I've never sat in the executive suite of a health system. But from recent conversations I've had with healthcare leaders, I imagine a version of this scene plays out regularly.

The CEO asks: "How are our patient satisfaction scores?"

Or maybe: "Why are we losing patients to the system across town?"

Or: "What's driving the complaints we're seeing?"

The VP of Patient Experience pulls up HCAHPS scores. Maybe some NPS data. Perhaps a few patient comments from a recent survey.

And everyone nods. Because what else is there?

Here's the problem: nobody's asking about trust.

They're asking about satisfaction. About scores. About complaints. But trust? That word rarely makes it into the executive conversation. And that's the gap I'm trying to close.

By the time survey data arrives, patients have already made their decisions. The "satisfied" ones have already scheduled with a competitor. The ones who felt unseen have already told their friends.

You're measuring moments. Trust is built over journeys.

The instruments we've relied on weren't designed to detect trust in real-time. They weren't even designed to measure trust at all. HCAHPS measures satisfaction with a hospital stay. NPS measures likelihood to recommend. Neither measures whether a patient actually trusts you with their health.

According to Deloitte, hospitals that consistently deliver superior patient experiences achieve operating margins 2.6 times higher than their peers. But that margin advantage doesn't come from satisfaction scores. It comes from trust. From operational behaviors that create confidence, consistency, and connection over time.

Those behaviors happen every day. In contact centers. In scheduling systems. In billing operations. In the handoff between a chatbot and a live agent. They're measurable. Right now. Without waiting for next quarter's survey results.

The Trust Algorithm gives you a new lens for an old question. When your CEO asks "How are our patient satisfaction scores?" you'll have a better answer: "Here's what our Trust Signals are telling us."

The Trust Algorithm is a diagnostic framework built on five operational signals. Each signal answers a question patients are asking, whether they articulate it or not.

These aren't sentiment metrics. They're behavioral indicators you can measure in real-time across your existing systems. Together, they reveal whether your organization is building trust or just processing transactions faster.

The 5 Trust Signals:

1. ACCESSIBILITY: Can patients reach you on their terms?

This isn't just about phone hours. It's about channel availability, escalation paths to humans, and language accommodation. When a patient needs you at 2am, what happens?

2. RESOLUTION: Are problems solved in ways that build confidence?

Not "was the ticket closed?" but "does the patient believe their issue is actually fixed?" The difference between system-defined resolution and patient-confirmed resolution is where trust leaks hide.

3. CONTINUITY: Do patients feel known?

When they call back, do they start over? Does your system recognize them? Does your agent see their history before asking for their date of birth for the third time?

4. PROACTIVITY: Do you reach out before they have to chase you?

Care gap notifications. Billing alerts before due dates. Appointment reminders that feel like caring, not nagging. Trust is built when you solve problems before patients know they have them.

5. RECOVERY: When things go wrong, do failures become trust-building moments?

The Service Recovery Paradox: customers who experience a problem that gets resolved excellently can become MORE loyal than those who never had a problem. Are you architecting for recovery, or just hoping failures don't happen?

I didn't pick these signals arbitrarily. They emerged from two decades of watching contact centers either build or destroy patient relationships.

Here's what the research shows:

Accessibility matters financially. Every abandoned call represents potential revenue walking to a competitor. For a high-volume contact center, that's $45,000 per day in lost opportunity. Intermountain Health deployed 24/7 AI-assisted access and achieved an 85% reduction in call abandonment. Not because the AI was magic. Because patients could reach them when they needed help.

Resolution drives retention. According to SQM Group research, every 1% improvement in First Contact Resolution correlates with a 1% improvement in customer satisfaction and a 1% reduction in operating costs. But here's the catch: most organizations measure whether they closed the ticket, not whether the patient believes the problem is solved. Houston Methodist achieved 91% of calls resolved without agent escalation by architecting for true resolution.

Continuity builds relationships. Research from Accenture shows that 68% of patients want providers who know their history without having to repeat it. Yet how many times does your system ask for the same information the patient already provided?

Proactivity predicts loyalty. Systems with proactive outreach see 40% higher patient engagement, according to KLAS Research. Patients who receive proactive care gap notifications are significantly more likely to complete recommended screenings and follow-up appointments.

Recovery transforms detractors into advocates. The Service Recovery Paradox is well-documented in customer experience literature. Harvard Business Review research confirms that effective service recovery can increase customer loyalty beyond pre-failure levels.

Here's where this gets complicated.

According to a 2024 Tebra survey, 71% of patients now trust AI tools like ChatGPT for health information. They're not doing that because they distrust their doctor. They're doing it because they want answers without friction. Without hold times. Without repeating their story.

AI can accelerate every Trust Signal. Or it can destroy them.

AI that builds

✅  Recognizes returning patients and preserves context across channels (Continuity)

✅      Handles routine inquiries instantly so humans can focus on complex cases (Accessibility)

✅     Surfaces patient history to agents before they answer (Resolution)

✅      Triggers proactive outreach for care gaps and billing alerts (Proactivity)

✅      Flags service failures in real-time for immediate recovery (Recovery)

AI that breaks trust

❌       Forces patients through IVR mazes with no human escape path

❌     Deflects calls without actually resolving the underlying issue

❌     Loses context when transferring from chatbot to agent

❌      Creates one more system patients have to navigate

❌       Delivers scripted responses that make patients feel like ticket numbers

The technology was never the problem. The strategy was.

According to Gartner, 73% of healthcare AI initiatives fail to scale beyond pilot phase. Not because the AI didn't work. Because organizations deployed it to cut costs without thinking about trust.

The question isn't whether to use AI. It's whether your AI deployment strengthens or weakens each Trust Signal.

Let me show you how this works in practice.

Scenario: A patient calls about a billing question.

Transactional System:

The satisfaction score looked fine. The trust was never built.

Trust-Building System:

Same call type. Completely different trust outcome.

Here's the practical part. For each signal, there are specific operational metrics you can track starting this week.

ACCESSIBILITY Metrics:

RESOLUTION Metrics:

CONTINUITY Metrics:

PROACTIVITY Metrics:

RECOVERY Metrics:

If you're a hospital system executive, here's the strategic shift:

Stop waiting for survey data to tell you whether you're building trust. By the time HCAHPS scores arrive, the decisions have been made. The Trust Algorithm gives you leading indicators you can monitor weekly, not quarterly.

Start connecting operational metrics to trust outcomes. Your contact center already generates the data. Your scheduling system already generates the data. The question is whether anyone is looking at it through a trust lens. When your team reports on abandonment rates, FCR, or handle time, ask: "What does this tell us about whether patients trust us more or less than last month?"

Evaluate every technology investment through the Trust Signal framework. Before you deploy that new AI chatbot or upgrade your IVR, ask: Which of the five signals does this strengthen? Which might it weaken? If you can't answer that question, you're not ready to deploy.

Protect the margin advantage. The 2.6x margin differential between high-performing and low-performing patient experience organizations isn't theoretical. It's real. But it doesn't come from satisfaction scores. It comes from operational behaviors that create trust at scale. The Trust Algorithm tells you whether you're capturing that advantage or letting it leak.

Use the diagnostic before the investment. Most organizations discover trust gaps after the damage is done. The assessment tool I've built scores your organization across all five signals and maps your results to the 3A Maturity Model. You'll see exactly where trust is leaking before you spend another dollar on technology that might make it worse.

Before you build a comprehensive Trust Signal dashboard, start with one question:

Call your own contact center at 2am tonight.

What happens? Voicemail? IVR maze? AI assistant? Human? Nothing?

That single test reveals your Accessibility signal. And it costs nothing.

If you want the full diagnostic, I've built an interactive assessment tool that scores your organization across all five Trust Signals and maps your results to the 3A Maturity Model (Automate, Augment, Amplify).

You already know trust drives margins. The Trust Thesis is established.

The question was always HOW to measure it. How to see the trust leaks before they show up in your retention data. How to know whether your AI investments are building trust or just processing transactions faster.

The Trust Algorithm gives you the diagnostic framework.

Five signals. All operational. All measurable. All predictive in ways that satisfaction surveys aren't.

Over the next five weeks, we're going to break down each signal in depth. You'll get case studies, metrics, quick wins, and implementation strategies for each one.

But you don't have to wait. The assessment tool is live. Your baseline score is waiting.

The question isn't whether you can afford to measure trust. It's whether you can afford not to while your competitors figure it out first.

Next week: Trust Signal #1, ACCESSIBILITY. We're going to talk about the 2am test, why 67% of patients have tried to reach their provider after hours, and what Intermountain Health did to turn accessibility into a competitive advantage.

What's one operational signal you suspect is leaking trust in your organization? Hit reply and tell me. I read every response.

Let's get to work.

— Ebony