OpenELIS is the open-source laboratory information system used by high-volume labs in lower-resource settings. The study examined how usability issues compound when infrastructure, staffing, and workflow conditions vary site to site — and which interventions would matter most.
The system had been deployed in over a dozen countries, but the team didn't have a comparative picture of how usability issues showed up across sites. Single-site reports were rich but anecdotal; bug trackers captured failures but not friction. The study filled that gap.
The study deliberately staged contextual interviews before usability sessions so that the moderation script could be calibrated against each site's actual workflow vocabulary. A structured but human protocol kept four moderators consistent without sacrificing probing depth.
Synthesis after the third site revealed that most issues weren't site-specific — they were workflow-stage specific. The bubble cluster below shows issues grouped by emergent theme, scaled by frequency.
Friction score = severity (1–4) × frequency (% sessions affected) × site count (1–4). The top three issues alone accounted for 41% of session-time lost across all four countries.
Issues plotted by severity (y) and frequency (x). The top-right quadrant — high frequency, high severity — became the must-fix set for the next deployment cycle.
The report distinguished interface changes (immediate) from workflow changes (next release) from training-material changes (parallel). Each recommendation was tied to specific issues from the matrix.
Delete-sample and re-accession actions in the current build proceeded without confirmation. In 11 of 16 sessions, participants triggered one of these by mistake and had no clear recovery path. A two-step confirm + 30-second undo eliminates the failure mode entirely.
Interface · immediate · ties to #001, #004, #007The current screen optimizes for keystroke count. Participants consistently treated it as a verification step — re-reading the printout, cross-checking — but the layout encouraged keyboarding past the very fields that were most error-prone. Reorder columns, surface prior values, add inline mismatch highlighting.
Workflow · next release · cluster T-03Finding "who changed what when" required leaving the working context, pulling a separate audit report, and matching by timestamp. Three of four sites had developed informal workarounds (paper logbooks) that this fix would replace.
Interface · next release · ties to #022–#031Côte d'Ivoire participants encountered three distinct French translations of the same destructive-action confirmation. Two of the three could be read as the opposite of the intended meaning. A short translation review cycle resolves this without a code change.
Localization · immediate · ties to #034, #035All sites had intermittent connectivity. None of the participants could correctly describe what happened to data captured during an outage. The behavior is well-defined in the codebase but invisible to operators. Documentation alone closes most of the perceived risk.
Training · parallel · cluster T-06A meta-recommendation: deployment partners reported they were often surprised by behavior changes between releases. A short standardized panel would let trainers prepare site staff before a rollout.
Operational · ongoing · cross-clusterThe deployment team prioritized the confirmation + audit + translation fixes (R-01, R-03, R-04) for immediate inclusion. The verification-flow restructure (R-02) became a roadmap item; the documentation pass (R-05) shipped alongside the next operator manual update.
Run a pilot earlier. An earlier pilot, ideally with international participants, would have helped refine task wording, post-task questions, timing, and the cultural shape of feedback before the full study began.
A structured but human interview script made the sessions stronger. It created consistency across moderators while leaving room to probe, clarify, and respond naturally.
This study deepened my appreciation for well-documented qualitative and quantitative analysis. Ranking issues by frequency and severity made the recommendations much more credible and actionable.
OpenELIS supports clinics and laboratories doing essential infectious-disease work. The methodological discipline wasn't academic posturing — it was the bridge between what we observed and what could actually ship.
Parent case study spanning discovery, design systems, AI tooling, and operations.
Read parent → 03 / 2018–2019Dual-path checkout shipped across two brands from a single spec.
Read case → 06 / TemplateThe data-vis scrollytelling template the synthesis above could be re-rendered through.
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