Oct 5, 2025
6
min read
Medically Reviewed
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Security and Data Governance as the Starting Point
The first and most important area of due diligence for any AI investment is security and data governance, because no other consideration matters if patient data is not adequately protected. Boards must understand where data is hosted, how it is encrypted both in transit and at rest, what access controls are in place, and whether the vendor’s security posture has been independently verified. In the Australian healthcare context, data residency is a critical concern: patient health information should be hosted in Australia and subject to Australian privacy law, and boards should verify this directly rather than relying on vendor assurances.
Independent security certifications provide the strongest evidence that a vendor takes data protection seriously. ISO 27001 certification demonstrates that the vendor has implemented a comprehensive information security management system. SOC 2 accreditation provides assurance about the vendor’s controls for security, availability and confidentiality. Compliance with healthcare-specific standards such as HIPAA and GDPR, while not Australian requirements, signals a vendor that operates at an international security standard. Boards should treat the absence of these certifications as a significant risk factor, particularly when the AI tool will handle identifiable patient information.
MediQo’s security architecture is built on Microsoft Azure and is compliant with ISO 27001, SOC 2, GDPR and HIPAA. Data is fully encrypted in transit and at rest, hosted in Australia, and the platform uses triggered-only listening, meaning the AI only processes audio when it detects speech directed at it rather than continuously recording. For boards evaluating an AI investment, these details are not technical trivia. They are the foundational evidence that the vendor has designed for security from the ground up rather than adding it as an afterthought.
Integration Architecture and Data Portability
The second critical area for board evaluation is integration architecture. An AI tool that cannot share data with the practice’s existing systems is not a solution. It is a new data silo that will require manual bridging, create reconciliation overhead, and generate the kind of fragmented technology landscape that undermines practice efficiency over time. Boards should ask whether the AI vendor uses open interoperability standards such as HL7 and FHIR, which are the accepted frameworks for healthcare data exchange in Australia and internationally. Proprietary integration approaches that lock the practice into a single vendor ecosystem represent a long-term risk that boards should carefully weigh.
Data portability is the corollary of integration. If the board approves the AI investment today, can the practice extract its data and move to a different vendor in three years if needed? Vendors that make data export difficult, charge for data extraction or store data in proprietary formats are creating switching costs that effectively lock the practice into their ecosystem. Boards should insist on contractual guarantees about data ownership, export formats and the process for transitioning data if the relationship ends. These protections are especially important in the AI space because the market is evolving rapidly and the vendor that leads today may not lead tomorrow.
MediQo’s development on FHIR and HL7 frameworks, combined with its integration with Best Practice, MedicalDirector, Halaxy, Cliniko and Nookal, provides boards with confidence that the platform is built for interoperability rather than vendor lock-in. The commitment to open standards means that data flows freely between MediQo and the practice’s existing systems, and the practice retains control over its data regardless of its long-term technology decisions. For boards evaluating AI investments, this architectural transparency is a strong governance signal.
Expert Tips
"I sit in board meetings where the conversation about AI focuses entirely on what the technology can do and almost not at all on whether the organisation is ready to receive it. Boards ask about accuracy rates but rarely about integration architecture, data residency or what happens if the vendor is acquired. Those are the questions that separate a prudent investment from a gamble. Some of the vendors that look solid today will not exist in three years. Boards that do their due diligence on governance, security and vendor viability will be the ones whose AI investments actually deliver sustained value." — Arash Zohuri, CEO, MediQo
Vendor Stability and Long-Term Viability
The AI healthcare technology market is growing rapidly, but rapid growth also means rapid change, and some vendors that appear strong today will not survive the next five years. Boards have a fiduciary responsibility to evaluate vendor stability and assess the risk of investing in a platform that may not be supported in the medium term. The key indicators include the vendor’s funding position, revenue trajectory, customer retention rates, and the depth of its management and engineering teams. A vendor that is reliant on a single funding round or a single large customer represents a concentration risk that boards should factor into their decision.
The risk of vendor failure is particularly relevant in the AI space because of the high cost of switching. If an AI documentation tool stops being supported, the practice does not simply lose a software licence. It loses the clinical notes, workflow configurations and embedded knowledge that have accumulated in that system over time. The cost of transitioning to an alternative is far higher than the initial investment, which means the due diligence on vendor stability is not just about protecting the initial spend but about avoiding the much larger cost of an unplanned transition.
Boards should also evaluate the vendor’s approach to product development and roadmap transparency. A vendor that shares its product roadmap openly and demonstrates a disciplined approach to feature development is more likely to be a reliable long-term partner than one that treats its roadmap as a competitive secret. Regular security updates, demonstrated responsiveness to regulatory changes and a pattern of delivering on past commitments are all positive indicators that a vendor is building for the long term rather than pursuing a short-term commercial exit.
Key Takeaways
Boards need a structured framework for evaluating AI that covers security, integration, vendor stability and clinical governance.
The first question is not what the AI can do but what happens to patient data and where it is hosted.
Integration capability matters more than AI model sophistication because disconnected AI creates more problems than it solves.
MediQo's compliance with ISO 27001, SOC 2 and FHIR/HL7 standards gives boards confidence in governance and security.
Healthcare boards across Australia are increasingly being asked to approve AI investments, and many are discovering that their existing governance frameworks were not designed for the questions these decisions raise. A traditional technology procurement review covers budget, scope, implementation timeline and vendor references. An AI investment review must cover all of those plus security architecture, data governance, integration dependencies, clinical governance implications, vendor stability, and the longer-term risk of vendor lock-in or technology obsolescence. The complexity of the evaluation has increased significantly, but the board processes and due diligence checklists have not always kept pace.
The stakes are high because AI in healthcare is not like other software purchases. An AI tool that handles patient intake data, assists with clinical documentation or supports billing decisions is operating at the intersection of clinical care, data privacy and financial compliance. A poor AI investment does not just waste money. It can expose the practice to regulatory risk, compromise patient data, distort clinical workflows and erode the trust that patients place in the organisation. Boards that treat AI investments as routine technology procurements are taking on risks they may not fully appreciate.
This article provides a practical framework for healthcare boards evaluating AI investments. It outlines the key due diligence areas that every board should examine, the specific questions to ask at each stage, and the governance indicators that distinguish a well-governed AI vendor from one that represents unacceptable risk. Whether the board is considering an AI receptionist, a clinical documentation assistant or a billing optimisation tool, the framework applies across the full range of AI-enabled healthcare technology.
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