Digital Health Engagement Is a Journey Design Problem

A member opens a health platform after receiving an abnormal laboratory result.

The platform may contain the result, an interpretation, educational content, provider options, benefits information, messages, an AI assistant, reminders, and a recommended action. Every element may be useful.

But the member’s immediate questions are narrower:

What does this result mean, how concerned should I be, and what should I do next?

If the experience does not help the member answer those questions in a trustworthy sequence, the platform can technically contain everything they need and still fail at the moment that matters.

Nothing has to be functionally broken. The page can load, every button can work, and the task can be possible to complete. The failure can occur because the receiver cannot determine what deserves attention, which explanation applies to them, or why the recommended next step is credible.

This is not merely a copy problem. It is a digital health journey-design problem.

Platform consolidation changes the product problem

Digital health companies are trying to reduce fragmentation by bringing more of a person’s health experience together.

Personify Health describes care navigation that combines benefits, point solutions, educational resources, digital guidance, and human advocates. Carrot describes personalized support across fertility care, pregnancy and postpartum, adoption, menopause, men’s health, and other family-building needs. Fullscript Journeys connects patient intake, laboratory testing, AI-assisted interpretation, wellness plans, recommendations, and provider follow-up.

Other platforms make a similar strategic promise. Included Health presents integrated care, navigation, advocacy, and AI-powered guidance. Transcarent’s WayFinding combines benefits navigation, clinical guidance, scheduling, care delivery, and human support.

These descriptions come from the companies’ public materials. They are not evidence that any named product has the failures discussed below.

They do establish the strategic pattern: platforms are expanding from a point solution toward a connected front door, personalized guide, or longitudinal care journey.

That can remove fragmentation. It can also create a new design challenge:

As the platform becomes capable of doing more, it must become better at determining what should matter now.

The next product advantage may not come only from adding another service or generating a more personalized recommendation. It may come from making the appropriate next decision understandable, trustworthy, and feasible for the person receiving it.

A working concept: attention debt

Violet uses attention debt as a working product-strategy term, not as an established clinical measure.

Attention debt accumulates when a product repeatedly adds information, options, recommendations, alerts, and actions without preserving a clear path through them for a particular receiver and decision.

It can appear when:

a high-stakes result competes with promotional or educational content;
several clinically reasonable options are presented without a comparison path;
an AI recommendation appears before its source, limits, or escalation route;
the platform knows the member’s demographic profile but not their current question;
a new feature introduces another call to action into an already difficult decision;
different parts of the journey use different language for the same benefit, condition, or action;
engagement metrics reward clicks or repeat visits without showing informed action or benefit.

This is not a claim that less information is always better. Some health decisions require detail, deliberation, and productive friction. The problem is unnecessary competition and unsupported transitions—not thought itself.

Real issue 1: access to a result is not comprehension

Patient portals have made laboratory and imaging results more available. That access is valuable, and patients generally want it.

But access and understanding are separate outcomes.

In a mixed-methods study of adults who viewed test results through portals, 63% reported receiving no explanatory information or interpretation with the result, and 46% searched online for more information. Participants receiving abnormal results reported negative emotions more often than those receiving normal results. The authors concluded that access alone was insufficient to support meaningful interpretation. Read the study in the Journal of the American Medical Informatics Association.

A larger survey later found that most respondents still preferred immediate access, even before a practitioner reviewed the result. A subset experienced greater worry, particularly when results were abnormal. Read the JAMA Network Open study.

The design implication is not “hide abnormal results.” It is to support the transition from result to meaning to appropriate next action.

For example, a result journey may need to make the following distinctions obvious:

What was measured?
Is the result inside or outside the relevant range?
What can and cannot be inferred from that result alone?
Who supplied the interpretation?
What action is recommended, how urgent is it, and why?
When should the patient contact a clinician or seek urgent care?
What happens after the patient selects the next action?

A dashboard can expose the correct data while leaving several of those transitions unsupported.

Real issue 2: “personalized benefits” still arrives in a human moment

Consider two members looking at the same fertility-benefits experience.

One has researched IVF for months and wants to determine whether a specific clinic, medication, and treatment cycle are covered. The other has just learned that conceiving may be difficult and does not yet know which kind of support to request.

Both may meet the eligibility rules for the same benefit. They do not have the same immediate job.

The first member may need:

plan-specific coverage and exclusions;
clinic and provider-network information;
cost, authorization, and timing;
a direct route to verify an edge case.

The second may need:

a plain-language orientation;
reassurance about privacy and confidentiality;
help understanding available paths without premature commitment;
a low-pressure way to speak with a qualified person.

Showing each member “personalized content” is not enough if the experience does not distinguish between exploration, comparison, coverage confirmation, and care initiation.

Context changes the interpretation again. A benefits page reviewed privately at home is not the same experience as the identical page opened at work, immediately after a clinical conversation, or while a partner is present.

The receiver is not merely a stable member profile. They are a person with a current job, prior knowledge, social surroundings, time constraints, and a consequential stake.

Real issue 3: a recommendation can mix several kinds of authority

Connected care journeys increasingly combine data, interpretation, clinical guidance, product recommendations, and purchasing or booking actions.

Imagine a patient receives:

laboratory results;
an AI-assisted interpretation;
a provider-reviewed wellness plan;
supplement, nutrition, and lifestyle recommendations;
a purchase action;
a reminder to retest later.

The journey may be coherent operationally. The patient still has to distinguish:

the observed result from its interpretation;
clinical guidance from a commercial recommendation;
a general educational statement from advice tailored to them;
evidence from model-generated explanation;
an optional action from a medically necessary one;
automated assistance from accountable human review.

The stronger experience does not merely make the purchase or booking button prominent. It makes the source, rationale, limits, alternatives, and follow-up path visible at the point each becomes necessary.

This matters especially when AI is involved. A fluent recommendation can be easy to understand linguistically while remaining difficult to evaluate clinically.

Engagement is often measured too early in the path

Digital health teams frequently use “engagement” to describe several different events:

opening a message;
logging into a platform;
reading content;
completing an assessment;
speaking with an advocate;
booking care;
following a plan;
achieving a clinical or financial benefit.

Those events are not interchangeable.

A 2024 systematic review found more than 60 terms used to define engagement across mobile-health studies. Log-in data was the most common measurement approach, while engagement often decreased over time. Read the JMIR review.

Another systematic review and meta-analysis estimated a 43% pooled dropout rate across app-based chronic-disease interventions, but also reported extremely high variation across studies and definitions. The responsible conclusion is not that every health app loses 43% of its users. It is that attrition is common, context-dependent, and easy to obscure with inconsistent measurement. Read the review.

This distinction changes product diagnosis.

A patient who does not book may:

never have noticed the recommendation;
have noticed it but not understood the reason;
understand it but distrust the source;
trust it but face cost, access, time, or authorization barriers;
intend to act but lose the path between systems;
understand everything and make an informed decision not to proceed.

“Low engagement” describes the observed outcome. It does not identify the failed transition.

Audit the path with five connected questions

The Violet 5S™ method examines a message or journey relative to a receiver, context, and target transition.

Lever	Digital health journey question
Spotlite	What is likely to receive attention first, and does it point toward the decision that matters now?
Substance	Does the receiver have the evidence and explanation needed to form an accurate interpretation?
Source	Is it clear who produced, reviewed, or is accountable for the result, recommendation, or benefit information?
Strain	What unnecessary cognitive, emotional, procedural, financial, or access burden interrupts the path?
Stake	Does the experience recognize what the receiver may gain, protect, fear, postpone, or reasonably decline?

These questions should not become a universal score.

A high-stakes cue can make a recommended action salient while also increasing anxiety. More substance can improve informed choice while increasing strain if it appears too early. A familiar clinical source can increase trust, but a commercial source may require clearer disclosure and evidence.

The useful configuration depends on the actual receiver and transition.

Test journeys, not only isolated screens

Functional tests answer whether a workflow can be completed.

Usability studies can reveal whether representative participants can complete it, what they say, and where they struggle.

An attention-oriented journey audit adds another layer:

At each step, what must become noticeable, interpretable, credible, and possible before the receiver can move appropriately?

For a benefits-navigation journey, the sequence might be:

Recognize the relevant benefit → understand eligibility → compare appropriate options → estimate cost and access → choose digital or human support → book or decline

For an abnormal-result journey:

Notice the result → understand its meaning and limits → determine urgency → identify the accountable source → select follow-up → receive confirmation

At each transition, product teams can test:

what participants noticed first;
how they described the result or recommendation in their own words;
which source they believed was responsible;
what they predicted would happen after acting;
where they hesitated, backtracked, searched elsewhere, or requested help;
whether the action produced an appropriate downstream outcome;
whether comprehension and performance differed across health and digital literacy, role, device, language, or context.

The goal is not to replace interviews, usability studies, accessibility testing, clinical safety review, or behavioural data. It is to connect those methods to an explicit model of the transition being designed.

AI increases the need for attention governance

AI can help health platforms interpret data, select content, personalize guidance, answer benefits questions, and coordinate next steps.

It also creates new questions:

When should a recommendation appear?
What evidence and uncertainty should accompany it?
Which nearby content could change its interpretation?
What should the system temporarily suppress?
When must a clinician, advocate, or other qualified person enter the path?
Can the receiver tell what was automated and who remains accountable?
Is the recommended action optimized for patient benefit, platform engagement, cost, or another objective?

The product problem is not solved when the model selects a plausible next action. The person still has to notice, understand, trust, and appropriately act on it.

A practical pre-release review

Before releasing a new digital health journey or materially changing an existing one:

Define the target transition. Replace “increase engagement” with a specific, observable, and ethically appropriate next state.
Identify materially different receivers. Use actual research where available; keep unknown states as hypotheses rather than synthetic facts.
Map the contextual field. Include device, location, timing, social setting, institutional rules, and competing tasks when they can alter the experience.
Capture the real sequence. Review the path from entry cue through explanation, evidence, action, confirmation, and follow-up.
Apply the 5S questions. Identify cue competition, missing substance, unclear authority, avoidable strain, and receiver stakes.
Elicit competing interpretations. Ask participants what they think is happening, why the action is recommended, and what they expect next.
Measure the chain. Separate exposure, attention, comprehension, trust, action, downstream benefit, and guardrails.
Test the rival explanation. Determine whether the real barrier is information design, cost, access, policy, service quality, or an appropriate decision not to act.

This process can be conducted manually before it becomes a recurring or automated product capability. The evidence standard matters more than whether an agent produced the first draft.

Attention may become a digital health product moat

The original strategic claim can now be stated more carefully:

As digital health platforms integrate more services and AI-generated guidance, the ability to organize attention around an informed next step may become a meaningful product advantage.

That advantage will not be demonstrated by an “attention score.” It will be demonstrated when teams can show that people with different needs understand the right information, reach appropriate care more reliably, make fewer avoidable errors, and receive better outcomes without coercion or hidden trade-offs.

More capability should not require the patient or member to perform more integration in their head.

The strongest platform may be the one that can make a comprehensive system feel like a coherent next step.

Violet applies this perspective through free focused audits of one message and consulting on larger transition problems. The objective is not to predict what every patient will do. It is to identify evidence-backed risks, redesign the path, and define a test that could prove the diagnosis wrong.