Digital Transformation in Healthcare: Preliminary Results from a Senior Leadership Study.
Notice bibliographique
Résumé
Organizations of all types are facing challenges in the new digital age to remain competitive (Hess et al, 2016). With customer expectations changing and new technologies emerging, organizations need to change their business models to remain relevant and sustain competitive advantage. Healthcare in particular is one segment that is undergoing digital transformation. This is motivated largely by the desire to improve cost, patient satisfaction, patient outcomes, quality of care, provider experience and other important facets of the healthcare experience. According to some, strategic changes enabled by digital technology can allow healthcare organizations to re-shape their business models and improve the aforementioned facets. Digital transformation in healthcare therefore emphasizes strategic endeavors enabled by emerging digital technologies to improve patient care and enhance patient outcomes in particular (Gupta, 2016). Although achieving strategic advantage is the overall stated goal of digital transformation, advancements are needed to understand the link between digital transformation activities and the overall business strategy, specifically in the healthcare industry. The key question business leaders need to respond to is - how to incorporate digital transformation and use it as a means for competitive advantage (Hess et al, 2016). The concept of digital transformation has also been one of interest to information systems academics as of late. Several scholars have attempted to argue the link between digital transformation strategies and integration efforts of various new technologies. Despite these efforts, the link between digital transformation strategies and the overall approach as to how the firm expects to achieve competitive advantage is unclear. As well, several scholars have argued that digital transformation lacks a substantive underlying theory, and work in this area will further guide future research and innovation activities. The purpose of this research is to determine how digital transformation efforts of a healthcare firm explain their approach to achieve competitive advantage. This paper reports on the preliminary results of an ongoing qualitative study involving 18 C-level information technology leaders in American healthcare organizations. The purpose of this TREO talk is twofold. First, the authors wish to share the preliminary findings of the study. Second, the authors wish to discuss the potential of integrating various theoretical approaches into research on digital transformation in healthcare, to give the concept of digital transformation a better theoretical underpinning for future research.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,004 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, pas un consensus.
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