Introduction of a Single Electronic Health Record for Maternity Units in Ireland: Outline of the Experiences of the Project Management Team
Bibliographic record
Abstract
Electronic health records (EHRs) are being introduced worldwide. The change from paper to electronic records has not always been a seamless or quick process; however, EHRs are viewed as central to updating modern health care, especially organization structures and delivery of sustainable care with the potential for joint decision-making with the patient. The objective of this viewpoint paper is to outline how an EHR is being developed in Ireland. The focus of the Maternal & Newborn Clinical Management System project is the design and implementation of an EHR for all women and babies in the maternity services in the Republic of Ireland. The paper also outlines the lessons learned from the planning to the optimization stage of the project. The paper was developed through discussions with the project management team and their completed reports that outline the lessons they acquired from each project stage. Key lessons learned from each stage of the project are highlighted. This viewpoint paper explains how the national project management team is implementing the EHR and outlines the experiences and lessons learned and the challenges ahead following the phase one introduction. The Maternal & Newborn Clinical Management System is an example of a clinician-led, patient-focused, change management project from its inception to implementation. The introduction of EHRs is essential in modernizing health care and optimizing patient outcomes through the accurate and appropriate use of data.
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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 | 0.071 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".