DIGITAL HEALTH ADOPTION ENGINE · CONCEPT DEMONSTRATION ·
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— Door 02 · Show, don't tell
Adoption strategy,
already structured.
The fastest way to show I can do this brief is to do a slice of it. Here's a digital health adoption engine — a readiness/maturity model for adopting digital health technology, a map of how remote-monitoring data flows into a real clinical workflow, and an AI data-quality & evaluation rubric (Alignerr's own domain). The strategy thinking, demonstrated.
CONCEPT ONLY — This is my own concept work, built to demonstrate strategy and AI-evaluation craft. It is not Alignerr's programme; the model, workflow and rubric are illustrative starting points to be refined with clinical and technical subject-matter experts, and no real patient data is shown.
01Digital health readiness model · 5 levelsadopt only as fast as the workflow can absorb
01
Ad hoc
Digital tools used in pockets; data trapped in silos; no shared measure of whether they help.
02
Piloting
First telehealth or remote-monitoring pilots running; promising, but not yet integrated into routine care.
03
Integrated
Digital data flows into the clinical workflow; providers act on it; basic outcome tracking in place.
04
Optimised
Trusted real-time data embedded in decisions; measured improvement in care and operations; scaling across teams.
05
Continuous
Digital health is business-as-usual; outcomes monitored continuously and fed back into the model.
Every initiative is placed on this scale — so a strategy can say "this clinic is at Level 2; here's the staged path to Level 4," instead of pushing technology faster than the workflow can absorb. Levels would be validated with clinical SMEs.
02Clinical-workflow integration map · remote monitoringdata → decision, with trust gates
Capture
Wearable / home device records vitals
Trust gate · data quality
Validate, de-noise & flag bad readings
before they reach a clinician
Surface
Real-time view in the provider's existing tool — no new screen to learn
Trigger
Threshold breach raises an alert, routed to the right person
Act & record
Provider acts; outcome captured to measure impact
Design principle: the value isn't the device — it's trusted data arriving inside the workflow the clinician already uses. The two highlighted "trust gates" are data-quality steps; that's the part of this brief that is squarely Alignerr's, and mine. Operational specifics would be designed with clinical SMEs.
03AI data-quality & evaluation rubricthe "preferred" skill, demonstrated
Does the data / AI output match a verified ground truth? Wrong vitals or a wrong summary is worse than none.
0Wrong
1Risky
2Minor
3Verified
Is anything missing that a provider needs to act safely? Gaps are flagged, not silently dropped.
0Gaps
1Partial
2Mostly
3Full
Does trusted data arrive in time to change a decision? Late data is data-quality failure too.
0Stale
1Slow
2Timely
3Real-time
Safety / risk flag
weight ×3
Are uncertain or high-risk outputs flagged for human review rather than auto-trusted? The line I won't blur.
This is the kind of weighted evaluation rubric I build for AI and data systems — the "data annotation, data quality, evaluation systems" your brief prefers. A score below threshold on Accuracy or Safety holds the output out of a clinical decision until a human verifies it.