Effectiveness Of Evidence-Based Pain Assessment Tools Used By Healthcare Professionals In Emergency, Intensive Care, Oncology, And Public Health Settings: A Systematic Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.130 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.017 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".