An Australian survey of health professionals’ perceptions of use and usefulness of electronic medical records in hospitalised children’s pain care
Bibliographic record
Abstract
Pain in hospitalised children is common, yet inadequately treated. Electronic medical records (EMRs) can improve care quality and outcomes during hospitalisation. Little is known about how clinicians use EMRs in caring for children with pain. This national cross-sectional survey examined the perceptions of clinician-EMR users about current and potential use of EMRs in children’s pain care. One hundred and ninety-four clinicians responded ( n = 81, 74% nurses; n = 21, 19% doctors; n = 7, 6% other); most used Epic ( n = 53/109, 49%) or Cerner ( n = 42/109, 38%). Most ( n = 84/113, 74%) agreed EMRs supported their initiation of pharmacological pain interventions. Fewer agreed EMRs supported initiation of physical ( n = 49/113, 43%) or psychological interventions ( n = 41/111, 37%). Forty-four percent reported their EMR had prompt reminders for pain care. Prompts were perceived as useful ( n = 40/51, 78%). Most agreed EMRs supported pain care provision ( n = 94/110, 85%) and documentation ( n = 99/111, 89%). Only 39% ( n = 40/102) agreed EMRs improved pain treatment, and 31% ( n = 32/103) agreed EMRs improved how they involve children and families in pain care. Findings provide recommendations for EMR designs that support clinicians’ understanding of the multidimensionality of children’s pain and drive comprehensive assessments and treatments. This contribution will inform future translational research on harnessing technology to support child and family partnerships in care.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".