Barriers and Opportunities for the Use of Digital Tools in Medicines Optimization Across the Interfaces of Care: Stakeholder Interviews in the United Kingdom
Notice bibliographique
Résumé
BACKGROUND: People with long-term conditions frequently transition between care settings that require information about a patient's medicines to be transferred or translated between systems. This process is currently error prone and associated with unintentional changes to medications and miscommunication, which can lead to serious patient consequences. One study estimated that approximately 250,000 serious medication errors occur in England when a patient transitions from hospital to home. Digital tools can equip health care professionals with the right information at the right time and place to support practice. OBJECTIVE: This study aimed to answer the following questions: what systems are being used to transfer information across interfaces of care within a region of England? and what are the challenges and potential opportunities for more effective cross-sector working to support medicines optimization? METHODS: A team of researchers at Newcastle University conducted a qualitative study by performing in-depth semistructured interviews with 23 key stakeholders in medicines optimization and IT between January and March 2022. The interviews lasted for approximately 1 hour. The interviews and field notes were transcribed and analyzed using the framework approach. The themes were discussed, refined, and applied systematically to the data set. Member checking was also performed. RESULTS: This study revealed themes and subthemes pertaining to 3 key areas: transfer of care issues, challenges of digital tools, and future hopes and opportunities. We identified a major complexity in terms of the number of different medicine management systems used throughout the region. There were also important challenges owing to incomplete patient records. We also highlighted the barriers related to using multiple systems and their subsequent impact on user workflow, a lack of interoperability between systems, gaps in the availability of digital data, and poor IT and change management. Finally, participants described their hopes and opportunities for the future provision of medicines optimization services, and there was a clear need for a patient-centered consolidated integrated health record for use by all health and care professionals across different sectors, bridging those working in primary, secondary, and social care. CONCLUSIONS: The effectiveness and utility of shared records depend on the data within; therefore, health care and digital leaders must support and strongly encourage the adoption of established and approved digital information standards. Specific priorities regarding understanding of the vision for pharmacy services and supporting this with appropriate funding arrangements and strategic planning of the workforce were also described. In addition, the following were identified as key enablers to harness the benefits of digital tools to support future medicines optimization: development of minimal system requirements; enhanced IT system management to reduce unnecessary repetition; and importantly, meaningful and continued collaboration with clinical and IT stakeholders to optimize systems and share good practices across care sectors.
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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 | 0,015 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,009 | 0,006 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,002 |
| 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 ».