Evaluation of the provincial Electronic Health Record HEALTHe NL and the HEALTHe NL online learning module: the nurse practitioner perspective
Bibliographic record
Abstract
Background: Newfoundland and Labrador’s Electronic Health Record, HEALTHe NL, provides clinicians with a holistic view of their patients’ health information. The HEALTHe NL online learning module was implemented in April 2018 to facilitate the orientation and adoption of HEALTHe NL into clinical practice. The current versions of these electronic systems had not been evaluated to date. Purpose: Evaluation of HEALTHe NL and the online learning module from the perspective of nurse practitioners (NPs) was important to determine the benefits and challenges of these electronic systems, to enhance patient care, and to improve the availability of relevant content to healthcare providers. Methods: The following methods were used: 1) literature review, 2) consultations with NPs and the Newfoundland and Labrador Centre for Health Information employees via semi-structured interviews and email, 3) environmental scan with other provinces throughout Canada via email, and 4) survey development using SurveyMonkey. Results: A survey was disseminated to 147 NPs in Newfoundland and Labrador who are active HEALTHe NL users. Questions focused on the content featured under each of the five tabs in HEALTHe NL and organized under headings associated with the Delone and McLean framework: 1) system and information quality, 2) service quality, 3) use and intention to use, 4) user satisfaction, 5) net benefits and, 6) demographic information. Survey results were positive. Participants were ‘very satisfied’ with HEALTHe NL, ‘definitely’ likely to recommend it, and ‘one-on-one demonstration’ was rated as the preferred method of training. Conclusion: The results of this evaluation survey will help support the continued use of these electronic systems, help promote the continuity of patient care, and help to identify ways to improve utilization of HEALTHe NL in the future.
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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 | 0.088 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".