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The Conversation About Your Practice — Without You in the Room
- Cindy Hansen
- 24 hours ago
- 4 min read
Every digital mental health platform has a privacy policy.
Very few can explain—clearly and simply—what happens to your client’s data after a session ends.
That gap isn’t just a practice issue. It’s one of the key questions being debated at digital mental health leadership tables around the world—including one I’ll be joining next week in Ottawa.
Before I go, I want to share what those conversations actually sound like—and what’s already being built in response.
The Problem Beneath the Problem
If you’ve been using outcome measures in your practice, you may have noticed something:
The most common tools weren’t designed with everyone in mind.
The GAD-7 and PHQ-9 are widely used—but they were validated primarily on Western, educated, white populations. When these tools are digitized and AI systems start drawing conclusions from their results, that narrow foundation doesn’t disappear—it scales.
This is the deeper issue.
It’s not just that outcome tools are imperfect. It’s that when imperfect tools are embedded in AI systems, they can reproduce inequity at speed—and often invisibly.
A Thinking Lens: The Equity Equation
When I evaluate whether a digital mental health tool works for everyone, I use a simple framework:
Equity = Access × Engagement × Effectiveness ÷ (Barriers + Bias)
This isn’t meant to be calculated. It’s a way to make your assumptions visible—and to see what may be missing.
Access: Can people actually use the tool? Consider cost, language, devices, connectivity, and cultural safety.
Engagement: Do people use it consistently? Does it fit their lives? Do they trust it?
Effectiveness: Does it help—for this person, in this context?
The denominator matters just as much:
Barriers: Practical obstacles to use.
Bias: When a tool works better for some groups than others—often due to narrow validation.
Here’s the key insight:
If we don’t reduce barriers and bias, we risk digitizing inequity instead of solving it. Expanding access to a biased tool is not progress.
What It Looks Like When It’s Built Well
Addressing bias in outcome measurement isn’t about refining symptom checklists. It’s about measuring what truly generalizes.
Decades of psychotherapy research point to the same conclusion:
Common factors—such as functioning, therapeutic alliance, and the client’s experience of care—predict outcomes across cultures and settings.
Not because they are abstractly “universal,” but because they are relational.
MyTOMS is an open-source outcome monitoring system being developed on this principle. It uses four core questions—Wellbeing, Goal, Support, and Progress—designed to work across:
in-person care
online environments
AI-mediated interactions
Because it focuses on common factors, it avoids anchoring bias from a single population.
Just as importantly, its design builds in:
Consent
Data sovereignty
Accountability
These are not retrofitted—they’re foundational.
The tools will be freely available under a CC BY 4.0 licence and designed to integrate with any EHR or platform—reducing cost barriers and avoiding vendor lock-in.
The technical specification is complete.
The Early Innovators program includes 10 co-designers across Canada, the U.K., the U.S., and Puerto Rico.
Holistic Research Canada is a self-founded social enterprise, and MyTOMS is mission-driven rather than investor-driven. Open-source release will follow psychometric validation.
A Practical Tool You Can Use Now
MyTOMS focuses on what’s being built next.
But most of us are already working inside systems that weren’t designed this way.
That’s where the Clinical Data Governance Checklist (CDGC) comes in.
Think of it as a structured reflection tool—not a compliance audit or legal framework. It helps you understand what your current platforms are actually doing with client data, including:
Consent language
Data flows
Vendor relationships
AI-enabled features
When I guide clinicians through it, the most common reaction is:
“I didn’t know that was happening.”
Not because they weren’t diligent—but because the technology has evolved faster than guidance.
The checklist takes about an hour to complete and is free and open access.
Two Gaps That Still Need Closing
These tools address specific issues—but two broader gaps remain.
1. The Evidence Gap
We don’t yet have strong, practice-level evidence showing where governance frameworks break down in real clinical workflows.
Without this, reforms stay theoretical.
What’s needed is clear evidence of which domains fail, in which contexts, so solutions can be targeted and effective.
2. The Infrastructure Gap
When care crosses boundaries—between health authorities or countries—trust frameworks don’t travel easily.
Consent doesn’t transfer
Accountability becomes unclear
Governance structures fragment
The infrastructure needed to carry trust alongside data hasn’t been fully built.
Both gaps are actively being addressed—and both are solvable.
Why This Matters Now
The decisions being made today—at policy and system-design levels—will shape:
The platforms you use
The standards you’re accountable to
The conditions that either support or undermine your therapeutic work
The Equity Equation, the CDGC, MyTOMS, and the broader structural work underway are all connected.
They form a bridge:
between clinical care and digital systems
between research evidence and real-world infrastructure
That conversation is already happening.
What it needs now is the perspective of clinicians who understand what’s at stake.
Cindy Hansen is Founder and Chief Science Officer of Holistic Research Canada, based on the ancestral, traditional, and unceded territory of the Syilx Okanagan Nation in Vernon, BC. She has worked across more than 25 countries over two decades at the intersection of digital mental health, clinical outcomes, and AI governance.
