Five digital priorities for medical device companies

Where digital effort actually pays in a device business: AI in design, compliance as a system, interoperability, security controls and predictive service.

19 May 2025 6 min read Updated 28 September 2026

Five digital priorities for medical device companies

Digital programmes in device companies fail in a characteristic way: they are run as IT projects in a business where almost everything that touches the product is a regulated change.

The priorities below are ordered by where the effort actually returns something, given that constraint.

1. AI in design and development, before AI in the device

Putting a model inside a cleared device is the ambitious version and carries the full regulatory weight — under the EU AI Act, a system influencing a clinical decision is high-risk, on top of MDR obligations.

The faster return is AI applied to the work around the device: searching prior design history, drafting documentation, summarising complaint data, finding patterns in post-market reports that a person would need weeks to surface.

These share a useful property — a qualified person reviews the output, so the system is decision support rather than a decision. That keeps it in a far lighter regulatory tier while removing real effort from a team that is usually short of it.

Sequence it that way: AI in the process first, AI in the product when the evidence and the governance are ready.

2. Compliance as a system, not a department

The single biggest efficiency difference between device companies is whether compliance evidence is generated or assembled.

Assembled means a team compiles the traceability matrix, collects test evidence and reconstructs the change history before an audit. It works, it is slow, and it scales badly — the effort grows with every release while the team does not.

Generated means the pipeline emits it: requirements linked to tests, tests linked to results, every build traceable to a commit, every change classified as it is made. Under IEC 62304 this is the same deliverable either way; the difference is whether it costs a week per release or nothing.

This is usually the highest-return digital project in a device business, and it rarely appears on a transformation roadmap because it produces no customer-facing feature.

3. Interoperability, which is where clinical value shows up

A device generating data that cannot enter a clinical workflow produces reports rather than outcomes.

HL7 and FHIR for clinical data exchange, DICOM where imaging is involved. These are international standards and carry over unchanged between markets — one of the few areas in this industry where that is true.

The practical priority is not exposing the interface immediately. It is making sure the internal data model can express the standard, because retro-fitting that onto a proprietary model is a migration that always arrives with a customer deadline attached.

4. Security and privacy controls, at the level of access

Device companies hold some of the most sensitive data there is, and the regulatory expectations reflect it.

Under revDSG/nDSG for Swiss data and GDPR for EU data, health data carries the strictest handling requirements. The controls that matter in practice:

Access, not just storage. Where data sits is the easy half; who can reach it — including supplier support staff in other jurisdictions and sub-processors behind them — is the half that gets audited.
Data minimisation as an architectural choice. Pseudonymising at collection permanently reduces obligation. It is the cheapest control available and the most often skipped.
Non-production separation. A copy of production data in a test environment is a common and serious finding. Synthetic or pseudonymised data only.
Audit trails as regulated artefacts. Retained deliberately, with a defined retention period — not left to a default.

Add to this the security expectations that arrive from customers: hospitals and insurers ask about incident response, sub-processors and exit paths during procurement, and a slow answer costs time in a sales cycle that is already long.

5. Predictive service and digital twins, where the installed base justifies it

Predictive maintenance and simulation are genuinely valuable for devices with a substantial installed base and meaningful service costs. They are also the priority most often started too early.

The precondition is telemetry — structured, reliable data from devices in the field. Without it, a predictive model is trained on service tickets, which record what was done rather than what happened.

The useful sequencing: build the telemetry for post-market surveillance, which MDR expects anyway, and the predictive capability becomes an incremental step on data you already had to collect. Starting from the model rather than the data is how three-year initiatives with nothing to show begin.

What this means for a roadmap

Two of these five — compliance generation and telemetry — are foundations that the other three depend on. They produce no demo and they determine whether the rest is affordable.

A defensible device-company roadmap puts those first, states what changes with a number and a date, and says explicitly what is not being attempted this year.