Charting the course: Insights into EMR usability from Australian clinicians – A national survey
Bibliographic record
Abstract
Electronic Medical Record Systems (EMRs) are integral to the work of nursing, medical and allied health professionals in Australia and other countries. Successful adoption of EMR systems is reliant upon their usability and effective use. Usability issues impact safety and quality, workflow, communication, and collaboration. The objective of the study was to measure clinician (nurse, medical and allied health professionals) experience of EMR usability in Australia.We conducted an observational study using a validated, cross-sectional survey, the National Usability-focused Health Information System Scale (NuHISS). Thirteen usability statements collect clinician impressions of EMRs related to ease of use, benefits and collaboration and technical quality. This paper presents responses of Australian clinicians using EMRs in primary care, hospitals and public and private sectors.In 2023, 534 health professionals from Australia submitted valid survey responses. The largest respondent group comprised nurses and midwives, working in publicly funded hospitals and having over three years of experience with the EMR mainly used. A majority (69%) agreed that the EMR system is stable and does not crash and 62% felt that the system responds quickly to inputs. Regarding ease of use of the EMR, 50% disagreed that the arrangement of fields and functions is logical, while 58% found the terminology clear and understandable. Sixty-two percent (62%) disagreed that routine tasks can be performed without extra steps, and 65% felt that significant training to learn the EMR is required. Although 63% agreed it is easy to obtain necessary patient information, 45% disagreed that entering and documenting data is quick and smooth. There were mixed responses regarding the EMR system's role in preventing medication errors, with 50% agreeing that it helps prevent errors and 27% disagreeing. There was agreement (74%) that the EMR system supports collaboration and information sharing within the same health service. Respectively, 51% and 47% disagreed regarding support of their EMR for collaboration between different health services and between clinicians and patients.We highlight the importance of understanding clinicians’ experiences with EMR usability. Our findings suggest areas where EMR usability can be strengthened to enhance user experience and support clinicians in delivering high quality, safe care. The study’s findings provide valuable insights for EMR system developers, vendors, and healthcare organisations, emphasising the need to improve usability to realise the full benefits of EMRs and support a digitally enabled healthcare system. Addressing these issues through targeted interventions is essential to enhance clinician satisfaction with the EMRs used, reduce burnout and improve patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".