Eighteen contextual inquiries, synthesised into the evidence base NHS Digital adopted as the baseline for alpha funding.
Discovery for a national Electronic Patient Record programme, building the evidence base for an alpha funding decision.
Discovery's job is to turn opinion into evidence a funding panel can act on. This one did.
An EPR is not a product clinicians choose. It is the room they work in for an entire shift. Get it wrong and the cost is not churn, it is risk at the point of care. The health service needed evidence, not opinion, about what that room should contain before committing alpha funding to build it.
Clinicians and administrative staff worked across a patchwork of paper notes and legacy systems, with a single task often spanning four to seven separate platforms before it was complete.
My brief was to lead the discovery research: understand how clinicians and administrative staff actually work, turn that into evidence a governance panel could fund against, and define the record at specification level.
Interviews tell you what people believe they do. Watching them shows you the workarounds, and the workarounds are where the real requirements hide. I ran 18 contextual inquiry sessions across a mix of remote video and on-site visits, with consultants and doctors, medical secretaries, outpatient and clinic administrators, ward nurses, and frontline staff including ambulance crews.
I was looking for the texture of the work: where tasks get interrupted, where information is re-keyed because two systems refuse to talk, where staff keep a private paper backup because they do not trust the screen, and where a handoff between roles silently drops context.
The moment that organised the rest was small: an administrator working from a spreadsheet she kept herself, off to the side of the official system, because it was the only place the information she needed actually sat together. The same gap had a bigger shape across the service. No two hospitals ran the same system and none could pass a record to another, so when a patient was transferred their information went with them on paper, carried by hand. The record was already moving between people and places; it was just moving in private files and printouts rather than through anything designed. That set the problem: not a cleaner screen, but a way for the information itself to travel.
Raw research does not move funding decisions. Structure does. I synthesised the sessions into GOV.UK-style personas grounded in observed behaviour rather than job titles, a service blueprint connecting front-stage clinical tasks to the back-stage administrative and data flows that make them possible, and journey maps for the highest-risk workflows.
The choice of GOV.UK grammar was deliberate. The audience for this evidence was a national health governance panel that reads service assessments in that format every week. Familiar structure lowers the cost of trust: the panel could interrogate the content instead of decoding the format.
The backlog came last: 40 user stories, prioritised, each traceable back to an observed behaviour. Traceability was the discipline. A story that could not point to a session did not make the list.
Runs a medical admissions ward. Her shift is a sequence of interruptions; any task may be paused four times before it finishes.
Confident user of clinical systems; no patience for ones that waste her time. Uses assistive technology herself, so a screen that defeats a screen reader is not a nuisance, it is a wall between her and the work.
The output of discovery is not polished UI, and pretending otherwise misleads everyone downstream. I defined the EPR through user flows and wireframes at the fidelity the funding decision needed: enough structure to estimate against, enough honesty to leave room for alpha to learn.
The deeper question was structural. The record was fragmented not only across screens but across organisations: each site held its own and none could pass it on. So I specified the EPR less as one large system and more as a set of smaller, connected apps sharing a common record, on the model of an office suite where each tool does one job but works on the same documents. The design work was defining what each app owned and what they shared, and how a patient's record moved between them and across hospital boundaries, so the information travelled through the system instead of beside it on paper.
The flow I am most proud of is the clinical handover. Handover is the moment the paper bundle existed for: a patient crossing a boundary, a shift change or a transfer to another site, with their context at risk of being dropped. I designed it so the handover assembled itself from the record, the current picture and what was still outstanding held in one place, and travelled with the patient as data the receiving clinician acknowledged rather than as a printout someone hoped had arrived. The lo-fi flow below traces that path.
I led inclusive usability testing on the proposed flows: screen-reader walkthroughs, WCAG 2.2 checks, sessions with clinicians who rely on assistive technology and administrative staff across a range of ages and digital confidence.
Between the baseline flows and the revised designs, task-completion errors fell by 32%, measured across eight to ten defined tasks in moderated testing. In most products an error means frustration. In an EPR an error can mean the wrong information attached to the wrong patient. The 32% is a safety figure wearing a usability costume.
Three calls shaped the discovery more than any single deliverable.
Breadth against depth. Eighteen sessions across a national service is thin coverage by volume, so I traded breadth across specialties for depth in the highest-risk workflows, handover and patient transfers above all, where a mistake reaches the patient fastest. I chose them by clinical risk rather than by frequency.
Access against the clinical day. The constraint that bit hardest was not the COVID winter or the budget, it was getting time at all with overstretched frontline staff. Recruiting was the bottleneck, so I fit research around shifts rather than scheduling around mine, observing during real work and taking the people I could reach when I could reach them.
What we said no to. The programme wanted alpha to cover more than it responsibly could. Testing a record that wide would have meant testing none of it properly, so I argued to narrow scope to the workflows where getting it wrong carried real clinical risk, and to prove those before widening. Saying no to breadth was how the evidence stayed worth anything.
I presented the evidence base to the panel: the personas, the blueprint, the tested flows, the backlog, and the line from each recommendation back to an observed behaviour. The findings were adopted as the baseline for alpha funding.
That is the whole job of discovery, done: the programme moved forward standing on observed behaviour rather than assumption, and the alpha team inherited a map instead of a guess.
The design was never really the screens. It was how the record moved: what each part of the system owned, and how information crossed between people and between hospitals. The screens were where that became visible, but the structure underneath was the work. The other thing clinical software taught me, a lesson that held just as firmly when I moved into financial risk work, is that trust in a system is built at the moment of data entry, not at the dashboard. If the person putting the information in does not believe it will be held and used safely, every view downstream is standing on sand.