Efects of COVID‑19 on Irish general practice activity from 2019 to 2021: a retrospective analysis of 500,000 consultations using electronic medical record data
Bibliographic record
Abstract
Background General practice (GP) is crucial to primary care delivery in the Republic of Ireland and is almost fully com?puterised. General practice teams were the frst point of contact for much COVID-19-related care and there were concerns routine healthcare activities could be disrupted due to COVID-19 and related restrictions.Aims The study aimed to assess efects of the pandemic on GP activity through analysis of electronic medical record data from general practice clinics in the Irish Midwest.Methods A retrospective, descriptive study of electronic medical record data relating to patient record updates, appointments and medications prescribed across 10 GP clinics over the period 2019–2021 inclusive.Results Data relating to 1.18 million record transactions for 32 k patients were analysed. Over 500 k appointments were examined, and demographic trends presented. Overall appointment and prescribing activity increased over the study period, while a dip was observed immediately after the pandemic’s arrival in March 2020. Delivery of non-childhood immunisations increased sixfold as a result of COVID-19, childhood immunisation activity was maintained, while cervical smears decreased in 2020 as the screening programme was halted. A quarter of consultations in 2020 and 2021 were teleconsultations, and these were more commonplace for younger patients. Conclusions General practice responded robustly to the pandemic by taking on additional activities while maintaining routine services where possible. The shift to teleconsulting was a signifcant change in workfow. Analysing routinely col?lected electronic medical record data can provide valuable insights for service planning, and access to these insights would be benefcial for future pandemic responses
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".