The Role of Health Administration, Social Services, Health Informatics, and Public Health in Driving Digital Transformation in Healthcare Systems: An Integrative Review
Notice bibliographique
Résumé
Introduction: Machine learning, Artificial intelligence and mobile medical software and apps that help physicians' everyday clinical choices are just a few examples of how digital technologies have been revolutionizing healthcare. Tools for digital health have great possibilities for enhancing the performance of personal healthcare in addition to our ability to identify and treat illnesses. Aim of work: for exploring the vital role that public health, social services, health administration, and health informatics play in promoting digital transformation in healthcare systems. Methods: We used the following search phrases to perform an extensive search in the MEDLINE database: role, digital transformation, public health, enhancing, health informatics, and health administration. We conducted a Google Scholar search to discover and look over scholarly reviews related to my review. Certain criteria for inclusion had an impact on the choosing of articles. Results: With distinct headers in the discussion part, the study was divided into several sections. Conclusion: The complete integration of data analytics, digital technologies and creative procedures to improve the quality of healthcare services is known as "digital transformation" in the healthcare section. For the treatment of non-communicable diseases, digital services can be both economical and effective; yet, their generalizability is constrained by the variable treatment effects. To collect, analyze, and apply data to improve health outcomes, health informatics integrates data science, data analytics and information management. To increase the effectiveness of healthcare organizations, technologists create and evaluate data collecting and usage platforms. The usage of advanced analytics technologies and the ongoing geometric development in the quantity of data that can be analyzed will affect almost every side of healthcare, including the management procedures automation, the quality of insurance rates, and the usage of AI in diagnosis. Some important aspects of digital healthcare transformation include Electronic health records, Data analytics, Artificial intelligence, Telemedicine and Wearable devices. Patients can utilize patient portals to access personal health information such as appointment data, medication information and test results. The digital services effects are on the health of population, expenses, and healthcare professional and patient satisfaction, as well as to pinpoint factors that promote and hinder the use of digital services in social welfare and healthcare. Growing in popularity, health information technology enables healthcare medical facilities to use pertinent data to scale forecast and operations treatment outcomes through information systems communication. Automation in the medical field can considerably boost efficiency in numerous healthcare administration jobs like: Streamlining appointment scheduling, maintaining compliance with healthcare regulations and laws, Managing work schedules for caregivers and other staff members and Keeping patient health information.
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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 | 0,005 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,011 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».