HEOR for Digital Health Tools

Digital health success means better outcomes at a reasonable cost—engagement alone is not the goal. Begin by naming outcomes that matter and a timeframe; the checklist in choosing outcomes that matter keeps teams honest. Because evidence will rely on routine data and program telemetry, align stakeholders with real‑world evidence in healthcare decision‑making and maintain input quality with EHR data quality for real‑world evidence.

Outcomes that reflect benefit

Tie endpoints to clinical or behavioral changes, not clicks:

• A1c reduction and medication persistence for diabetes apps
• Symptom control and exacerbation‑free days for respiratory tools
• Postpartum BP checks and severe events for maternal programs
• Depression scores and remission for mental health tools

Pair outcomes with experience measures (“felt respected,” privacy) and equity views by language and neighborhood.

Pragmatic comparators and designs

Pick comparators people actually have: usual care, waitlist, or alternative tools. Favor designs that fit busy clinics: staggered rollouts, registry‑based randomization, or encouragement designs per pragmatic trials and RWE: better together. Pre‑specify outcomes and windows.

Adherence and engagement, measured usefully

Define meaningful use up front (e.g., completed modules linked to outcomes). Track persistence, switching, and reasons for discontinuation. Publish a model card if prediction personalizes content; include subgroup performance and calibration, echoing fairness habits from AI for population health management.

Costs, value, and budget impact

Show incremental costs (licensing, devices, staff time) and effects (outcomes moved, ED visits avoided). Present both cost‑effectiveness and budget impact. For framing, borrow basics from Health Economics 101 for Clinical Teams.

Privacy and trust

Minimize data; encrypt; log access; provide clear consent. Avoid secondary uses that surprise patients. For reproductive health features, follow autonomy‑preserving guidance in AI‑supported contraceptive counseling.

Case vignette: remote coaching for hypertension

Context: A health system evaluates a coaching app plus home BP cuffs.

• Outcomes: day‑10 postpartum checks for high‑risk patients; mean systolic reduction at 90 days.
• Design: stepped‑wedge rollout across clinics; registry‑based outcomes.
• Costs: licensing, cuffs, staff time; avoided ED visits and admissions.
• Results: day‑10 checks rise to 67%; severe events fall by 24%; net savings in high‑risk cohorts.

Common pitfalls (and fixes)

• Engagement as the endpoint → tie use to outcomes and value.
• Fancy models with no fairness checks → publish subgroup performance and calibration.
• No comparator → design a feasible rollout that supports causal inference.
• Hidden costs → include staff time and device logistics.

Implementation checklist

• Name outcomes and windows; align to patient‑centered metrics.
• Choose pragmatic comparators and register a simple evaluation plan.
• Define meaningful use; track persistence and reasons for discontinuation.
• Present both cost‑effectiveness and budget impact.
• Publish a privacy plan and fairness metrics when personalization is used.