Ask any New Zealander who has tried to find urgent care for a sick child on a Sunday, and they will tell you the hard part is rarely the treatment. It is knowing where to go for help. 

Our health system is full of the right services. They are simply hard to find. Consider something as ordinary as “finding a GP”. Healthpoint profiles at least ten distinct types of general practice and urgent care: standard general practice, after-hours practice, accident and urgent medical care, nurse-led and paramedic-led practices, youth and tertiary student health, virtual care, aged residential care, and armed forces clinics. 

From the outside they look alike, but each serves a different population, under different rules.

A perfect problem for AI

Knowing what kind of service can help is only the first step. The questions that actually decide whether someone gets care are practical and personal: who is open right now? Who will see me, given the access criteria? What will it cost, and do I qualify for a lower fee? 

As the Minister for Mental Health has noted, not knowing where to go or who to talk to is the reported reason for unmet mental health needs in one third of children and more than a quarter of adults. 

When people cannot find the door, they wait, they give up, or they arrive
in the wrong part of the system. Cost, distance, language, literacy and disability stack the barriers higher.

This is the unmet need - not a shortage of services, but a shortage of navigation.

Navigation across a complex, forever-changing system is exactly the kind of problem AI appears built for. 

Type what you need in plain words; receive a clear answer, but in health, a useful answer is only trustworthy when it is grounded in verified data, shaped by domain expertise, and constrained by careful design. 

That is my central argument to colleagues in health informatics: what looks easy is not.

The pillars of good AI

Good AI over a health system rests on three pillars: take away any one of them, and the whole structure collapses.

The first is the data. Healthpoint operates New Zealand's national health services directory: more than 12,000 verified public, private and NGO services, authored by 8,500 service editors who own their own records, and changing constantly, between 100 and 400 updates a day. 

This trusted foundation is only possible because thousands of health and social
service organisations across Aotearoa maintain accurate, up-to-date information and
contribute to the shared digital health ecosystem. 

Ask Healthpoint is New Zealand's first public-facing health AI to draw on international data standards, FHIR and SNOMED CT, so that clinical meaning is exchanged precisely rather than guessed at.

The second is domain expertise. Twenty-two years have taught us that the real world is messy: eligibility, referral pathways, cost to access. Data alone does not capture that.

Understanding how the sector fits together is what lets us make the directory usable, keep it current, and connect people with confidence. Crucially, it is what lets us know what good navigation looks like.

The third is carefully controlled and implemented AI. We use AI narrowly, to understand what a person means, and then return real services through conventional queries against
verified data. 

That design choice deliberately sidesteps the hallucinations generative models
produce. 

Ask Healthpoint does not diagnose or triage. It holds no personal information.
Neither directory data nor conversations are ever used to train the underlying model.

Knowing what good looks like

I want to dwell on why that third pillar demands the first two, because this is where the sector is most exposed. 

AI is a confident performer, producing answers that sound fluent, plausible and authoritative, and be wrong. 

Ask a general model where to find a needle exchange and it offers generic advice and a helpline number. Ask Healthpoint and it names the pharmacy, the opening hours, that it is a free service, and that no identification is required. 

The difference is not cleverness. It is verified data and disciplined constraint.
You cannot govern what you cannot recognise.

If you do not know what good looks like, you cannot tell when an AI is quietly leading people astray. Domain expertise is therefore not a nice-to-have wrapped around the technology; it is the thing that lets you judge the technology at all.

So we built to constrain, not to impress. Over nearly twelve months we ran 25 rounds of testing across 600 real-world scenarios, and had the tool independently security-tested the way a real attacker would probe it. 

Humans remain in the loop, reviewing and escalating.

Enabling the consumer experience 

None of this is visible to the person who types “I have a sore ear” and gets a useful answer and that is the point. The ease is earned; behind it sits governance, expertise and restraint.

My caution to the sector is simple. The barrier to deploying AI in health has never been lower, and the temptation to move fast never greater, but plausible is not the same as correct, and in health the consequences of that gap fall on real people. 

Before we trust AI to guide people through the system, we should be able to say, precisely, what good looks like, and prove our tools meet it.

If you want to contact eHealthNews.nz regarding this View, please email the editor Rebecca McBeth.

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When people talk about artificial intelligence in healthcare, it is often in theoretical terms. During a recent visit to Renji Smart Hospital in Shanghai, I had the opportunity to see what happens when AI moves beyond the pilot stage and becomes embedded in everyday clinical practice.

The experience was both impressive and thought-provoking. 

What struck me most was not the technology itself, but how seamlessly it has been integrated into the hospital's workflows. AI isn't replacing clinicians, it is acting as an intelligent assistant, reducing administrative burden, surfacing clinical insights and helping clinicians make faster, better-informed decisions.

An AI-powered hospital experience 

Renji's AI-powered Internet Hospital platform allows registered patients to consult online with their specialist. Before the clinician even reviews the case, an AI Doctor Assistant (AIDA) analyses the patient's medical history, medications, laboratory results and previous clinical notes. It drafts a structured clinical summary and suggested management plan based on evidence-based medical decision-making. 

Importantly, nothing is sent to the patient until the treating specialist has reviewed, edited and approved the AI-generated recommendations. The clinician remains firmly in control, while the AI significantly reduces the time required to prepare for each consultation. 

The system also recognises when a patient's condition appears to fall outside a clinician's specialty. It cross-checks recommendations against the latest clinical guidelines and can suggest involving another specialist if appropriate. 

AI also plays a major role in Renji's intensive care units. As laboratory, radiology and pathology results become available, they are automatically reviewed alongside the patient's medications and clinical history. If AI detects deterioration or predicts a worsening condition, it generates alerts recommending additional investigations or interventions for clinicians to consider. Rather than waiting for problems to emerge, clinicians are supported by continuous, real-time analysis. 

This level of clinical decision support is possible because AI is only one component of a much larger digital transformation. 

Triple I Hospital 

Renji launched its ‘Triple I Hospital’ strategy around six years ago, focusing on becoming Integrated, Intelligent and International. 

The integration piece came first, with medical services, equipment and data across intensive care units, operating theatres, wards and hospital facilities all brought together into a single platform. Staff can securely access information from mobile devices according to their level of authority, giving clinicians a comprehensive view of patient care wherever they are. 

The hospital has also integrated healthcare delivery with health management and insurance services, creating a one-stop platform where patients can receive treatment while insurance claims are processed quickly and directly. 

Only after building these strong digital foundations did the hospital progressively introduce more advanced AI capabilities. 

The scale of the operation is remarkable. 

History of Renji Hospital 

Founded in 1844 by Dr William Lockhart of the London Missionary Society, Renji Hospital is one of the major teaching hospitals affiliated with Shanghai Jiao Tong University School of Medicine. Across four campuses it provides 54 clinical specialties, more than 3,000 beds and employs over 4,600 staff, including more than 1,800 doctors and 2,800 nurses. 

In 2025 alone, the hospital managed approximately 7.5 million outpatient and emergency visits, discharged around 260,000 patients and performed more than 130,000 surgical procedures. 

Its Internet Hospital has become equally significant, with over 1,200 doctors providing online services to more than eight million registered patients. Collectively, it has delivered care to over 14 million patients, with more than two million prescriptions issued online and delivered to 300 cities across China. 

The hospital has also maintained DNV international accreditation for nine consecutive years and, in 2025, became the first hospital in the world to be assessed against DNV's Internet Hospital Accreditation Standard. 

Lessons for Aotearoa 

My visit highlighted several lessons that New Zealand could consider as we continue our own digital health journey. 

First, success requires a long-term strategy. Renji's achievements were not built overnight but through a carefully staged programme of digital integration before introducing sophisticated AI. 

Second, leadership matters. Large-scale transformation requires sustained commitment from hospital executives alongside dedicated multidisciplinary teams with the right technical and clinical expertise. 

Third, high-quality clinical data is essential. AI is only as good as the information used to train and support it, making consistent, structured clinical data a critical asset. 

Fourth, significant investment is unavoidable. While I do not know the total cost of Renji's programme, there is no doubt that funding has been a key enabler. 

Finally, New Zealand's relatively small population presents both a challenge and an opportunity. Rather than developing isolated systems, greater collaboration and shared digital infrastructure across our health system would create richer datasets, improve interoperability and provide a stronger foundation for clinically useful AI.

If you want to contact eHealthNews.nz regarding this View, please email the editor Rebecca McBeth.

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The case for AI in healthcare has moved beyond theoretical potential to a necessity. Clinicians spend significant time on documentation and administration during every care encounter. Medical knowledge doubles every 73 days, creating an almost impossible cognitive burden to stay up to date. Meanwhile, healthcare costs continue to spiral upward, demanding solutions that improve quality while containing expenditure.

Yet the question confronting healthcare leaders today is not whether to adopt AI, but how to implement it to deliver sustainable value rather than creating new complexities.

Growing evidence of AI’s impact on healthcare makes it critical to any future healthcare strategy. Recent studies show that AI-assisted clinical documentation can save clinicians time while improving productivity and patient focus. Predictive analytics used for population health programs can identify high-risk patients. Diagnostic AI can detect diseases earlier and with greater accuracy. Assisted coding can reduce billing errors and accelerate revenue cycles.

The Data-Ready Imperative

There is a catch, however. Artificial intelligence is only as powerful as the data infrastructure that supports it. An AI algorithm trained on fragmented, inconsistent data will produce fragmented, inconsistent results. And multiple AI tools bolted onto legacy systems may create new complexities and negative user impacts.

A data-ready approach centres on ensuring data completeness and continuity across the care continuum. This means creating unified data architectures where patient information flows seamlessly across systems and departments, while maintaining the flexibility to integrate with external health information exchanges as they evolve.

Equally critical is establishing robust data quality and governance frameworks that ensure information accuracy, security and compliance. These foundational capabilities create the integration points necessary for both current operations and future AI systems to access the comprehensive, longitudinal patient context they require to deliver meaningful insights.

Organisations with mature data foundations (e.g., governance and data platforms) achieve measurably better AI outcomes. In BCG’s 2024 DAICAMA survey, leading organisations scaled four times more use cases and realised five times greater average financial impact than laggards. Conversely, Gartner predicts that through 2026, organisations will abandon 60 per cent of AI projects that are not supported by AI ready data. Mature data foundations support not just today’s AI applications, but tomorrow’s innovations that we cannot yet imagine.

Inbuilt AI: The Strategic Difference

Traditional approaches to healthcare AI follow a bolt-on model – purchasing point solutions from multiple vendors, each addressing a specific use case and requiring separate integration, governance and management. This approach may appear expedient at first, but it creates long-term challenges that ultimately limit AI’s transformative potential.

The alternative – a solution with inbuilt AI – embeds intelligence directly within the electronic health record platform. This approach delivers several strategic advantages. Unified solutions eliminate the workflow friction that drives clinician resistance. Unified governance simplifies oversight and ensures consistent policy application. Consolidated vendor management reduces complexity and often the total cost of ownership. Most importantly, AI algorithms gain direct access to comprehensive patient data, enabling more accurate insights and recommendations.

InterSystems has supported healthcare transformation globally for over four decades. Our TrakCare® platform is deployed in more than 600 hospitals worldwide in 29 countries, and our healthcare solutions help manage over one billion health records across the world. With InterSystems IntelliCare™, we have created an AI-at-centre architecture that eliminates integration complexity while delivering the intelligent automation that clinicians need: ambient clinical documentation, assisted coding and clinical workflow support.

A Call to Strategic Action

Healthcare leaders face a choice that will define the next decade for their organisations. The path of fragmented AI adoption, multiple vendors, complex integrations and disparate governance may satisfy short-term pressures but creates long-term technical debt that becomes increasingly difficult to manage.

The alternative path requires discipline and strategic thinking. It means investing in data readiness before rushing to deploy AI applications. It means selecting architecture that unifies rather than fragments. It means partnering with vendors who understand the unique requirements of healthcare and who commit to long-term relationships, not transactional sales.

This is precisely the moment when strategic choices matter most. The organisations that get this foundation right will achieve improved quality, enhanced efficiency, innovation leadership and, ultimately, better health outcomes for the populations we serve.

The question is not whether AI will transform healthcare. The question is which organisations will lead that transformation, and which will struggle to keep pace because they built on the wrong foundation.

Source: Darren Jones, Country Manager, Australia and New Zealand, InterSystems media release

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Health New Zealand, through its HealthX programme, has entered into an agreement with UCLPartners, a United Kingdom academic health science partnership, in a move that will provide a boost to its digital and AI innovation. 

The agreement supports HealthX’s work to carefully assess, test and scale digital and AI innovation that helps reduce administrative burden, support clinical workflow, and improve patient experience. 

UCLPartners works across the National Health Service (NHS), the publicly funded health system that provides healthcare to people across England, alongside universities and major hospitals to support the safe, consistent, and scalable adoption of innovation. 

Health NZ Chief Executive Dr Dale Bramley says the partnership is about learning from best-practice in the United Kingdom to understand whether improvements can be applied locally.

The partnership supports a deliberate and evidence-led approach, Dr Bramley says.

“The agreement allows us to draw on international experience from the NHS, one of the largest public health systems in the world.” 

HealthX Director Sonny Taite says insights from UCLPartners are already helping to inform Health NZ’s decision-making. 

“For example, their horizon scanning has provided additional confidence for the rollout of AI scribe technology in emergency departments, by showing how similar approaches are being used in other health systems,” Mr Taite says.

“This partnership allows us to test our thinking against real world experience, so we can make informed decisions about where to focus and how to apply digital and AI in practice.

“It also gives us access to international expertise in areas we’re actively exploring, including the use of AI in mental health settings, AI enabled diagnostics, and better ways to bring patient information together across systems to support care.” 

UCLPartners Chief Executive Chris Laing says he is excited to be partnering with Health New Zealand. 

“We share many healthcare and health inequalities challenges across our populations,” Dr Laing says. “We can learn a great deal from collaborating on shared approaches for the responsible use of data, digital and AI in healthcare.  

“I’ve been hugely impressed by the ambition of the Health New Zealand and Health X New Zealand teams and I’m excited about us working together in the years ahead.” 

Source: Health NZ | Te Whatu Ora

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Kiwi start-up mA.I Health has secured ISO/IEC 27001:2022 certification, confirming its systems for managing and protecting sensitive health information meet rigorous international standards. 

The certification, awarded following an independent audit and registered on 20 May 2026, provides added assurance for families and healthcare partners relying on secure, patient-controlled access to health data. 

For individuals and families, health data is deeply personal, and protecting it is essential. mA.I Health recognises the responsibility that comes with this and has built its platform around strong, internationally aligned safeguards to keep information secure and within users’ control.

For people managing complex, lifelong or multi-provider healthcare, keeping track of health information can be a significant burden. 

Health records often sit across different clinics, hospitals, providers and even countries, leaving individuals and families to piece together the information needed to support informed and timely care. 

mA.I Health addresses this challenge by bringing health information together in one secure, patient-controlled platform, making it easier to organise, access and share records when and where they are needed most.

The company's commitment to protecting that information has now been recognised through ISO/IEC 27001:2022 certification. 

Following an independent audit by ARS Assessment Private Limited, the certification confirms that mA.I Health meets rigorous international standards for information security management. 

Registered on 20 May 2026, it covers the design, development and operation of the mA.I Health platform for the management, storage, sharing and retrieval of health-related information.

Arlene Goodwin, co-founder of mA.I Health, says the certification provides external assurance that security has been built into how the organisation operates.

“People using mA.I Health are trusting us with deeply personal information. That trust must be earned through disciplined systems and clear accountability,” says Goodwin. 

“This certification gives families, healthcare partners, and other stakeholders greater confidence that the protections around their information have been carefully designed, assessed and maintained.”

Its security framework combines several layers of protection. mA.I Health complies with New Zealand’s Privacy Act 2020 and Health Information Privacy Code 2020, while its architecture is designed to meet  the requirements of US health legislation (HIPAA) and the EU’s data security law (GDPR).

Its in-app AI search also operates within mA.I Health’s secure environment, meaning users’ health information is not sent to external AI models or used to train them.

Goodwin says this is particularly important as more health services adopt digital and AI enabled tools.

“AI can make it easier for people to find relevant information within a large medical history, but that usefulness cannot come at the expense of privacy,” she says.

“Security risks evolve, and our controls need to evolve with them. This gives us a clear, independently assessed framework for continuing that work as the platform grows and as we engage with families, healthcare organisations and partners in New Zealand and overseas.”

What sets mA.I Health apart

Unlike traditional patient portals or provider-specific apps, mA.I Health is provider-agnostic, globally accessible and centred around the individual. Key features include:

• Universal record keeping – across all providers and regions, locally and globally
  
• Secure sharing and collaboration – supports coordinated care amongst carers and family to optimise better health outcomes 
  
• Family accounts – for children, spouses, and parents – all managed from one device
  
• Private access only – only you or authorised individuals can view or transmit data 
  
• Strong privacy protections – your data is encrypted and safeguarded under local and internationally recognised health and data privacy standards 
  
• Private AI overlay – retrieve relevant data instantly, and in the future will translate medical info when traveling
  
• Personalised health summaries – alerts clinicians to critical history and contraindications
  
• Total user control – you own your data, not institutions
  
• No data mining – your information is never sold or used by big tech or corporates
  

For more information about mA.I Health and its approach to health information security, visit https://maihealthapp.co.nz/news/your-health-deserves-privacy-you-can-trust

Source: mA.I Health

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ACC is reshaping how musculoskeletal injuries reach secondary care. Instead of a GP or physiotherapist referring directly to a surgeon or specialist, referrals will run through a triage process that confirms the referral is complete, appropriate for ACC cover, and headed to the right place. With around 160,000 secondary-care referrals a year, that process has to be digitally enabled — it simply can’t be done by hand at that volume.

I’m a sports and exercise physician, and for the past six months I’ve been running an AI history-taking platform in my own practice. When ACC signalled the change, I adapted it into a dedicated triage platform, Ara Health.

Here’s how it works. A referral arrives from a GP, physiotherapist or other allied health provider. Ara first screens it — checking the ACC claim, the date of injury, that an examination is recorded, that the body site matches the cover. It sends the patient a history-taking form, pulls relevant past imaging from radiology, and collates everything into one place. From there it drafts a decision across five outcomes: decline (not an ACC injury), decline with a redirect (wrong coding, or too early to refer on), more information needed (an MRI before re-review, for example), specialist care, or ongoing management within an ICP musculoskeletal service.

The part that matters most to me: a registered clinician reviews and approves every single decision. The AI makes suggestions and does the paperwork — the GP letter, a patient letter written in plain language, the ACC read-code update, the return-to-work request to the employer — but a clinician signs off every call. And when something isn’t right for ACC, the patient isn’t simply turned away. The seventy-year-old with arthritis is told it isn’t an ACC injury and given the exercises that will actually help.

I think ACC’s direction is the right one. But it has to be done well, or we risk creating bottlenecks where there shouldn’t be any — the obvious rotator-cuff tear that sits in a loop for weeks instead of reaching the surgeon who can fix it. Preventing that is the whole point.

I’m now working with established ICP musculoskeletal providers toward combined bids for the new pathway. If you’re thinking about the same problem, I’d be glad to compare notes.

Source: Dr Stuart Armstrong, sports and exercise physician, founder of Ara Health media release

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A new Collaborative Aotearoa survey shows artificial intelligence (AI) is already being used across primary and community care, with health professionals calling for clearer national guidance, endorsement processes, and practical support to ensure AI is used safely.

The AI in Primary and Community Care Survey (Phase 4) received 47 responses from across Aotearoa. Almost half of respondents reported using AI daily, and more than two-thirds reported using AI daily or weekly in their work. The most common uses were clinical documentation and transcription, followed by administration and communication support, clinical decision support, and data analysis or reporting.

“AI is moving quickly into primary and community care, particularly through tools that support clinical notes, transcription, inbox management, clinical decision support and administration. These tools have real potential to reduce workload and free up health professionals to spend more time with patients and whānau,” says CEO and rōpū chair Jess Morgan-French.

The survey found strong support for a nationally coordinated approach to assessing or endorsing AI tools. Of those who answered the question, almost all supported some form of national endorsement to confirm the safety and effectiveness of AI tools used in healthcare.

Awareness of existing national endorsement processes remains low. Many respondents said they were unsure whether the AI tools they use had been endorsed, or were not aware of endorsement pathways such as the National AI and Algorithm Expert Advisory Group (NAIAEAG).

Collaborative Aotearoa CEO says the findings reinforce the need for a nationally consistent approach that supports innovation while protecting patients, whānau, clinicians and practices.

“AI has the potential to help primary and community care respond to increasing demand, workforce pressure and administrative burden. But it must be introduced in ways that are safe, equitable, clinically useful, and grounded in trust,” says CEO, Jess Morgan-French

AI is already here. The question now is, how we as a sector, make sure AI works for patients, whānau, clinicians, practices and the wider health system.

Source: Collaborative Aotearoa media release

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A major clinical trial involving 50 hospital intensive care units (ICUs) across New Zealand and Australia will test whether artificial intelligence (AI) can help doctors improve survival for patients on life support.

Paul Young, deputy director of the Medical Research Institute of New Zealand and co-clinical leader at Wellington Hospital ICU, has received a $5 million grant from the Health Research Council (HRC) to lead the the Randomised EValuation of Oxygen Limits Using Tailored IndividualisatiON (REVOLUTION) trial.

He says that hundreds of thousands of lives could be saved each year in ICUs around the world, even if the trial confirms only a modest reduction in deaths

The trial will recruit more than 24,000 patients and is the first of its kind worldwide to evaluate whether AI-guided treatment using machine learning improves survival in the ICU. 

A HRC statement says that if successful, it could set an international benchmark for the safe and rigorous application of AI-derived models in clinical decision-making.

The grant is one of two programme grants with a combined value of $10 million and 38 project grants, with a combined value of $46.8 million, announced by the HRC. 

Clinical trials are a key government priority and this year's funding is supporting eight across the country, including $1.5 million for a project called SENSAI which will investigate a smartwatch early notification system for asthma intervention.

Young explains that oxygen is the most widely used therapy in intensive care, but there is growing evidence that the optimal oxygen level differs between patients.

The aim of the trial is to use machine learning to “transform critical care from a 'one-size-fits-all' model to a personalised care model.

“If successful, this research could fundamentally change how oxygen therapy is delivered in intensive care units around the world, improving outcomes for thousands of critically ill patients each year while demonstrating how artificial intelligence can be safely and effectively integrated into routine clinical care,” he tells eHealthNews. 

“It could also pave the way to use this method to personalise treatments in other areas of medicine.”

Young says they will first refine and optimise their machine learning model using data from their recently completed HRC-funded Mega-ROX trial, the world’s largest ICU trial involving 40,003 patients from 137 ICUs in 14 countries. 

The model will use these data, together with personal data submitted by clinicians when patients are admitted to the ICU, to estimate the individual benefit, or harm, of higher versus lower oxygen levels for patients on life support.

From there, they will compare the personalised delivery of oxygen treatments to patients when doctors use machine learning to help them decide what level of oxygen each patient receives, versus the standard approach when doctors decide the oxygen level without AI.

Young says a limitation of trials like Mega-ROX is that they measure average treatment effects across a population. 

“Applying those averages to individual patients assumes uniform benefit, an assumption that may not reflect clinical reality,” he says.

REVOLUTION will use machine learning to identify the oxygen target most likely to benefit each individual patient.

Of the 50 ICUs involved, 12 are based in New Zealand and are expected to contribute around 7,000 patients. 

Young says funding has also been secured in Australia to recruit the remaining patients from Australian ICUs. The trial is expected to begin recruiting in 2028, with participants being adults who require unplanned life support in the ICU.

Young says the team is delighted and relieved to get the HRC funding.

"We have a highly skilled research co-ordinator workforce whose employment depends on grants like these," says Professor Young. 

"As funding levels for health research have reduced in recent years, it has become increasingly challenging to get even the very best projects funded."

HRC director of investments and co-chief executive (acting) Stacey Pene says the trial bridges the gap between research and clinical practice at a national level.

"This study has the potential to fundamentally change the way clinical trial results are translated into patient care by integrating machine learning with high-quality data from a randomised clinical trial," he says.

Image: Paul Young, deputy director of the Medical Research Institute of New Zealand and co-clinical leader at Wellington Hospital ICU 

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A new report from the Health Quality & Safety Commission Te Tāhū Hauora finds that disability is “largely invisible” in national health data, meaning the system cannot easily identify, measure or address the health needs and inequities experienced by more than 850,000 disabled New Zealanders.

Disability advocates spoken to by eHealthNews support the HQSC’s call to accurately reflect disability in national health data, saying this will help ensure disabled people are visible in health planning, service design, quality improvement initiatives, and investment decisions.

The report, A Window on Disability, says key systems such as the National Minimum Dataset, the National Non-Admitted Patients Collection and the National Health Index (NHI) do not record disability status or the support a disabled person may need.

It recommends the health system “commit to, and fully resource, the mandatory inclusion of standardised disability identification questions across all existing and future health data collection processes”.

A significant gap
The HQSC describes the gap in disability data as so significant that Window on Disability was the hardest of all the reports it has compiled as part of this series yet.

Researchers instead used the Integrated Data Infrastructure (IDI), a system that links otherwise disconnected government datasets.

The data the Commission was able to gather shows that the rate of deaths from conditions that could have been prevented with timely health care is more than five times higher for disabled people than for non-disabled people.

Disabled people also present to emergency departments at almost three times the rate of non-disabled people, are hospitalised more frequently, and spend an average of nine days in hospital compared with two days for non-disabled people.

New Zealand Disability Support Network chief executive Debbie Hughes describes the absence of reliable disability data as a longstanding issue that disabled people and disability support providers encounter regularly.

"If disabled people are not identified within health data systems, they effectively become invisible to planners, policy makers, and service designers," Hughes says.

"This makes it difficult to understand whether disabled people are receiving equitable access to healthcare, where health disparities exist, and whether health outcomes are improving or worsening over time."

Standardising information
Hughes says that consistency in how disability is identified across the health system is critical, as “if different parts of the health system identify disability in different ways, the data cannot be effectively compared or used to inform decision-making”.

She says that disabled people and their representative organisations should be involved in designing how disability information is collected and used and that trust and transparency are essential.

Blind Low Vision NZ's Policy and Advocacy team says its own research using the IDI found that blindness and low vision were nearly invisible in health data and showed why standardised coding matters.

"As there was not a singular code for vision impairment, multiple disparate sources of information had to be used to piece together a mosaic of a population of people who are blind or low vision," the team says.

Blind Low Vision says that different data sources within the IDI use different coding for severe vision impairment and other vision conditions and the inconsistency creates significant practical barriers.

"As a charity with limited resources, it meant we were, and continue to be, limited by what research we can afford to do," the team says.

Disability and the NHI
eHealthNews reported in November last year that Health New Zealand was developing a digital system to allow disabled people to record their healthcare access requirements and enable the health system to collect disability data for the first time.

The Patient Profile National Health Index (PPNHI) project aims to address the lack of disability data in New Zealand's health system and the need for healthcare providers to understand people’s access needs before appointments.

Rachel Noble, Health NZ head of disability says the organisation acknowledges the issues raised in the report and says improving disability data is a key activity under the New Zealand Disability Strategy

“We recognise the experience some disabled people have had with Health NZ has not been positive or led to good health outcomes,” she says.

“We take this seriously and have work underway to improve the experiences of disabled people in the health system. These include actions arising from the New Zealand Health Plan and the New Zealand Disability Strategy 2026–2030.

“We remain committed to working with the disability community, sector partners including Disability Support Services, and the Health and Disability Commission and the Health Quality & Safety Commission to strengthen our system so we provide safe, respectful, and accessible care.”

A personal experience
Jacinta Tevaga, a Samoan researcher focused on disabled person-led monitoring of the United Nations Convention on the Rights of Persons with Disabilities, tells her story in the HQSC report.

She says that despite being blind, nothing in her hospital notes flagged her vision impairment during a surgical admission, leaving her to repeatedly explain her disability to every new staff member.

Two years after surgery, she discovered by chance during a routine GP visit that surgeons had found endometriosis during her operation and no one had told her.

"Through all of this, I learned how heavy the burden is on disabled people to carry information between services," Tevaga says.

"The systems do not talk to each other, so we are forced to act as our own messengers, even when we are sick and overwhelmed."

"What the health system needs is simple, but essential. Disabled people deserve to be listened to. We need staff who are trained in disability awareness - not just nurses, but health care assistants, doctors, everyone."

The 2023 Household Disability Survey estimates that 17 percent of New Zealanders, approximately 851,000 people, are disabled.

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Primary Healthcare Limited (PHCL), the not-for profit operator of general practices across Waikato and Taranaki, will begin controlled rollout of Valentia Technologies’ OctansCare AI-powered patient access and engagement services across some of its practices, as part of its ongoing work to explore practical ways of supporting patients and general practice teams.

The OctansCare will enable PHCL to provide around-the-clock AI-powered patient access and engagement services, giving patients more flexible ways to engage with their practice, including through AI voice services. The new capability will complement the myindici patient portal, which is already used extensively by PHCL patients.

Deputy Chief Executive, Batami Pundak, said PHCL is looking forward to the benefits the new AI-driven capability will bring for both patients and staff.

“As part of the Pinnacle network, PHCL serves as a hub for innovation and transformation. We test models of change to understand what works in real general practice settings. Implementation of OctansCare with Valentia will help us explore how AI-supported tools could improve access for patients, support our teams, and enhance patient-centred care for the communities we serve,” said Mrs Pundak.

“AI-driven digital front doors are being adopted rapidly across New Zealand primary care, and the OctansCare platform is playing a vital role in enabling practices to increase capacity, improve productivity and deliver a better user experience for patients,” said Malik Rizwan, Virtual Care Strategy Lead, Valentia Technologies.

OctansCare is designed to support healthcare organisations with intelligent patient engagement, AI-assisted reception services, digital triage, workflow automation and integration with existing digital health platforms. By enabling patients to interact with services through modern digital and voice-based channels, OctansCare helps practices manage demand more effectively while improving the overall patient journey.

This  PHCL initiative reflects the increasing role of AI-enabled tools in New Zealand’s primary care sector, where practices are seeking new ways to improve timely access, reduce administrative pressure and support sustainable models of care.

Source: Valentia Technologies media release

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Medical AI scribes have the potential to change the way clinicians take medical notes. By automatically converting patient-clinician conversations and generating structured clinical notes for electronic health records, they can reduce administrative tasks for clinicians, enabling them to focus more on their patients. AI scribes are also purported to increase accuracy and standardisation and support up-to-date patient records.

The most advanced systems use a combination of AI techniques (speech recognition, natural language processing, and large language models) to interpret, summarise and format medical conversations.

With the potential of AI scribes comes important risks to consider, including to privacy, security, data sovereignty, patient consent, as well as through bias, inaccuracy, and “hallucinations”. It can be difficult to manage these risks as they haven’t yet been fully understood or quantified.

This research is timely given the recent endorsement of two AI scribe tools by the National AI and Algorithm Expert Advisory Group. To help promote the safe and effective deployment of this technology, Assoc Prof. V Selak (UoA) and a team from HealthNZ/Te Whatu Ora (HNZ/TWO) have received HRC funding to develop a roadmap for their safe, effective, and equitable use in Aotearoa. 

One key part of our study is to hear directly from clinicians and healthcare managers working across NZ, all of whom are invited to complete an anonymous online 5-10 minute survey: https://redcap.auckland.ac.nz/surveys/?s=RXPY38PXC3CA774Y. Participants don’t need to have used an AI scribe themselves.

“We will start by reviewing the available evidence and then survey clinicians and health managers to understand service-specific use cases, preferences for features, benefits/risks, training needs and implementation needs. We will also facilitate hui with experts and other stakeholders; clinical, consumer, Māori, Pacific, management, information technology, and health professional regulation representatives as part of developing guidance on risks and the roadmap”. – Assoc Prof V Selak.

The resulting AI scribe Roadmap for Aotearoa will include a set of recommendations for optimising the use of AI scribes in clinical settings across Aotearoa, tailored to the unique needs and context of our healthcare system, to ensure that these tools support quality healthcare for all New Zealanders.

Source: AI in Health Research Network media release

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Bupa New Zealand has announced the full rollout of its AI-supported care platform Nexus AI, designed to help frontline teams deliver safer, more responsive and consistent care and services to its residents.

The technology has been introduced to support frontline decision-making, by giving Bupa’s carers and nurses faster access to information, policies and organisational knowledge in the moments it matters most.

By reducing time spent searching across multiple systems, the platform is helping teams spend more time focused on people and less time on administration.

Already, the AI-supported platform is helping employees:

• Access policies, procedures and guidance instantly
• Respond more quickly and consistently to resident needs
  
• Reduce administrative workload and duplication
  
• Spend more time focused on care, support and customer interaction
  

Bupa New Zealand Clinical and Quality Director Sandy Turnwald said that platform was designed to make every day work simpler and support better outcomes for residents.

“At Bupa we have extensive guidance and procedures covering all aspects of care, from wound dressing to requirements for resident van trips. Previously, employees needed to know where information was stored across multiple platforms. With Nexus AI, our people can simply ask a question and quickly access the relevant current information and reference documents,” she says.

“It removes barriers for our people and helps frontline teams make more informed decisions, while always maintaining human oversight and professional judgement.”

Importantly, the technology is designed to support clinical, operational and care teams, not automate care itself. Decisions remain firmly in the hands of trained employees and care professionals.

"Healthcare is human. It requires compassion, connection and professional judgement. Technology should support our people to work smarter and reduce administrative burden, so they can focus on what matters most, our residents,” says Sandy,

"AI helps reduce friction by surfacing important information faster. Ultimately, it’s about enabling safer, more connected and responsive care and service delivery," she says.

Since becoming fully operational, the platform has also improved collaboration across teams, reduced time spent resolving queries and made organisational knowledge easier to access and apply consistently.

As with any AI tool, Bupa will continue refining the platform alongside frontline employees to ensure it remains safe, practical and focused on delivering meaningful improvements in care and customer outcomes.

The rollout follows Bupa’s recent implementation of its digital Deterioration Early Warning System (DEWS) across all its care homes, a clinical decision-support system that helps care teams identify early and subtle signs that a resident’s health may be declining. 

“Like many aged residential care providers across New Zealand, the sector is facing significant challenges, including funding pressures, significant demand, and increasing complexity of care needs,” says Sandy.

“In this environment, digital solutions that simplify processes, support our people, and help teams spend more time focused on resident care are more important than ever,” added Sandy.”

Source: Bupa media release

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To help enterprises deploy tailored, secure, and production-ready AI, global digital experience leader Fujitsu has revealed its strategic approach that combines the world's leading models with its own proprietary technology.

Central to this approach are Fujitsu’s strategic partnerships in Japan with both Anthropic and OpenAI. These collaborations allow Fujitsu to integrate the most appropriate AI model into customer solutions, ensuring they are tailored to specific enterprise requirements for performance, data sovereignty, security, and compliance.

Separately, Fujitsu has developed self-evolving multi-AI agent technology that enables AI agents to collaborate and continuously learn from execution results, human feedback, policy changes and specification updates.

It allows the AI to adapt and improve over time, automating the enhancement of domain-specific models. In testing, this improved model accuracy by an average of 28 points.

For healthcare organisations, it signals how AI could better support complex clinical and operational information environments, including the structured extraction of information from medical records and test results.

These global developments reveal Fujitsu’s strategic blueprint for the next wave of enterprise AI. 

Read the full global releases below 

• Fujitsu signs strategic partnership with Anthropic
• Fujitsu to accelerate AI transformation in Japan's enterprise sector through collaboration with OpenAI
• Fujitsu develops self-evolving multi-AI agent technology that learns and adapts to business operations

Source: Fujitsu media release

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Health New Zealand | Te Whatu Ora has consolidated multiple systems inherited from over 20 supporting subsidiaries, District Health Boards and the Ministry of Health covering clinical, finance, payroll, supply chain, procurement and asset management data.   

Director of health analytics Delwyn Armstrong says the organisation now has nationally available Qlik apps driving transformation and monitoring performance. 

“Qlik acts as not only a data visualisation tool, but also a secure distributor of data across the country, a self-service tool for clinicians and managers to answer their own questions, a forecaster, scenario tester and even a data collection tool,” she says.   

Armstrong says health is hugely complex and data can be explored in a myriad of different ways depending on where you sit in the system. 

"The Planned Care application alone has over 200 pages within it, each of which can be mined for insights in thousands of ways." 

Real-time data replaces monthly spreadsheets 

Health NZ serves over 100,000 employees and manages over 6,000 digital systems, making data consolidation and visibility critical for effective operations and decision-making across the health system. 

Armstrong says when the organisation was established in July 2022, it required data and analytics in near real-time to understand waitlist sizes, acute demand and other care pathways.  

“We also need to understand how access to and quality of care vary by geographic area and for priority population groups,” she says. 

Previously, national data collections had a lag of two to three months, but the new analytics platform provides near real-time data feeds covering waitlists, emergency department presentations, inpatient events, operating room efficiency, GP referrals and hospital occupancy. 

“We have taken the approach of building apps underpinned by large data models at the patient level. That means the same app is used for identifying individuals to book through to reporting national performance by ethnic group, rural/urban and other geographic spread, age group, and many other ways,” explains Armstrong. 

“The near real-time data feeds coupled with the analytics platform have been key enablers for healthcare transformation; reducing waitlists, improving acute care responsiveness, improving cancer care pathways, and across all care, reducing unwarranted variation.” 

Improving patient outcomes 

Clinical innovations enabled by the new analytics function include opportunistic vaccination programmes linking vaccination data to real-time hospital admission data and identifying patients not receiving optimal treatment or with undiagnosed kidney disease.  

In February 2025, the World Health Organisation adopted a resolution on ‘promotion of kidney health and strengthening prevention and control of kidney disease’ encouraging member states "to strengthen and integrate the monitoring of kidney disease burden, access to care, quality of care and morbidity and mortality outcomes into national health information systems".    

Nephrologist Andy Salmon says using Qlik to create an interactive national chronic kidney disease dashboard means the country is on track to achieve these purposes, “using integrated data insights to reveal opportunities to optimise kidney disease care for whānau across Aotearoa New Zealand”. 

Armstrong says the platform also enables scenario testing and forecasting for health planners. Users can input variables, such as increasing patient appointments by 10 per week, to determine the impact this would have on wait lists. 

Finance expands access 

Health NZ’s Finance function has expanded access to Qlik apps from regional services to 3,000 budget managers across the organisation, providing real-time budget visibility, enabling managers to monitor cost centres independently. 

"Using these applications has transformed the way of working each month for the accounting team of accountants to review of results," says interim director of finance systems enablement Nicolas Harrison. 

"A key highlight has been a multi-purpose General Ledger app for 3,000 staff providing accurate and timely financial information, overcoming security, privacy, data literacy, training and governance challenges." 

He says the improved quality and access to financial data is a key part of cementing functional teams as enablers, allowing conversations to focus on actionable insights rather than data requests. 

The success could not have been achieved without the support of Acumen BI who contributed skills, knowledge, relationships and hard work to support the project. 

“The impact of our partners at Qlik and Acumen BI has been hugely integral and impactful,” Harrison says. 

A Qlik Cloud environment for national Procurement and Supply Chain analytics also includes 35 applications providing, for the first time, a comprehensive national view of procurement, supply chain and inventory data.   

Dan Gargiulo, Acumen BI managing director says the program is a great example of what is possible when strong data foundations, well-designed analytics, and committed teams come together. 

“We are not just consolidating data; we're giving people across the system the confidence to act on trusted, near-real-time insights quickly and consistently,” he says.

Image: Stuart Bloomfield, director of data and analytics, Health NZ

If you have any questions re the above feature article, please contact the editor Rebecca McBeth.

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Health New Zealand | Te Whatu Ora has released a tender worth $4.4 million over three years covering AI reading for screening mammograms and breast density reporting.

The organisation expects AI reading to improve cancer detection rates whilst reducing recall rates and interval cancer rates.

Health Minister Simeon Brown says Health NZ is taking a careful, evidence-based approach to introducing AI into screening and the AI will support, not replace, skilled clinicians.

"Every diagnosis and follow-up decision will continue to include qualified health professionals," he says.

The Request for Proposal is for AI mammography solutions for BreastScreen Aotearoa, and solutions must integrate into the national breast screening programme’s existing blind double-read screening workflow. 

BreastScreen Aotearoa screens around 270,000 women annually, with numbers expected to increase as age extension rolls out to include women up to 74 years old.

Brown says the proposed AI technology would undertake one of the two independent reads currently required in the mammogram assessment process.

He says that international studies show that AI-supported mammogram reading can improve cancer detection rates, including identifying cancers that might otherwise be missed or detected between routine screening rounds.

“As demand for breast screening continues to grow, we need to ensure we are making full use of safe, proven technology to support timely, accurate results for women," says Brown.

The roll-out will be via three-phases, beginning with validation in July 2026. Phase one includes retrospective trials comparing AI reading performance against historical ground truth, including datasets using Māori, Pacific and Asian ethnicity mammograms, followed by prospective trials comparing AI performance against radiologist reading.

Phase two is implementation, including integrating AI solutions into existing breast screening workflows and technical platforms before deploying into production. The final phase covers post-implementation delivery across eight lead providers. 

Solutions must work with all BreastScreen Aotearoa mammogram machine types and integrate with both Sectra PACS and Intelerad systems used across the programme.

Data sovereignty requirements mean that patient health information must remain within New Zealand or Australia jurisdictions and all AI models must be locked, so they cannot learn from production data, with any updates requiring customer approval before release.

The deadline for proposals is 22 June 2026.

This RFP follows the release of an RFI in February this year.

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Valentia Technologies today announced the development of an advanced AI navigation and safety component for OctansCare, its existing digital front-door platform.

The new capability has been designed to help people more safely and confidently navigate complex health and mental health support pathways. Built on advanced research in responsible AI, clinical safety, service navigation, and human-centred design, the component adds a sophisticated layer of AI-assisted intake, guidance, routing, and safeguarding to the OctansCare platform.

Rather than functioning as a general-purpose chatbot, the technology has been developed as a governed AI navigation layer for sensitive health environments. It is designed to support users in describing their needs in natural language and being guided toward appropriate services, support options, or next steps, while maintaining clear medical boundaries, safety controls, and escalation pathways.

The initial focus is on strengthening mental health navigation, helping users identify suitable support pathways and better understand where to go for help. The system is designed with safeguards for high-risk interactions, privacy-aware information handling, culturally safe and locally appropriate guidance, and response controls intended to reduce unsafe, biased, stigmatising, or inappropriate outputs.

A key part of the new capability is its emphasis on responsible deployment in real-world care environments. This includes design considerations for data residency, secure handling of sensitive information, auditability, observability, and alignment with local privacy and governance requirements. The component has also been designed to maintain clear clinical and medical boundaries, ensuring that users are guided toward appropriate care pathways rather than being provided with diagnosis, treatment decisions, or medical advice outside the intended scope.

Valentia Technologies is also working with a major clinical provider in New Zealand on a separate research and development workstream exploring how this AI capability could help open the door to future AI-assisted clinical triage. This work is being approached carefully, with an emphasis on clinical governance, validation, safety, cultural safety, data protection, and appropriate professional oversight.

“AI has significant potential to improve how people access health and mental health support, but only if it is developed with the right safeguards from the outset,” said Malik, Executive Lead at Valentia Technologies. “With this new component for OctansCare, we have focused on building a safety-first AI capability that supports navigation, escalation, and responsible guidance in complex care environments, while respecting medical boundaries, privacy, data residency, and cultural safety.”

The clinical triage workstream is not intended to replace clinicians or automate clinical decision-making. Instead, it is focused on exploring how advanced AI could responsibly support structured intake, prioritisation, and routing, while keeping clinical responsibility within appropriate professional and organisational governance frameworks.

“This is about creating the foundations for safer, smarter access to care,” added Malik. “Mental health and clinical systems are often difficult for people to navigate. Our goal is to use AI responsibly to help people find the right pathway faster, while ensuring that safety, accountability, cultural appropriateness, and clinical oversight remain central.”

The new OctansCare AI component reflects Valentia Technologies’ broader commitment to developing practical, governed AI systems for healthcare, mental health, and human services. Its development is guided by principles of safety, transparency, privacy, cultural respect, local compliance, and responsible innovation in care settings.

Source: OctansCare media release

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Ora Toa PHO is trialling a predictive analytics approach to help identify people at higher risk of undiagnosed respiratory conditions and support earlier outreach, review, and care. 

The primary health organisation is working with Swevnz® Research Science LP to combine patient management system (PMS) data with geospatial and socioeconomic information through the Swevnz® predictive analytics capability. 

Michael Rongo, director of health at Ora Toa, Te Rūnanga o Toa Rangatira, presented the PHO's work at the Digital Health Leadership Forum in Wellington on 12 May 2026. 

He said the approach is designed to help Ora Toa better understand unmet need, emerging risk and where earlier support may be helpful. 

"We use this predictive approach to help us better understand unmet need and where health risks might be emerging," he told the forum. 

"Too often we respond once harm has already occurred or someone is vulnerable or very unwell.

Population health insights allow us to see patterns earlier: chronic conditions, service gaps, emerging risks, and areas where outreach may be needed." 

The Swevnz® capability brings together PMS data with geospatial and socioeconomic data. Its algorithm analyses the combined information to support identification of at-risk populations and areas where earlier action may be helpful. 

As part of the trial, Ora Toa has focused on respiratory conditions because they present a significant health challenge for Māori and Pacific communities, who experience higher rates of respiratory illness compared with other populations. Earlier identification can support earlier clinical review, outreach, and whānau-centred care. 

Rongo said the work with Swevnz® Research Science LP demonstrates how technology can be used to strengthen rather than replace human-centred care. He said the algorithm is a tool to help health teams identify where their attention and resources may be most needed. 

"Digital health must sit with that foundation. It needs to serve the people, not replace," he said. 

"For us, digital innovation is not about chasing technology for technology's sake, it is about asking how it strengthens mana, supports whānau to live well, helps people access care earlier, and supports greater control over their own wellbeing." 

Rongo also highlighted the Whaitua Mapping Tool, developed by Āti Awa Toa Hauora Iwi Māori Partnership Board in partnership with Te Tāhū Hauora (Health Quality & Safety Commission), noting its value in helping communities and health providers better understand the wider determinants of health. 

The tool visualises environmental, social, economic and health data across the region. It can display information such as the locations of vape shops, liquor stores and takeaway outlets and overlay this alongside school locations and deprivation index heat maps. This helps illustrate how lower socioeconomic communities are disproportionately impacted by these factors 

"Health outcomes are shaped long before someone walks through the clinic door," Rongo said. 

"Housing, environment, transport, income, education, food security, and connection all influence wellbeing." 

Rongo said digital success should not be measured by logins, dashboards, or the number of devices deployed. Instead, success should be evaluated by whether the technology saves time, avoids unnecessary travel, builds trust, and improves health outcomes. 

"If iwi are not steering the waka, then technology will reflect someone else's assumptions, someone else's values, someone else's priorities," he said. 

Image: Michael Rongo, director of health at Ora Toa, Te Rūnanga o Toa Rangatira, presenting at the Digital Health Leadership Forum, May 12.

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New Zealand should position itself as a global leader in deploying artificial intelligence (AI) for healthcare, particularly to address rural access challenges, according to international health expert Ezekiel Emanuel.

Speaking at the nib NZ Health Innovator’s Summit, US writer and oncologist Emanuel said that AI could provide 24-hour access to care in rural and underserved areas by reducing the need for patients to travel to urban centres to access specialist expertise.

"We are at the cusp of the AI era, make no mistake about it. It is not here today, necessarily, but it is going to be here tomorrow and New Zealand can either be a leader or it can be a follower. There's no reason it should not be a leader,” he told the Summit.

Emanuel said that several companies developing AI systems are interested in testing their technology in the real-world and this is an opportunity for New Zealand to pioneer clinical AI implementation.

He also advocated for a single electronic health record system for the country as well as using ambient AI technology nationally to reduce administrative burdens on clinicians.

"You look at almost anything, and either today or certainly by 2030, AI is going to outperform doctors in almost every area," he said. 

"New Zealand needs to be a leader in this space."

Health Minister Simeon Brown spoke at the Summit and said Health New Zealand has established HealthX to accelerate digital health innovations and AI by working with technology companies to rapidly trial new solutions.

He described HealthX as a platform to "partner closely with private sector digital health innovators to find ways that we can rapidly innovate, trial new things in our health system and make sure that Health New Zealand is a place of innovation and opportunity.

"The system does not need more complexity, it needs to focus on the problems that face in front of it, making available tools that make existing processes faster, safer and more efficient, that give clinicians more time with patients and make it easier for New Zealanders to access care when and where they need it," Brown told attendees.

Brown also spoke about the 10-year health digital investment plan released in November which he said identifies where investment in digital technology will lift productivity and signals opportunities for partnership within the health system.

He said there are several innovation initiatives already underway, including AI scribes being rolled out in emergency departments nationwide, genomic testing being brought closer to home and AI being used in screening programmes.

Image: Ezekiel Emanuel speaking at the nib NZ Health Innovator’s Summit

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The HealthX team at Health New Zealand | Te Whatu Ora is working on five initiatives as part of its monthly rollout programme designed to rapidly deploy AI and innovative solutions across the public health system.

Sonny Taite, director of innovation and AI at Health NZ | Te Whatu Ora, outlined the initiatives during a presentation at the Clinical AI in Practice conference in Auckland on April 30, saying the aim of HealthX is to be, “a workforce multiplier to improve care for our communities”.

The five are; AI scribes, remote patient monitoring for heart failure patients, AI-enabled skin lesion assessments, AI-enabled diagnostics, and CoPilot for leadership and digital services.

Taite says the AI scribes programme, launched in September 2025, became the team's first major success story. Initially planned for six or seven emergency departments (EDs), demand from clinicians drove rapid expansion across the country.

"In a very short period… we were in every ED in the country, which when I looked around the world, turns out is world leading for a public health system," Taite said.

The remote patient monitoring (RPM) initiative, rolled out in October, targets heart failure patients and has shown significant improvements in medication management. Without the solution, patients typically take five to six months to reach recommended medication levels. With telehealth and RPM, 80 percent of patients reach optimal levels within six weeks.

The AI-enabled skin lesion assessment programme is aimed at addressing substantial dermatology wait lists across the country and an announcement on this is expected soon.

Taite said HealthX operates under strict criteria for selecting initiatives. Each programme must be clinician-led with clinical champions, demonstrate feasibility within monthly budget constraints, show sufficient impact, and avoid heavy integration requirements with existing systems.

"We are not here to sell to our clinicians, we are actually here to find out what problems they have already solved,” he explained.

The team focuses on three key pressure areas: workforce shortages, rural and remote access challenges, and clinical inefficiencies. The AI scribes programme particularly addresses after-hours documentation work that ED clinicians do.

The programme incorporates security by design, privacy by design and clinical safety protocols.

"We have governance ethics groups closely connected to our team. Sometimes ethics is should we do it, often now ethics is why aren't we doing it? This is the right thing: it is ethically not right not to do it," Taite said.

Looking ahead, Taite envisioned expanding the HealthX model to multiple teams across different organisations while maintaining the same rapid deployment approach. He said the appetite amongst workforce is strong as the team continues to get requests from clinicians nationwide looking to implement AI solutions.

HealthX has also established partnerships to support longer-term research initiatives and pilot programmes that may not fit the monthly deployment schedule. 

Image: Sonny Taite, director of innovation and AI at Health NZ

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Three years ago, I started working with an algorithm I called Smartmāori, extracted from ChatGPT.  

What began as a simple experiment has evolved into something I never expected: a way to operationalise tikanga through artificial intelligence, making technology more accessible and culturally grounded for our people. 

When Health NZ offered 40 Copilot licenses to Hauora Māori Services staff I saw an opportunity so I asked for those 40 licenses, then went back asking for more and we now have 170 amongst our 200 staff.  

Finding your level on the digital shoreline 

I approach everything through a Māori lens first, rather than trying to embellish Māori elements onto existing products. Like our tamariki learning to harvest on the beach, starting with safe pūpū before progressing to kūtai, then diving for kina, and eventually reaching pāua and kōura when they are truly skilled, I see succession in everything. 

You need to identify where everyone is in their digital journey and start there. You can't be ahead of yourself or behind yourself, you are where you are, so I created space for people to sit, observe, and listen without judgment.  

That's where BroPilot came from, giving Copilot a face change to make people curious rather than scared. 

What started as a two-week interim training program while waiting for Microsoft's official sessions has now been running regularly since November last year. 

The first thing I get people to do is put their job descriptions into Copilot to define their KPIs, understand alignments, and clarify reporting structures. Instead of ambiguous weekly goals, we get granular: what exactly are you doing, how many, who is involved, and by when? 

Aligning with tikanga 

I have created personas within the BroPilot whare that align with tikanga principles and others have built their own to reflect their roles and functions.  

Mine include Smartmāori for research and deep thinking, Nanny for warm, human-centred advice on kaupapa, Coco for day-to-day tasks, and Doc for polished, executive-level documents.  

These personas remove deficit thinking and are mana-enhancing. Where Western audits often catch people out for discipline, our approach is about "shaking the trees and lifting the mats." When teams are fatigued, we practice the concept of emptying our pockets of everything unaddressed, being completely honest, reviewing skill sets, and delegating to clear the decks. 

Protecting Mātauranga while enabling access 

I have built agents such as a service improvement tool that provides a rubric for application quality and mana-enhancing feedback. Rather than rejecting poor applications, it guides people to strengthen them. 

One of my most significant creations is He Kāmaka Waiora, based on a 35-year-old document by Dame Naida Glavish. This agent provides practical tikanga-aligned guidance for non-Māori, for the interim, as Māori kaimanaaki become available. 

The beauty is that creators maintain full control of their mātauranga. No one else can access the knowledge within their agents and when people create something useful, their name stays attached to it. 

Humanising the digital experience 

I am not building all these tools myself: I am teaching people to build them for their specific purposes. My training is steeped in tikanga first, if people do not align with protecting our mātauranga, I direct them to mainstream training until they are ready. 

The return on investment should be the people, with efficiency as a byproduct. We are enabling voices that have been missing, particularly our neurodiverse and introverted whānau. People with literacy challenges can now be poets because they can talk to the device: they can be storytellers, articulate feelings, and have a confidant. 

I test as an introvert in everything, but te ao Māori has countered that limitation. When a reluctant Pākehā gentleman left my session with an agent he called Rufus (from Bill and Ted's Adventure) for testing coding insights, I knew we were onto something. 

Pushing potential over perfection 

When people criticise AI because it is not perfect, I remind them: I don't care if it is perfect, it is about the journey. Instead of pushing perfection, push potential.  

Our whānau who are nowhere near their own marae would excel in this space because of how they innovate and think, having multiple ways of looking at problems. 

We have always had to make do with what we have. Give us one capability like Copilot, and we will make it work in ways others have not imagined. I was not even coding six months ago or creating agents two years ago, but I did all of this without permission. 

When I faced resistance, people saying, "that's not Māori," I responded, "cool, you do you" as I will not let others' opinions stop progress. Two years later, those same people are asking for help and manaaki. 

Creating experiences, not just training 

While others focus on numbers and digital enablement, I am creating experiences.  

Last week, I put my out-of-office on with a sign saying the whakawhangaungatanga continues. Fourteen people still turned up and talked to each other and that is when I knew we have built something sustainable. 

BroPilot is not just about making AI accessible, it is about ensuring our people are not left behind in the digital transformation while maintaining the integrity of our tikanga. It is about reciprocity, finding that point where different worldviews can agree and move toward the right thing together. 

That is what I am most interested in: creating pathways for our people to thrive in digital spaces while remaining authentically Māori. 

Read more about BroPilot in eHealthNews. 

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A recent conversation with digital health experts left me feeling that it’s hard to gauge AI maturity, or even define it, and so my mind turned to AI myths.  

The Cambridge Dictionary defines ‘myth’ as a traditional explanatory story, or a false idea that is commonly believed. Myths abound about health information technology, more so about AI. 

Myth 1 - AI thinks like a human 

There are many types of AI and they have all been developed to process information the way humans do, i.e., they are built to mimic human thought processes. For example, neural networks are built on what is known about how the human brain works. AI is provided with a human-computer interface that is programmed to seem helpful, friendly, polite, eager to please, and seeks reinforcing feedback from the user. AI appears to think like a human. It makes a close approximation, and like a human it makes mistakes and fabricates answers that don’t exist in its database.  

It is tempting to anthropomorphise technology of any kind, more specifically AI. This is evident in the widely contested Turing Test which claims that, if a human using a computer can’t tell if the computer output is from a human or a computer, artificial intelligence exists in that interaction. 

When an interaction with AI becomes frustrating because the AI appears to go on a tangent that the operator is not interested in, but the AI persists, the human is tempted to use the metaphor of a stubborn teenager. When the AI tool presents hallucinations, one is tempted to think about this as a guess from someone who is eager to please.  

AI mimics human thought by design. Its functional maturity is hard to pin down – AI technologies only recently became commercially available en masse, but the technology itself covers a range of sophistication. Has the machine come of age? Not yet.  

Myth 2 - AI is reliable, so we can use it without gathering scientific evidence 

Marketing and commercialisation at scale have led us to believe that sufficient testing has been done for safe implementation and continued use in healthcare contexts. In contrast, medicines and medical devices cannot be implemented in clinical settings unless research has demonstrated their utility, effectiveness, efficiency, and outcomes on the health of those for whom it is prescribed. Some AI tools have been rigorously tested when designed as a medical intervention, e.g., radiology image analysis has been tested for reliability, validity, specificity and scope.  

Narrow AI enables predictions of specific phenomena, while predictive AI uses past data to predict a described future, and generative AI creates new content. Chatbots use conversational AI and the theory of mind AI to produce, such as mental health companions.  

Each clinical application of AI requires rigorous research about the clinical implications, but also about the effects of the AI on how we work, interact with one another and the technology, and how the tools affect work processes, policies, workload, and the cognitive loads and shifts of its users. 

Marketing and commercialisation at scale fill this gap and we respond to the need that the tools fill (if it works, we will use it) – this is a dangerous approach and requires the governance that is rapidly developing in this space.  

Myth 3 - AI makes clinical work easier and quicker 

It feels like it at first. The promise of information systems and technologies in health care has always been to automate the drudgery, simplify work, and augment what we re usually able to achieve in a day’s work. Research on digital health technology implementations reveals a mixed picture of mostly failure and some success. The ‘productivity paradox’ has emerged whereby the promise of easier and quicker work has not been kept, and digitally supported work can become more difficult, time consuming, and less productive than analogue work.  

AI offers the promise of expanding, amplifying, extending, and enhancing clinical work. At first use, it seems to keep this promise. The implementation of AI scribes has seen rapid and eager adoption in Aotearoa New Zealand. Its core function is to relieve clinical staff (doctors, nurses, allied healthcare professionals) of note taking during consultations with patients – it does this well. But AI does not do reasoning unless specific forms of reasoning and critical thinking are part of its programme. Initial signs are there about its utility in this scenario, but the cracks are emerging where some indicate that critical thinking is delayed until the next appointment where the clinician reviews and analyses the AI-recorded summary before starting the consultation.  

An uneasy relationship

Three myths that form our uneasy relationship with AI. AI mimics human thinking – we are still learning how to introduce critical thinking into its programming. For now, we should resist anthropomorphising the technology and sharpen our own critical thinking skills.  

We do not know how reliable it is, especially when it’s not explainable or where the black box of its reasoning hides what it is doing – research is needed to establish reliability and explainability. Until we have enough evidence of the right kinds, our relationship with AI remains awkward – how do you trust a technology?  

AI is still a ‘new kid on the block’ and we don’t know how well it makes clinical work easier or quicker, implementation science research should document this and help us avoid the productivity paradox. In the end, the humans should always be in charge. We bring humanity to clinical work.  

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An artificial intelligence (AI) call centre agent has reduced missed appointments to zero at one GP clinic, while cutting DNA rates by an average of 3 percent across three practices at New Zealand largest primary healthcare provider. 

Tāmaki Health, which operates 52 clinics nationwide and serves over 360,000 enrolled patients, has deployed the AI agent to handle appointment bookings and repeat prescription requests as part of broader AI initiatives across its network. 

Chief executive Lloyd McCann spoke at a recent HiNZ event – from IT to Digital and AI in Health – in Auckland where he told the audience the agent now successfully books about 40 percent of appointments at participating clinics. 

This means patients are not waiting in phone queues for human operators and Net Promoter Scores have improved by 10 to 12 points across the three clinics using the technology to date. 

McCann told the event that most patients still ring either their practice or centralised call centre to try and book an appointment and the second highest volume of inbound calls is for those people wanting to manage or arrange for a repeat prescription. 

The AI agent handles these high-volume, routine calls, freeing human staff for more complex tasks including outbound calls for smoking cessation programmes and cardiovascular disease screening. 

One practice, which already had a low DNA rate of two percent, saw missed appointments drop to zero after implementing the AI reminder system, which calls patients on the day of their appointments. Tāmaki Health is now working on a second language option after feedback and requests from patients. 

McCann said the call centre initiative is one of three major AI programmes at the organisation which has also piloted an AI patient summary system and rolled out AI scribes across its network. 

The patient summary system was developed and piloted with Deloitte and interrogates Tamaki Health's single Medtech Evolution database to provide a summary of key patient information, with a chatbot to answer additional queries about patient records. 

"This had a huge impact on our clinicians lives by surfacing relevant information and useful data at the touch of a button,” he said.  

“It was very well integrated into the clinician's workflow and it is something we are now looking at rolling out at a production level for our clinicians across the network.” 

Tamaki Health's AI scribe programme has also achieved 91-92 percent adoption by clinicians using Heidi Health. 

This has delivered both time savings and productivity improvements, as well as reduced cognitive load and less fatigue at the end of workdays, he said. 

Future plans include expanding the AI call centre agent to handle outbound calls for preventive care programmes and potentially managing more complex inbound calls.  

"The focus for the organisation going forward is to be very thoughtful and deliberate about where we are applying these algorithms so that they solve real world problems for our clinicians and for our patients,” said McCann. 

Image: Tāmaki Health chief executive Lloyd McCann speaking at - Srom IT to Digital and AI in Health

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Hauora Māori Services within Health New Zealand | Te Whatu Ora has transformed Microsoft 365 Copilot into ‘BroPilot’, an artificial intelligence tool grounded in tikanga Māori to support its staff.

The programme, led by Senior ICT Specialist Troy Baker, customised the platform to become a true digital reflection of te ao Māori, using Dame Naida Glavish's Tikanga Best Practice Guidance as its foundational document to create 16 measurable standard operating procedures that reflect Māori culture and values.

"The thing that is really important to me as a Māori is to influence other Māori to adopt AI using mātauranga Māori," Baker says. 

"We started with honest conversations grounded in values and what matters most to people."

Hauora Māori Services teams manage high workloads across reporting, assurance, governance and programme design.

Baker started regular drop-in sessions where staff learn by using real documents in a secure Enterprise Copilot environment. He says this allows people to test prompts and make mistakes without fear.

"We did not want this to feel like a technology programme happening to people," he explains. 

"The focus has been on helping our teams explore AI through real mahi, in ways that reflect how they already work and what they need support with."

The sessions begin with visual exercises such as creating pictures or personas using prompts, then progress to practical applications like putting job descriptions and KPIs into Copilot to create visual representations of goals and roles.

Within the BroPilot "whare", staff have created agents and personas that reflect their roles and functions. Baker’s AI personas include Smartmāori for research and deep thinking, Nanny for warm, human-centred advice on kaupapa, Coco for day-to-day tasks, and Doc for polished, executive-level documents.

Ronald Karaitiana, a team member who completed the training, says he was initially cautious about AI and whether it would dilute thinking, accountability or kaupapa Māori.

"What changed was seeing Troy introduce AI not as a replacement for thinking, but as a partner, positioned within Māori values, governance and accountability," Karaitiana says. 

Karaitiana now uses BroPilot with his favourite persona called Pāua to transform drafts and conversations into executive-facing documents and provide oversight that understands both corporate and governance requirements as well as Māori culture.

Feedback from BroPilot users shows they feel happier and more supported, which Baker hopes will help keep them within the Health NZ whānau.

Sarah Carney, chief technology officer for Microsoft ANZ, says "this is a world-leading example of how to make supposedly “faceless” AI totally relevant to local people and healthcare workers, through really smart and sensitive engagement upfront and a robust governance structure that clearly sets out principles for how it should be used".

Image:  (Top) Troy Baker and Ronald Karaitiana meeting Microsoft CEO Satya Nadella at the Microsoft AI Tour in Auckland, (Bottom) Senior ICT Specialist Troy Baker speaking at the Microsoft AI Tour in Auckland

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Whakarongorau Aotearoa will launch an AI-powered Welcome service in May 2026 to support people waiting to connect with mental health counsellors through its 1737 and Women's Refuge web chat services. 

The Microsoft Azure AI system gathers background information, helps understand why people are contacting the service and holds them until they connect with a counsellor.

Whakarongorau chief support services officer Anna Campbell says this allows clinicians to focus on care from the first moment of contact.

The telehealth provider manages more than 7,000 interactions daily across its health, mental health and social services, connecting with over 735,000 people across Aotearoa each year. 

Campbell says the organisation is facing growing demand for its services and the complexity of support needed is increasing.

At-risk contacts - people at risk of harming themselves or others - have nearly doubled over the past five years in Whakarongorau's mental health services and these complex interactions now take about 50 per cent longer on average than previously.

"When someone's waiting in a queue and they are in distress, that wait time really matters," says Campbell. 

“It is about making the experience more connected, more responsive and more human, even as demand continues to grow.”

The Welcome service sits at the front of SMS interactions for 1737 and web chat for Women's Refuge services. 

It introduces itself as an AI and callers can choose whether to engage or not. As well as gathering information it can offer non-clinical support like breathing techniques if they agree.

Staff currently start with no information about who they are speaking to on a call, why the person is reaching out, or how urgent the situation is, but the new service means that when a counsellor joins the conversation, they have context from the start.

"We support a high number of people through those web and SMS based interactions, so we know from speaking to people that they are ready and comfortable to deal with an agentic support as well, knowing that they will speak to one of our people at the end of that process,” says Campbell.

She tells eHealthNews that Whakarongorau developed the service with clinical oversight and input from frontline staff and tested it with staff, trusted mental health service partners and people with lived experience.

She says success for the Welcome service is about connection and care rather than efficiency metrics. 

"For us it is about connection, it is experience and it is getting people that pathway to wellbeing that they need and deserve," Campbell said.

The Welcome service is part of Whakarongorau's broader digital transformation, including migration to Microsoft's New Zealand datacentre region. This move saves the organisation $10,000 monthly in IT fees whilst enabling AI innovation and improved service delivery.

Image: Whakarongorau chief support services officer Anna Campbell

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Health New Zealand | Te Whatu Ora has released a Request for Proposal to establish an open panel of suppliers for AI ambient scribe solutions to improve clinical productivity and experience.

The initiative aims to reduce administrative burden by providing clinicians with access to tools that generate accurate structured clinical documentation, allowing them to spend more time on direct patient care.

New Zealand already has the Heidi Health AI-powered scribe deployed in every emergency department nationwide, with 1,250 clinicians currently using the technology and another 1,000 licences being added to support mental health teams.

Health Minister Simeon Brown said in March 2026 that, "this places New Zealand among the fastest health systems in the world to move from pilot to nationwide frontline AI use in emergency departments, helping clinicians spend more time with patients and less time on paperwork”.

The procurement will create a pre-qualified panel of suppliers that meet defined clinical, technical, privacy and assurance requirements.

"The panel will provide access to AI-enabled tools that support clinicians to generate accurate, structured clinical documentation from patient-clinician interactions, while maintaining appropriate clinician oversight and clinical safety controls," the RFP states.

The open panel approach will provide flexibility and choice to Health NZ districts and services by enabling access to multiple qualified suppliers, the documents say.

All production data, including patient health information processed or generated by the solution, must be hosted in data centres physically located in New Zealand or Australia and solutions must comply with the Privacy Act 2020 and the Health Information Privacy Code 2020.

"Clinical safety, patient trust, and responsible use of AI are fundamental," the documents say. 

"Solutions must demonstrate mature governance, transparency, and safeguards to protect patient information and support safe clinical practice."

Responses should be in by 7 May and the expected contract start date is July 2026.

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