Data-Led Healthcare Transformation Depends On More Than Just Numbers
Asif Mujahid, Chief Data and Analytics Officer at Quartz Health Solutions.
There is a saying that data alone doesn't change outcomes—trust does. As health plans and provider systems strive to modernize care delivery, improve population health and manage cost trends, the success of these efforts increasingly hinges on something less technical and more human: whether people trust the data they're given.
We have seen the pattern too often: We deliver a thoughtfully designed dashboard. The metrics are right. The logic is sound. However, the reaction in the room isn't action—it's doubt. "Are these numbers right?" "That's not what we're seeing on the ground." "Can we trust this data?"
This challenge isn't about technology. It's about trust. Until we solve for the trust factor, data-driven transformation will continue to underdeliver on its promise.
The erosion of trust in data doesn't happen all at once—it accumulates slowly across multiple factors:
• Data Quality: Inconsistent values, missing fields or outdated records fuel skepticism.
• Interpretability By Audience: Technical teams may understand what an "impactable opportunity" or "risk-adjusted cost ratio" means, but front-line teams often don't. Thus, you are disenfranchising a segment of the audience from partaking in the benefits of using data.
• Competing Truths: Health plans and providers frequently work from different systems and coding standards. When a physician's panel doesn't match the health plan's attribution list, both sides dig in—and mutual confidence erodes.
• Lack Of Co-Ownership: When analytics are delivered to operational or clinical teams rather than built with them, people treat the outputs like surveillance rather than support.
Now that we have established the problem statement, what does it take to rebuild trust in data?
1. Co-create the narrative. Involve clinicians, case managers, finance leads and operations to define which insights matter. Let them shape the use case, not just review the output. The more they help build the story, the more likely they are to believe it.
2. Validate in the open. Make data validation a collaborative process. Invite stakeholders to compare datasets, surface mismatches and resolve discrepancies together.
3. Explain, then recommend. Don't assume everyone understands PMPM, MLR, RAF or any other acronym we've come to normalize. Use plain language, and always connect the insight to an operational decision. ("What should we do differently because of this?")
4. Embed data in the workflow. Insights need to show up where decisions happen—not just in a dashboard but inside clinical notes, staffing huddles or care management prioritization. When data becomes part of daily operations, it gains legitimacy.
5. Measure trust as a metric. Start tracking how often reports are used, whether action plans stem from analytics and how confident your stakeholders feel in the data. Treat trust like a KPI, not an assumption.
How do we know we've reached the promised land? We know we are doing something right when the conversation turns from whether something is right to what we can do about it. Health plan and provider teams align more easily on shared goals. Innovation takes root—not because the technology is better but because the belief in its value is stronger.
In healthcare, data will never be perfect, but it can be trusted if we build that trust deliberately. That's the work ahead of us.
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