Clinical Diagnostics:
Head of Commercial & IT
in a regulated, NHS-facing lab.

The role

Head of Commercial & IT at a UK company specialising in cellular pathology — histopathology, cytology, and molecular diagnostics. The company operated as both an NHS service provider and a commercial diagnostics laboratory, which meant navigating two distinct procurement environments simultaneously: NHS framework agreements and commercial contract negotiation.

The role spanned commercial pipeline management, supplier contract negotiation, IT systems ownership, and the evaluation and procurement of emerging diagnostic technology — including AI-assisted digital pathology platforms at a time when the technology was moving from research to clinical deployment.

NHS procurement and commercial leadership

NHS procurement operates within a framework that rewards organisations that understand the rules and can structure proposals accordingly. Tender responses require financial modelling that accounts for NHS cost pressures, pathway integration evidence, and governance documentation that demonstrates quality assurance beyond what most commercial vendors provide by default.

Key commercial responsibilities:

• NHS trust-level contract negotiations for outsourced histopathology and cytology services — understanding both the clinical pathway requirements and the commercial constraints on NHS commissioners
• Supplier contract management across laboratory consumables, equipment service agreements, and digital platform licensing
• Commercial pipeline: identifying new NHS and private healthcare opportunities, managing bid timelines, coordinating bid teams across clinical, operational, and technical stakeholders
• Tender strategy and writing — framing technical capability in the language NHS procurement panels respond to: patient outcomes, pathway efficiency, governance maturity

Digital pathology AI platform evaluation

Between 2021 and 2023, digital pathology AI transitioned from academic proofs-of-concept to platforms seeking clinical deployment. As Head of IT, I was responsible for evaluating these platforms against real clinical deployment criteria — not benchmark accuracy on curated datasets, but performance in the operational context of a busy diagnostic lab with existing LIMS infrastructure, pathologist workflow constraints, and ISO 15189 quality governance requirements.

Platforms evaluated

The evaluation criteria were not purely technical. For each platform, the procurement decision required:

• UKCA/CE marking status and clinical evidence base — not just published accuracy claims, but evidence of performance in NHS-equivalent settings
• LIMS integration capability — most labs run HL7-based LIS/LIMS; a pathology AI that can't integrate without manual workarounds creates more risk than it removes
• Pathologist workflow compatibility — adoption failure is a technology failure, regardless of algorithmic performance
• Vendor clinical governance support — what does the vendor provide for validation documentation, change control, and post-deployment monitoring?
• Commercial terms — licensing models ranged from per-scan to site licence to outcome-linked; understanding the true cost at deployment scale required detailed modelling

HL7 and LIMS operations

The laboratory ran a HL7-native LIMS (Laboratory Information Management System) as the authoritative system of record for all specimen, result, and report data. HL7 v2 messaging governed the interfaces between the LIMS, the scanning infrastructure, the reporting system, and the NHS trust EPR (Electronic Patient Record) systems.

Operating in this environment meant understanding data integrity at a level where errors have patient-level consequences — a mislabelled specimen, a dropped message, a result attributed to the wrong encounter. IT responsibility included:

• Interface monitoring: watching HL7 message queues, identifying and resolving stuck messages, liaising with NHS trust IT for EPR-side issues
• New interface commissioning: working with vendors and NHS trust IT teams to configure and validate new HL7 interfaces for new clients
• Data integrity incidents: root cause analysis for any data discrepancy, documentation of findings and corrective action in the quality management system

ISO 15189-adjacent governance

ISO 15189 is the international standard for medical laboratory quality management systems. The laboratory operated within an accreditation framework that required documented evidence of competence, validated procedures, and controlled change management for any process or system that could affect diagnostic accuracy.

In practice, this meant:

• Every IT system change that touched clinical data flows required a documented change request with risk assessment, implementation evidence, and post-change verification
• Software validation for any new clinical application — not just installation testing, but documented evidence that the system performs as intended in the specific operational context
• Audit trail discipline as an operational norm — not a compliance checkbox, but the standard expectation for every system that handles patient data
• Supplier qualification: vendors supplying clinical-facing technology required documented assessment of their quality management and post-market surveillance practices

This background is the direct source of the architecture principles I now apply to AI systems: human gates before destructive actions, full audit trails, fail-safe rather than fail-silent, validated change control. These aren't abstract engineering values — they're practices I learned in an environment where the cost of getting them wrong was measured in patient outcomes.

NGS pipeline context

The company was developing next-generation sequencing (NGS) capability for molecular diagnostics — somatic mutation panels, BRCA testing, and liquid biopsy applications. As Head of IT, I was involved in the pipeline infrastructure decisions: sequencer selection, bioinformatics pipeline tooling, data storage architecture for the large file sizes that WGS and panel sequencing produce, and the LIMS integration required to move from sequencer output to reportable clinical result.

This is not deep bioinformatics expertise — but it is genuine first-hand familiarity with the operational and commercial reality of deploying NGS in a regulated diagnostics environment, which is a meaningfully different context from understanding NGS in the abstract.

What this experience transfers to

The instincts developed in this role inform everything I build now:

• Operator error is a design failure. If a clinical system can be used incorrectly by a trained BMS with no alert, the interface has failed — not the user. The same principle applies to any tool I build.
• Audit trails are a feature, not overhead. In a regulated lab, you can reconstruct every decision from the log. In my AI systems, every tool call, every model decision, every data write is logged.
• AI adoption requires workflow integration. A pathology AI that requires pathologists to break their existing workflow will not be used regardless of its accuracy. The lesson generalises.
• Regulated-sector thinking scales down. The governance disciplines from ISO 15189 don't require an accreditation body to be worth applying. They're just good engineering discipline under a different name.