“Consulting is very complex work. It involves hard engineering knowledge, painful trench experience and gentle, persuasive and context-appropriate speech. And, as things so often behave differently to how we expect them, it also requires a fine and indomitable humour. John Carter brings these properties to every project and that makes him such a great person to work with. In addition, John also has a great sense of engineering ethics and the feeling of community that produces the giant's shoulders we all stand on. John will have an eye to sensible, effective, lean and open processes which produce the excellent quality and growth that the FHIR community sees. Finally, John brings to projects the daring and plain-spoken attitude of the US mid-west, wrapped in a self-deprecating Kiwi package. When things are ever uphill, or up the creek without a paddle, he is the person to count on.”
About
25 years of progressively responsible health information technology leadership.
Activity
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✨ 𝗡𝗲𝘄 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗧𝗲𝗿𝗺𝗛𝘂𝗯 𝘁𝗲𝗮𝗺: Organizations can now deploy a lightweight, 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗙𝗛𝗜𝗥…
✨ 𝗡𝗲𝘄 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗧𝗲𝗿𝗺𝗛𝘂𝗯 𝘁𝗲𝗮𝗺: Organizations can now deploy a lightweight, 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗙𝗛𝗜𝗥…
Liked by John Carter
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The pain of modeling FHIR I’m a FHIR modeler. I build profiles, extensions, and implementation guides — a lot of them — for national projects in…
The pain of modeling FHIR I’m a FHIR modeler. I build profiles, extensions, and implementation guides — a lot of them — for national projects in…
Liked by John Carter
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We’re excited to introduce the SR Referrals integration in myindici – making healthcare simpler for both patients and practices. What’s new All…
We’re excited to introduce the SR Referrals integration in myindici – making healthcare simpler for both patients and practices. What’s new All…
Liked by John Carter
Experience
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Volunteer Experience
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Treasurer
Auckland Big Band
- Present 4 years 6 months
Arts and Culture
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Committee Member, Industry Representative
Health Information Standards Organisation (HISO), NZ
- Present 6 years 4 months
Languages
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English
Native or bilingual proficiency
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French
Limited working proficiency
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Spanish
Elementary proficiency
Recommendations received
6 people have recommended John
Join now to viewMore activity by John
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Manage My Health product team — Asfahaan Mirza and Anna-Marie Scroggins — had the pleasure of attending an insightful workshop organised by HL7 New…
Manage My Health product team — Asfahaan Mirza and Anna-Marie Scroggins — had the pleasure of attending an insightful workshop organised by HL7 New…
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CyberTeam, Propagator and Talent Army have partnered up with the wonderful folks at Health Informatics New Zealand (HiNZ) to bring you The Waiting…
CyberTeam, Propagator and Talent Army have partnered up with the wonderful folks at Health Informatics New Zealand (HiNZ) to bring you The Waiting…
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I am truly honored to have been re-elected as a member of the Board of Directors of HL7 International. My deepest thanks to the HL7 community, the…
I am truly honored to have been re-elected as a member of the Board of Directors of HL7 International. My deepest thanks to the HL7 community, the…
Liked by John Carter
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HealthLink is proud to be a Platinum Sponsor of HL7 New Zealand and “The Business of Interoperability” event, happening today at Deloitte, Auckland.…
HealthLink is proud to be a Platinum Sponsor of HL7 New Zealand and “The Business of Interoperability” event, happening today at Deloitte, Auckland.…
Liked by John Carter
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Dzisiaj odbyło się pierwsze spotkanie Grupy roboczej HL7 FHIR, która będzie koordynować wdrażanie standardu HL7 FHIR w Polsce. W pierwszej kolejności…
Dzisiaj odbyło się pierwsze spotkanie Grupy roboczej HL7 FHIR, która będzie koordynować wdrażanie standardu HL7 FHIR w Polsce. W pierwszej kolejności…
Liked by John Carter
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We’re proud to see the new Practice PHO Claims Portal now live across the Pinnacle network, following a successful pilot with seven practices. This…
We’re proud to see the new Practice PHO Claims Portal now live across the Pinnacle network, following a successful pilot with seven practices. This…
Liked by John Carter
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✨We’re so grateful for you being a bright light in the HL7 community! ✨ (And this photo really makes me smile 🤩)
✨We’re so grateful for you being a bright light in the HL7 community! ✨ (And this photo really makes me smile 🤩)
Liked by John Carter
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In 2024, Konnect NET launched in Australia, strengthening our mission to support healthcare providers and businesses across the region. Through our…
In 2024, Konnect NET launched in Australia, strengthening our mission to support healthcare providers and businesses across the region. Through our…
Liked by John Carter
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The Medtech ALEX® team has participated in many HL7 NZ Connectathons, but this was our first time attending the Australian Connectathon in Brisbane a…
The Medtech ALEX® team has participated in many HL7 NZ Connectathons, but this was our first time attending the Australian Connectathon in Brisbane a…
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C. Ola Landgren, M.D., Ph.D.
Tempus AI has acquired the digital pathology developer Paige, including its FDA-cleared, artificial intelligence-powered programs for spotting the signs of cancer. The deal includes Tempus paying out Paige’s remaining commitment to Microsoft Azure for its cloud-computing services. Tempus set its eyes on Paige for its massive, anonymized dataset, which encompasses nearly 7 million digitized pathology slides and clinical data licensed from Memorial Sloan Kettering Cancer Center #artificialintelligence #miami #precisionmedicine
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Abdel Mahmoud, MD
Ever wondered how we build AI systems that truly understand the nuances of a highly complex industry? Our very own Dr Christopher Lovejoy, MD breaks down at AI Engineer Summit one of our core playbooks at Anterior: Creating domain-native Large Language Model (LLM) platforms. He argues our view that for specialized AI, the system's ability to incorporate deep domain knowledge is more critical than the model's sophistication. It's all about solving the "last mile problem" – giving the AI the context and workflow understanding it needs to be truly effective. Dr Lovejoy introduces the concept of an adaptive domain intelligence engine, which continuously improves AI performance by learning from domain experts. This involves two key components: Measurement: Defining user-centric metrics and creating a detailed ontology of potential failure modes. This allows for targeted improvements based on real-world feedback. Improvement: Using the data from failure modes to iterate and enhance the AI's performance. This creates a rapid feedback loop where issues can be identified and addressed quickly. The key takeaway? Building successful vertical AI requires a deep partnership between AI engineers and domain experts (in this case, our clinical scientists), powered by purpose-built systems that enable easy learning and adaptation so that we can unlock the full potential of AI. #AI #LLM #ArtificialIntelligence #MachineLearning #DomainExpertise #Tech
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Eyal Klang
GPT-5 did not fix the problems it was marketed as improving. Systematic variation in care decisions across groups, often larger than with GPT-4o, despite identical clinical details. In a separate test, GPT-5 adopted a fabricated chart detail in 65% of runs. Mitigation cut this to 7.7%. Bias remains. Vulnerability remains. Mahmud Omar, reem agbaria, Donald Apakama, Carol Horowitz, MD, MPH, Robbie Freeman, Alexander Charney, Girish Nadkarni https://lnkd.in/de4V69tj
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Daniel Yang, MD
While an obvious point to many, it's not universally known - Equity 🚫 Fairness. These terms are often used interchangeably, but they may be optimizing for different values and outcomes. When it comes to health AI - fairness of an algorithm "aspires to have equal performance across all populations, with no regard for these populations’ differential needs and processes." Equity on the other hand - "requires considering that individuals with 'larger barriers to improving their health require more and/or different, rather than equal, effort to experience this fair opportunity.'" What does this look like in practice? Ziad Obermeyer's now famous Science paper gives a great illustration of an unfair algorithm. The purpose of the algorithm is to identify patients that would benefit from complex case management. However, white pts with the same health status as black pts were disproportionately offered additional support. This was due to conflating health care cost with health status. While many argued for fairness in correcting this algorithm (pts with the same health status should be equally likely to receive additional support, regardless of their race) - an equity driven algorithm might actually result in a persistently unfair algorithm but restructure it to prioritize black pts over white pts, given historical barriers to accessing care. What we prioritize is not a matter of math or computer science; but rather a deeply human, social and political question. A JAMA Health Forum perspective from earlier this year(Kevin Johnson, Eric Horvitz, Ivor Horn, MD, MPH) dives deeper into this topic, while providing specific / actionable guidance on how to include fairness + equity considerations throughout the AI lifecycle. https://lnkd.in/g5iY5kS9
9 Comments
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