A Sense of Coherence Approach to Improving Patient Experience Using Information Infrastructure Modeling: Design Science Research
Notice bibliographique
Résumé
BACKGROUND: Health care provider organizations are complex and dynamic environments. Consequently, how the physical and social environment of such organizations interact with an individual is a primary driver of an individual's experience. Increasingly, the capabilities required for them to successfully interact with those within their care are critically dependent on the information infrastructure they have in place, which enables people, both patients and staff, to work optimally together to deliver their clinical and operational objectives. OBJECTIVE: This study aims to design a framework to address the challenge of how to assemble information systems in health care to support an improved sense of coherence for patients, as well as potentially innovate patients' experiences, by connecting and orchestrating the synergy among people, processes, and systems. METHODS: It is necessary to understand the needs of health care providers and patients to address this challenge at a level relevant to information process design and technology development. This paper describes the design science research method used to combine the sense of coherence, which is a core concept within the Antonosky salutogenic approach to health and well-being, with an established information infrastructure maturity framework, demonstrating the coalescence of 2 distinct conceptual perspectives on care delivery. This paper provides an approach to defining a positive and supportive health care experience and linking this to the capabilities of an information- and technology-enabled environment. RESULTS: This research delivers a methodology for describing the patient experience in a form relevant to information infrastructure design, articulating a pathway from information infrastructure to patient experience. It proposes that patient experience can be viewed pragmatically in terms of the established sense of coherence concept, with its ability to identify and guide resources to modulate a patient's environmental stressors. This research establishes a framework for determining and optimizing the capability of a facility's information infrastructure to support the sense of coherence defined by the experiences of its patients. CONCLUSIONS: This groundbreaking research provides a framework for health care provider organizations to understand and assess the ability of their information infrastructure to support and improve the patient experience. The tool assists providers in defining their technology-dependent operational goals around patient experience and, consequently, in identifying the information capabilities needed to support these goals. The results demonstrate how a fundamental shift in thinking about the use of information infrastructure can transform the patient experience. This study details an approach to describing information infrastructure within an experience-oriented framework that enables the impact of technology on experience to be designed explicitly. The contribution to knowledge is a new perspective on modeling how information infrastructure can contribute to supportive health-promoting environments. Furthermore, it may significantly affect the design and deployment of future digital infrastructures in 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 | 0,040 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,011 |
| Communication savante | 0,009 | 0,008 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».