“If you can't measure it, you can't manage it” – essential truth, or costly myth?
Notice bibliographique
Résumé
If you can't measure it, you can't manage it" -essential truth, or costly myth?In this issue of the journal, Kilbourne et al 1 make a reasoned and cogent argument for a more structured approach to the delivery of mental health care, with the aim of driving up the quality of care and its outcomes.They see mental health services as innovators in models of care delivery (i.e., communitybased, multidisciplinary, and person-centred) but as laggards in learning from and adopting advances in monitoring and improving the quality of continuing care derived from other chronic disease disciplines.The paper advocates enhancements oriented to the structure proposed in Donabedian's framework, namely the organization of care, the clinical care processes, and the health care outcomes achieved.The authors are right to highlight the importance of measurement, on the principle that "if you can't measure it, you can't manage it".Health management information systems are a core "building block" for well-functioning health systems.The purpose of those information systems is to routinely generate quality health information, and they are used directly for management decisions to improve health care delivery -supporting resource management, service monitoring, supervision and quality improvement.On the other hand, W. Edwards Deming 2 warned that the above principle could be "a costly myth".He could be right, and for several reasons.First, intuitively obvious qualitative enhancements can be made without data either to diagnose the problem or confirm the benefits.Data can provide a mechanism to support incremental improvement, but the fundamental transformation towards a "learning health system" is cultural.Well-functioning health management information systems are just one essential component of an interactive suite of health system strengthening measures that may prove necessary developing: a peer-driven quality improvement culture; non-technical workforce skills (leadership, teamwork and communication); and person-centred care practices guided by values and preferences, with agreed care plans, and patients empowered for self-management.Beyond evidence-based guidelines, consideration may also be given to the introduction of care pathways 3 .Every patient goes through a care process, and this varies among patients with particular conditions.Care pathways are about planning and managing those processes, in advance, for defined groups of individuals.Critically, this establishes explicit standards for care processes and outcomes, against which performance can then be judged.Not all health care activities lend themselves to this approach, since not all care is provided for a "welldefined patient group" and a "well-defined period of time".For continuing care of mental health conditions, pathways may need to be drawn up and delivered flexibly, contingent upon differing needs, clinical trajectories and treatment responses.A "stepped care" approach is often used, whereby a patient first receives the most effective, least invasive, least
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 | 0,046 | 0,115 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,007 | 0,029 |
| Communication savante | 0,019 | 0,029 |
| Science ouverte | 0,006 | 0,006 |
| Intégrité de la recherche | 0,023 | 0,057 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,004 |
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 ».