Telstra Health today announced a major expansion of its aged care analytics capabilities. Telstra Health’s new Care Insights delivers more than twenty new and enhanced analytics capabilities designed to help providers strengthen governance, support better care delivery, and navigate increasing regulatory complexity across Australia’s aged care sector. 

As providers respond to strengthened Aged Care Quality Standards requirements, greater transparency through Star Ratings, and a rising demand for services, timely and actionable insights have become more critical. However, many aged care providers still struggle to have a reliable view of performance across the organisation because their data is spread across multiple systems and reporting tools.

Telstra Health’s Care Insights suite brings clinical and operational information into a single reporting environment, designed to give providers greater visibility of resident care, quality, risk and organisational performance, and help support timely identification of risks, more efficient reporting and more informed decision-making across both frontline care and executive leadership.

Alina Zhang, Chief Operating Officer for Regents Garden Group, comments, “I would like to thank the Telstra Health team for delivering reporting tools that make a real difference to our clinical and quality teams. The new reports make it much easier to access and review clinical data, already reducing reporting time by around 30% per week, while providing a clearer overall picture of resident care and quality indicators. With better visibility, our teams can make more informed decisions, identify areas requiring attention, and spend more time focused on delivering safe, high-quality care for residents."

The need for timely, connected insights is becoming increasingly important as the aged care sector faces growing demand and complexity. By 2063, about one in five Australians will be aged over 65, with the number of people aged over 85 expected to more than triple. At the same time, providers are under pressure to deliver high-quality care more efficiently amid workforce shortages, funding constraints and increasingly complex resident needs.

Dr Monica Trujillo, Telstra Health’s Chief Health and Risk Officer, comments, “Aged care providers are being asked to make faster, better decisions in a complex and evolving regulatory environment using information that is often spread across multiple systems. Our focus with Care Insights is to bring critical care and operational data together, so leaders and care teams can see what is happening, act earlier and make decisions with greater confidence, supporting better, more responsive care for the people they serve.”

Care Insights: Key Analytics Capabilities for Aged Care 
Telstra Health’s key data analytics uplift is led by three flagship capabilities: 

• Organisation 360: Brings compliance, clinical and operational data from across facilities into one view, designed to help executive leaders and boards strengthen multi-site governance, support Standards compliance and make more informed strategic decisions. 
  
• Resident 360: Brings key clinical and care information together in a single resident view, designed to help care teams see a more complete picture, support care planning and identify opportunities for earlier intervention.
  
• Resident Events: Brings incidents, falls, wounds, assessments and follow-up activities together in one view, designed to help care teams identify patterns, strengthen clinical oversight and support continuous quality improvement. 
  

Caroline Parker, Clinical Adoption Specialist for Clinical Innovation and Effectiveness at Telstra Health, adds, “Analytics should do more than report on past events. Providers need timely, meaningful insights that help care teams identify risks earlier, support better care delivery and demonstrate quality care. By creating a single source of truth across clinical, operational and medication data, we’re helping providers spend less time compiling reports and more time focusing on what matters most - providing the best possible residential care.” 

Telstra Health has also introduced enhanced incident and wound reporting capabilities, designed to provide greater visibility into clinical risks and supporting continuous quality improvement, compliance and care delivery.

Built for Reform and Transparency 
The Care Insights suite is built on structured data aligned to the strengthened Aged Care Quality Standards, designed to help providers monitor quality indicators, support Star Ratings performance, strengthen compliance reporting and improve operational outcomes. 

Unlike traditional reporting approaches that rely on multiple third-party systems, Telstra Health's integrated model brings clinical, medication and operational data together. This can help providers build a more complete understanding of resident wellbeing, organisational risk and service performance. 

Creating the Foundation for AI-Powered Insights 
The new Care Insights analytics capabilities extend the value of Telstra Health’s Assessment Framework, developed in collaboration with twenty-three aged care organisations and informed by more than 850 hours of research. Together, they create a connected data foundation that supports more consistent assessment and reporting, while paving the way for future innovations including AI-assisted resident summaries, natural language analytics and predictive insights.

Parker said, “High-quality analytics starts with high-quality data. By aligning assessments with the strengthened Quality Standards and capturing clinically meaningful information from the outset, we’re helping providers deliver more consistent, data-informed care while building the foundation for the next generation of AI-powered aged care.”

Care Insights is ready to use for eligible customers now within Clinical Manager and lays the groundwork for what comes next. As Telstra Health develops Corus™, its new care intelligence platform, these analytics will become part of a connected ecosystem where data, workflows and insights flow seamlessly between aged care, primary care and broader healthcare settings.’

Source: Telstra Health media release

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Heidi, the AI Care Partner for clinicians globally, today announced the close of a US$100 million (AU$140 million) Series C funding round led by Blackbird, with participation from other existing investors including Phoenix Court (the home of LocalGlobe, Latitude and Solar funds), Point72 Private Investments, and Headline. The round values Heidi at US$900 million (AU$1.26 billion). Alongside the equity round, Heidi has also secured a US$240 million (AU$335 million) growth investment led by General Catalyst’s Customer Value Fund (CVF), providing large scale go-to-market (GTM) investment capacity to support Heidi's rapid growth and scale, bringing the total new funding to US$340 million.

The investment supports Heidi's next phase of growth: deepening adoption across global health systems, bringing agentic capabilities to support the work around patient visits, and continuing to build the regulatory and clinical safety foundations those capabilities require. 

"From the day I started Heidi, the ambition was always bigger than writing doctor’s notes. I imagined that AI would sit alongside clinicians and complete real work under their supervision.” said Dr Thomas Kelly, Co-founder and CEO of Heidi. “That’s the shift we’re now making: from documenting care to helping clinicians act on it. This round is about getting an AI Care Partner to every doctor in the world, and putting clinicians back at the center of care with their patients."

Built for healthcare's defining problem 
The world faces a projected shortfall of 11 million health workers by 2030, and the gap between clinical demand and capacity is widening in every country. Heidi's ambition is to double the world's healthcare capacity while protecting what matters most: the human connection between clinicians and patients. Heidi is building an AI Care Partner that supports clinicians across the whole working day, from administration and clinical knowledge to the work around the patient visit. Every hour of admin lifted from a clinician's day is time that can be given back to caring for patients - more time listening, explaining, and treating. 

Two years since launch, that demand is evident in the numbers. Heidi's AI Care Partner has supported more than 175 million patient visits (up from 73 million at Series B), and over 67 million clinical hours, more than tripling since September 2025. The platform is being used in 190 countries and 110 languages. Annual recurring revenue grew from US$1 million to US$50 million (April 2026) in just two years.

Trusted by enterprise health systems
Since its Series B, Heidi has been adopted across major public and private health systems with its enterprise activation rate sitting at around 62%. In Australia, deployments include The Royal Children's Hospital Melbourne, Children's Health Queensland Hospital and Health Service, and Metro South Health. In New Zealand, Heidi was deployed across every emergency department in the country. In the UK, Heidi was selected as sole supplier for the NHS England Midlands, the largest clinical AI procurement in NHS history. In the US, Heidi is used by Beth Israel Lahey Health, one of the largest health systems in Massachusetts. 

From documentation to the full clinical work day
Heidi has moved well beyond the scribe. It is now a connected platform that health organisations trust with the whole clinical day, with deeper connections into the major electronic health record (EHR) systems worldwide. Heidi Evidence, which brings context-aware clinical guidelines at the point of care, has answered over 10 million queries since launching in March 2026. Heidi also shipped its first wearable device, Remote, built to work across settings, from surgery to community clinics, and Dictate, a voice-to-text feature.

Clinical work itself is changing. AI agents can now work through complex healthcare workflows and come back with what needs review, which means the manual administration that has clogged health systems for decades is starting to move off clinicians' desks. What comes back is capacity, and more time for the care only a person can give.

Purpose built clinical AI
Heidi invested early in purpose-built clinical AI rather than relying on frontier models. Today, transcription and note generation, the vast majority of Heidi’s compute, run almost entirely on technology developed in-house. This gives Heidi the flexibility to use the right approach for each clinical task rather than defaulting to the largest available system, while optimizing accuracy, latency, and cost together. The result is a more efficient AI stack that enables Heidi to reinvest in the product.

These products are designed for safety and quality before they ever reach a clinician. Heidi's clinical research team leads that work through ongoing testing, validation and published research. This funding takes the same discipline into agentic AI, with the clinician in control of every decision. Part of this round also funds the clinical evidence, quality management systems and regulatory submissions that agentic capability in healthcare demands. Heidi will continue to partner closely with regulators as clinical AI takes shape, to ensure every product goes through the appropriate pathway.

"We’ve backed Tom and the team since their pre-seed in 2021 and our conviction is greater than ever. The healthcare deficit won’t be solved with generic AI solutions that clinicians ignore. Heidi invested early in deeply understanding what clinicians want, building its own models and training capability to deliver this. This approach has earnt them the right to capture valuable downstream workflows in the most rigorous clinical settings globally," said Michael Tolo, General Partner, Blackbird.

The coming product capabilities will not be available in the UK and EU. 

Source: Heidi media release

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New Zealand's Cancer Control Agency Te Aho o Te Kahu, has successfully trialled an AI-assisted structured pathology reporting system that converts histology findings directly into fully coded structured data, and is preparing for its second phase.

John Fountain, data and analytics manager at Te Aho o Te Kahu, presented on ASAP (AI-assisted Structured Anatomical Pathology reporting) at the HiNZ Digital Health AI Summit where he said it significantly improves on the manual coding process that leaves the New Zealand Cancer Register months behind.

Pathology findings are traditionally sent from laboratories as unstructured text, which clinical coders must read, interpret, and manually translate into codes for registers and databases. 

This process is slow, resource-intensive, and replicated across multiple organisations that need structured cancer data, such as BreastScreen Aotearoa and the Breast Cancer Foundation NZ.

"Text is not data: we cannot use text directly for analytics, we cannot use text to auto-populate other systems that clinicians can interact with, and text basically must be read if you want to actually understand what is going on,” explained Fountain.

The trial, supported by Deloitte, AWS, and Health New Zealand, produced 117 fully coded data reports.
ASAP was developed using national interoperability standards - FHIR for data exchange and SNOMED CT for clinical terminology - to produce structured, coded outputs that are clinician-verified. 

It uses Te Aho o Te Kahu's national cancer data platform CanShare, which sits across a FHIR-based HealthLake platform within Health New Zealand and a Snowflake environment used for analytics.

The AI component involves AI scribe Tui, which converts the pathologists’ speech to text, then maps that text to SNOMED CT codes. The pathologist reviews the structured output before it is verified and sent through the system.

Coding accuracy during the trial was close to 100 percent said Fountain. However, the pathologists involved in the trial did not want to use the AI scribe as they preferred structured forms for them to follow.

"The takeaway message is you need to fit your solution to the clinical need, what the clinicians want, not replace the norm,” he told attendees.

The next phase, ASAP 2, will expand the scope to the full clinical data loop, from surgical request through to pathology report and into multidisciplinary meetings (MDMs).

Under this model, surgeons would submit coded requests to pathologists, pre-populated with relevant clinical information, and surgical notes would also feed into the system. The resulting structured data would then flow into MDMs, reducing the time needed for staff to prepare information for these meetings.

“Clinicians want instant gratification, so if their forms have been pre-auto-populated with information from the person that first collected data, it makes life easy,” Fountain said.

The Summit presentation was on September 17 in Christchurch.

Image: John Fountain, data and analytics manager at Te Aho o Te Kahu

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The Royal New Zealand College of General Practitioners is trialling an AI-supported learning platform with a targeted group of GP registrars. 

The College is partnering with Gestalt, a New Zealand-built clinical reasoning platform, to test the tool with a select cohort of registrars, including a group of International Medical Graduates.  

Registrars will complete simulated consultations through Gestalt's platform and receive individualised feedback, creating additional opportunities for independent practice between their regular supervised sessions. 

College chief executive Toby Beaglehole says the pilot is focused on understanding where AI genuinely adds value to specialist general practice training, rather than assuming it does. 

"AI could give registrars more opportunities to practise and learn," Beaglehole says.  

"We need evidence that it works in the real training environment, and that it supports the standards and relationships at the heart of general practice. 

"This pilot will show us where the technology helps, where it does not, and what safe, useful adoption looks like." 

Gestalt co-founder, chief executive and college fellow Alastair Dunne says the platform is built to extend and amplify the learning that medical educators already provide. 

"Practice, feedback and reflection are critical to developing clinical skills," Dunne says.  

"This partnership will test how AI can give learners more of those opportunities alongside established education programmes." 

The platform has been built on a proprietary medical knowledge graph covering around 3,000 medical conditions and more than 500,000 interlinking terms. 

Dunne tells eHealthNews it was built to simulate patient consultations, giving medical students a low-stakes environment to practise clinical reasoning. The College saw the potential of simulated consultation practice for its registrars, noting that they have different learning needs from undergraduate students. 

“We have also built an educator overview for the College facilitators to track the learning needs of their groups, helping them better target their facilitated sessions,” he says. 

Dunne says the tool is designed to supplement, not replace, clinical placements as well as amplify and not replace educators.   

Following the pilot, the College will assess registrar engagement, user experience and educator feedback before deciding whether AI-supported learning should have a wider role in its training programmes.  
Image: College chief executive Toby Beaglehole

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Health New Zealand’s HealthX team is developing an AI Foundation Service to enable approved health use cases to use selected AI models safely within a governed environment. 

The work brings together HealthX, HX Digital, Digital Services, and clinical and assurance teams.

Sonny Taite, director of innovation and AI at Te Whatu Ora, told the co:lab 2026 conference on September 2 that HealthX has grown to become a core cross-functional team of around 20 people and a national Clinical Innovation Advisory Group involving clinicians from across the country.

The Foundation Service is in its initial build and validation phase, but Taite said it is intended to provide a safe, controlled way to apply appropriate AI models to genuine health-service problems.

The aim is to enable clinicians and other subject-matter experts across the country to work in small, cross-functional teams to develop solutions to real health-service problems. These teams would bring together clinical, engineering, data, and assurance expertise rather than expecting clinicians to develop or deploy unsupported tools themselves.

He told attendees that the intention is for the service to become a national capability, with AI-assisted software engineering among its first use cases.

HealthX has also developed a structured pipeline for problems and potential solutions submitted from across the sector. An early-stage AI-assisted tool is being developed to scan this material and identify potential connections for review by the team. 

Taite is testing the tool against the HealthX pipeline and said that as it develops, it will look for links among opportunities, existing problems, and emerging evidence.

He said those working in the sector are best placed to identify the challenges that need solving, and that clearly defining the problem is critical when making a submission.

HealthX operates on a clinically led model, meaning every initiative must have a clinical champion.

“For every initiative, we need clinical champions who agree that this is the right thing,” he said, adding that this means ensuring the initiative responds to a genuine problem, addresses a real need, and focuses on delivering meaningful value. 

Clinical leadership is supported by appropriate privacy, security, clinical safety, and workforce impact assessments.

Taite said the team is exploring how selected early research, clinical evaluation, and proof-of-concept activity could be better supported, helping promising ideas build the evidence needed to enter the HealthX delivery pipeline. Options being considered include a future dedicated funding pathway, subject to approval. 

The team has also formalised an international partnership, signing a memorandum of understanding with UCL Partners, a United Kingdom academic health science partnership. 

The relationship gives HealthX access to evidence and practical experience from across the NHS, helping the team test its thinking and identify lessons that could be applied in New Zealand.

Image: Sonny Taite, director of innovation and AI at Te Whatu Ora, speaking at co:lab 2026 

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Labour will make sure New Zealand is ready to seize the opportunities AI presents, manage the risks and ensure people benefit from the technology. 

“AI is changing how we work, access services and make decisions, Labour will make sure it works for Kiwis," Labour Leader Chris Hipkins said.

"Used well, AI has huge benefits. It can cut hospital paperwork so doctors and nurses have more time with patients. Workers and small businesses can be more productive. Certain services can be delivered more efficiently.

“But we cannot leave it to big international tech companies to decide how it shapes our future. Labour has a plan to make AI work for New Zealanders, not just the companies that build and own it,” Chris Hipkins said.

Labour's AI Action Plan will:

• Establish an Office of AI to coordinate AI policy across government
• Establish an online safety regulator within the Department for Internal Affairs.  
  
• Develop a copyright framework so creators control and are paid for their work
  
• Set clear rules for data centres so households aren't left carrying the cost
  
• Support small businesses and community organisations to adopt AI safely
  
• Establish an AI in Education team to support schools, teachers and students
  
• Work with businesses and unions to make sure productivity gains show up in wages and conditions
  

“New Zealanders can see the opportunities of AI, but they also see the risks, whether that's job losses, harmful content, data centres driving up energy costs, or a lack of accountability for important decisions that affect people's lives,” Labour science, technology and innovation spokesperson Reuben Davidson said.

"Labour will protect what matters, unlock the benefits, and make sure New Zealand sets the rules on its own terms. That means protecting peoples’ rights, safety and privacy, creative work, and ensuring that there is always human accountability. 

“We'll also make big data centres pay their own way, securing their own renewable energy and covering their own connection costs. New Zealand households shouldn't be subsidising the power bills of overseas tech companies.

"We can't afford three more years of National letting big tech call all the shots. It's time New Zealand set its own rules, so that all New Zealanders benefit," Reuben Davidson said.

Source: Labour media release

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AI was a hot topic at Co:Lab this year with speakers highlighting patient concerns about its use in clinical care, the importance of selecting the right combination of tools and the need to upskill the workforce to take advantage of them.

 If we keep coming back to what matters to the patient, we will create a far better healthcare system than we currently have.”

National chief medical officer Helen Stokes-Lampard flagged growing patient anxiety around artificial intelligence in clinical care and spoke about genomics and precision medicine as significant opportunities for the health sector.

She described a case where a hospital patient tried to sign a consent form refusing any AI involvement in their care. 

She said task-specific AI is integrated into all levels of the health system and the patient's real concern was about sensitive health data being processed by large language model platforms such as ChatGPT.

Stokes-Lampard pointed to the ‘black box’ problem, where the reasoning behind AI outputs cannot be fully understood, as a key concern for regulators and health leaders globally.

She highlighted the opportunity of genomics and personalised medicine for New Zealand, saying the Māori and Pacific populations give the country a distinctive genomic profile. 

"We are uniquely placed to provide a massive contribution to that world genomic database of information," she said.

"This is not about AI being done to the organisation, it is about people in the organisation leaning into AI to help the tasks that they undertake."

Spark Health chief executive John Macaskill-Smith told attendees that the organisations making the most progress with AI are not buying off-the-shelf products, but training their own people to build and manage AI agents themselves. 

He said one of the Big Four firms has set a target of matching its 40,000 human consultants with an equivalent digital workforce, creating a one-to-one ratio of human to AI agents. 

"Rather than being threatened by the opportunity, why don't you reach out and have a go at it and start to explore what the opportunities might be?" he said.

For New Zealand's pathology sector Macaskill-Smith identified four workflow areas suited to agentic AI: pre-analytical errors that lead to incorrect test orders, patient uncertainty about where results are and what they mean, critical result follow-up, and the management of complex ‘send-away’ testing.

“5 years ago the microscope was my main tool… now I have ChatGPT Enterprise and openevidence.com open on the desk and use it all day, every day.”

David Roche anatomical pathologist at Awanui Labs gave examples of what AI can do in pathology labs right now:
- Detecting and grading tumours in prostate, breast, and colon cancer cases
- Identifying cancer spread into lymph nodes
- Flagging slide quality issues before they reach the pathologist

He told attendees that multimodal AI models are also beginning to combine imaging, genetics, clinical records, and patient data into integrated diagnostic outputs.

When it comes to challenges and risks, Roche said training data quality really matters, accountability frameworks need to be in place before AI makes a consequential error, deskilling is a genuine risk and the environmental cost of AI infrastructure is also significant.

"The order in which you use these tools, and how you actually implement this with people, is so incredibly important.”

Poor workflow design can neutralise even the most capable AI tools, but a well-structured combinations of models - including older, traditional ones -  consistently outperform single-model approaches, Colby Raley told co:lab 2026.

Raley is a strategist in Microsoft’s NZ public sector team and spoke about a diagnostic study using GPT-4 where clinicians given access to the tool achieved 74 percent diagnostic accuracy, while the AI working alone reached 92 percent.

Another pathology study of more than 100 slides showed a different risk: clinicians without confident diagnoses were unduly swayed by AI recommendations of negative results.

"That is the flip side, the risk of building in too much confidence," Raley said. 

"There is quite a lot of consideration around how you want to bring these tools in and how you need to upskill the people that are going to be using them."

She said organisations working with low-resource languages such as te reo Māori found that traditional, purpose-trained models outperformed generative AI tools, and mixed-language conversations required hybrid approaches combining both model types.

"It is not just about the model, it is how you put things together," said Raley.

The Co:Lab conference is for the pathology sector and was held in Wellington on September 2-3, 2026.

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I have found myself increasingly involved in the debate about whether New Zealand’s health system can innovate fast enough to outrun the pressure it is under. Some people have suggested that is driven by self-interest and they are probably right.

By the time New Zealand’s older population reaches its peak, I will be part of it. I am self-interested because I want to grow old in a country where illness is picked up earlier, a clinician can draw on the full picture of my health, and AI can spot patterns a human might miss while giving health professionals more time with patients.

Most of all, I want us to use AI to move healthcare beyond treating people once they become sick and towards keeping them well for longer.

That future is possible. AI is developing quickly and some of it is already being used in healthcare, but New Zealand is not ready to make full use of it. AI needs information that is connected, trusted and available when decisions are made. 

Much of our health information still sits across thousands of applications that do not work well together. Clinicians search for records, repeat processes and piece together patient histories while people navigate disconnected portals, websites, phone lines and services. 

Putting advanced AI into that environment will not repair the fragmentation, it risks carrying the same gaps and inconsistencies into a new generation of technology. We need to get the fundamentals right first.

An ageing country needs a smarter system 
Around 17 per cent of New Zealanders are aged 65 or older. Over the coming decades, that is expected to rise to somewhere between a quarter and a third of the population.

In 2020, there were around 24 people aged 65 and over for every 100 working-age New Zealanders. By 2040, that figure is expected to reach between 33 and 41. 

More of us will live with several conditions at once and need care for longer. We will move between GPs, hospitals, pharmacies, specialists, community providers and aged-care services. Alongside this a proportionately smaller workforce will be available to provide and fund healthcare. 

We cannot meet that challenge by asking already stretched people to work harder, but nor can we keep adding disconnected services and expect them to form a coherent whole.

Make the basics work 
A clinician should be able to see the essential information needed to care for the person in front of them. People should not have to repeat their history each time they enter a different part of the health system.

That information must also appear within the tools clinicians already use. If accessing it requires another portal or password, we have created more work for people who already have too much of it.

Patients need a clearer way into the system too: booking an appointment, checking a result, or working out where to seek help should not require navigating multiple websites, apps and phone numbers. A national digital front door could give people one clear place to begin, while retaining in-person for those who need them.

Underneath this sits less visible but essential work: secure infrastructure, common standards and systems that can exchange information safely.

None of this is especially futuristic, but it is the groundwork that makes the future possible.

I am not going to stop   
When I said I was self-interested, I am sure the fact I lead Orion Health flashed across your mind, so I should say, yes, Orion’s capability can form part the solution if that is the path the country takes. 

But before we can talk about what software to use, I see our role being much more about making sure that the right people know positive change is possible. I see firsthand examples around the world of healthcare systems innovating faster than us in way that reduces ED visits, increases health outcomes and reduces costs.

I worry that too many people think it is impossible to create the healthcare system we all want in New Zealand, so I am going to keep banging on that it is possible. 

The hardest hurdles we have in front of us are not about vendors, they are about leadership, standards, governance, trust and whether successive governments will stay the course.

We released our election manifesto because those of us working in health technology should be solutions-focused, rather than just critical of what is happening now. 

Orion Health is prepared to commit time, people and expertise, and to work alongside clinicians, government, health organisations, researchers, patient groups and other technology providers to help deliver the healthcare system we need. 

The next government must create the conditions for that collective effort to succeed. New Zealand needs a clear national direction, shared standards, systems required to interoperate and accountability for delivery. The work must continue beyond one budget and one election term. 

When I reach my 60s, I do not want to be discussing what AI is doing for healthcare overseas and what it could do here someday, I want it to be the bedrock of the health system I’m happily growing old in.

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More than 300 people attended the MedTech Explorer Showcase 2026 where 20 exhibitors presented innovations ranging from AI-powered diagnostics to a tool designed to reduce stillbirth risk in high-risk pregnancies.

Clinicians, students, academics and industry representatives gathered for the annual showcase, hosted by Te Tītoki Mataora | MedTech Research Translator, as part of a national initiative bringing together universities, industry partners and Health New Zealand to accelerate the development of medical technologies and improve health outcomes across Aotearoa New Zealand.

Jo James, a professor in the Department of Obstetrics and Gynaecology at the University of Auckland, was exhibiting SADIE, which uses physics-based models to predict stillbirth risk in high-risk pregnancies with greater accuracy than available methods.

She explained that when a baby is not growing and is being closely monitored, clinicians face a critical decision about when to deliver. 

SADIE brings together the relevant clinical information to support that decision with greater precision. The project has secured funding for a retrospective trial in the United Kingdom, where the clinical lead is based.

Also on display was a bone reconstruction algorithm which combines simple joint measurements with data such as a patient's age, sex and available imaging to recreate what a healthy bone looked like before disease set in.

Ted Yeung, a research fellow at the Musculoskeletal Group at the University of Auckland, said reconstructive surgery relies heavily on a surgeon’s experience and intuition so the algorithm can help to inform their decisions.

"A lot of times doctors want to reconstruct a patient's function, so this gives them a good idea of how the joint worked before it was diseased," he told eHealthNews.

An afternoon session highlighted Pacific-led digital health innovations where Amio Matenga-Ikihele of Moana Connect, a partner organisation of MedTech-IQ Aotearoa, talked about the role of emerging technology in addressing health disparities.

"Our aim is to inspire communities and clinicians to explore how digital health and emerging technologies, including AI, can be harnessed to address health challenges and create healthier futures for Pacific peoples," Matenga-Ikihele said.

Diana Siew, who co-leads the Te Tītoki Mataora programme, said the showcase gives rangatahi a direct connection to the people shaping the future of healthcare.

"For young people, this showcase provides a chance to meet researchers, clinicians and entrepreneurs who are creating solutions that can change lives,” Siew said.

“By being part of the health technology, engineering, digital innovation and research sector, they can be an instrumental part of shaping the future."

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A Dunedin medical centre has developed what it calls the Broadway Intelligence Suite, combining a number of AI and other digital tools to help it solve multiple friction points. 

Doug Hill, specialist general practitioner and practice owner at Broadway Medical Centre, spoke at HiNZ’s Southern Connections event in Dunedin on July 29 where he said the suite includes Heidi AI, Heron AI, the Well Patient Portal, Copilot and Claude. 

He told attendees the centre started by asking staff about the points of friction that stop the practice from delivering ideal care. The response targets clinical administration, after-hours access, telephone demand, and broader practice management. 

"We could not find a single product that would do the whole thing for us, so we have put together a suite of products which we are now calling the Broadway Intelligence Suite," said Hill. 

The first problem they tackled was clinical administrative burden, which Hill said meant clinicians were finishing notes, completing referrals and managing prescriptions and patient queries well outside normal working hours. 

Broadway introduced Heidi AI as an ambient scribe across the practice, supported by 15 practice-built templates such as a specialist referral template. 

He said a radiology referral template was built to suit community-referred radiology guidelines and the clinic has had no referrals declined since introducing it.  

The practice also replaced its patient portal with the Centrik Well portal to help with after-hours access. This allows patients to book appointments around the clock, order and pay for prescriptions, receive results and view their medical notes. 

He said the migration was straightforward with 1,000 patients onboarded in a day without a single phone call to the practice and three months in, 3,500 patients are onboarded. 

Another challenge was managing telephone demand, particularly during surge times like before work and lunchtime, when staff are either unavailable or stretched. 

The practice introduced an AI-powered receptionist from Heron AI, which means when a patient calls, they can choose to wait for a staff member or enter the AI answering service.  

If they choose AI, the call is transcribed and summarised, and that summary is placed on a workflow board which reception staff can work through during quieter periods. 

The practice handles 5,000 calls per month, with five percent managed through Heron.  

The practice also uses Claude and Copilot across the entire team for tasks including drafting emails, writing policy, analysing budgets, taking meeting minutes, budget preparation and financial modelling. 

“That is the power of what we can do if we introduce these tools across our practices,” Hill said. 

"We have a lot of work to do and we are not perfect, but this is our approach to the problem.” 

Image: Doug Hill, practice owner at Broadway Medical Centre, speking at HiNZ’s Southern Connections event in Dunedin on July 29

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There is a conversation happening across the health sector right now about artificial intelligence, and frankly, I think we are framing it wrong.

The dominant narrative around AI in organisations is that it exists to eliminate menial tasks, to strip out cost, to reduce headcount. If you are the person doing that so-called menial task today, that framing is not empowering. It is threatening. And for a sector like primary health care, where our mission is fundamentally about people, that framing does us a disservice.

At Pinnacle, we have taken a deliberately different approach. We see AI as a way of augmenting and amplifying what our teams can do, not replacing them. The goal is to give a primary health organisation the potential capability of a much larger organisation, to bring the scale and heft, without the weight. That is the lens through which every decision we are making in this space is filtered.

Building on a solid foundation

One advantage I have coming into this role is that my predecessors did the hard work of modernising our infrastructure. Pinnacle has no legacy on-premise servers, everything sits in clean, self-contained cloud platforms. That starting position gives me the freedom to focus on realising value rather than untangling technical debt, and I am genuinely grateful for it.

We have chosen two platforms that work in concert with each other. The first is Amazon Quick, Amazon's AI-powered business intelligence platform. What attracted me to Amazon Quick was that the platform is built on a business-critical security model. 

It is understandable and relatable and it delivers. We have domiciled our tenant in Australia, developed our own logging to assure ourselves the platform is behaving as it should, and configured it so that interactions generate continuous improvement tickets for our service team rather than disappearing into a void.

Amazon Quick is now being rolled out across our organisation. It brings together our different data sources, our email, our HR system, our CRM, into a single experience. A task that might have taken five hours - pulling licensing data, cross-referencing our organisational hierarchy, building and distributing a ranked spreadsheet to functional leaders - can now be done in minutes. 

This is not a hypothetical, it is happening now.

The death of the dashboard

The second platform is where things get particularly interesting for our practices and our population health work. We are calling it Ask Pinnacle, built on Snowflake, and it is currently live in pilot with two practices.

Historically, data in a PHO context gets shared through reports or dashboards and while both have their place, but both have real limitations. 

Health New Zealand, for example, has been criticised for the sheer volume of reports it generates. I actually see that as a symptom of something understandable: everyone in an organisation has slightly different data needs, and those needs get expressed as reports that accumulate into what we call technical debt.

Ask Pinnacle changes that dynamic as instead of a static dashboard, users can ask questions in natural language, questions that are specific to them, framed in a way that makes sense to them, and get answers with context. 

Our data sets include national enrolment data, prescribed and dispensed medicines information, clinical, administrative and financial data, and reference sets including PubMed, so users can benchmark what they are seeing against published research.

Rebuilding agency

One of the most unexpected learnings from this process has been what I can only describe as learned helplessness. We have, over time, almost coached people out of asking questions because either the answer was too hard to get, too expensive, or the question was not deemed important enough to prioritise. This meant people stopped asking.

Now, when you sit someone in front of a tool that can genuinely answer almost any question they have ever wanted to ask, the response is not always excitement, sometimes it is paralysis - what do I even ask?

We are working through that with our pilot practices, sitting alongside people, asking them what they have always wanted to know, helping them type it in, and then coaching them through what comes back. It is a process of re-engaging people's agency, and it is one of the most important parts of this work.

There is also a virtuous cycle emerging as the more useful the data outputs become, the more motivated people are to put quality data in at the source. When data has visible value on the other end, the incentive to code it correctly in the first place becomes real.

A core responsibility, not an optional extra

I believe organisations have passed the threshold where AI tools can be treated as optional extras, they are now a core part of any organisation's digital footprint, and in health, that carries a legal and ethical weight.

Many of our people are already using consumer AI tools in their day-to-day work. This is natural, but when that happens outside of organisational oversight, we lose visibility of potential privacy risks, and we cannot meet our obligations under the law to protect the data we are entrusted with.

By providing a governed, assessed, understood and paid-for platform, we give our people equality of access to these tools while meeting our fiduciary responsibilities to our stakeholders, our contractual partners, and the communities we serve.

That is not a technology story, that is a leadership story, and one I think every PHO, and every health organisation in New Zealand, needs to be telling itself right now.

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Pinnacle PHO has launched a natural language data query tool called Ask Pinnacle that lets clinical and administrative staff interrogate aggregated health data by typing questions in natural language.

Pinnacle general manager of digital and innovation Garry Johnston says the new tool replaces the static dashboards and reports that primary health organisations regularly use to access population health insights.

Built on Snowflake, the platform is being piloted in two practices. Ask Pinnacle draws from multiple data sets such as; national enrolment data, prescribed and dispensed medicines information, clinical, administrative and financial data, and reference sets including PubMed.

"Everyone has a slightly different need in an organisation for data, and therefore what happens is their needs get expressed as reports that become seen as technical debt,” explains Johnston.

“In this new era, using AI, that need to have technical debt disappears because genuinely everyone can ask questions that are relevant to them in a way that makes sense to them."

He says Pinnacle's chief medical officer Jo Scott-Jones used Ask Pinnacle to explore whether there is a statistical correlation between three diseases prevalent in deprived communities, a question he had always wanted answered but would have been too costly and complex to request.

"Within five minutes it came back with an answer and it was categorically yes, there is absolutely a correlation and it is immutable,” Johnston tells eHealthNews.

He says findings such as this have the potential to change clinical practice by providing the data to back up what clinicians may have known anecdotally for years.

Pinnacle is running coaching sessions with pilot practices, sitting alongside staff and working through the questions they have always wanted to ask but never been able to pursue.

Johnston says the new approach is also changing how the organisation thinks about data quality. 

"The quality of the data is not as important as it used to be because the AI can provide context that a dashboard or a report cannot," he says.

"The more we look at the data, the more we explore it to find the origins of where the data is wrong, the more likely it is that people will code it correctly in the first place.”

Ask Pinnacle also introduces a governance capability that traditional dashboards lack, because it logs every query, so Pinnacle's digital team can now assess user intent - flagging questions that appear outside a user's role or that raise privacy concerns, he says.

Read more in Garry Johnston's My View - Why I'm done with Dashboards.

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Expansion of an AI-powered rostering tool aimed at freeing free up time for radiologists has been confirmed as one of the monthly initiatives being delivered by HealthX.

Sonny Taite, director of innovation and AI at Health New Zealand, spoke about the expansion at the Te Tītoki Mataora Forum in Auckland on July 2 where he said the use of RosterLab will expand from around 500 clinicians rostered to around 900.

He described it as a “very tightly controlled expansion across four locations” and said the benefits they are looking for involve a reduction in hours to create a roster, which includes the clinical requirements and workforce preferences. 

“We do not want clinicians creating rosters, it is time consuming and often requires more than one person getting involved,” he said.

The pilot, will include Medical Radiation Technologist (MRT) services at Counties Manukau, Waikato, Starship and Waitematā. 

“The pilot will be delivered through a structured implementation programme designed to move from initial scoping and stakeholder alignment through to live roster deployment and ongoing support,” RosterLab says.

“Implementations can run in parallel across multiple sites, allowing several healthcare services to be onboarded simultaneously.”

Daniel Ge, co-founder of RosterLab, says engagement from clinical teams has shaped how the platform is being implemented. 

"What has stood out most is how much clinicians and roster coordinators care about getting this right," he said.

"Rostering affects people's day-to-day lives, their weekends, their recovery time, their family commitments and their sense of fairness at work. Seeing teams engage so deeply with improving that experience has been incredibly encouraging."

The pilot will evaluate how improved rostering can support better roster quality and work-life balance while reducing the administrative burden associated with creating and maintaining complex healthcare schedules.

Taite said, “this is the kind of thing HealthX aims to do: find those initiatives and opportunities where it makes a lot of sense with really obvious benefits and then just deliver it”.

Image: Sonny Taite, director of innovation and AI at Health New Zealand, speaking at the Te Tītoki Mataora Forum in Auckland

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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.

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More than $3.3 million in government funding has been announced to develop an AI-enabled triage and referral tool to help connect people contacting mental health and addiction helplines with the best service for their needs. 

The tool is part of a broader $10 million package announced by mental health minister Matt Doocey, aimed at strengthening mental health and addiction telehealth services across the country.

Doocey says the $3.35 million in funding is to develop and roll out the AI-powered tool across mental health and addiction helplines over four years. It will be designed to consistently identify callers' needs and route them to the provider best placed to support them.

"It can be disheartening to reach out for support while in distress and find yourself waiting for too long," Doocey says. 

"For someone reaching out, whether it be for the first time or not, that moment on the other end of the line can be incredibly important."

Health New Zealand director mentally well Lisa Gestro says the project is in its early development phase, with the final scope and procurement approach still being worked through.

"The key objective is to ensure people can access the right support as quickly as possible when they reach out for help," Gestro says.  

She says Health NZ will have greater clarity on the delivery model, lead organisation, and the specific activities involved as the project progresses. The work will be led by the National Mental Health & Addiction team working alongside Digital Services.

The broader funding package is expected to support around 16,000 additional calls and interactions and increase sector capacity by around 15 percent, with support available by phone, text and other digital channels.

Lifeline receives $1.2 million to support its continued operation through 2026/2027 and Youthline receives $2 million over four years to expand its government-funded capacity by 44 percent, enabling it to respond to around 4,400 additional contacts from young people. 

Whakarongorau also receives additional funding to help meet growing demand.

Acting chief executive Brian O’Connell says the announcement reflects a growing reality facing mental health services across New Zealand.

"People are not just reaching out more often, they sre reaching out with more complex needs, higher levels of distress, and increasingly difficult situations to navigate,” he says.

The additional funding will enable Whakarongorau to recruit more counsellors, increasing frontline staffing for 1737 and Whakarongorau is also continuing to invest in digital tools designed to improve access to support. 

Around 5,000 people access the 1737 Digital Hub each month, and new initiatives include an AI-powered Welcome Service and a Mental Health Navigation Platform to help people find the support that's right for them.

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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.

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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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Home and community care provider New Zealand Health Group has implemented automation and AI into its service centres that process over 2 million calls annually. 

An AI-powered triage system has eliminated a daily backlog of 600 emails and reduced roster change processing times from 15 minutes to near-instant handling. 

Chief digital officer Peter Radich says the organisation's AI programme has transformed operations across multiple service areas while maintaining human oversight and choice for clients. 

New Zealand Health Group operates several well-known brands, including Geneva Healthcare and Healthcare New Zealand, supporting around 10,000 community workers across the country who provide care for people we support. 

The service centres implementation includes real-time call analysis and transcription. When clients call, the system displays their details and next appointment times while analysing, transcribing and summarising the conversations. 

"A human checks the summary before it enters the system, we do not let anybody just automatically accept what the AI generates," Radich says. 

The system has identified patterns in call types and found that a number involve simple appointment confirmations. 

He says this has enabled the organisation to use AI to give people a choice: either wait to speak to a person or choose to talk to the AI, which can tell you instantly when your appointment is. 

This approach has reduced wait times while improving customer service, explains Radich. 

The system also handles the large volume of referrals from ACC, automating data ingestion to improve accuracy while eliminating manual data entry tasks. 

Radich says it is critical to measure the return on investment for AI implementations, as well as measuring the value of all their technology investments. 

NZ Health Group’s approach is based on choice and consent for clients and on optimising rather than replacing the human workforce, he says.

Image: New Zealand Health Group chief digital officer Peter Radich

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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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