The ecosystem of health decision making: from fragmentation to synergy
Notice bibliographique
Résumé
Clinicians, patients, policy makers, funders, programme managers, regulators, and science communities invest considerable amounts of time and energy in influencing or making decisions at various levels, using systematic reviews, health technology assessments, guideline recommendations, coverage decisions, selection of essential medicines and diagnostics, quality assurance and improvement schemes, and policy and evidence briefs.The criteria and methods that these actors use in their work differ (eg, the role economic analysis has in decision making), but these methods frequently overlap and exist together.Under the aegis of WHO, we have brought together representatives of different areas to reconcile how the evidence that influences decisions is used across multiple health system decision levels.We describe the overlap and differences in decision-making criteria between different actors in the health sector to provide bridging opportunities through a unifying broad framework that we call theory of everything.Although decision-making activities respond to system needs, processes are often poorly coordinated, both globally and on a country level.A decision made in isolation from other decisions on the same topic could cause misleading, unnecessary, or conflicted inputs to the health system and, therefore, confusion and resource waste.partnership of systematic review authors and guideline developers.Under the aegis of WHO (Regional Office for Europe, the Country Office in Estonia, and the headquarter WHO Department of Health Product Policy and Standards), and in collaboration with the Estonian Health Insurance Fund, we brought together representatives of different areas to discuss similarities in the criteria and processes encompassing evidence evaluation across different health system decision levels.This process, beginning in 2019, was inclusive, involving key stakeholders, such as systematic reviewers, guideline developers and panelists, regulators, policy makers, and payers, whose roles were not mutually exclusive.Participants served in their personal capacity as usually requested by WHO.However, several members had recognised experience in organisations that are active in informing or making health decisions, such as the Guidelines International Network, the International Network of Agencies for Health Technology Assessment, the Professional Society for Health Economics and Outcomes Research, or the Cochrane Collaboration (appendix p 10).We evaluated selected examples of how healthcare questions about interventions are created, the factors (ie, criteria) that influence a decision, the type of evidence required, where this evidence is used, who is making recommendations and decisions, and in what context these recommendations and decisions are being made.We used case studies from Estonia on the use of direct oral anticoagulants (DOACs) and a Canadian Agency for Drugs and Technology in Health (CADTH) case study on dialysis (appendix pp 11-19), during large and small group sessions in a 2day workshop to identify what elements substantially apply to all types of decision making, regardless of intervention, population, or topic.
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 | 0,056 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,002 |
| Bibliométrie | 0,006 | 0,007 |
| Études des sciences et des technologies | 0,003 | 0,037 |
| Communication savante | 0,019 | 0,025 |
| Science ouverte | 0,003 | 0,025 |
| Intégrité de la recherche | 0,005 | 0,008 |
| 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 ».