Process Re-Engineering and Data Integration Using Fast Healthcare Interoperability Resources for the Multidisciplinary Treatment of Lung Cancer
Notice bibliographique
Résumé
Unlabelled: Multidisciplinary team (MDT) meetings play a critical role in cancer care by fostering collaboration between different health care professionals to develop optimal treatment recommendations. However, meeting scheduling and coordination rely heavily on manual work, making information-sharing and integration challenging. This results in incomplete information, affecting decision-making efficiency and impacting the progress of MDT. This project aimed to optimize and digitize the MDT workflow by interviewing the members of an MDT and implementing an integrated information platform using the Fast Healthcare Interoperability Resources (FHIR) standard. MDT process re-engineering was conducted at a central Taiwan medical center. To digitize the workflow, our hospital adopted the NAVIFY Tumor Board (NTB), a cloud-based platform integrating medical data using international standards, including Logical Object Identifiers, Names, and Codes, Systemized Nomenclature of Medicine-Clinical Terms, M-code, and FHIR. We improved our hospital's information system using application programming interfaces to consolidate data from various systems, excluding sensitive cases. Using FHIR, we aggregated, analyzed, and converted the data for seamless integration. Using a user experience design, we gained insights into the lung cancer MDT's processes and needs. We conducted 2 phases: pre- and post-NTB integration. Ethnographic observations and stakeholder interviews revealed pain points. The affinity diagram method categorizes the pain points during the discussion process, leading to efficient solutions. We divided the observation period into 2 phases: before and after integrating the NTB with the hospital information system. In phase 1, there were 83 steps across the 6 MDT activities, leading to inefficiencies and potential delays in patient care. In phase 2, we streamlined the tumor board process into 33 steps by introducing new functions and optimizing the data entry for pathologists. We converted the related medical data to the FHIR format using 6 FHIR resources and improved our hospital information system by developing functions and application programming interfaces to interoperate among various systems; consolidating data from different sources, excluding sensitive cases; and enhancing overall system efficiency. The MDT workflow reduced steps by 60% (50/83), lowering the coordinated activity time from 30 to 5 minutes. Improved efficiency boosted productivity and coordination in each case of manager feedback. This study optimized and digitized the workflow of MDT meetings, significantly enhancing the efficiency and accuracy of the tumor board process to benefit both medical professionals and patients. Based on FHIR, we integrated the data scattered across different information systems in our hospital and established a system interoperability interface that conformed to the standard. While digitizing the work of MDT meetings, we also promoted the optimization and transformation of related information systems and improved their service quality. We recommend additional research to assess the usability of a tumor board platform.
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 | 0,017 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».