Bridge Point proposes to attend a specialist Health & AI conference in Australia, including associated workshop sessions, travel, accommodation, and related expenses.

The purpose of this activity is not general networking or passive conference attendance. It is targeted external capability development in a market that is further advanced in healthcare AI adoption, governance, clinical workflow integration, and commercial deployment.

The workshops and conference sessions will expose Bridge Point to current thinking on:

• safe use of AI in healthcare settings;
• patient data governance and privacy expectations;
• practical deployment of AI-enabled clinical and administrative workflows;
• integration patterns between AI tools and health-sector systems;
• clinical risk, auditability, and human-in-the-loop design;
• lessons from vendors, health providers, and regulators already operating in this space.

This will help Bridge Point develop a more mature understanding of what responsible healthcare AI deployment looks like in practice. The learning will be captured internally and translated into future R&D planning, product design, compliance processes, and client implementation models.

This activity directly supports the revised capability objective: building Bridge Point's internal ability to research, design, and deploy safe AI-enabled systems for healthcare environments.

Bridge Point proposes to engage an external AI/software specialist to design and implement a structured R&D information management system.

As Bridge Point's R&D activity becomes more complex, informal documentation is no longer sufficient. The business needs a structured way to capture, organise, and reuse R&D knowledge across projects. This is particularly important where the work involves healthcare data, AI services, integration decisions, privacy considerations, technical uncertainty, and repeated experimentation.

The proposed system will support the capture of:

• research questions and technical uncertainties;
• hypotheses and experiment design;
• test results and lessons learned;
• architecture decisions and rationale;
• integration findings;
• compliance and privacy considerations;
• prompt, workflow, and automation design patterns;
• reusable technical knowledge;
• evidence required to distinguish R&D from ordinary commercial delivery.

The objective is not simply to build an internal database. The objective is to create a repeatable R&D knowledge-management capability that improves the quality, traceability, and reusability of Bridge Point's future R&D work.

This capability will allow Bridge Point to make better technical decisions, reduce duplicated learning, onboard team members more effectively, and maintain a stronger evidence base for future R&D investment.

Bridge Point proposes to engage an external healthcare technology architect to provide specialist advisory input on health-sector system integration.

The main technical challenge in healthcare AI is no longer simply accessing an AI model. Commercial AI services are now readily available. The real capability gap is understanding how those services can be safely integrated into healthcare workflows, systems, and data environments.

This advisory work will focus on areas such as:

• integration with NZ healthcare platforms and practice management systems;
• API design and data flow architecture;
• authentication, access control, and audit logging;
• patient data handling and minimisation;
• privacy-by-design architecture;
• secure use of third-party AI services;
• human review and escalation pathways;
• production readiness for healthcare environments.

The purpose is to transfer specialist architectural knowledge into Bridge Point, not simply outsource development. The outcome will be improved internal capability to assess healthcare AI opportunities, design safe integration patterns, and make better technical decisions during future R&D projects.

This is a high-value capability activity because it directly addresses the practical barrier between AI prototypes and usable healthcare products.

Bridge Point proposes to obtain specialist intellectual property legal advice in relation to the AI and healthcare integration capability being developed.

As the company invests in healthcare AI R&D, it is important to understand which parts of the work may create protectable intellectual property or valuable confidential know-how. This may include integration methods, workflow designs, data structures, automation logic, documentation frameworks, product methodology, and reusable implementation patterns.

The IP advice will help Bridge Point understand:

• what IP is being created through the R&D work;
• whether any elements should be protected through copyright, confidentiality, trade secrets, or contractual controls;
• how to manage ownership of IP created by external consultants;
• how to protect reusable methods and frameworks;
• what should be documented internally as proprietary knowledge;
• what should be disclosed, licensed, or kept confidential.

This advice will strengthen Bridge Point's ability to commercialise future R&D outputs and avoid losing control of valuable capability developed through the programme.

Bridge Point proposes to obtain specialist R&D tax and structuring advice to ensure future R&D activity is properly classified, recorded, and managed.

This advice is not ordinary accounting support. The purpose is to build internal capability around how R&D activity should be identified, separated from commercial delivery, documented, costed, and governed.

The advice will help Bridge Point understand:

• how to distinguish eligible R&D from routine software development;
• what records should be maintained for technical uncertainty and experimentation;
• how staff time, contractor input, and external advisory costs should be captured;
• how future R&D expenditure should be structured;
• how to maintain compliance with grant and tax requirements;
• how to create repeatable internal processes for future R&D investment.

This capability is important because Bridge Point expects healthcare AI to become a long-term R&D focus. The business therefore needs stronger systems for managing R&D activity in a compliant, auditable, and commercially sustainable way.