Effectiveness Of Evidence-Based Pain Assessment Tools Used By Healthcare Professionals In Emergency, Intensive Care, Oncology, And Public Health Settings: A Systematic Review
Notice bibliographique
Résumé
Background PPain is one of the most common and clinically significant symptoms in all medical practice environments. Proper and timely pain assessment is important for successful pain management and safety in treatment. There are many valid pain assessment tools that have been developed during recent years; they may include self-assessment scales, observations of behaviour and even composite tools. Yet the efficacy of using these tools to conduct proper pain management and the proper use of these assessment tools by healthcare providers in EDs, ICUs, oncology and public health care settings vary greatly. No systematic reviews on this topic were conducted recently to compare all four types of settings in terms of effectiveness of pain assessment tools and their proper use by healthcare professionals. Objectives The aim of this systematic review is to examine the validity of evidence-based pain assessment tools in use by healthcare professionals in emergency, ICU, oncology and public health settings in terms of pain management outcomes and assessment accuracy. Methods For studies published in the period from January 2020 to December 2025, databases such as PubMed/MEDLINE, EMBASE, CINAHL, PsycINFO, and Cochrane Library were searched for original research articles. Studies that used one or more validated pain measurement tools, collected data about healthcare professionals' use of the measurement tools, and evaluated at least one clinical or patient reported outcome were considered eligible. The risk of bias was judged through Mixed Methods Appraisal Tool (MMAT) and Cochrane RoB 2.0. Results A total of 10 studies were included in our review, involving 31,847 patients and 4,219 healthcare professionals from 18 different countries. Measurement tools used by these studies included Numerical Rating Scale (NRS), Visual Analogue Scale (VAS), Behavioural Pain Scale (BPS), Critical-Care Pain Observation Tool (CPOT), the Edmonton Symptom Assessment System (ESAS), the Brief Pain Inventory (BPI), and the Wong-Baker FACES Pain Rating Scale. Pooled concordance between healthcare professionals' estimation and self-report among patients was 68.4% (95% CI: 61.2-75.6%; I²=59%). Observational tools (CPOT, BPS) exhibited greater sensitivity (0.83, specificity 0.79) in non-verbal ICU patients. The rate of undertreatment of pain was higher in the oncology setting (mean 34.7%). Inter-rater reliability was found to be least consistent in the emergency department setting. Use of structured pain assessment tools was also found to correlate with decreased numerical pain scores upon discharge and with fewer analgesic breaks. Conclusion :It can be seen that evidence-based pain scales are highly effective when used correctly; however, clinician compliance and education on use, along with the correct selection of tool per setting, still present a major challenge to success. It is suggested that CPOT and BPS be used for assessment of patients in ICU who are unable to communicate verbally; the NRS scale for emergency department and oncology patients able to understand the questions presented; and the ESAS scale for use in oncology/palliative care.
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 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 | 0,050 | 0,059 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,007 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».