Time for a Paradigm Shift: Managing Smarter by Moving from Data and Information to Knowledge and Wisdom in Healthcare Decision-Making
Notice bibliographique
Résumé
Senior decision-makers in the Canadian healthcare system have to continuously make significant, and complex, policy and program decisions.However, it appears that, often, the evidence they have available is fairly simple descriptive information, collected for operational purposes.Trying to solve complex problems with fairly simple data may lead to suboptimal decisions.This article presents a new knowledge development system (KDS) that should allow senior decision-makers and others to manage smarter and take their decision-making to the next level.A KDS represents the integration of information systems, and research and analysis, into one system.It can generate sophisticated, strategic information around complex issues, which should ultimately lead to wiser decisions.This article describes the KDS, provides an example of a current KDS and concludes by presenting a self-diagnostic tool for decision-makers to allow them to determine whether their organization could benefit from a KDS.H ealthcare organizations such as ministries of health, regional health authorities and other organizations collect large amounts of data.However, they appear to struggle with translating these data into strategic knowledge and insights that can be used as inputs into evidence-based decision-making at the clinical, operational, administrative, policy and executive levels.Several reasons seem to account for this difficulty.Information systems are often developed to meet the operational needs of different organizational components.For example, separate systems are developed for finance, human resources and care delivery.Some types of data that exist outside the organization and are critical for certain types of analyses -such as data on the population served (for population health and epidemiological analyses)may not be readily available or, if available, may not be systematically integrated into the data architecture of the organization.In addition, over the past several years, the focus seems to have been on developing information systems and electronic health records rather than on analyzing data to take maximum advantage of the data that are already available.Thus, organizations may have suboptimal knowledge development, not because of a lack of data but because the data that exist are not fully used to generate new knowledge.Finally, because data may not be used to meet the real needs of organizational actors (e.g., front-line care delivery staff, policy developers, planners etc.), people may not recognize the potential of existing information systems to provide insights into key issues.Thus, a separation often exists between collecting data and using the data to develop new knowledge in healthcare organizations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,048 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,011 | 0,044 |
| Communication savante | 0,028 | 0,042 |
| Science ouverte | 0,003 | 0,013 |
| Intégrité de la recherche | 0,007 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».