5PSQ-134 Unit dose in a cyberattack scenario
Bibliographic record
Abstract
Background and Importance At dawn on the 26th of April 2022, our hospital suffered a cyberattack. All hospital´s computer systems and applications were inaccessible, and the network and most workstations inoperable. The only few computers that remained operational were standalone, that is, not connected to a network. The institutional email was only available on mobile phones. At that time, we were considered a paper-free hospital, totally computerised, with electronic patient records and online prescription totally implemented, and pharmaceutical procedures highly dependent on technology and automation so, it was particularly challenging to continue to provide pharmaceutical care in this scenario. Aim and Objectives Description of procedures implemented in a scenario of cyberattack by the pharmacy department and establishment of preventive measures for the future. Material and Methods This study is a description of a case. Results Due to lack of access to clinical and pharmacotherapeutic profile of patients, it was necessary to reverse the prescription for paper support, in inpatient wards. The Kardex System remained operational, having been disconnected from the network in a timely manner, allowing the reconstitution of the history treatment of patients through the previous day therapeutic map files. Microsoft Excel files were created for all patients admitted to services with unit dose distribution, using laptops stand-alone. The communication with the nursing team was made daily, by telephone, with conference of all the patients. The Excel files with the transcription of the prescriptions, per patient, were manually coded by service, patient and drug, and, at the end of the day, transformed into the appropriate format to be correctly read by Kardex system, transferred to it by pen-drive, allowing the Unit Dose preparation.Contact was strengthened with the medical and nursing staff to avoid duplication of drugs or inadequate posology errors. Paper file folders were created by service for all prescriptions made, and updated daily.All Excel files were posteriorly accounted for regularisation of consumption. Conclusion and Relevance In this cyberattack context, it was evident the difficulty in reversing the prescriptions for paper support, especially by young doctors. It will be necessary to implement validated procedures with periodic measures, including training in contingency protocols and cloud backup information maintenance. References and/or Acknowledgements 1. Canadian Medical Association Journal 2020;192(4):E101-2. Conflict of Interest No conflict of interest
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".