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Agile Product Development
Digital Therapeutics & Life Sciences

Agile Delivery of a Regulated Digital Therapeutic App


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Overview

What we built

A digital therapeutics company was shipping one validated release every nine months while competitors shipped monthly. We rebuilt its delivery around agile release trains that live inside its design controls and end audit-ready every six weeks.

In plain terms: the company makes a prescription app that treats behavioural-health conditions, so every change must be documented and validated the way a medical product demands. Its quality system assumed big, infrequent releases, which meant even small patient-experience fixes dragged a full manual paperwork and validation pass behind them. Improvements queued for months, engineers spent more time producing documentation than product, and rivals were shipping monthly while the company managed one release every nine months.

We did not ask the company to loosen its controls; we rebuilt delivery to run inside them. Requirements, risk items and test cases were linked in one traceability system, the build pipeline was rewired to generate validation evidence automatically, and documentation templates were mapped to sprint outputs, so each six-week release train ends audit-ready by construction. Release cadence improved from one validated release every 9 months to six-week validated release trains, and 47 patient-experience improvements shipped in the first year, up from 6 the year before.

The Problem

Nine-month regulated release cycles

The nine-month cycle was not a process preference, it was baked into the quality management system. The QMS assumed big-bang releases, so every change, however small, pulled a full manual documentation and validation pass behind it. There was no light path for a copy fix and no heavier path for a clinical change: everything paid the same toll.

The people costs compounded. Patient-experience fixes queued for months while the current release ground through validation, engineers spent more time on paperwork than on the product, and the roadmap became a story of what would not ship this cycle. In a category where competitors were shipping monthly, a nine-month cadence was a strategic problem wearing compliance clothing.

Full regression validation alone consumed 3 weeks of manual testing per release, and the documentation regulators reasonably require was assembled by hand at the end of each cycle rather than produced as the work happened. The result was a team that dreaded releases, because every one meant weeks of retrospective evidence-gathering.

Big-bang quality system

The quality management system assumed large, infrequent releases, so even a trivial change triggered the full manual documentation and validation pass designed for major ones.

Queued patient fixes

Patient-experience improvements waited months for the next release window, leaving known irritations live in a prescription app for the whole queue's duration.

Paperwork over product

Engineers spent more time assembling documentation and validation evidence by hand than building the therapeutic features the roadmap promised, and the imbalance grew with every release.

Manual regression burden

Every release closed with 3 weeks of manual regression testing, a fixed toll that made frequent releases arithmetically impossible under the old model.

What it was costing them

One validated release every nine months meant only a handful of chances a year to improve a prescription product patients used daily, while competitors iterated monthly. Engineering capacity drained into hand-built documentation, patient-experience fixes aged in a queue, and each release carried the accumulated risk of everything bundled into it, exactly the fragility big-bang releasing is supposed to avoid.

The Solution

Agile inside design controls

The founding decision was to treat the design controls as a constraint to build within, not an obstacle to argue with. We restructured delivery into agile release trains that live inside the company's quality framework, so compliance and cadence stopped being opposites. Each train runs six weeks and is expected to end audit-ready, not to become audit-ready afterwards.

Traceability became infrastructure. Requirements, risk items and test cases were linked in a single traceability system, so the chain from a requirement to its risk assessment to its passing test exists continuously instead of being reconstructed at release time. The CI pipeline was rebuilt to generate validation evidence automatically on every build, turning what had been a manual end-of-cycle effort into a byproduct of normal engineering.

Documentation templates were mapped to sprint outputs, so the artefacts the quality system requires are produced as the work happens, already in the shape auditors expect. The overnight automated regression suite replaced 3 weeks of manual testing, removing the last structural blocker to a six-week cadence and giving every train the same exit bar.

Key decisions

01

Agile inside design controls

Release trains were designed to satisfy the existing quality framework rather than bypass it, so regulatory confidence survived the change in cadence.

02

One traceability spine

Requirements, risk items and test cases live linked in a single system, making the compliance chain continuous instead of reassembled for each release.

03

Evidence generated, not assembled

The rebuilt CI pipeline produces validation evidence automatically on every build, so proof of quality accumulates continuously rather than being compiled at the end.

04

Templates mapped to sprints

Documentation templates were aligned to sprint outputs, so completing the work and completing the paperwork became the same activity rather than two competing ones.

05

Automate the regression toll

Full regression validation moved from 3 weeks of manual testing to an overnight automated suite, removing the fixed cost that had made frequent releases impossible.

Measurable Impact

What changed after launch

Release cadence improved from one validated release every 9 months to six-week validated release trains, and the product felt the difference immediately: 47 patient-experience improvements shipped in the first year, up from 6 the year before. Fixes that would once have queued for months now ride the next train to patients.

The compliance burden fell as the cadence rose. Per-release documentation effort was cut by roughly 60% through automated requirements-to-test traceability, and full regression validation dropped from 3 weeks of manual testing to an overnight automated suite. Engineers now spend their time on the therapeutic product, and audits start from evidence that already exists.

Release cadence

One validated release every 9 months

Validated release trains every six weeks

Patient improvements

6 patient-experience improvements shipped in a year

47 shipped in the first year on the new cadence

Documentation effort

Full manual pass assembled at each release

Cut by roughly 60% via automated traceability

Regression validation

3 weeks of manual testing per release

An overnight automated suite on every train

Headline results

Release cadence improved from one validated release every 9 months to six-week validated release trains

47 patient-experience improvements shipped in the first year, up from 6 the year before

Per-release documentation effort cut by roughly 60% through automated requirements-to-test traceability

Full regression validation reduced from 3 weeks of manual testing to an overnight automated suite

Tech & Tools Used

What powered the build

Every tool below earned its place in this engagement. Here is the part each one played.

Jira logo

Jira

Ran the six-week release trains, with sprint outputs structured so that closing work in the board also produced the delivery records the documentation templates expect.

Jama Connect

The single traceability system linking requirements, risk items and test cases, keeping the compliance chain continuous from stated intent through to passing evidence.

GitHub Actions logo

GitHub Actions

The rebuilt CI pipeline, generating validation evidence automatically on every build and packaging it for each release train's audit-ready close.

React Native logo

React Native

The framework behind the prescription behavioural-health app itself, letting patient-experience improvements reach patients from a single codebase on every train.

Node.js logo

Node.js

Powered the backend services supporting the therapeutic app, updated inside the same release trains and covered by the same automatically generated evidence.

PostgreSQL logo

PostgreSQL

Stored the app's clinical and operational data, with schema changes flowing through the traceability system like any other risk-assessed change.

Cypress logo

Cypress

Ran the automated regression suite that replaced weeks of manual testing, executing overnight so every train enters validation already exercised end to end.

SonarQube logo

SonarQube

Enforced code-quality gates inside the pipeline, adding static analysis results to the evidence pack each build generates for the quality team.

AWS ECS

Hosted the backend services and kept deployments repeatable, so each six-week train releases to production through the same validated, containerised path.

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