Developing Internet of Things-related ISO 10001 Hand Hygiene Privacy Codes in healthcare
Bibliographic record
Abstract
Purpose Augmentation of an ISO 10001 code system for healthcare worker (HW) satisfaction with ISO/IEC 27701 and ISO/IEC 29184 privacy-related subsystems is shown. Four specific codes regarding the privacy of HWs using electronic devices for hand hygiene (HH) monitoring and the related activities are presented. Design/methodology/approach HWs’ concerns involving automated hand hygiene monitoring technologies were identified through a literature review and classified. Privacy codes (PCs) that deal with such concerns were developed. ISO/IEC 27701 requirements for privacy information were mapped to the elements of these codes, labelled as “Healthcare Workers’ Hand Hygiene Privacy Codes (HW-HH-PCs)”. Both ISO/IEC 27701 and ISO/IEC 29184 guidelines for Privacy Notices and consent were linked with the activities for preparing the code resources. Findings Components of an ISO/IEC 27701 system, the guidance of ISO/IEC 29184 and the definitions provided in ISO/IEC 29100 can assist the preparation of HW-HH-PCs and the required resources. An ISO/IEC 29184 Privacy Notice can be used as input for developing an Informed Consent Form, which can be implemented to suit two of the four developed HW-HH-PCs. Practical implications HW-HH-PCs and the supporting resources, which healthcare organizations could implement to potentially increase quality assurance of an automated HH monitoring service, are illustrated. Originality/value Integrative augmentation of ISO 10001:2018, ISO/IEC 27701:2019 and ISO/IEC 29184:2020 within an underlying framework from ISO/IEC 20000–1:2018 for information technology service, together with the related examples of privacy-related customer satisfaction codes and the corresponding resources, is introduced.
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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 | 0.034 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".