Improving Implementation of an Electronic Prescription System for COVID-19 Vaccination in the Czech Republic: Process Modeling Approach
Notice bibliographique
Résumé
BACKGROUND: It is very difficult to find a consensus that will be accepted by most players when creating health care legislation. The Czech electronic prescription system was launched in 2011 and new functions were introduced in 2018. To ensure that these functions will not conflict with any other existing law, a process modeling tool based on the patent "Method and system for automated requirements modeling" was used successfully in the Czech Republic for the first time. OBJECTIVE: The aim of this project was to develop another successful application of process modeling to add COVID-19 vaccination records to the existing electronic prescription system. METHODS: The method employed was based on the mathematical theory of hierarchical state diagrams and process models. In the first step, sketches that record the results of informal discussions, interviews, meetings, and workshops were prepared. Subsequently, the architecture containing the main participants and their high-level interactions was drafted. Finally, detailed process diagrams were drawn. Each semiresult was discussed with all involved team members and stakeholders to incorporate all comments. By repeating this procedure, individual topics were gradually resolved and the areas of discussion were narrowed down until reaching complete agreement. RESULTS: This method proved to be faster, clearer, and significantly simpler than other methods. Owing to the use of graphic tools and symbols, the risk of errors, inaccuracies, and misunderstandings was significantly reduced. The outcome was used as an annex to the bill in the legislative process. One of the main benefits of this approach is gaining a higher level of understanding for all parties involved (ie, legislators, the medical community, patient organizations, and information technology professionals). The process architecture model in a form of a graphic scheme has proven to be a valuable communication platform and facilitated negotiation between stakeholders. Moreover, this model helped to avoid several inconsistencies that appeared during workshops and discussions. Our method worked successfully even when participants were from different knowledge areas. CONCLUSIONS: The vaccination record process model was drafted in 3 weeks and it took a total of 2 months to pass the bill. In comparison, the initial introduction of the electronic prescription system using conventional legislative methods took over 1 year, involving immediate creation of a text with legislative intent, followed by paragraph-by-section wording of the legislation that was commented on directly. These steps are repeated over and over, as any change in any part of the text has to be checked and rechecked within the entire document. Compared with conventional methods, we have shown that using our method for the process of modification of legislation related to such a complex issue as the integration of COVID-19 vaccination into an electronic prescription model significantly simplifies the preparation of a legislative standard.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,003 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».