Assessing the Usability of a Clinical Decision Support System: Heuristic Evaluation
Bibliographic record
Abstract
BACKGROUND: Poor usability is a primary cause of unintended consequences related to the use of electronic health record (EHR) systems, which negatively impacts patient safety. Due to the cost and time needed to carry out iterative evaluations, many EHR components, such as clinical decision support systems (CDSSs), have not undergone rigorous usability testing prior to their deployment in clinical practice. Usability testing in the predeployment phase is crucial to eliminating usability issues and preventing costly fixes that will be needed if these issues are found after the system's implementation. OBJECTIVE: This study presents an example application of a systematic evaluation method that uses clinician experts with human-computer interaction (HCI) expertise to evaluate the usability of an electronic clinical decision support (CDS) intervention prior to its deployment in a randomized controlled trial. METHODS: We invited 6 HCI experts to participate in a heuristic evaluation of our CDS intervention. Each expert was asked to independently explore the intervention at least twice. After completing the assigned tasks using patient scenarios, each expert completed a heuristic evaluation checklist developed by Bright et al based on Nielsen's 10 heuristics. The experts also rated the overall severity of each identified heuristic violation on a scale of 0 to 4, where 0 indicates no problems and 4 indicates a usability catastrophe. Data from the experts' coded comments were synthesized, and the severity of each identified usability heuristic was analyzed. RESULTS: The 6 HCI experts included professionals from the fields of nursing (n=4), pharmaceutical science (n=1), and systems engineering (n=1). The mean overall severity scores of the identified heuristic violations ranged from 0.66 (flexibility and efficiency of use) to 2.00 (user control and freedom and error prevention), in which scores closer to 0 indicate a more usable system. The heuristic principle user control and freedom was identified as the most in need of refinement and, particularly by nonnursing HCI experts, considered as having major usability problems. In response to the heuristic match between system and the real world, the experts pointed to the reversed direction of our system's pain scale scores (1=severe pain) compared to those commonly used in clinical practice (typically 1=mild pain); although this was identified as a minor usability problem, its refinement was repeatedly emphasized by nursing HCI experts. CONCLUSIONS: Our heuristic evaluation process is simple and systematic and can be used at multiple stages of system development to reduce the time and cost needed to establish the usability of a system before its widespread implementation. Furthermore, heuristic evaluations can help organizations develop transparent reporting protocols for usability, as required by Title IV of the 21st Century Cures Act. Testing of EHRs and CDSSs by clinicians with HCI expertise in heuristic evaluation processes has the potential to reduce the frequency of testing while increasing its quality, which may reduce clinicians' cognitive workload and errors and enhance the adoption of EHRs and CDSSs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.092 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".