tasks, he now has a flexible workflow that adapts to whatever the consult requires, without adding unnecessary time to the end of his day.

Disclaimer: Hendrix Health is the official New Zealand partner for Heidi Health.

3. The Pulse - Your Fortnightly Update

India Launches AI-Enabled E-ICU to Extend Private Specialist Critical Care to Public District Hospitals

An AI-enabled E-ICU Command Centre has been launched in Ghaziabad, India, linking a tertiary “base” command centre at a private hospital with the ICU at a public district hospital. The model is designed to strengthen critical care capacity by combining real-time, remote specialist oversight with AI-supported monitoring.

Image Source: IndiaMedToday

When a critically ill patient is admitted to the district hospital ICU, the system connects bedside monitors and the hospital information system to a centralised dashboard. AI-driven analytics then continuously assess patient data, support risk stratification, and generate early alerts for clinical deterioration. A central team of critical care specialists will be available 24/7 at the command centre to assist the on-ground medical teams with timely interventions and management.

This allows high-quality intensive care to be more accessible and affordable for economically weaker sections of society.

Key Features:

• Continuous remote oversight: Round-the-clock specialist supervision from a central command centre supports on-site ICU teams.

• Integrated clinical data: Bedside monitors and hospital systems feed into a unified dashboard for consolidated patient visibility.

• AI-supported early warning: Algorithms assist with deterioration detection and risk stratification, aiming to enable earlier intervention.

• District-level strengthening: The model focuses on expanding access to structured critical care support in non-tertiary settings.

Implications for the Health System and Clinicians:
For clinicians, the value proposition is the integration, earlier detection and access to immediate expert input when risk escalates. This results in more equitable healthcare delivery. For practice managers and health system leaders, this is infrastructure-level AI adoption. The technology is embedded within workflow, data, monitoring, escalation pathways, and governance, rather than deployed as an isolated tool.

The unanswered question remains outcomes. There is currently no published data on mortality, ICU length of stay, transfer rates, or adverse event reduction. The implementation architecture is clear, while the clinical impact still needs to be demonstrated.

AI Virtual Triage in Australia: What 1.5m Patient Encounters Show

A cross-sectional study in Mayo Clinic Proceedings: Digital Health evaluated Australia’s AI-based Virtual Triage and Care Referral (VTCR) system, deployed by Healthdirect Australia as a 24/7 digital front door to care.

Analysing 1,552,592 patient encounters between April 2023 and March 2025, the study examined whether the AI-driven symptom assessment improved alignment between patient care intent and recommended pathways. This includes self-care, GP, urgent care, virtual emergency, or in-person emergency services.

Image Source: Medical Republic

Key Findings:

• Care alignment: The propotion of patients appropriately selecting lower-acuity, non-urgent care mroe than doubled, from 21% to 53%.

• Uncertainty eliminated: Patients unsure what care to seek dropped from 43.2% to 0.2% – a 99.6% reduction.

• Emergency department diversion: Intent to attend in-person ED fell from 37% to 25% as virtual pathways became available.

• Virtual care uptake: Among those who initially planed to attend in-person ED, virtual emergency care rose from < 1% to 11%, with additional growth in telehealth and urgent care use.

• System-level impact: If results seen in Victoria were replicated nationally, modelling suggests around 2,400 unnecessary non-urgent visits and 19,000 unnecessary ED visits could be avoided annually.

Implications for Healthcare Systems:
AI virtual triage demonstrates potential for improving care alignment, reducing patient uncertainty, and redirecting low-acuity demand away from emergency departments toward telehealth and urgent care pathways. The early signals are strong, but validation of actual service utilisation, cost savings, and hard clinical outcomes will be needed before large-scale adoption.

Read the full review here.

4. Vitals – Quick Bytes

• NZ Government funds $4.6m for 10 AI-in-health research projects

  The Health Research Council has awarded $4.6 million across ten artificial intelligence research projects aimed at transforming healthcare in Aotearoa. Funded initiatives span across AI-assisted stroke and radiology imaging, heart failure management tools, digital pathology for gastrointestinal cancers, postoperative monitoring, primary care diagnostic pilots, and ethical frameworks for responsible AI use. All projects are scheduled to run through to completion between late-2026 and late-2027. This investment reflects continued national focus on evaluating both technical accuracy and implementation, safety, and equity of AI within New Zealand’s health system. (hrc.govt.nz)

• PRIMARY-AI: a propsed framework for judging whether AI helps or harms primary care

  A Nature Medicine correspondence argues that AI is already being deployed at scale in primary care, but most evaluation still focuses on technical performance metrics rather than outcomes that actually matters in general practice, such as continuity, coordination, accessibility, and people-centered care. The authors propose PRIMARY-AI, an outcomes-based standards framework with a three-tier approach: safety and fairness, real-world effectiveness and reliability, and primary care-specific attributes including people-centeredness, coordination, and continuity. They plan to develop validated outcome sets and minimum evidence thresholds via a multi-stakeholder Delphi method beginning in 2026, with final standards targeted for October 2026.

We’d love to hear your thoughts, so join the conversation by leaving a comment below:

Leave a comment

Stay tuned for more insights in the next edition of The Pulse.

Have a great day & see you in two weeks!

Your Hendrix Health Team

1. Triage – Your Fortnightly Rundown

Hey Pulse Readers, we’re back! And we will be in your inbox every second Wednesday from now on. Let’s dive right into it:

• promising results in AI physiotherapy clinic with expansion across the UK,

• what 11 randomised trials found about the impact of AI in cardiology clinical outcomes and operations,

• and how you can get Heidi to write notes that are more “like you”.

Subscribe now

2. Case Study – Your Fortnightly Practical

Image Source: Heidi Health

Designing Documentation That Matches Complex Consults

Case Presentation: Harry is a busy GP in Aotearoa who recently started offering adult ADHD assessments after the 1 February 2026 rule change by the New Zealand Government.

These appointments are long, often over 60 minutes and across multiple visits. He found that his standard GP note templates (SOAP style, short problem lists) do not fit the structure of an ADHD assessment or the f/u plan.

He is stuck rewriting the same sections every time and has to stay behind after hours to finish his notes.

Approach: Build a fit-for-purpose ADHD assessment template by uploading a de-identified example document to Heidi, then iterating until the output matches how you want to document.

1. Pick a “gold standard” example note
Choose one ADHD assessment note you are happy with (3^rd party or self-drafted), focusing on headings and structure (not patient specifics). Aim for the exact output style you want to reuse as your baseline structure.

2. De-identify properly, do not be sloppy
Replace names, NHI, DOB, address, and other unique identifiers with placeholders (for example, [PATIENT], [DOB], [ADDRESS], [DATE]).

3. Upload and create a template in Heidi
Use Heidi’s template creation workflow to upload your de-identified note and have Heidi generate a “Adult ADHD assessment” template according to your preferred structure and wording. Heidi supports building templates through conversation and adapting from uploaded existing templates. Alternatively, reach out to the Hendrix team, and they can do this for you.

4. Test and refine
Run a dummy case through the template, check the output, then adjust the template instructions until it reliably produces the level of detail you need and you have to make as little edits as possible before copying and pasting into your PMS or EHR. You will still need to review the notes for accuracy in any case.

Save and favourite it for fast access.

Outcomes: With a dedicated ADHD template built from his own documentation style, Harry stops re-writing the assessment from scratch each time. His consult notes become consistent across clinicians, easier to review at follow-up, and super quick to complete, which matters when assessments routinely take > one hour.

Instead of fighting his usual GP templates, he now has a repeatable workflow that fits the new reality of ADHD care in primary care.

Disclaimer: Hendrix Health is the official New Zealand partner for Heidi Health.

3. The Pulse - Your Fortnightly Update

AI Physiotherapy Clinic Now Expanding Across 11 NHS Regions After Pilot Programmes

Flok Health, an AI powered physiotherapy clinic, has expanded across 11 NHS regions to provide patients with same-day video appointments for back pain via a smartphone app. This follows large-scale pilots in Scotland and England in early 2025, and the rollout now covers more than 2.4 million patients across England.

Image Source: Flok Health

The service begins with a mobile phone video assessment, with the AI system evaluating patient symptoms. If approved, they receive weekly video appointments with the AI physiotherapist, available at any time. Patients can also send voice messages to the clinical team and receive responses during working hours.

Those with more complex symptoms have a further telehealth assessment with a clinician before digital treatment; or otherwise, referral to a more appropriate NHS service. This keeps clinician time focused on higher complexity care.

Key Features:

• Waiting list impact: In one of the pilots, waiting lists for back pain were cut by more than half, and waits for all MSK conditions fell by 44 percent in under 10 weeks.

• End-to-end pathway: The AI physiotherapist manages patients from initial assessment through treatment and discharge, recording interactions and outcomes into the patient’s EHR.

• Regulatory readiness: Class IIa medical device approval under EU regulations supports autonomous delivery of diagnostic decisions and treatment pathways without requiring clinician input.

• Patient reported improvement: Average MSK-HQ score improvement of 6.16 points, with anything above 5 considered to be clinically significant.

Implications for the Health System and Clinicians:
For the UK, this is a clear signal that commissioners will back regulated, integrated digital pathways where the operational case is strong. For ANZ services watching from a distance, the useful lesson is the combination of regulatory clearance, and measurable waiting list impact.

In the long term, the real test will be sustaining outcomes across diverse populations, maintaining clinical oversight, and ensuring equitable access for patients who might not engage well with smartphone-delivered care.

AI in Cardiovascular Care: What 11 Randomised Trials Tell Us

A new systematic review in Journal of the American College of Cardiology examined randomised controlled trials of the application of AI in cardiovascular care. It aims to investigate AI’s impact on cardiology patient outcomes and health systems.

Image Source: Journal of the American College of Cardiology (JACC)

Key Findings:

• ECG: > 50% of the trials focused on ECG-based AI, mainly to screen and flag higher-risk patients earlier, prompting early detection or escalation. One trial showed reduced 90-day mortality, but another trial showed worse outcomes. Overall, modest clinical benefits and often seen in high-risk subgroups.

• Computed Tomography: CT-based AI tools were used to support coronary risk assessment and procedural planning. It helped reduce unnecessary downstream invasive testing, and promoted consistent preventive care (e.g. statin use) when actionable findings are identified.

• Echocardiography: Results were mixed. For routine echo workflows (e.g. EF assessment), AI performed as well as sonographers and helped save time, but performance was not strong enough in specialised context such as stress echo for coronary disease detection.

• Evidence and reporting gaps: Only 11 out of 930 RCTs (2021 to 2024), met inclusion criteria. None of which reported patient-reported outcomes (e.g. quality of life, symptoms) and only 5 RCTs declared adherence to AI-specific reporting standards (CONSORT AI or SPIRIT AI). Many studies had moderate to high risk of bias linked to industry funding or financial interests.

Implications for Healthcare Systems:
AI in cardiology shows promise for screening, early detection, standardised interpretation, as well as operational / workflow efficiency. More high-quality evidence is needed to validate AI’s effectiveness in hard clinical outcomes (e.g. treatment response rates, patient-reported outcomes) before widespread clinical reliance and scaling.

Read the full review here.

4. Vitals – Quick Bytes

• AI in ED sepsis, early alerts and smarter diagnostics, but limited evidence

  A narrative review outlines how AI could support sepsis care in the emergency department, from early warning alerts (e.g. TREWS) to improving diagnostic work ups such as targeting blood cultures and combining biomarkers with routine data (for example Sepsis ImmunoScore). The main caution is that most sepsis AI has only been validated in observational studies, with limited randomised evidence. The incomplete ED data capture and generalisability also remain challenges. The takeaway is simple: promising tools, but they still need rigorous trials and careful workflow integration before wide rollout.

• US-based Optain Health acquires EyePACS to scale AI retinal screening in primary care

  Optain Health is combining their AI-powered robotic retinal imaging with EyePACS’ teleophthalmology network, expanding retinal screening in primary care and community clinics. A portable, robotic camera captures retinal images at the point of care to detect diabetic retinopathy, glaucoma, and age-related macular degeneration. Optain is also positioning this platform for “oculomics”, using signal from the eye to inform broader cardiometabolic risk assessment. The combined platform’s specialist multi-condition grading, EHR integration and analytics helps support population health programmes. (fiercehealth.com)

We’d love to hear your thoughts, so join the conversation by leaving a comment below:

Leave a comment

Stay tuned for more insights in next week's edition of The Pulse.

Have a great day & see you in two weeks!

Your Hendrix Health Team

1. Triage – Your Weekly Rundown

Hi Pulse Readers - this week, we’re focusing on:

• how AI is transforming physiotherapy care in Scotland with NHS Lothian’s innovative pilot,

• what 86 clinical trials reveal about the impact of AI in healthcare delivery,

• and how you leverage AI tools like ChatGPT to achieve your New Year’s resolutions.

Subscribe now

2. Case Study – Your Weekly Practical

Image Source: generated with Recraft.ai

Achieve your Practice’s New Year’s Resolutions with AI

Case Presentation: New Year’s resolutions can drive personal and professional growth, but busy schedules in healthcare make it difficult to stay on track. Whether you want to improve clinic efficiency, foster team well-being, or enhance work-life balance, LLM tools like ChatGPT or Claude can act as your planning assistant to help you take actionable steps.

Approach: Prompt an AI tool to break your resolutions into manageable tasks, optimise time management, and ensure steady progress throughout the year.

1. Set Professional and Personal Goals
Prompt Example:
"I am a [GP, practice manager, nurse, etc] at a busy medical centre in [country]. Help me set New Year’s resolutions for streamlining clinic workflows and improving staff morale. Also, suggest a goal to maintain a better work-life balance. I would like to especially improve my [fitness] [sleep] [stress management] [etc] this year."

Type “Regenerate” and refine as needed to create additional options.

2. Develop Actionable Plans for Practice Improvements
Prompt Example:
"I want to improve patient wait times and reduce admin workload. Create a step-by-step plan that excludes [technology upgrades, etc], but includes [staff training, etc]."

3. Build a Well-Being Strategy for the Team
Prompt Example:
"Suggest a weekly wellness program for my team, including short mindfulness sessions, healthy snack options, and team-building activities."

4. Track Key Metrics and Celebrate Achievements
Prompt Example:
"Create a system for tracking staff engagement levels. Suggest ways to recognise and celebrate milestones with the team."

5. Manage Time Effectively
Prompt Example:
"Provide tips for managing my time as a [GP, practice manager, nurse practitioner], including strategies for delegation and maintaining focus on high-priority tasks."

Outcomes: By integrating AI into your New Year’s resolution planning, you can balance personal well-being with professional goals, enhance clinic operations, and cultivate a supportive team environment. Make 2025 a year of growth and achievement for you and your practice!

3. The Pulse - Your Weekly Update

AI-Powered Physiotherapy Clinic Now Available in Scotland

Image Source: Flok Health

NHS Lothian has launched a large-scale pilot of an AI-powered physiotherapy clinic designed to provide personalised care to nearly one million patients across the region.

Based on video footage of human physiotherapists, the system replicates a structured video-call experience, offering rapid access to musculoskeletal (MSK) care.

Key Features:

• CQC Approval: The solution is the first digital MSK clinic to be approved by the Care Quality Commission in the UK.

• Patient Feedback: During earlier trials, 100% of patients rated their experience as equal to or better than with a human physiotherapist, with 86% reporting symptom improvement.

• Streamlined Access: Patients can self-refer through NHS Lothian’s existing portals or be referred by participating GPs for same-day digital appointments.

• Data Integration: The service links seamlessly with NHS systems, ensuring coordinated care and robust clinical and cost outcomes analysis.

Implications for the Health System and Clinicians:
This pilot highlights the potential of AI to address treatment delays and improve care accessibility. It also demonstrates a shift towards integrating digital solutions into routine clinical workflows. While promising, challenges such as ensuring long-term patient engagement, clinical oversight, and equitable access remain critical to address.

With plans to expand the service to other MSK conditions, solutions like Flok Health could redefine how physiotherapy is delivered at scale.

Evaluating AI’s Impact: What 86 Clinical Trials Reveal

Image Source: The Lancet Digital Health

A comprehensive review in The Lancet Digital Health analysed 86 randomised controlled trials (RCTs) assessing AI’s role in clinical practice, providing insights into its potential and limitations.

Key Findings:

• Care Management Improvements: AI systems for insulin dosing and hypotension monitoring enhanced patient outcomes by increasing the time patients stayed within target glucose and blood pressure ranges. In oncology, AI-assisted radiation therapy and prostate brachytherapy showed reductions in acute care rates and tumour volumes.

• Behavioural and Symptom Impact: Trials revealed AI-driven diabetic retinopathy risk predictions boosted patient referral adherence, while nociception monitoring systems decreased postoperative pain scores.

• Clinical Decision Support: AI predictions on cancer mortality prompted more serious illness discussions between oncologists and patients. However, other trials, such as AI tools for atrial fibrillation stroke risk, showed limited impact on prescribing behaviour.

Implications for Healthcare Systems:
While AI shows promise in improving care management, enhancing patient adherence, and assisting in clinical decision-making, the variability in outcomes highlights the need for more robust, multicenter trials. To ensure effective integration, future research should prioritise patient-centred outcomes, equitable access, and transparent reporting.

Read the full review here.

4. Vitals – Quick Bytes

• AI Advances Mammography Screening in Germany

  Germany’s PRAIM study reveals that AI integrated into mammography screenings has improved cancer detection rates by 17.6%. Using AI for triage and safety net alerts streamlined workflows, reduced radiologist workload by 56.7%, and enhanced positive predictive values for recalls and biopsies. This large-scale implementation, over 460,000 screenings, underscores AI's potential to improve early cancer detection while maintaining accuracy and efficiency.

  Explore the study here for more on its groundbreaking findings.

• CHAI Releases Open-Source “Nutrition Label” for Healthcare AI

  The Coalition for Health AI (CHAI) has launched an open-access model card on GitHub, offering a comprehensive "nutrition label" for healthcare AI algorithms. Designed to improve transparency, the card details key information like training data, bias mitigation strategies, and ongoing maintenance, enabling healthcare providers to assess AI models effectively. (fiercehealth.com)

We’d love to hear your thoughts, so join the conversation by leaving a comment below:

Leave a comment

Stay tuned for more insights in next week's edition of The Pulse.

Have a great day & see you next week!

Your Hendrix Health Team

1. Triage – Your Weekly Rundown

Ho ho ho Pulse Readers - this week, we’re highlighting:

• China’s virtual hospital with self-evolving AI doctors,

• how OpenAI’s Sora is opening new doors for visual patient education,

• and how you can use AI to take the stress out of planning your next medical centre team event.

Subscribe now

2. Case Study – Your Weekly Practical

Image Source: generated with Recraft.ai

Planning Your Next Event with ChatGPT

Case Presentation: Organising a festive Christmas party for your medical centre can be challenging, especially with busy schedules. Tools such as ChatGPT can help simplify the process, offering creative ideas for food, activities, and decor while considering budget and time constraints.

Approach: Use ChatGPT as your planning assistant by providing detailed prompts to generate ideas tailored to your team’s preferences and resources.

1. Define Your Theme and Vision
Prompt Example:
"I’m planning a Christmas party for our medical centre staff. We want a fun and festive theme suitable for doctors, nurses, and admin staff. Please suggest 3 creative themes with decor, outfit ideas, and activities."

2. Create a Festive Menu
Prompt Example:
"Plan a Christmas party menu for 30 people, including finger foods, a main dish, desserts, and non-alcoholic drinks. We have a mix of vegetarians and meat eaters. Include easy-to-prepare and budget-friendly options."

3. Plan Activities and Entertainment
Prompt Example:
"Suggest engaging Christmas party activities for a medical centre team. We want team-building games, a Secret Santa gift exchange, and some fun prizes. Include instructions for at least one activity."

4. Set a Budget and Shopping List
Prompt Example:
"Help me create a budget for a Christmas party for 30 people. We need decor, food, drinks, and small gifts. Break it down into categories, and provide a detailed shopping list."

We hope these tips help to get the creative juices flowing… try it for your next event!

3. The Pulse - Your weekly Update

China’s Self-Evolving AI Virtual Hospital

Image Source: Tsinghua University’s Institute for AI Industry Research

Tsinghua University’s Agent Hospital, a self-evolving virtual healthcare platform, is slated for public release in 2025. Utilizing AI-generated doctors, nurses, and patients, the system simulates real-world clinical workflows, offering advancements in training of AI agents for clinical applications.

Key Features:

• MedAgent-Zero Framework: Allows AI doctors to learn continuously from interactions, literature, and outcomes.

• Simulated environment covers diagnostics, treatments, and follow-ups.

• AI doctors achieved up to 93.06% accuracy in tasks like diagnosis of respiratory conditions

Implications:
By simulating tens of thousands of cases in days without the need for human-labeled data, the system accelerates the proficiency of AI agents to carry out clinical tasks such as triaging, diagnosing and treating patients with increasing accuracy.

While those agents could help to alleviate clinician shortages and free clinician’s time for complex tasks, it raises ethical concerns about accountability in patient care, and broader questions about the respective roles of doctors and AI agents in the future.

Still a long way to go, but definitely one to watch.

For more insights, check the full study here.

OpenAI Unveils Sora: What are the implications for healthcare?

Image Source: generated with Sora, via Sora.com

OpenAI has launched Sora, an AI model that generates videos from text prompts. This innovation shows potential to significantly enhance healthcare communication and education by enabling personalised, visual content creation.

Key Features:

• Dynamic Video Creation: Sora can generate videos up to 20 seconds long based on user-provided text prompts.

• Versatile Applications: Tools like "Remix," "Loop," and "Blend" allow users to customize and enhance video content.

• Accessible: Available to ChatGPT Plus and Pro subscribers, Sora democratizes video production with minimal technical requirements.

Implications for Patient Care:
Sora could redefine how healthcare providers communicate with patients:

• Personalised Patient Instructions: Clinicians could create tailored videos explaining post-operative care, medication usage, or chronic disease management.

• Physiotherapy Demonstrations: Visual guides for physical therapy exercises could improve patient adherence and outcomes.

• Health Education: Public health campaigns and condition-specific education could be delivered in engaging, accessible video formats.

Challenges and Considerations:
While promising, Sora's applications in healthcare are not without challenges:

• Quality and Accuracy: Ensuring videos are clinically accurate and free from visual inconsistencies is still currently challenging, especially with the early maturity state of the technology. Also, videos cannot be generated with accompanying audio as of today.

• Ethical Use: Safeguards will be critical to prevent misuse, such as generating misleading or harmful content.

• Integration with Workflows: Providers will need efficient processes to incorporate text-to-video tools into clinical practices.

As Sora evolves, its potential to enhance patient education and support healthcare professionals makes it a tool worth watching in the AI healthcare landscape.

We also expect to see other solutions develop in the text-to-video space that are more suited to narrower healthcare use cases.

4. Vitals – Quick Bytes

• Scientists Propose Global Collaboration on AI Virtual Cell
  Researchers from Stanford, Genentech, and the Chan-Zuckerberg Initiative propose a global effort to create an AI-powered virtual human cell. This model could simulate cellular behavior, accelerate drug discovery, and advance personalised medicine. Achieving it will require massive datasets and unprecedented collaboration across disciplines. (stanford.edu)

• Neuralink Launches Study to Connect Brain Implants with Robotic Arms
  Neuralink has announced the CONVOY study to test its N1 brain-computer interface implant for controlling assistive robotic arms. The implant interprets neural activity, enabling individuals with quadriplegia to operate devices or robotic limbs with their thoughts. This advancement marks a step toward integrating robotics with neural technology for restoring mobility and independence. (mobihealthnews.com)

• AI Surpasses Experts in Neuroscience Predictions

  A new study shows large language models (LLMs) outperform human neuroscientists in predicting experimental outcomes, achieving 81.4% accuracy compared to 63.4% by experts. By analysing patterns in vast scientific datasets, models like BrainGPT can uncover hidden insights to guide future studies and streamline discovery processes.(nature.com)

We’d love to hear your thoughts, so joint the conversation by leaving a comment below:

Leave a comment

We are taking a break to recharge the batteries over the holiday period and will be back with your next issue of The Pulse the week of January 6th.

Wishing you all a relaxing festive season & see you in the New Year!

Your Hendrix Health Team

1. Triage – Your Weekly Rundown

Hi Pulse Readers - this week, we’re highlighting:

• how TrialGPT is streamlining patient recruitment for clinical trials,

• how a Microsoft open-source model is democratizing medical imaging analysis,

• and how you can quickly create impactful public health posters with AI.

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2. Case Study – Your Weekly Practical

Image Source: generated with Recraft.ai

Creating Public Health Posters with Recraft.ai

Case Presentation: Public health campaigns require engaging visuals to effectively communicate messages. Designing these posters can be time-intensive and resource-heavy.

Approach: Recraft.ai simplifies the process by using AI to create professional-grade public health posters tailored to your target audience. The platform, which recently shot to the top of the text-to-image leaderboard, provides a generous free tier and enables rapid poster creation with minimal effort.

How to Use Recraft.ai for Public Health Posters

1. Set Up Your Project:
   Log in to Recraft.ai and select “New project” and then use the “Frame” object to select the format for your poster (1:1, 4:3, etc).

2. Input Your Key Message:
   Use the text prompt feature to describe your poster’s goal, e.g., “Create a poster encouraging flu vaccination for young adults.” You can use the text tool to add specific text you would like to include in the frame, as well as any reference images.

3. Customize the Design:
   Once generated, edit colors, fonts, and layouts to align with your branding. Add images, icons, or QR codes for additional information or links to resources.

4. Generate Variations:
   Let Recraft.ai generate multiple design options. Review and refine the one that best fits your needs. You can also choose different styles such as hand-drawn or scientific styles.

5. Download and Share:
   Export the finished poster in high resolution for printing or digital distribution.

Outcomes: Recraft.ai empowers practice managers and clinicians to create visually appealing, impactful posters quickly and cost-effectively, ensuring critical messages reach their intended audience.

3. The Pulse - Your weekly Update

Matching Patients to Clinical Trials Using TrialGPT

Image Source: Nature Communications

Recent advancements in artificial intelligence have transformed patient recruitment for clinical trials, a traditionally labor-intensive process. Researchers have developed TrialGPT, a large language model (LLM)-driven framework that matches patients to clinical trials with remarkable efficiency and accuracy.

Key Features:

• Three-Step Matching: TrialGPT employs retrieval, matching, and ranking to identify trials suited to individual patients, recalling over 90% of relevant trials with less than 6% of the initial collection analyzed.

• Enhanced Accuracy: With an accuracy rate of 87.3% in predicting patient eligibility at a criterion level, TrialGPT delivers results comparable to human experts while offering detailed explanations and sentence-level evidence.

• Efficiency Gains: A pilot study demonstrated a 42.6% reduction in screening time, significantly accelerating patient-trial matching processes.

Impact:
TrialGPT has the potential to revolutionise clinical trial recruitment by reducing human errors, expediting trial matches, and enabling greater scalability.

For more insights, check the full study here.

Microsoft Unveils Open-Source Model for Medical Imaging Analysis

Image Source: Microsoft

Microsoft has unveiled BiomedParse, a GPT-4-powered AI foundation model designed to transform biomedical image analysis. This new tool unifies object recognition, detection, and segmentation, enabling clinicians to analyse medical imagery - from tumors to COVID-19 infections - using simple text prompts.

Key Features:

• Unified Framework: Unlike previous tools that separately perform object detection and segmentation, BiomedParse integrates these tasks for a holistic image analysis experience.

• Natural Language Input: Users can specify objects for analysis using simple text prompts, eliminating the need for manual bounding boxes.

• Data-Driven Precision: The model was trained on BiomedParseData, a dataset of over 6 million image-mask-description sets covering 64 object types and 82 subtypes.

Implications:
BiomedParse outperformed previous best methods like MedSAM and SAM in all evaluated metrics, particularly excelling in segmenting irregularly shaped objects, a common challenge in medical imaging. Its ability to identify and segment objects without additional user input reduces errors and increases efficiency.

Available under an Apache 2.0 license and deployable on Azure AI, BiomedParse is positioned to democratize access to advanced biomedical imaging tools.

4. Vitals – Quick Bytes

• Study Highlights Bias in AI Driven by Racial Differences in Lab Testing
  A recent study from the University of Michigan highlights how racial disparities in laboratory testing could introduce bias into AI models used in healthcare. The research found that African-american patients were significantly less likely than Caucasian patients to receive certain tests, such as complete blood counts or metabolic panels, during emergency department visits. This discrepancy risks skewing AI models trained on such data, potentially amplifying existing healthcare inequities. Read the study here.

• Hippocratic AI’s Patent Advances Safety in Healthcare LLMs
  Nvidia-backed start up Hippocratic AI has patented Polaris, a safety-focused framework that combines multiple AI models to verify outputs in healthcare applications. Its architecture, involving engines for tasks like medication reconciliation and chronic care management, aims to prevent errors while assisting with pre-op, discharge, and billing processes. With accuracy exceeding 99%, Polaris reflects a growing trend in safety-driven AI in healthcare (business wire.com)

• AI "Virtual Sitter" for Patient Monitoring
  Teladoc Health has launched Virtual Sitter, an AI-driven tool designed to detect and alert hospital staff to patient movements that may lead to falls. Using computer vision, the system monitors movements, identifies risky actions, and notifies remote staff for intervention. This system aims to improve patient safety and optimize staff resources in inpatient settings. (fiercehealthcare.com)

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PS: Thanks for your patience, we had a family emergency last week that prevented us from publishing our weekly newsletter, back to our usual weekly schedule now.

Have a great day & see you next week!

Your Hendrix Health Team

1. Triage – Your Weekly Rundown

Hi Pulse Readers - this week, we’re diving into:

• the heated debate over a study claiming AI outperforms doctors in diagnostic reasoning,

• a groundbreaking “ChatGPT for DNA” unlocking new possibilities in genomics,

• and we show you how AI-powered voice simulations can help clinicians hone their communication skills.

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2. Case Study – Your Weekly Practical

Image Source: generated with Recraft.ai

Practising Communication Skills with AI

Effective communication is a vital skill for healthcare professionals. Traditional training methods, such as role-playing and simulations, require significant time and resources.

Approach: AI tools like ChatGPT with voice mode offer an innovative solution for communication practice by simulating realistic patient interactions. Using simple prompts, healthcare professionals can engage in virtual role-play to refine their bedside manner and communication techniques.

Watch a video tutorial here, and/or follow the step by step below.

How to Use AI for Communication Practice:

1. Access Voice Mode: Open advanced voice mode in an AI tool like ChatGPT (premium feature - available as a mobile app or web application).

2. Set the Scenario: Begin with a clear prompt, such as: "Act as a patient. I am a physician practicing to deliver bad news regarding a serious diagnosis. Respond as a concerned patient."

3. Conduct the Simulation: Engage in the conversation, practising your communication skills. For example, deliver test results with empathy and offer supportive follow-up questions, such as: "How are you feeling about this news?"

4. Request Feedback: Ask the AI to provide feedback on your tone, clarity, or approach to ensure you improve over time.

Outcomes: Practising communication with AI allows clinicians to simulate a variety of scenarios, from delivering bad news to de-escalating potentially aggressive situations, in a low-pressure environment. This approach is flexible, scalable, and accessible, helping professionals build confidence and refine critical soft skills.

3. The Pulse - Your weekly Update

AI Outperforms Doctors? A Closer Look at a Controversial Study

Image Source: Dr Terence Tan LinkedIn via AI Health Uncut

A recent study published in JAMA claimed that a large language model (LLM) outperformed clinicians in diagnostic reasoning tasks, which was then later sensationalised in a NYT article and amplified widely, including by Elon Musk. Many experts have raised concerns about the study's validity due to its questionable methodology and small sample size, as explained in this excellent deep dive by Sergei Polevikov.

• The Study's Claim: The LLM achieved a 92% accuracy in diagnostic reasoning across six hand-picked medical cases, compared to 74% for physicians and 76% for physicians using the LLM as a tool.

• The Issues: Critics argue the study’s reliance on only six medical case histories from an original dataset of 105 undermines its credibility. Hand-picking curated cases and excluding 94% of the sample without transparency raises questions about bias and real-world applicability.

• Real-World Implications: While the study showcases the potential of AI in assisting clinicians, its limitations—lack of training for physicians on using the LLM, reliance on structured vignettes rather than real patient interactions, and minimal sample size—make its findings difficult to generalize.

Implications: This study highlights the need for rigorous standards in AI research, particularly in medicine. Oversimplified claims, amplified by media outlets, risk creating unrealistic expectations for AI's capabilities in healthcare, as well as frustrations in the medical workforce.

Cracking the Code: Evo’s Role in the Future of Genomics

Image Source: Science.Org

Scientists at the Arc Institute in Palo Alto and the Stanford Department of Bioengineering have unveiled Evo, a revolutionary AI model dubbed “ChatGPT for DNA.” Trained on 2.7 million microbial genomes, Evo excels at both interpreting and generating genetic sequences, opening new frontiers in genome research and engineering.

• What It Does: Evo simultaneously analyzes DNA, RNA, and protein sequences, enabling zero-shot function prediction and generating realistic genome-length sequences exceeding 1 million base pairs. It has already designed functional genetic editing tools and accurately predicted DNA changes' effects on bacterial fitness.

• Safety: Researchers intentionally excluded human-affecting viral genomes from Evo's training dataset to mitigate potential risks.

• Applications: Evo could drastically accelerate research, design proteins for drug development, predict disease-causing mutations, and provide unprecedented insights into biological systems.

Implications: Evo’s potential is groundbreaking, but its ability to “write genetic code” with ease raises significant ethical and safety questions. Striking a balance between innovation and responsible use will be crucial as this technology advances.

4. Vitals – Quick Bytes

• AI Journaling for Mental Health: AI-powered journaling tools like Mindsera and Rosebud are transforming the journaling experience by providing personalised prompts, emotional insights, and detailed feedback. These tools aim to enhance emotional processing and self-awareness, making journaling more accessible and effective for users coping with stress, grief, or chronic illness. While not a substitute for professional care, AI journaling offers a supplemental tool for maintaining mental well-being.
  (popsugar.com)

• Personalized Stroke Treatment with AI: The TRUSTroke project at Vall d’Hebron Hospital in Spain uses AI to analyse medical records and patient data, providing tailored treatment plans to optimise stroke recovery and prevent complications. (ft.com)

We’d love to hear your thoughts, so joint the conversation by leaving a comment below:

Leave a comment

Stay tuned for more insights in next week's edition of The Pulse.

Have a great day & see you next week!

Your Hendrix Health Team

1. Triage – Your Weekly Rundown

Hi Pulse Readers - this week, we’re diving into:

• wearable AI cameras that can help to prevent medication errors,

• DaVinci robots mastering surgical skills through video training,

• and we explore practical ways to use AI tools like ChatGPT for mind-mapping, empowering clinicians to explore and dive deeper into complex topics.

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2. Case Study – Your Weekly Practical

Image Source: ChatGPT.com

Case Presentation: A mind map can be useful tool to get a quick overview of a topic area, and provides a good starting point to identify specific areas of study interest. It can also be helpful as a starting point for planning training sessions for RMOs and other team members.

Approach: Large Language Models (LLMs) can be used to quickly familiarize yourself with concepts that you want to learn more about.

How to Use LLMs for Mind Mapping:

1. Browse to an LLM tool: such as ChatGPT, Perplexity (no sign in required) or Claude (requires sign in) and find the prompt window to start creating your mind-mapping prompt.

2. Begin your prompt with a role: e.g. “Act as an experienced medical educator in <country> “, or “Act as a master builder with specific experience in home renovations and <country> building regulations” are just some of the options.

3. Define the topic: Include “Create a mind map for <topic>” in your prompt.

4. Format the output: Useful additions to your prompt include “list all the main branches and sub-branches”, “add a short description”, “use a numbered list format”, “output as a table”, and so on.

5. Review: Review the generated output, as LLM tools can provide inaccurate information.

6. Diver Deeper: Once happy, you can dive deeper into any areas of interest by writing another prompt such as “I have X hours a week to study. Create a lesson plan for a X week course to help me understand <all concepts/a specific concept> and include links to specific <videos/ journal articles> that help illustrate the concept”; or “Create an outline for a 5 minute refresher training plan for a resident medical officer”; or any other question you may have.

Outcomes: Using LLMs, we can quickly familiarize ourselves with an area of interest. Try it today and let us know your results in the comments below.

3. The Pulse - Your weekly Update

AI Wearable Cameras Prevent Medication Errors

Image Source: NPJ Digital Magazine via nature.com

A groundbreaking study published in npj Digital Medicine highlights the potential of wearable AI-powered camera systems to prevent medication errors. Developed using head-mounted cameras worn by anesthesiologists, this system employs deep learning to detect and classify drug labels on syringes and vials in real time, identifying vial-swap errors before medications reach patients.

• How It Works: The camera captures drug preparation events, streams video to an edge server, and uses AI to compare syringe and vial labels for mismatches.

• Performance: In trials across 17 operating rooms, the system achieved 99.6% sensitivity and 98.8% specificity in detecting errors, showcasing its potential to act as a secondary safety measure.

• Applications: Designed primarily for anesthesiology, this technology could be adapted for broader use in emergency departments, ICUs, and routine care settings to reduce preventable drug-related harm.

Implications: This technology showcases potential to be a significant step in reducing medication errors, with currently up to 12% of these errors resulting in serious harm or death¹. Estimate range from 140,000 up to 440,000 deaths annually in the United States are due to medical errors^2,3,4, with 41% of adverse events occurring in the operating room.

There are still challenges to overcome until this technology is widely deployed to save lives however - clinician acceptance being the major factor - which could be addressed through tighter workflow integration and overcoming hardware limitations (e.g., battery life, weight/size and connectivity).

Robots Trained with Surgery Videos Achieve Human-Level Skill

Researchers at Johns Hopkins University have developed a breakthrough system using imitation learning to train surgical robots by analyzing videos of skilled surgeons using the DaVinci system. This method eliminates the need for painstakingly coding every robotic movement and opens the door to fully autonomous surgical procedures.

• How It Works: The system uses video data captured by cameras on da Vinci surgical robots, training models to predict robotic movements based on visual input and relative motion rather than absolute commands.

• The AI Edge: Similar to the architecture behind ChatGPT, the model translates video into robotic actions, enabling it to adapt in real-time and even recover from errors, such as dropped needles.

Implications: This advancement could shorten training times for new robotic procedures, reduce medical errors, and provide precise surgical care in remote or underserved areas.

We’ll keep you updated on regulatory and clinical trials involving this technology. Safety validation, surgeon acceptance, and integration into diverse clinical workflows will be key hurdles.

4. Vitals – Quick Bytes

• Google Open-Sources AlphaFold 3: Google DeepMind has open-sourced AlphaFold 3, the latest iteration of its groundbreaking AI model for protein structure prediction. This version boasts faster computations and enhanced accuracy, making it a powerful tool for researchers worldwide to accelerate drug discovery, understand diseases, and innovate in biotech. By open-sourcing the tool, Google aims to democratize access and foster collaboration in solving complex biological challenges.
  (GitHub)

• Contactless Health Screening: An AI-powered tool presented by a team of Japanese researches offers no-contact blood pressure and diabetes screening using analysis of high speed video only.
  (American Heart Association)

• AI Predicts Cancer Gene Activity from Biopsies: Stanford researchers have unveiled SEQUOIA, an AI program that analyzes tumor biopsy images to predict gene activity without costly genetic sequencing. This breakthrough could accelerate clinical decision-making and reduce costs in cancer care.
  (Stanford Medicine)

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Stay tuned for more insights in next week's edition of The Pulse.

Have a great day & see you next week!

Your Hendrix Health Team

1. Triage – Your Weekly Rundown

Hi Pulse Readers - this week, we’re spotlighting:

• NVIDIA’s latest advancements which hint at a future with assistive robotics and AI healthcare agents.

• We also take a look at new diagnostic imaging models from Microsoft,

• and show you how you can supercharge your research tasks by getting practical with AI.

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2. Case Study – Your Weekly Practical

Image Source: Google NotebookML

Case Presentation: Staying updated with the latest guidelines and/or research is essential but time-consuming.

Approach: Notebook ML, a machine learning-powered notebook tool developed by Google, helps clinicians and practice managers quickly make sense of complex documents, guidelines or research - in turn saving valuable time. With a few simple steps, Notebook ML can turn dense content into concise, actionable summaries, study guides or even an engaging audio podcast.

How to Use Notebook ML for Research Summaries:

1. Upload or Import Text: Start by importing documents, research papers or any other web link into Notebook ML via drag-and-drop or file upload options for quick access. Don’t upload sensitive, non-public or patient-identifiable information.

2. Utilise the Notebook Guide: The Notebook Guide is a quick start tool that provides a variety of out-of-the box options such as generating study guides, FAQs and even audio summaries. Alternatively use the prompt window to input any specific research question you may have.

3. Adjust Length and Tone: Use the prompt window to customise the length and tone of the response to fit your needs (e.g., shorter summaries for quick reviews, more detailed summaries for in-depth understanding).

4. Review: Review the generated output as the tool can provide inaccurate information. Ensure it covers essential points accurately, then copy for easy sharing with colleagues or integration into workflows.

Outcomes: Notebook ML can save hours on research tasks, helping healthcare professionals stay current with the latest evidence and guidelines, even with limited time.

3. The Pulse - Your weekly Update

Microsoft Unveils New AI Models for Medical Imaging

Image Source: Microsoft

Microsoft’s new AI-powered medical imaging tools in Azure aim to improve diagnostic accuracy and efficiency. These models support multimodal analysis, meaning they can process diverse data types like CT scans, MRI images, and more, assisting radiologists in better identifying complex conditions.

• Tool: Available in Microsoft’s Azure AI catalog for healthcare providers.

• Functionality: Multimodal capabilities enable more comprehensive insights from medical images.

• Potential Impact: Reduces diagnostic errors and accelerates image interpretation.

Implications: For clinical settings, these tools could transform how quickly and accurately clinicians diagnose conditions, especially in radiology-heavy fields.

Actionable Steps:

• Explore Partnerships: Clinics and hospitals could consider partnerships with Microsoft or Microsoft partners to access these models.

• Trial in Practice: Pilot these tools in departments that heavily rely on imaging, like oncology or neurology.

NVIDIA's Strategy in AI-Driven Healthcare and Robotics

NVIDIA, known for its powerful graphics technology, is making waves in healthcare with cutting-edge AI solutions. The company is expanding beyond graphics to develop AI-driven tools like assistive robots and virtual healthcare agents, aiming to make healthcare more efficient and accessible. Their latest announcement showcases new tools for training humanoid robots, including the Isaac Lab open source robot learning framework and project GR00T.

Nvidia is not hiding it’s ambitions In the healthcare realm, embodied by NVIDIA’s partnership with Hippocratic AI, a start-up developing non-diagnostic AI healthcare agents that provide support in chronic care, remote monitoring, aged care and other patient-centric roles; but have been criticised for potentially replacing nurse tasks for $9 an hour.

• Project GR00T: Focuses on providing foundational AI models for advancing robotics capabilities for lifelike interactions, potentially positioning robots as future clinical aides.

• Hippocratic AI: A partnership delivering generative AI agents specialized in healthcare, prioritizing safety and utility in various non-diagnostic clinical settings.

Implications: NVIDIA’s investment in both robotics and healthcare AI suggests a future where AI and humanoid robots and virtual AI agents could support clinical staff, from patient handling to administrative assistance, helping to alleviate workforce shortages. While NVIDIA’s technology holds promise for enhancing clinical efficiency, there are potential downsides to consider:

• Reliability and Safety: As humanoid robots and healthcare agents integrate more deeply into clinical workflows, issues such as data security, patient privacy, and system reliability must be addressed.

• Cost and Workforce Impacts: The high costs associated with these advanced technologies may limit accessibility and divert investment away from human workers.

• Impact on Patient Experience: There are fears reliance on robotic assistance and AI agents might lead to reduced human interaction, potentially impacting patient satisfaction and patient experience.

Actionable Steps:

• Evaluate Potential and Risks: Healthcare leaders may consider the long-term applications of humanoid robots and virtual agents for clinic tasks.

• Stay Updated: Follow NVIDIA’s updates as they refine tools aimed at healthcare.

4. Vitals – Quick Bytes

• Oracle’s AI-First EHR: Oracle is developing an AI-first electronic health record (EHR) system, aiming to revolutionize data management in clinical settings by providing real-time insights, predictive analytics, and a user-friendly interface for clinicians. This development promises to enhance patient care and streamline administrative workflows. Watch the fireside chat with Oracle Founder and CTO Larry Ellison from Oracle Health Summit 2024 here.

• AI Platform for Arthritis Detection Secures Funding: A UK-based AI platform designed to improve early detection of arthritis has secured a £1.2 million grant. The platform, developed by a team of researchers and clinicians, uses advanced imaging and machine learning algorithms to detect early signs of arthritis more accurately, potentially reducing the need for invasive procedures. This funding aims to expand its research and development, with hopes of improving diagnostic pathways and patient outcomes in rheumatology.
  (digitalhealth.net)

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Stay tuned for more insights in next week's edition of The Pulse.

Have a great day & see you next week!

Your Hendrix Health Team

1. Triage – Your Weekly Rundown

Hi Pulse Readers, this week we explore AI's role in improving patient safety, delve into a groundbreaking AI diagnostic tool for sepsis, take a look at how the future of search could be shifting dramatically, and highlight a new AI model accelerating drug discovery.

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2. Case Study – Your Weekly Practical

Image Source: Prenosis Immunoscore

Case Presentation: Sepsis remains a leading cause of mortality in hospitals, with timely diagnosis being critical.

Approach: Prenosis has developed the Sepsis ImmunoScore, the first AI diagnostic test for sepsis approved by the U.S. FDA. This tool analyzes 22 health metrics to provide a sepsis risk score, predicting severe cases within 24 hours.

Why: Integrating the Sepsis ImmunoScore into clinical workflows can aid in early detection, allowing for prompt intervention and improved patient outcomes.

Outcomes: Early adoption of this AI tool has shown promise in reducing sepsis-related mortality by enabling timely and targeted treatments.

3. The Pulse - Your weekly Update

ChatGPT takes on Google Search

Image Source: OpenAI

Highlights:

OpenAI’s ChatGPT has recently introduced a web search function, a significant move that positions it as a direct competitor to traditional search engines like Google. This feature allows ChatGPT users to access real-time, up-to-date information directly within the chat, expanding its capabilities beyond static knowledge.

• Tool: ChatGPT’s new web search feature is accessible to premium users (ChatGPT Plus and Enterprise).

• Functionality: The search retrieves current information on a wide range of topics, answering questions and summarizing recent events or findings from reputable sources.

• Potential: Early reports suggest that ChatGPT’s search tool may offer a more conversational and contextual search experience than traditional engines, designed to provide nuanced, human-like explanations and direct insights.

Implications:
For clinicians and practice managers, this development could streamline access to healthcare and medical updates. ChatGPT’s search ability, combined with its natural language capabilities, might support clinicians by quickly synthesizing complex information and summarizing emerging studies, regulatory updates, or clinical best practices directly within the conversation.

Actionable Steps:

• Stay Informed: Premium ChatGPT users may consider using the web search function to access newly published studies, or updated patient care recommendations.

• Trial in Practice: Explore this tool as a quick reference resource, or for non-clinical research.

• Monitor Developments: Keep an eye on how ChatGPT’s search results evolve, especially in healthcare contexts, as OpenAI refines its algorithms and accuracy.

New AI tool may halve drug development R&D cost

Image Source: Iambic.AI

Highlights:
Iambic Therapeutics, supported by Nvidia, has launched "Enchant," an AI model that can predict the performance of drugs at the early stages of development. This innovation holds potential to streamline the drug discovery process, cutting both costs and development time significantly by allowing researchers to identify promising candidates faster.

• Tool: Enchant leverages Nvidia’s powerful AI infrastructure to model drug efficacy in preclinical stages.

• Performance: This predictive AI model has shown the potential to reduce pharmaceutical R&D costs by up to 50%.

• Clinical Impact: By accelerating the identification of effective drug candidates, Enchant could reduce the time required for new drugs to reach clinical trials, enhancing overall pipeline efficiency.

Implications:
For clinicians, Enchant’s predictive capabilities in drug development could result in faster access to innovative treatments. By shortening the development cycle, this technology may lead to more rapid availability of new therapies for chronic or hard-to-treat conditions, benefiting both patients and healthcare providers.

Actionable Steps:

• Stay Updated: Monitor developments in AI-driven drug discovery to anticipate upcoming therapies that may impact patient care.

• Consider Applications: As this technology matures, assess how it might influence treatment options available for chronic disease management in clinical settings.

4. Vitals – Quick Bytes

• AI in Mental Health Support: AI chatbots are gaining popularity as accessible mental health resources, offering 24/7 availability and free use. However, experts caution against viewing these tools as replacements for human therapists.

  Le Monde

• AI in Mammography: AI mammography is gaining traction for its potential in improving breast cancer detection. Developed through deep learning from vast mammogram datasets, AI can identify patterns that indicate possible cancers faster and perhaps more accurately than humans.

  PopSugar

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