Health Information Exchange Usage in Japan: Content Analysis of Audit Logs
Notice bibliographique
Résumé
Background: In Japan, research on the types of medical data requested by health care workers in health information exchanges (HIEs) is limited. Examining the number of views for each data type is important to quantify its benefits. Objective: This study aimed to identify the types of medical data that are frequently viewed on demand using HIEs in Japan. Methods: We analyzed audit log data from two HIEs, Choukai Net and PicaPicaLink, covering the period from April 1, 2017, to March 31, 2022. First, we calculated the cumulative monthly usage days of the HIEs by each institution for the financial year (FY) 2021/22. Second, we calculated the cumulative annual usage days of the HIEs by each user type for FY 2021/22. Third, we calculated the view rate for each output field and content within each HIE, using institution type or year as the aggregation unit. Fourth, we calculated the cumulative annual usage days of the HIEs for days with and without progress note viewing, and for days without any content viewing. Fifth, we calculated the cumulative number of viewed days for content scheduled to be included in the national HIE compared to that which was not. Results: In 32.6% (47/144) of hospitals connected to Choukai Net and 2.3% (20/875) of hospitals connected to PicaPicaLink, cumulative monthly usage days exceeded 101. Additionally, in 32.4% (56/173) of visiting nursing stations connected to Choukai Net, cumulative monthly usage days were over 51. User types viewing HIEs were heavily biased toward institution types other than hospitals. The overall view rate for progress notes was highest among all content types, at 67.4% (83,476/123,915) for Choukai Net and 32.9% (26,159/79,612) for PicaPicaLink. In both HIEs, when comparing by institution type, the view rate for progress notes was highest for visiting nursing stations, reaching 91.8% (5553/6052) for Choukai Net and 65.3% (126/193) for PicaPicaLink. We also found that 17% (5417/31,944) of Choukai Net usage and 9.6% (1802/18,862) of PicaPicaLink usage involved referencing only progress notes in FY 2021/22. The view rate of content scheduled to be included in the national HIE was 45.6% (56,499/123,791) for Choukai Net and 47.7% (37,972/79,612) for PicaPicaLink. Conversely, the view rate for content not scheduled to be included in the national HIE was higher, at 80.2% (99,234/123,791) for Choukai Net and 56.6% (45,052/79,612) for PicaPicaLink. Conclusions: In both HIEs analyzed in this study, progress notes were the most viewed content. As more health care organizations disclose the progress notes they manage to their HIEs, progress notes are likely to be viewed more frequently. The cost-benefit of disclosing progress notes to HIEs remains unclear, and both health care providers and patients have concerns about privacy risks. Future research is needed to quantify and maximize the benefits of disclosure while mitigating the associated privacy risks.
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 | 0,004 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».