Use of Electronic Patient Record Systems for Rapid Response to an MHRA Public Assessment Report: Retrospective Observational Study (Preprint)
Notice bibliographique
Résumé
Background: Digital health data and infrastructure facilitate rapid analysis to provide actionable data, thereby fulfilling the principles of a learning health system. In response to a report from the UK Medicines and Healthcare Products Regulatory Agency (MHRA), a rapid service evaluation was carried out to identify patterns of modified-release (MR) opioid use after elective surgery. Objective: We aimed to describe the prescribing patterns of MR opioids, methods to repurpose existing infrastructure, and the experience of collaboration between clinical and research teams using shared data pipelines. Methods: A retrospective case-control study was conducted at a tertiary care organization across multiple hospital sites in London, United Kingdom. Prescription and administration data for adult patients undergoing elective surgery between March 31, 2019, and June 20, 2025, were extracted from a standardized research data pipeline within 4 weeks of the publication of the MHRA report. Patients were screened for MR opioid prescriptions in the postoperative period and at hospital discharge. Counts and proportions of encounters in which MR opioids were administered or prescribed were evaluated across the study period. Reflections on the application of the infrastructure for this purpose were also documented. Results: Of 126,882 elective surgeries screened, 102,879 (81.1%) met the eligibility criteria. Over the study period, patients received a new MR opioid prescription after 7525 (7.3%) of the 102,879 eligible encounters, with 2438 (2.4%) encounters receiving a new MR opioid prescription at hospital discharge. Postoperative administration of MR opioids and prescribing at discharge have declined since 2020. As a result of this study, a new context-aware alert system was developed to monitor and reduce MR opioid prescribing in this surgical cohort. Reflections on the implementation experience demonstrated how collaboration between clinical and research teams in conjunction with integrated and seamless research pipelines allowed rapid knowledge generation. Key issues raised were the difficulty of validation between parallel data extraction systems and how the two different teams compared nonequitable data points and results. Conclusions: Mature digital and analytical infrastructure within health care institutions can enable swift evaluation of local practices in the context of national medication safety alerts. This can shorten action response times and improve patient care but requires close collaboration between clinicians and research teams. Shared infrastructure between teams across the learning health system improves data quality and provides easy access to the key users. Further work is needed to understand the benefits and challenges of infrastructure built for other use cases and the effectiveness of the intervention.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».