19-Soft-B2 User Interface · May 2015
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
National Exams, May 2015 — 04-Soft-B2, User Interface (closed book, 3 hours). Part A: answer any FIVE of the NINE questions (10 marks each); Part B: answer ALL FIVE questions (10 marks each), all based on the same case study — Happy Medical Clinic, a Toronto medical clinic switching from paper-based practice to an electronic health record (EHR) system purchased from VisualEHR Inc., an off-the-shelf vendor willing to customize the product to the clinic's needs. Most questions call for essay-format answers; clarity and organisation count. This solution answers all fourteen questions as a full study resource.
Reference texts. Rogers, Sharp & Preece, Interaction Design: Beyond Human-Computer Interaction, 5th ed., Ch. 1–3 (interaction design, cognitive aspects, mental models), Ch. 9–10 (prototyping, personas), Ch. 11–12 (data gathering, requirements), Ch. 15–16 (evaluation, lab vs. field studies); Nielsen, Usability Engineering, Ch. 4–6 (usability heuristics, iterative design, usability testing); Shneiderman, Designing the User Interface, 6th ed., Ch. 2 (guidelines, principles), Ch. 12 (internationalization); Norman, The Design of Everyday Things, Ch. 1–4 (visibility, affordances, feedback, conceptual models).
Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.
A requirements analysis for the customization would combine several complementary data-collection methods, since Happy Medical Clinic itself has asked for (1) an assessment of current staff tasks, (2) an assessment of VisualEHR's off-the-shelf capabilities, and (3) recommendations for customization — a single method cannot answer all three on its own.
| Method | Advantage | Disadvantage |
|---|---|---|
| Interviews (with receptionists, nurses, physicians, clinic management) | Elicits rich, individual detail and the reasoning behind current practices, including workarounds and pain points not visible from outside | Time-consuming to schedule and conduct one-on-one across a working clinic; relies on what people say they do, which can differ from what they actually do |
| Direct observation of current (paper-based) workflow | Captures how work is actually performed, including informal steps, interruptions and workarounds staff may not think to mention in an interview | Presence of an observer can change behaviour (observer effect); observing a full range of scenarios (rare but important cases) may require a long observation period |
| Document analysis (existing paper forms, the current VisualEHR off-the-shelf feature list/manual) | Grounds requirements in concrete, existing artefacts — the exact data fields currently captured, and exactly which off-the-shelf features already exist — without needing to schedule anyone's time | Documents describe the intended/nominal process, not necessarily what staff actually do day to day; a vendor feature list may not reveal a feature's real usability in practice |
| Questionnaires/surveys (broader staff sample) | Efficient way to gather structured, comparable data (e.g. satisfaction ratings, frequency of specific pain points) from every staff member with minimal disruption to clinic operations | Limited depth — cannot probe or follow up on an unclear or surprising answer the way an interview can; response rate and honesty depend on question design |
The analysis would proceed by first using document analysis and a small set of interviews to build an initial task/feature picture, then observing a representative sample of real clinic sessions to validate and enrich that picture against actual practice, and finally using a broader staff questionnaire to check how widely-shared the pain points found in the smaller interview/observation sample actually are — triangulating across methods so that no single method's blind spot silently shapes the customization recommendations.
