Application of an Innovative Methodology to Build Infrastructure for Digital Transformation of Health Systems: Developmental Program Evaluation
Notice bibliographique
Résumé
BACKGROUND: The current public health crises we face, including communicable disease pandemics such as COVID-19, require cohesive societal efforts to address decision-making gaps in our health systems. Digital health platforms that leverage big data ethically from citizens can transform health systems by enabling real-time data collection, communication, and rapid responses. However, the lack of standardized and evidence-based methods to develop and implement digital health platforms currently limits their application. OBJECTIVE: This study aims to apply mixed evaluation methods to assess the development of a rapid response COVID-19 digital health platform before public launch by engaging with the development and research team, which consists of interdisciplinary researchers (ie, key stakeholders). METHODS: Using a developmental evaluation approach, this study conducted (1) a qualitative survey assessing digital health platform objectives, modifications, and challenges administered to 5 key members of the software development team and (2) a role-play pilot with 7 key stakeholders who simulated 8 real-world users, followed by a self-report survey, to evaluate the utility of the digital health platform for each of its objectives. Survey data were analyzed using an inductive thematic analysis approach. Postpilot test survey data were aggregated and synthesized by participant role. RESULTS: The digital health platform met original objectives and was expanded to accommodate the evolving needs of potential users and COVID-19 pandemic regulations. Key challenges noted by the development team included navigating changing government policies and supporting the data sovereignty of platform users. Strong team cohesion and problem-solving were essential in the overall success of program development. During the pilot test, participants reported positive experiences interacting with the platform and found its features relatively easy to use. Users in the community member role felt that the platform accurately reflected their risk of contracting COVID-19, but reported some challenges interacting with the interface. Those in the decision maker role found the data visualizations helpful for understanding complex information. Both participant groups highlighted the utility of a tutorial for future users. CONCLUSIONS: Evaluation of the digital health platform development process informed our decisions to integrate the research team more cohesively with the development team, a practice that is currently uncommon given the use of external technology vendors in health research. In the short term, the developmental evaluation resulted in shorter sprints, and the role-play exercise enabled improvements to the log-in process and user interface ahead of public deployment. In the long term, this exercise informed the decision to include a data scientist as part of both teams going forward to liaise with researchers throughout the development process. More interdisciplinarity was also integrated into the research process by providing health system training to computer programmers, a key factor in human-centered artificial intelligence development.
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 | 0,172 | 0,191 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,011 | 0,008 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,006 | 0,010 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 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 ».