I regularly get asked by clinicians about career paths in clinical informatics. The questions have shifted recently: “How do I get into clinical informatics?” has become “How do I position myself for clinical AI work?”

The field is evolving rapidly. Here’s my current thinking on career development in this space.

What Clinical Informatics Actually Is

Clinical informatics sits at the intersection of clinical practice, information systems, and health service management. Clinical informaticists:

• Lead clinical system implementations
• Design and optimise clinical workflows
• Bridge between clinical staff and IT teams
• Develop and implement decision support
• Govern clinical data and information use
• Evaluate health IT effectiveness

It’s not coding (usually). It’s not pure clinical work (anymore). It’s translation and leadership work that requires understanding of both domains.

Why AI Changes Things

AI is becoming a significant part of clinical informatics work:

• AI systems need clinical oversight and governance
• Implementation of clinical AI requires informatics expertise
• Clinical validation of AI requires people who understand both AI and clinical context
• AI ethics and safety require clinical informatics perspectives

Organisations building AI capabilities increasingly need informaticists who understand AI—not just traditional health IT systems.

This creates career opportunity for those who develop relevant skills.

Career Entry Points

People enter clinical informatics from various backgrounds:

Clinical to Informatics

The most common pathway. Clinicians (doctors, nurses, allied health) who become interested in information systems and take on informatics roles.

Advantages: Deep clinical credibility. Understanding of clinical workflows. Trust from clinical colleagues.

Challenges: May need to build technical knowledge. May face career structure uncertainties leaving clinical path.

IT to Health

Technology professionals who move into healthcare IT and develop clinical understanding.

Advantages: Strong technical foundation. Understanding of systems and integration.

Challenges: Building clinical credibility. Understanding clinical workflows and culture.

Hybrid Entry

Roles that combine clinical and informatics elements from the start: clinical systems analysts, health information managers, quality and safety roles with IT focus.

Advantages: Build both domains simultaneously. See the intersection from the beginning.

Challenges: May need to deepen both areas as career progresses.

Essential Skills for Clinical AI Work

For informaticists wanting to work in clinical AI, several skill areas matter:

Clinical Expertise

You need genuine clinical depth. Not just familiarity with healthcare—understanding of how clinical decisions are made, what information matters, what workflows look like, what safety considerations apply.

This doesn’t require being a current practitioner, but it requires having been one or having developed deep clinical understanding through long experience.

Technical Literacy

You don’t need to code machine learning algorithms. You do need to understand:

• How AI systems work conceptually
• What training data means and why it matters
• Performance metrics (sensitivity, specificity, AUROC, calibration)
• How AI integrates with clinical systems
• What can go wrong technically

Enough technical knowledge to evaluate AI, ask intelligent questions of vendors, and communicate with technical teams.

Governance and Ethics

AI governance is increasingly important. Skills include:

• Risk assessment for AI applications
• Developing and implementing AI policies
• Navigating regulatory requirements
• Addressing ethical considerations
• Building oversight structures

These governance skills transfer from clinical governance experience but need adaptation for AI context.

Change Management

Implementing AI is change management. Understanding how to:

• Build clinical buy-in
• Manage resistance
• Train and support users
• Communicate about change
• Sustain adoption over time

These skills apply across health IT but are particularly important for AI given clinician concerns.

Data and Analytics

AI is data-dependent. Understanding:

• Data quality and governance
• How to evaluate data for AI suitability
• Basic analytical thinking
• How to interpret AI outputs critically

You don’t need to be a data scientist, but data literacy matters.

Building These Skills

How to develop these capabilities:

Formal Education

Relevant qualifications include:

• Graduate certificates and masters in health informatics (available from several Australian universities)
• Specialist certifications (CHIA, AHIEC)
• AI-specific courses and certificates (increasingly available online)
• Data science fundamentals courses

Formal qualifications signal commitment and provide foundational knowledge.

On-the-Job Development

Many skills develop through experience:

• Leading or participating in clinical IT projects
• Working with AI vendors during implementations
• Serving on governance committees
• Participating in AI pilots
• Collaborating with data and analytics teams

Seek roles that provide exposure to AI work, even if it’s not your primary responsibility.

Self-Directed Learning

Significant learning happens through:

• Reading (journals, industry publications, vendor materials)
• Conferences and professional development events
• Online courses and tutorials
• Podcasts and webinars
• Professional community engagement

The field moves quickly. Continuous learning is essential.

Mentorship and Networks

Connecting with others in the field:

• Finding mentors who’ve built informatics careers
• Professional associations (HISA, AIDH, HIMSS)
• Informal networks and communities
• LinkedIn connections and engagement

Learning from others’ experience accelerates development.

Career Structures

Clinical informatics careers can follow several paths:

Operational Leadership

Roles like Chief Clinical Information Officer, Director of Clinical Informatics, or Clinical Informatics Manager. Leading informatics programs within health services.

Consulting and Advisory

Working with organisations as an external advisor. Either through consulting firms or independent practice. Broader exposure but less depth in any single organisation.

Vendor and Industry

Working for health IT or AI vendors. Product development, implementation support, clinical advisory roles. Different perspective on the field.

Academic and Research

University positions combining research and teaching. Contributing to the evidence base and training the next generation.

Policy and Government

Roles in health departments, ADHA, or other government bodies. Shaping policy and programs at system level.

Many careers combine elements of these over time.

The AI Opportunity

The expansion of clinical AI creates opportunity:

• New roles specifically focused on AI governance and implementation
• Existing informatics roles expanding to include AI
• Demand for people who can bridge clinical and AI domains
• Consulting opportunities for AI expertise

Organisations are looking for people with these skills. Demand currently exceeds supply.

For those building informatics careers, developing AI capabilities is wise positioning. For those already in the field, adding AI expertise extends your value.

Organisations seeking to build internal capability sometimes partner with AI consultants Brisbane and similar firms who can provide expertise while internal talent develops.

Practical Advice

For those considering or early in clinical informatics careers:

Start from clinical strength. Your clinical expertise is your differentiator. Technical people are common; clinical-technical people are rare.

Get involved in AI projects. Even in small ways. Volunteer for pilot projects, join governance committees, participate in evaluations.

Build technical literacy gradually. You don’t need to become a data scientist. Steady investment in technical understanding pays off.

Develop your network. Connect with others in the field. Learn from their experience. Create opportunities through relationships.

Stay current. This field changes quickly. Commit to ongoing learning.

Be patient. Career development takes time. Skills compound. Experience accumulates.

The clinical informatics field is more interesting and more important than it was when I entered it. The addition of AI makes it more so. For those drawn to this work, there’s meaningful career opportunity ahead. Organisations like AI consultants Melbourne are actively recruiting people with clinical informatics backgrounds, which reflects the growing demand across the sector.

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Dr. Rebecca Liu is a health informatics specialist and former Chief Clinical Information Officer. She advises healthcare organisations on clinical AI strategy and implementation.