Training the First Generation of Health Care Performance Intelligence Professionals in Europe and Canada
Notice bibliographique
Résumé
To the Editor: Fiske and colleagues1 argue that a new type of professional called an information counselor needs to be trained to turn data into meaningful information for clinical practice, supporting providers and patients. Indeed, there is a lack of training in producing valid, reliable, and actionable “health care performance intelligence,” and stakeholders using the intelligence lack critical assessment skills, especially with respect to intelligence derived from big data. We argue that such training should go beyond clinical practice to serve all stakeholders in health care: decisionmakers to steer the health care system; funders to purchase high quality health care; health care managers to optimize quality, costs, and patient experiences; and citizens to make informed health care decisions. Furthermore, training should cover all layers of the “health care performance intelligence pyramid,” turning big data into reliable, valid indicators (layer 1), inferring useful information from indicators (layer 2) that is translated into knowledge (layer 3) upon which stakeholders can act (layer 4).2,3 This covers three major research areas: health care performance measurement, performance-based health care governance mechanisms, and the utilization of health care performance intelligence by different end users. To cover all layers of the pyramid and all three research areas, the European Commission launched the international training network on Healthcare Performance Intelligence Professionals (HealthPros) in September 2018.4 Coordinated by an international consortium, it provides an innovative, three-year program of collaborative, multidisciplinary, and entrepreneurial training to 13 doctoral students with varying backgrounds (e.g., health sciences, medical informatics, medicine, biological sciences, business administration, statistics, and economics), who will work on a cohesive set of individual research projects to obtain a PhD degree. Students will be trained to master a set of required competencies and multidisciplinary skills that are not well covered in existing research and training programs. Moreover, through secondments, data hackathons, and network events, the HealthPros will closely interact with an immersion community (e.g., academia, industry, pharma, governance, and funders) as part of the educational process. This will train the HealthPros in understanding different perspectives, politics, and change processes of stakeholders in health systems and will simultaneously function as a means of increasing the uptake of health performance research results. The program involves Canada, Denmark, Germany, Hungary, Italy, the Netherlands, and the United Kingdom. The program design is geared toward a direct flow between research outputs and innovation that is enhanced through education. Ultimately, HealthPros is about using knowledge that is available on health care system performance to achieve improved quality of care for all and more sustainable health care. Dionne S. Kringos, PhDAssistant professor, Department of Public Health, Amsterdam Public Health research institute, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; [email protected]; ORCID: https://orcid.org/0000-0003-2711-4713.Oliver Groene, PhDVice chairman of the board, OptiMedis AG, Hamburg, Germany, and honorary senior lecturer in health services research, London School of Hygiene and Tropical Medicine, London, United Kingdom; ORCID: https://orcid.org/0000-0002-1099-2950.Søren Paaske Johnsen, MD, PhDProfessor in clinical health services research, Department of Clinical Medicine, Danish Center for Clinical Health Services Research, Aalborg University, Aalborg, Denmark; ORCID: https://orcid.org/0000-0003-0053-5649.
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Comment cette classification a été obtenuedéplier
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».