Pursuit of Digital Innovation in Psychiatric Data Handling Practices in Ireland: Comprehensive Case Study
Notice bibliographique
Résumé
Background: Ireland is ranked among the most disadvantageous European countries in terms of mental health challenges. Contrary to general health services that primarily focus on diagnosis and treatment, the mental health sector in Ireland deals with highly sensitive psychiatric case notes based on patient-doctor conversations. Such data, therefore, must be collected, analyzed, and stored with an approach customized specifically for psychiatry. Objective: This study's objective involves examining the state of data handling practices in the Irish Mental Health Services (MHS), identifying the shortcomings regarding privacy, security, and usability of psychiatric case notes, and proposing an innovative technological solution that addresses most of the surfaced challenges. Methods: The study was conducted using a comprehensive methodology. Our approach involved a thorough literature review, ethics approval, web-based surveys with mental health professionals as participants, interviews of psychiatrists, interactions with mental health organizations, analysis of inspection reports by the Ireland Mental Health Commission, and comparative evaluation of existing IT solutions. The thoroughness of our adopted research methodology instills confidence in the reliability and validity of our findings. Results: Our study revealed outdated data management, heavy reliance on paperwork resulting in serious repercussions, parallel workload, alarmingly low readability of notes, and a nonviable setup that hinders research and analytical examination. Our survey reported an average score of 4.37 of 10 (SD 1.25) given by participants in terms of technology use. Regarding privacy measures, 75% (n=12) of participants mentioned that staff members are allowed to keep their phones while accessing psychiatric case notes. Similarly, 80% (n=13) of submissions highlighted that multiple staff members can access sensitive notes and patients' contact information. On the other hand, Mental Health Commission reports showed that their inspections are limited to evaluating physical privacy only. Regarding technological comparative analysis, we observed that conventional IT solutions are vulnerable against cyberattacks and fall short in addressing multiple challenges simultaneously. Therefore, an innovative convergence of different technologies is needed. Our research supports speech-to-text transcription for data collection, interactive artificial intelligence for data analysis, and permissioned blockchain for data storage and retrieval. Our survey participants also estimated the proposed solution to optimize their workload by an average of 35%. Conclusions: Irish MHS seem to be handling psychiatric data under polycrisis circumstances; therefore, a single-dimensional digitization of records would not be sufficient in addressing the wide range of concerns. In addition to highlighting intertwined challenges in Irish psychiatry and validating the need for innovation in data handling practices in Irish MHS, this study culminated in the proposal of an innovative technological solution that offers a significant contribution to a considerably improved, efficient, and compliant service delivery in mental health care.
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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 | 0,008 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,007 | 0,006 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,003 |
| 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 ».