Tracking studies in healthcare have traditionally focussed on brand equity measures like commitment, loyalty, and advocacy. While useful, these can be overcomplicated and slow to act on. There’s a strong case for simplifying things. Going forward, tracking should focus on two essentials: mental availability (how easily your brand comes to mind) when the opportunity to purchase or prescribe it presents itself, and brand availability (how easy it is to access or choose). Alongside this shift, AI should play a central role in making tracking more useful, timely, and actionable.
Keep the metrics simple
Rather than measuring everything, focus on what actually drives behaviour.
- Mental availability: Is your brand front-of-mind when a clinician or patient makes a decision?
- Brand availability: Is it easy to prescribe, access, or obtain?
In practice, that might mean asking: does a GP think of your treatment first, and can they easily prescribe it within their system?
Simple metrics make it much easier to layer in AI and get clear, usable insights.
Where AI adds real value
AI isn’t just about speed—it helps turn tracking into something continuous and genuinely useful.
1. Bring data together automatically
AI can combine survey data with prescribing data, digital engagement, and other sources into one view. Instead of static reports, you get a live picture of what’s happening.
Example: Seeing whether a rise in brand awareness is actually leading to more prescriptions.
2. Make better use of open feedback
Open-ended responses often get ignored because they’re time-consuming. AI can analyse them instantly.
- Spot recurring themes and sentiment
- Pick up early warning signs or emerging needs
Example: Identifying growing concerns about side effects before they show up in key metrics.
3. Predict what’s coming next
AI can flag likely changes before they happen.
- Forecast dips in awareness or usage
- Highlight at-risk audiences
Example: Noticing that younger clinicians aren’t thinking of your brand, signalling a future drop in use.
4. Smarter segmentation
Move beyond basic demographics.
- Group audiences by behaviour and attitudes
- Identify who’s likely to switch—or not engage at all
Example: Clinicians who know your brand but don’t prescribe due to access issues.
5. Link perception to behaviour
One of the biggest gaps in tracking is proving impact. AI helps connect the dots.
- Show how awareness links to prescribing or uptake
- Quantify what actually drives change
Example: Demonstrating that improving mental availability leads directly to increased usage.
Making it work
To get the most out of AI-led tracking:
- Keep the framework simple and focused
- Make sure your data is clean and connected
- Use AI to support decisions, not replace them
- Stay compliant with data and privacy standards
The direction of travel is clear. Tracking in healthcare needs to be simpler, faster, and more actionable. By focusing on mental and brand availability—and using AI to do the heavy lifting—organisations can move from reporting what happened to actually shaping what happens next.
