Prioritisation tools for cataract surgery, knee replacement, and inguinal hernia repair waiting lists and their effectiveness in reducing elective surgery waiting times: A rapid systematic review

Prioritisation tools for cataract surgery, knee replacement, and inguinal hernia repair waiting lists and their effectiveness in reducing elective surgery waiting times: A rapid systematic review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prioritisation tools for cataract surgery, knee replacement, and inguinal hernia repair waiting lists and their effectiveness in reducing elective surgery waiting times: A rapid systematic review Wendy Nieto-Gutierrez, Melixa Medina-Aedo, M Soledad Isern Val This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9447805/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This review identifies and describes clinical prioritisation tools used to rank patients on surgical waiting lists for cataract surgery, knee replacement, and inguinal hernia repair, and evaluate the effect of these tools on elective surgery waiting times. Methods We conducted a systematic review following Cochrane Rapid Review methods and PRISMA guidelines. Searches were performed in PubMed, Embase, and Google Scholar to identify studies evaluating prioritisation tools for cataract surgery, knee replacement, and inguinal hernia repair. We described the tools, their criteria and domains, assessed psychometric performance, and synthesised evidence on waiting-time outcomes. The certainty of the evidence was evaluated using GRADE methodology. Results Forty-six studies were included: 25 on cataract surgery, 19 on knee replacement, and 2 on inguinal hernia repair. Nine prioritisation tools were identified for cataract surgery, six for knee replacement, and two for inguinal hernia repair. Across the three procedures, identified tools incorporated multiple domains reflecting differences in clinical characteristics and disease burden. Evidence on psychometric performance and waiting-time effects was available only for cataract surgery and knee replacement and showed weak to moderate correlations with other tools. Evidence on the impact of prioritisation on waiting times was heterogeneous. Non-randomised studies showed weak associations between priority scores and surgical order, with longer waits for lower-priority patients in some settings. Modelling studies suggested either overall reductions in waiting times or reductions confined to high-priority patients. Conclusions Prioritisation tools adopt procedure-specific, multidimensional approaches, but evidence supporting their effectiveness in reducing waiting times is heterogeneous and, in some cases, uncertain. waiting lists cataract extraction arthroplasty replacement knee herniorrhaphy (source: MeSH) Figures Figure 1 Background Waiting times for access to healthcare services represent an important challenge in many health systems, although their magnitude and policy relevance vary across financing and delivery arrangements ( 1 ). They are particularly relevant in publicly financed or heavily publicly regulated systems, where universal or population-based coverage seeks to ensure access according to need, but where capacity constraints may require explicit mechanisms to manage demand ( 2 ). In these contexts, waiting lists are not only an administrative challenge but also a major policy concern, as prolonged waits may undermine the principles of timely and equitable access on which such systems are based ( 1 , 3 ). Waiting times may arise across different stages of the patient care pathway, including access to primary care, specialist consultations, diagnostic services, and elective procedures. Delays at earlier stages may accumulate over time and contribute to longer waits for subsequent interventions. Elective surgery constitutes a particularly relevant setting because access is more commonly managed through formal waiting lists, and waiting times are frequently reported and used in health system performance assessment, particularly in publicly funded systems ( 4 , 5 ). Multiple strategies have been implemented to manage and reduce waiting lists, including increasing surgical capacity, improving care pathways, outsourcing procedures, and reorganising service delivery ( 6 ). Among these, prioritisation has been proposed as a mechanism to improve the allocation of limited resources by ranking patients according to explicit criteria such as clinical need, functional status, expected benefit from surgery, and, in some cases, selected sociodemographic factors. In this way, prioritisation seeks to enhance transparency, consistency, and equity in access to elective procedures ( 7 ). Despite the growing use of prioritisation approaches, evidence on their effectiveness in reducing waiting times remains limited. To our knowledge, no systematic review has comprehensively evaluated the impact of prioritisation tools, representing an important gap for policymakers and health system managers considering the adoption or refinement of these strategies. This gap is particularly relevant for common elective procedures such as cataract surgery, knee replacement, and inguinal hernia repair, which are widely used as indicators of waiting times and access to surgical care, and which represent different surgical specialties and decision-making contexts ( 8 ). Therefore, this study aims to: (O1) identify and describe the psychometric properties of the prioritisation tools used to rank patients on surgical waiting lists for cataract surgery, knee replacement, and inguinal hernia repair; and (O2) evaluate their impact on elective surgery waiting times. Examining both aspects will provide important evidence on the potential effects of these tools on waiting times and on their relevance for potential applicability across settings Methods Context The Spanish National Health System established a national working group on waiting lists to examine their causes, assess management strategies, and develop recommendations for Spain. This article reports one component of that work, a systematic review of three prioritised elective procedures. Although commissioned to inform decision-making in Spain, the review considered evidence beyond the Spanish setting. Design and structured question This systematic review followed the methodology of a Cochrane Rapid Systematic Review ( 9 ), and reporting adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( 10 ). he protocol was registered on the Open Science Framework (OSF) platform ( 11 ). The clinical question addressed was: What prioritisation tools are used to rank patients on surgical waiting lists for cataract surgery, knee replacement, and inguinal hernia repair, and what is their impact on elective surgery waiting times? This question was structured according to the PICO framework and is presented in Box 1 . Data sources and searches The systematic search was conducted in PubMed, Embase, and Google Scholar (until July 2025), with the latter limited to the first 100 records. Three search strategies were developed, one for each surgical procedure, combining terms related to waiting lists with procedure-specific terms (Additional file 1) . Records identified through PubMed and Embase were exported to EndNote X9, and duplicates were removed automatically. Records retrieved through Google Scholar were screened separately, and duplicates were addressed during full-text assessment. In addition, the reference lists of all included studies and relevant systematic reviews ( 6 , 12 , 13 ) were screened to identify further eligible studies not captured by the electronic search. The reviews were considered because they mapped prioritisation tools and cited primary studies related to their development and use. Study selection and data extraction Studies were eligible if they addressed all PICO components and had full text available in English or Spanish. Eligible designs included empirical studies, defined as trials or observational studies, and modelling studies, defined as studies reporting results derived from decision-analytic or other simulation-based models. Study selection was performed in two stages in Rayyan ( 14 ): title/abstract screening by one reviewer (WNG) and cross-check by a second reviewer (MMA), followed by independent, blinded full-text assessment by both reviewers. Before formal selection, a calibration exercise was conducted on 20% of records, and selection proceeded once agreement exceeded 80%. Disagreements were resolved by consensus. Data extraction followed the same calibration process. It was conducted by one reviewer (WNG) and cross-checked by a second reviewer (MMA) using a standardized form. Extracted data included study characteristics of the study and prioritisation tools, their psychometric properties (such as validity and reliability measures), and effects of prioritisation on waiting times. When studies did not explicitly report effect estimates such as mean differences and corresponding confidence intervals, but sufficient summary data were available, estimated mean differences were calculated assuming independence between comparison groups. For modelling studies, uncertainty intervals were interpreted under the assumption that, if the simulation were repeated a large number of times, the true mean estimate generated by the model would fall within the reported 95% interval in 95% of repetitions (where N represents the number of simulation runs). Risk of bias and certainty of the evidence Risk of bias and certainty of evidence were assessed only for studies addressing the second objective, as no validated risk-of-bias tools were identified for psychometric studies and studies under the first objective were considered supplementary, mainly to support identification of tools relevant to the second objective. Assessments were performed by one reviewer (WNG) and cross-checked by a second reviewer (MMA), with disagreements resolved by consensus. Risk of bias was assessed according to study type, observational studies were evaluated using the Newcastle-Ottawa Scale ( 15 ), while modelling studies were assessed using the ISPOR–AMCP–NPC Good Practice recommendations ( 16 ), and by evaluating the risk of bias of key model inputs (disease prevalence, surgical parameters, waiting list parameters, and priority score distributions). Certainty of evidence was assessed using the GRADE framework, applying specific guidances ( 17 , 18 ). Data analysis Data synthesis was structured according to the type of surgical procedure (cataract surgery, knee replacement, or inguinal hernia repair), the type of prioritisation tool, and the outcome measures assessed. Considering the heterogeneity of the reporting of the results quantitative synthesis was not feasible and results were summarized using a narrative synthesis. To synthesize psychometric performance, correlation coefficients were interpreted using predefined thresholds: values < 0.40 were classified as weak correlations; values between 0.40 and 0.69 as moderate correlations; values between 0.70 and 0.89 as strong correlations; and values between 0.90 and 1.00 as very strong correlations ( 19 ). For the second objective, visual representations of the forest plots were used to illustrate waiting-time outcomes when it was possible. These were conducted using R. In addition, the main findings of the review were summarized in a Summary of Findings Table. Results Systematic search The systematic search was conducted separately for each of the three surgical procedures, and the selection process is summarised in the flow diagrams (Additional file 2) . In total, 25, 18, and two studies were included for cataract surgery, knee replacement, and inguinal hernia repair, respectively. Full-text exclusions and reasons are reported in Additional file 3 . Among the included studies, 15 identified or evaluated the psychometric performance of prioritisation tools for cataract surgery ( 20 – 34 ), 13 for knee replacement ( 20 , 21 , 26 , 31 , 35 – 43 ), and two for inguinal hernia repair ( 44 , 45 ). However, only seven studies assessed their impact for cataract surgery ( 33 , 46 – 51 ) and four studies for knee replacement ( 47 , 52 – 54 ). Characteristics of prioritisation tools A total of nine prioritisation tools were identified for cataract surgery, six for knee replacement, and two for inguinal hernia repair. The characteristics of these tools are summarized in Table 1 . Table 1 Prioritisation tools identified and their characteristics for waiting lists for cataract surgery, knee replacement, and inguinal hernia repair Elective surgery Prioritisation tool Criteria Score Cataract surgery Catalan Agency for Health Technology Assessment and Research cataract priority system (CPPS) ( 20 – 23 ) 1. Visual Impairment 2. Probability of recovery 3. Difficulty performing activities of daily living 4. Ability to work 5. Having someone to care for the patient/ being caregiver Overall score, by sum of the scores of each criterion. Scores can range from 0 (lowest priority) to 100 (highest priority) IRYSS-Cataract Priority Score (ICPS) ( 23 , 24 ) 1. Relevance 2. Ocular comorbidities 3. Pre-intervention visual acuity in the eye with cataract 4. Visual function reported by the patient before surgery 5. Visual acuity in the contralateral eye. 6. Type of cataract (laterality) 7. Social dependence Overall score, by sum of the scores of each criterion. Scores can range from 0 (lowest priority) to 100 (highest priority) Manitoba Cataract Waiting List Program (MCWLP) prioritisation system ( 25 ) 1. Functional impairment 2. Waiting time 3. Work impairment 4. Impairment in work performance 5. Possible loss of driver’s license Overall score by the sum of each criteria. The higher the score, the higher the priority. Western Canada Waiting List Project tool (WCWL) ( 22 , 23 , 26 – 28 ) 1. Corrected visual acuity in the non-operated eye 2. Corrected visual acuity in the eye to be operated 3. Glare 4. Age-related macular degeneration and other ocular comorbidities 5. Impaired visual function 6. Other significant disabilities 7. Ability to work, live independently, or care for dependents Overall score, by sum of the scores of each criterion. Scores can range from 0 (lowest priority) to 100 (highest priority) WCWL modified* ( 29 ) 1. Corrected visual acuity in the non-operated eye 2. Anticipated visual acuity in the eye to be operated 3. Complexity of the case 4. Condition of the contralateral eye 5. Vision impairments 6. Impact of comorbidity on improvement after cataract removal 7. Impact of cataracts on the ability to monitor or treat comorbidity 8. Quality of Life questionnaire 9. Ability to work, care for dependents, or live independently. Cataract Clinical Priority Assessment Criteria (Cataract CPAC) ( 30 ) 1. Corrected visual acuity (Snellen chart) 2. Glare 3. Ocular comorbidity 4. Visual function impairment (VF-14) 5. Social/occupational/educational problems 6. Patient disability 7. Discretional clinical points (unspecified) Overall score, by sum of the scores of each criterion. Scores can range from 0 (lowest priority) to 100 (highest priority) The national priority project ( 31 , 32 ) 1. Visual acuity 2. Glare 3. Ocular comorbidity 4. Ability to work, care for dependents or function independently 5. Degree of visual function impairment 6. Other significant disabilities Overall score, by sum of the scores of each criterion. Scores can range from 0 (lowest priority) to 100 (highest priority) Fantini et. al tool ( 33 ) 1. Visual acuity in the eye to be operated on 2. Visual acuity in the contralateral eye 3. Visual Function 4. Patient's ability to work or live independently. Overall score, by sum of the scores of each criterion. Scores can range from 0 (lowest priority) to 100 (highest priority) Nationell Indikationsmodell for Kataraktextraktion (NIKE) ( 34 ) 1. Visual acuity of the operated eye 2. Visual acuity of the contralateral eye 3. Difficulty perceived by the patient in performing daily activities 4. Cataract symptoms (glare, difference between eyes) 5. Ability to live independently (work, driving, help at home, caring for family members, etc.) 6. Medical/ophthalmological reasons for urgent surgery Sum of the scores for each criterion. Scores can range from 0 (lowest priority) to 18 (highest priority). Knee replacement Catalan Hip and Knee Priority Score (CHKPS) ( 20 , 21 , 35 ) 1. Severity of the illness (clinical and radiological examination) 2. Probability of recovery 3. Difficulty performing daily activities 4. Limited ability to work 5. Does the patient have a caregiver? Overall score, by sum of the scores of each criterion. Scores can range from 0 (lowest priority) to 100 (highest priority) The hip and knee replacement priority criteria tool (HKPT) ( 26 , 31 , 36 , 37 ) 1. Pain on movement 2. Pain at rest 3. Ability to walk without significant pain 4. Other functional limitations 5. Abnormal findings on physical examination related to the affected joint 6. Potential for disease progression documented by radiographic findings 7. Threat to the patient's role and independence in society Sum of priority criteria scores. Maximum score 100; the higher the score, the higher the priority. IRYSS Hip and Knee Priority Score (IHKPS) ( 35 , 38 , 39 ) 1. Pain on movement 2. Functional limitations when walking 3. Abnormal findings on physical examination 4. Pain at rest 5. Social role (impact on the ability to care for dependents and to work or live independently) 6. Other conditions (which could improve with joint replacement) 7. Other functional limitations Sum of priority criteria scores. Maximum score 100; the higher the score, the higher the priority. Ontario tool ( 40 ) 1. Pain at rest 2. Pain during daily activities 3. Difficulty working or caring for others 4. Expected improvement in functional status Sum of the scores for the criteria (divided into functional classes) PATHWAY tool ( 41 ) 1. Pain 2. Mobility/function 3. Activities of daily living 4. Inability to work/care for others 5. Waiting time 6. Radiological severity 7. Mental well-being Sum of scores by criterion. Scores for each level are not clear. New Zealand Orthopaedic Association Score (NZOA) ( 42 , 43 ) 1. Pain 2. Personal functional limitation (due to an orthopaedic condition of the hip or knee) 3. Social limitation (due to an orthopaedic condition of the hip or knee) 4. Potential benefit of the operation 5. Consequence of delay > 6 months Sum of scores per criterion. Scores can range from 0 (best condition) to 100 (worst condition). Inguinal hernia repair Dynamic Priority Scoring for hernia (DPS) ( 44 ) 1. Pain 2. Reducibility 3. Risk of complications 4. Functional consequences 5. Other relevant factors Each clinical criterion has weighted values. The more severe the condition, the higher the score assigned to that criterion. The scores corresponding to all the conditions presented by the patient are added together. The total score reflects the overall clinical priority. (Category 1 - highest priority; 2 - intermediate priority; and 3 - lowest priority) Oudhoff et. al tool ( 45 ) 1. Physical symptoms 2. Psychological distress 3. Social limitations 4. Work impairments It starts with a baseline score that varies depending on the perspective (7 to 16.3), to which the values of the criteria categories (depending on the perspective) are added. The higher the score, the lower the priority; conversely, the lower the score, the higher the priority. *Changes: 1) Each comorbidity is included separately and its severity is assessed; 2) visual acuity was grouped into 7 categories; 3) glare was clinically measured as none, mild, moderate or severe; 4) questions about quality of life were grouped as none, mild, moderate or severe, and 3 additional items were included. The heat map showed marked differences in the distribution of prioritisation criteria across domains by surgical procedure. In cataract surgery, criteria were predominantly concentrated in the physical or organ-specific function domain (33%), followed by daily life and occupational impact (25%) and comorbidities and expected benefit (19%). In contrast, prioritisation tools for inguinal hernia repair placed greater emphasis on clinical severity and symptoms (44%), followed by daily life and occupational impact and contextual and social factors (22% each). For knee replacement, the highest proportions were observed for clinical severity and symptoms (37%) and daily life and occupational impact (23%), with physical or organ-specific function also well represented (17%). The least represented domains were contextual and social factors in cataract surgery (9%), comorbidities and expected benefit in inguinal hernia repair (0%), and comorbidities and expected benefit together with contextual and social factors (11% each) in knee replacement. ( Fig. 1 ) Prioritisation tools for cataract surgery For cataract surgery, the prioritisation tools most frequently evaluated for psychometric performance were the Western Canada Waiting List (WCWL) ( 22 , 23 , 27 – 29 ) and the Catalan Agency for Health Technology Assessment and Research cataract priority system (CCPS) ( 21 – 23 ), mainly in the Spanish context. These tools were validated against visual function, visual acuity, quality-of-life measures, and other prioritisation tools. The CPPS showed weak correlations with quality-of-life measures, visual function (VF-14), and visual acuity, whereas the WCWL demonstrated moderate correlations with pain (VAS) and weak correlations with visual function and visual acuity. Only a few studies compared tools directly, reporting moderate correlations between CPPS and WCWL. Reported reliability measures varied across tools. ( Table 2 ) Table 2 Psychometric performance of the prioritisation tools identified Elective surgery Prioritisation tool Country Validation Reliability Cataract surgery CCPS Spain • Medical opinion: Moderate correlation (r 0.65; CI95% 0.61–0.69) ( 21 )* • Patient perception: Weak correlation (r 0.31; CI95% 0.26–0,36) ( 21 )* • HUI3: Weak correlation (r 0.16; CI95% 0.07–0.24) ( 21 )* • EQ-5D: Weak correlation (r 0.36; CI95% 0.26–0.45) ( 21 )* • Assigns higher scores than WCWL; however, the scoring pattern was similar( 22 ) • VF-14: Weak correlation (r 0.11) ( 22 ) • Post-operative VA: Weak correlation (r 0.18) ( 22 ) • High scores on the tool were considered appropriate for surgery ( 22 ) • ICPS: Moderate correlation (r 0.56; p < 0.001) ( 23 ) • WCWL: Moderate correlation (r 0.62; p < 0.001) ( 23 ) • Inter-observer agreement: 0.79 (0.63–0.95) ( 21 )* • ICPS vs CCPS vs WCWL: Kappa ranged from 0.13 to 0.40 ( 23 ) ICPS Spain • Pre-intervention values ≤ 40 and 0.40 for VF-14 and VA, respectively, were associated with low ICPS priority values ( 24 ) • CCPS: Moderate correlation (r 0.56; p < 0.001) ( 23 ) • WCWL: Moderate correlation (r 0.71; p < 0.001) ( 23 ) • ICPS vs CPPS vs WCWL: Kappa ranged from 0.13 to 0.40 ( 23 ) WCWL Canada • VF-14: High correlation with criterion 4 (r 0.71) ( 27 ) • EVA: Moderate correlation (r 0.65) ( 28 ) • VFA-14: Weak correlation(r 0.35) ( 28 ) • Interrater agreement on the ratings of urgency of ophthalmologists: 0.44 ( 27 ) • Excellent agreement for four criteria items (> 0.75), fair to good agreement for one item, and low agreement for three items (< 0.40) ( 27 ) Spain • Assigns lower scores than CCPS assigns higher scores; however, the scoring pattern was similar ( 22 ) • VF-14: Weak correlation(r 0.11) ( 22 ) • Post-operative VA: Weak correlation (r 0.07) ( 22 ) • High scores on the tool were considered appropriate for surgery ( 22 ) • ICPS: Strong correlation (r 0.71; p < 0.001) ( 23 ) • CCPS: Moderate correlation (r 0.62; p < 0.001) ( 23 ) • ICPS vs CPPS vs WCWL: Kappa ranged between 0.13 y 0.40 ( 23 ) WCWL modified Canada - • Generalisation coefficient: 0.852 ( 29 ) • Low variance and good agreement between assessors and over time( 29 ) • Generalisation coefficient of the quality-of-life scale: 0.941 ( 29 ) • Agreement between assessors: 0.979 ( 29 ) • Reliability per item: 0.948 ( 29 ) Cataract CPAC New Zealand ● EQ-5D: Weak correlation (r 0.22) ( 30 ) ● SF-12: Weak correlation (r 0.01) ( 30 ) ● VF-14: Moderate correlation (r 0.45) ( 30 ) - The national priority project New Zealand ● EVA: Moderate correlation (r 0.41) ( 32 ) ● Correlations with EVA varied among assessors, with weak to moderate correlations estimated ( 32 ) - Fantini et. al tool Italy ● Inter-criterion: Weak correlation (< 0.41) ( 33 ) NIKE Sweden ● Inter-criterion: Weak correlation (r 0.28; CI95% 0.231 a 0.573) ( 34 ) ● Re-test: Coefficient of 0.53 (CI95% 0.32–0.68; p < 0.001) ( 34 ) ● Inter-observer agreement: Coefficient 0.92 (CI95% 0.88–0.95; p < 0.001) ( 34 ) Knee replacement CHKPS Spain ● Construct validity: 0.93 ( 20 ) ● Predictive validity: 0.99 ( 20 ) ● Medical opinion: Moderate correlation (r 0.64; CI95% 0.60–0.68) ( 21 )* ● Patient perception: Weak correlation (r 0.31; CI95% 0.24–0.38) ( 21 )* ● HUI3: Weak correlation (r 0.23; CI95% 0.11–0.36) ( 21 )* ● EQ-5D: Weak correlation (r 0.36; CI 95% 0.26–0.45) ( 21 )* ● WOMAC: Weak correlation (r 0.39; CI 95% 0.33–0.45) ( 21 ) ● Good discrimination for classification of urgent vs. other and ordinary vs. other (AUC of 0.91 and 0.94) ( 35 ) ● Lower AUC than IHKPS ( 35 ) ● IHKPS: Moderate correlation (0.43 to 0.64) with WOMAC, lower than IHKPS (0.50 a 0.74) ( 35 ) ● Agreement between observers: 0.79 (0.64–0.94) ( 21 )* HKPT Canada ● EVA: correlation between 0.36 and 0.67 (lowest for criterion 3 and highest for criterion 4) ( 31 ) ● EVA: No statistical differences in the assessment of the HKPT and the EVA. Similar correlation. ( 36 ) ● WOMAC: Results only for hip arthroplasty ( 36 ) ● WOMAC: Weak correlation (r 0.33), variables between items (r 0.08 to 0.32) ( 37 ) ● EQ-VAS: Weak correlation (r 0.26) ( 37 ) ● EQ-5D: Weak correlation (r 0.33) ( 37 ) ● MAWT: Weak correlation (r 0.38) ( 37 ) ● Inter-observer agreement: 0.25 to 0.85 (lowest criterion value 6). Criteria 2, 3, 4, 5, and 7 had values ≥ 0.70 ( 31 ) IHKPS Spain ● WOMAC: Strong correlation (0.78, function; 0.69, pain; and 0.50, stiffness) ( 39 ) ● Overall convergent validity: between 0.45 and 0,68 ( 39 ) ● IHKPS greater AUC than CHKPS ● WOMAC: Moderate to strong correlation (0,50 a 0,74) ( 35 ) - NZOA New Zealand ● Oxford: Moderate correlation (r 0.43) ( 43 ) ● RWS: Moderate correlation (r 0.34) ( 43 ) ● Agreement: 88.3% ( 43 ) ● Kappa 0.71 ( 43 ) VF-14: 14-item visual function scale, VA: visual acuity, VAS: visual analogue scale; HUI3: Health Utilities Index − 3; AUC: area under the curve; VAS: visual analogue scale; HUI3: Health Utilities Index − 3; AUC: area under the curve; RWS: reduced WOMAC score; MAWT: maximum acceptable waiting time; r: correlation coefficient *The values correspond to general psychometric estimates of the tool and not to specific estimates for the disease-specific tool. Evidence on the effect of prioritisation tools on waiting times was available only for the CPPS, WCWL, NIKE ( Nationell Indikationsmodell för Kataraktextraktion ), and the National Priority Project tools. Results from individual studies are reported in Additional file 4 . Compared with status quo and regardless of the type of prioritisation tool, estimates derived from non-randomised studies suggested a weak association between standardised differences in surgical entry-exit order and priority scores in one setting, and overall increases in waiting times following the implementation of prioritisation strategies in another context ( 47 ). These increases were driven exclusively by longer waiting times among patients assigned lower priority scores, with delays exceeding the status quo by 0.29 to 0.69 months and progressively increasing as priority decreased. In contrast, among higher-priority patients, reductions in waiting times compared with the status quo were observed ( 50 , 51 ). In contrast, most estimates derived from simulation modelling studies indicated reductions in mean waiting times compared with the status quo, including first-in, first-out systems ( 46 , 48 , 49 ). (Additional file 7) . However, when results were stratified by priority category, modelling studies consistently showed that waiting-time reductions were concentrated among patients in the highest priority groups, with relative reductions ranging from 9% to 27%, while waiting times increased among lower-priority categories ( 33 ) ( Table 3 ) . Table 3 Summary of finding table of the impact in waiting list of the priorization tools Outcomes Inter. Comp. № of studies Certainty of the evidence (GRADE)* Impact Cataract surgery Time differences in waiting times Priorization tool Status quo** 3 non-randomized studies ( 47 , 50 , 51 ) ⨁◯◯◯ Very low a One study reported a weak correlation (r 0.25) between standardized differences in entry–exit order for surgery and priority scores, whereas a very strong correlation was observed with the status quo (FIFO) system (r 0.99). Another study found that the probability of first-eye cataract surgery within three months was higher among patients with the highest prioritisation and progressively lower among groups with lower prioritisation. Differences between prioritized groups and the status quo were statistically significant across all comparisons; however, a reduction in waiting time was observed only in the highest priority category (-0.45 months), while longer waiting times were observed in lower priority categories (0.29 to 0.69 months). The same pattern was observed when analyses were stratified by sex (female-only and male-only cohorts). Time differences in waiting times Priorization tool Status quo** 4 modelling study ( 33 , 46 , 48 , 49 ) ⨁◯◯◯ Very low b,c,d One study reported that a prioritized waiting list system was associated with greater effectiveness, achieving an approximate 60% reduction in delays to access surgery. Other studies showed that differences in waiting times varied across regions; however, consistently shorter waiting times were observed among patients prioritized using the tool, with reductions ranging from − 0.65 to -2.73 months. Another study found that, while prioritisation reduced waiting times among prioritized patients, it was associated with increased waiting times among non-prioritized groups. Relative reductions in waiting time among patients with the highest priority ranged from 9% to 27%, depending on the time function modeled, whereas relative increases among patients with the lowest priority ranged from 14% to 57%. Time differences in waiting times Priorization tool Triage 1 modelling study ( 46 ) ⨁◯◯◯ Very low e,f The study reported that delays were greater under the triage system compared with the prioritized waiting list system. Knee replacement Time differences in waiting times Priorization tool Status quo** 1 non-randomized studies ( 47 , 53 ) ⨁◯◯◯ Very low g One study reported in two publications identified a weak correlation (r 0.09) between standardized differences in entry–exit order for surgery and priority scores, whereas a very strong correlation was observed with the status quo (FIFO) system (r 0.99). Time differences in waiting times Priorization tool Status quo** 2 modelling study ( 52 , 54 ) ⨁⨁◯◯ Low d,h,i One study reported that, on average, 9.3% more patients received surgery within their maximum recommended waiting time per year when a prioritisation tool was used. Overall, shorter waiting times were observed among patients prioritized using the tool compared with the status quo (FIFO) system. However, another study reported that waiting time differences varied by priority score and that, overall, waiting times were longer when the prioritisation tool was applied, with an adjusted difference of 4.49 months (95% CI 4.23 to 4.75). Time differences in waiting times Priorization tool + other policy Status quo** 1 modelling study ( 52 ) ⨁⨁⨁⨁ High h Adding waiting time guarantee policies to the prioritisation system yielded heterogeneous results compared with the status quo. Compared with status quo, the addition of a waiting time guarantee of 3 months for high-priority patients and 6 months for all patients was associated with a slightly lower proportion of patients receiving surgery within their maximum recommended waiting time (-0.6% and − 1.1%, respectively). In contrast, adding a waiting time guarantee of 3 months for both high- and medium-priority patients was associated with a higher proportion of patients receiving surgery within the recommended time (+ 6.1%). *Certainty of evidence: Modelling studies and non-randomized studies were initially rated as high certainty of evidence and downgraded **Status quo is defined as the baseline waiting list management system, including a first-in, first-out (FIFO) approach. Explanations a. Downgraded by one level for risk of bias due to some concerns in most of the included studies, mainly related to residual confounding, insufficient adjustment for baseline differences, and difficulties in isolating the effect of the intervention from concurrent system-level changes. b. Half of the modelling studies had good credibility and half had poor credibility. For this reason, downgraded by one level for risk of bias because this could potentially affect the simulation and the model outputs. c. Downgraded by two levels for risk of bias due to cumulative concerns related to key model inputs that were either insufficiently reported or derived from potentially non-representative sources, raising concerns about selection bias and applicability to the target population. d. Downgraded by one level due to imprecision, as effect estimates and trends varied depending on the type of prioritisation tool, prioritisation category, and the measure used to estimate waiting time differences. e. The model credibility was poor; for this reason, downgraded by one level for risk of bias because this could potentially affect the simulation and the model outputs. f. Downgraded by two levels for high risk of bias due to lack of reporting of crucial surgery and waiting list parameters used as model inputs. g. Downgraded by two levels for high risk of bias due to potential confounding from concurrent regional health policies, lack of adjustment for important confounders, and risk of attrition bias related to non-random withdrawal from the waiting list. h. Models had good credibility. i. Downgraded by one level for risk of bias due to some concerns regarding the representativeness of key model inputs, particularly the use of priority score distributions derived from a pilot sample from a single community. When it was compared with a triage one study ( 46 ) reported that delays were greater under the triage system compared with the prioritised waiting list system (no value reported). ( Table 3 ) . Prioritisation tools for knee replacement For knee replacement, the prioritisation tools most frequently evaluated for psychometric performance were the CHKPS ( 20 , 21 , 35 ) and the HKPT ( 26 , 31 , 36 , 37 ), mainly in the Spanish and Canadian contexts. These tools were validated against functional measures, such as the WOMAC, and generic quality-of-life instruments, including HUI3, EQ-5D, and EQ-VAS, consistently showing weak correlations. In contrast, the HKPT exhibited variable correlations with pain measured using a visual analogue scale, depending on the specific criterion assessed. High agreement between observers were identified in CHKPS but variable in the case of HKPT. ( Table 2 ) Compared with the status quo, and regardless of the type of prioritisation tool, estimates derived from non-randomized studies indicated a weak association between standardized differences in surgical entry–exit order and priority scores in one setting ( 47 , 53 ). Findings from modelling studies showed heterogeneous effects. One study reported that the implementation of a prioritisation tool increased the proportion of patients receiving surgery within the maximum recommended waiting time (9.3% per year) and reduced waiting times compared with a first-in, first-out system ( 52 ). In contrast, another study found that the effects varied by priority category and that, overall, waiting times increased following implementation of the tool (4.49 months) ( 54 ). ( Table 3 ) When prioritisation tools were combined with additional waiting-list management policies, such as waiting time guarantees, their impact on timely access to surgery was heterogeneous ( 52 ). The direction and magnitude of the effects varied according to how the guarantees were defined and which priority groups they targeted. In some scenarios, the introduction of waiting time guarantees was associated with a slight reduction in the proportion of patients treated within their recommended maximum waiting times, whereas in others, extending guarantees to both high- and medium-priority patients led to an overall improvement in timely surgical access. ( Table 3 ) Prioritisation tools for inguinal hernia repair For inguinal hernia repair, evidence on the psychometric performance and effect of prioritisation tools was extremely limited, and no studies directly assessing these outcomes were identified. One study reported a modelling simulation based on the DPS system; however, the results were presented at a general level and were not specific to hernia repair ( 44 ). Risk of bias and certainty of the evidence The risk of bias of the studies included is reported in Additional file 6 . For cataract surgery, the certainty of evidence on the impact of prioritisation tools on waiting lists was rated as very low, downgraded for risk of bias in most non-randomized studies and for cumulative risk of bias in key model inputs, imprecision, and, in some cases, poor model credibility in modelling studies. Similarly, for knee replacement, certainty ranged from high to very low: non-randomized studies were downgraded for high risk of bias, and modelling studies for some concerns regarding model input risk of bias and imprecision. ( Table 3 ) Discussion Our findings highlight substantial heterogeneity across cataract surgery, knee replacement, and inguinal hernia repair in the availability, composition, psychometric performance, and effects of prioritisation tools. Across procedures, these tools do not assess priority in the same way. Instead, they include different domains and criteria depending on what is considered most relevant for each clinical context. In cataract surgery, prioritisation criteria extend beyond functional limitations to include social and occupational impact and expected benefit, aiming to capture the broader consequences of vision loss for independence and social participation. This is particularly relevant because cataract surgery, although not life-saving, is highly effective in improving vision-related functioning and quality of life ( 54 ), especially in older adults ( 55 ), in whom visual impairment often interacts with functional decline. However, factors not always captured by prioritisation tools, such as social support and other contextual circumstances, may reduce the likelihood of meaningful improvement even when expected benefit appears favourable, as poor postoperative care and unmanaged comorbidities can compromise visual recovery ( 56 , 57 ). Knee replacement prioritisation tools emphasise pain, physical function, and social and occupational impact, reflecting the burden of knee osteoarthritis in older adults, in whom disability is strongly associated with loss of independence and increased care needs beyond clinical severity alone ( 58 ). Unlike cataract surgery, expected benefit is less frequently specified, likely because knee replacement is commonly considered a treatment of last resort after failed conservative management ( 59 ). As a result, prioritisation tends to focus on symptom burden, functional limitation, and their consequences for everyday life and social roles. However, this implicit assumption of benefit may overlook patients whose outcomes are attenuated by frailty, comorbidities, or limited rehabilitation capacity ( 60 ). As in cataract surgery, social support and other contextual circumstances are less frequently captured by prioritisation tools, despite their potential to influence postoperative recovery and, consequently, the extent of improvement achieved after surgery. This suggests that even when prioritisation frameworks are oriented towards need, they may remain insufficiently sensitive to factors that modify surgical benefit in routine practice. In contrast, prioritisation tools for inguinal hernia repair place greater emphasis on clinical severity and symptoms, reflecting an explicit risk-based logic ( 61 ). This is expected, as inguinal hernia carries a risk of acute complications such as incarceration or strangulation, which may require emergency surgery and can be life-threatening ( 62 ). This also helps to explain why comorbidities and expected benefit were not represented in the identified tools, since the anticipated benefit of surgery may be treated as relatively uniform once indication has been established. Despite the inclusion of broader domains in several prioritisation tools, our review found that, for cataract surgery and knee replacement, psychometric performance was mainly characterised by weak, and less frequently moderate, correlations with quality-of-life and functional scales, together with variable reliability. These findings are consistent with previous systematic reviews reporting acceptable face validity but limited construct validity, with heterogeneous associations between prioritisation scores and external outcome measures across patient subgroups ( 12 ). From a measurement perspective, this raises concerns about whether existing tools adequately capture the constructs they are intended to operationalise for priority setting and whether they can reliably identify patients with the greatest need and potential to benefit from surgery ( 63 ). Taken together, this evidence suggests that current frameworks may need to be re-examined, not necessarily through the development of entirely new instruments, but through the integration or adaptation of existing validated quality-of-life and functional scales to better capture relevant domains while avoiding duplication of prior methodological efforts, as previously suggested ( 64 ). Evidence on the effects of prioritisation tools on waiting times was limited and heterogeneous, with few validated tools reporting effect estimates, mainly for cataract surgery and, to a lesser extent, for knee replacement, and similar patterns were observed in both cases. We identified divergent findings in overall waiting-time differences across study designs. This discrepancy is likely driven, at least in part, by differences in adherence to prioritisation strategies. In modelling studies, all patients are prioritised strictly according to the tool, whereas empirical evidence suggests that this assumption may not hold in practice ( 47 ). This gap between theoretical allocation and real-world implementation is likely to be critical for understanding why apparently promising prioritisation strategies may yield attenuated or inconsistent effects in practice. For these tools to be implemented effectively, services need clear responsibility for oversight, explicit criteria for their use in practice, support from clinicians and managers, and regular assessment of their effects on prioritisation decisions and patient outcomes ( 65 ). One important challenge is that prioritisation tools may encounter resistance from clinical staff, thereby limiting adherence and weakening their intended effects, and underscoring the need to actively de-implement pre-existing prioritisation practices, such as reliance on subjective judgement or informal criteria, which may persist despite the formal introduction of structured prioritisation tools ( 66 ). However, implementation challenges are not limited to clinician acceptance. Even when clinical staff agree with prioritisation strategies, their routine use may be constrained by the organisational conditions under which care is delivered, particularly in publicly funded systems where demand is high and consultation time is limited. For example, if clinicians have only a short consultation in which to assess the patient, make clinical decisions, and complete an additional prioritisation instrument, the consistent use of complex tools may be unrealistic, which makes administration time an important, yet rarely reported, consideration for feasibility and system integration. An important finding was that empirical and some modelling studies assessed waiting-time effects by prioritisation category. These studies consistently showed that prioritisation tools reduced waiting times for patients in the highest-priority category, while waiting times increased for lower-priority groups compared with the status quo. From an equity perspective, this pattern may be desirable, as it reallocates limited surgical capacity towards patients with greater need and supports more timely access for those most likely to benefit within constrained health system resources. Accordingly, the effects of prioritisation should not be judged solely on the basis of average waiting-time reductions, but also on their distributive consequences across priority groups. For this reason, studies reporting only overall waiting-time differences should be interpreted with caution, as subgroup analyses are needed to determine whether increases in waiting times reflect the prioritisation policy itself or are concentrated among lower-priority groups. Evidence evaluating prioritisation tools in combination with other waiting-list management policies, such as maximum waiting-time guarantees, or against active comparators, was scarce. This is an important limitation, given that most countries already operate multiple waiting-time management policies ( 6 ), making it difficult to disentangle the effects of prioritisation tools from those of coexisting strategies. Future evaluations should therefore account more explicitly for the broader policy mix within which these tools are implemented. Limitations and strengths This review has several limitations. First, the available evidence was limited and unevenly distributed across procedures, with most studies identified for cataract surgery and, to a lesser extent, knee replacement, while evidence for inguinal hernia repair was extremely scarce. Second, substantial heterogeneity in intervention characteristics, outcome definitions, and analytical approaches precluded quantitative synthesis and limited direct comparability across studies and tools. Third, as the effects of prioritisation policies are likely to be context dependent, transferability should be interpreted with caution, particularly in settings other than Spain, from which most effect estimates were derived. Fourth, the included studies captured only the period from entry onto the surgical waiting list onwards, without accounting for earlier delays in primary care, referral, and specialist assessment, which may be relevant for patient-centred outcomes. Fifth, some empirical and modelling studies appeared to reflect pathways in which patients used the private sector to accelerate access, potentially influencing both entry onto the waiting list and subsequent waiting-time estimates. Finally, this review focused on tools specifically developed for prioritisation purposes and did not include other instruments that, although not originally designed for this purpose, might also be relevant to waiting-list management. This review also has important strengths. To our knowledge, it is the first to synthesise evidence on the characteristics, psychometric performance, and waiting-time effects of prioritisation tools across elective surgical procedures. By combining these dimensions, it can support decision-making by identifying which tools exist, how they have been validated, whether they may be transferable to other contexts, and whether they are associated with measurable effects on waiting times. In addition, it is the only review to include modelling studies, which provide a useful perspective on the potential effects of prioritisation under ideal implementation assumptions and on what might be achievable if some real-world implementation challenges were overcome. Conclusions We identified several multidimensional prioritisation tools designed to reflect differences in clinical risk, functional impairment, and social impact across cataract surgery, knee replacement, and inguinal hernia repair. However, evidence on their psychometric performance and impact was available only for cataract surgery and knee replacement. Overall, validation studies showed weak associations with commonly used quality-of-life and functional scales, variable reliability, and limited and inconsistent evidence on effects on waiting times. Abbreviations CCPS: Catalan Agency for Health Technology Assessment and Research cataract priority system CHKPS: Catalan Hip and Knee Priority Scoring system CPPS: Cataract Priority Scoring System DPS: Dutch Priority Score EQ-5D: EuroQol 5-Dimensions EQ-VAS: EuroQol Visual Analogue Scale GRADE: Grading of Recommendations Assessment, Development and Evaluation HKPT: Hip and Knee Priority Tool HUI3: Health Utilities Index Mark 3 ISPOR: International Society for Pharmacoeconomics and Outcomes Research NIKE: Nationell Indikationsmodell för Kataraktextraktion NPC: National Pharmaceutical Council O1: Objective 1 O2: Objective 2 OSF: Open Science Framework PICO: Population, Intervention, Comparison, Outcome PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses VAS: Visual Analogue Scale VF-14: Visual Function Index-14 WCWL: Western Canada Waiting List Declarations Ethics approval and consent to participate Not applicable Consent for publication All authors read and approved the final manuscript. Availability of data and materials All data analysed in this study were derived from publicly available published studies. No new datasets were generated. The data extracted and analysed are included in this published article and its supplementary materials. Competing interests The authors declare that they have no competing interests. Funding This study was commissioned and funded by the Spanish Ministry of Health in the framework of activities carried out by the Spanish Network of Agencies for Assessing National Health System Technologies and Performance (RedETS). The study was conducted independently of study sponsors. There was no sponsor involvement in the study design; collection, analysis and interpretation of the data; in writing of the manuscript; or in the decision to submit the manuscript for publication. Authors' contributions WNG and SIV conceived and designed the study and developed the review protocol. WNG and MMA conducted the literature search, screened studies, extracted data, analysed and synthesised the results, and drafted the first version of the manuscript. SIV provided methodological guidance and critically revised the manuscript for important intellectual content. All authors reviewed and approved the final manuscript and its content. Acknowledgements The authors would like to thank Maria Pilar Blas Diez for developing and validating the search strategy. They also thank Silvia Moler-Zapata and Analía Abt Sacks for their valuable review of, and comments on, the manuscript. Finally, they are grateful to all members of the National Working Group on Waiting Lists of the Spanish National Health System for their contributions and valuable discussions. The views expressed in this article are those of the authors and do not necessarily reflect the official position of their affiliated institutions. References McIntyre D, Chow CK. 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A single prioritisation tool could contribute one or more criteria to the same domain.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDomains: 1) Clinical severity and symptoms: Refers to the clinical seriousness of the condition and its directly attributable symptom burden, including objective indicators of disease status, urgency-related features, risk of deterioration or complications, pain, discomfort, and other patient-reported symptoms. 2) Physical or organ-specific function: Refers to the impact of the condition on physical functioning or on the function of the affected organ or body system, including disease-specific functional limitations such as visual function, mobility, walking ability, or joint-related impairment. 3) Daily life and occupational impact: Refers to the effect of the condition on activities of daily living, independence, work, caregiving responsibilities, driving, and broader role performance in everyday life. 4) Comorbidities and expected benefit: Refers to coexisting conditions or patient-related clinical factors that may influence surgical appropriateness, likely outcomes, or the expected benefit from the procedure, including anticipated recovery or improvement after surgery. 5) Contextual and social factors: Refers to broader non-clinical or contextual circumstances relevant to prioritisation, including social support, social dependency, mental well-being, waiting-time considerations, and other discretionary or contextual factors not fully captured by clinical or functional domains.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9447805/v1/3aaba70ed8e4a195976c1469.png"},{"id":108494032,"identity":"a7d6dcae-597d-44e5-a905-9fa2b99d86f6","added_by":"auto","created_at":"2026-05-05 10:02:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":721924,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9447805/v1/05fbb951-b6e8-4972-af3f-babf4866dadb.pdf"},{"id":107621546,"identity":"a9c7580d-21c5-4b6b-a0c8-023e529239e9","added_by":"auto","created_at":"2026-04-23 09:41:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":467951,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalfilesHRPS.docx","url":"https://assets-eu.researchsquare.com/files/rs-9447805/v1/64f83f094895f21238b87616.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prioritisation tools for cataract surgery, knee replacement, and inguinal hernia repair waiting lists and their effectiveness in reducing elective surgery waiting times: A rapid systematic review","fulltext":[{"header":"Background","content":"\u003cp\u003eWaiting times for access to healthcare services represent an important challenge in many health systems, although their magnitude and policy relevance vary across financing and delivery arrangements (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). They are particularly relevant in publicly financed or heavily publicly regulated systems, where universal or population-based coverage seeks to ensure access according to need, but where capacity constraints may require explicit mechanisms to manage demand (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In these contexts, waiting lists are not only an administrative challenge but also a major policy concern, as prolonged waits may undermine the principles of timely and equitable access on which such systems are based (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWaiting times may arise across different stages of the patient care pathway, including access to primary care, specialist consultations, diagnostic services, and elective procedures. Delays at earlier stages may accumulate over time and contribute to longer waits for subsequent interventions. Elective surgery constitutes a particularly relevant setting because access is more commonly managed through formal waiting lists, and waiting times are frequently reported and used in health system performance assessment, particularly in publicly funded systems (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultiple strategies have been implemented to manage and reduce waiting lists, including increasing surgical capacity, improving care pathways, outsourcing procedures, and reorganising service delivery (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Among these, prioritisation has been proposed as a mechanism to improve the allocation of limited resources by ranking patients according to explicit criteria such as clinical need, functional status, expected benefit from surgery, and, in some cases, selected sociodemographic factors. In this way, prioritisation seeks to enhance transparency, consistency, and equity in access to elective procedures (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Despite the growing use of prioritisation approaches, evidence on their effectiveness in reducing waiting times remains limited. To our knowledge, no systematic review has comprehensively evaluated the impact of prioritisation tools, representing an important gap for policymakers and health system managers considering the adoption or refinement of these strategies. This gap is particularly relevant for common elective procedures such as cataract surgery, knee replacement, and inguinal hernia repair, which are widely used as indicators of waiting times and access to surgical care, and which represent different surgical specialties and decision-making contexts (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, this study aims to: (O1) identify and describe the psychometric properties of the prioritisation tools used to rank patients on surgical waiting lists for cataract surgery, knee replacement, and inguinal hernia repair; and (O2) evaluate their impact on elective surgery waiting times. Examining both aspects will provide important evidence on the potential effects of these tools on waiting times and on their relevance for potential applicability across settings\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eContext\u003c/h2\u003e \u003cp\u003eThe Spanish National Health System established a national working group on waiting lists to examine their causes, assess management strategies, and develop recommendations for Spain. This article reports one component of that work, a systematic review of three prioritised elective procedures. Although commissioned to inform decision-making in Spain, the review considered evidence beyond the Spanish setting.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDesign and structured question\u003c/h3\u003e\n\u003cp\u003eThis systematic review followed the methodology of a Cochrane Rapid Systematic Review (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), and reporting adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). he protocol was registered on the Open Science Framework (OSF) platform (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe clinical question addressed was: \u003cem\u003eWhat prioritisation tools are used to rank patients on surgical waiting lists for cataract surgery, knee replacement, and inguinal hernia repair, and what is their impact on elective surgery waiting times?\u003c/em\u003e This question was structured according to the PICO framework and is presented in \u003cb\u003eBox 1\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003eData sources and searches\u003c/h3\u003e\n\u003cp\u003eThe systematic search was conducted in PubMed, Embase, and Google Scholar (until July 2025), with the latter limited to the first 100 records. Three search strategies were developed, one for each surgical procedure, combining terms related to waiting lists with procedure-specific terms \u003cb\u003e(Additional file 1)\u003c/b\u003e. Records identified through PubMed and Embase were exported to EndNote X9, and duplicates were removed automatically. Records retrieved through Google Scholar were screened separately, and duplicates were addressed during full-text assessment. In addition, the reference lists of all included studies and relevant systematic reviews (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) were screened to identify further eligible studies not captured by the electronic search. The reviews were considered because they mapped prioritisation tools and cited primary studies related to their development and use.\u003c/p\u003e\n\u003ch3\u003eStudy selection and data extraction\u003c/h3\u003e\n\u003cp\u003eStudies were eligible if they addressed all PICO components and had full text available in English or Spanish. Eligible designs included empirical studies, defined as trials or observational studies, and modelling studies, defined as studies reporting results derived from decision-analytic or other simulation-based models.\u003c/p\u003e \u003cp\u003eStudy selection was performed in two stages in Rayyan (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e): title/abstract screening by one reviewer (WNG) and cross-check by a second reviewer (MMA), followed by independent, blinded full-text assessment by both reviewers. Before formal selection, a calibration exercise was conducted on 20% of records, and selection proceeded once agreement exceeded 80%. Disagreements were resolved by consensus.\u003c/p\u003e \u003cp\u003eData extraction followed the same calibration process. It was conducted by one reviewer (WNG) and cross-checked by a second reviewer (MMA) using a standardized form. Extracted data included study characteristics of the study and prioritisation tools, their psychometric properties (such as validity and reliability measures), and effects of prioritisation on waiting times. When studies did not explicitly report effect estimates such as mean differences and corresponding confidence intervals, but sufficient summary data were available, estimated mean differences were calculated assuming independence between comparison groups. For modelling studies, uncertainty intervals were interpreted under the assumption that, if the simulation were repeated a large number of times, the true mean estimate generated by the model would fall within the reported 95% interval in 95% of repetitions (where N represents the number of simulation runs).\u003c/p\u003e\n\u003ch3\u003eRisk of bias and certainty of the evidence\u003c/h3\u003e\n\u003cp\u003eRisk of bias and certainty of evidence were assessed only for studies addressing the second objective, as no validated risk-of-bias tools were identified for psychometric studies and studies under the first objective were considered supplementary, mainly to support identification of tools relevant to the second objective. Assessments were performed by one reviewer (WNG) and cross-checked by a second reviewer (MMA), with disagreements resolved by consensus. Risk of bias was assessed according to study type, observational studies were evaluated using the Newcastle-Ottawa Scale (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), while modelling studies were assessed using the ISPOR\u0026ndash;AMCP\u0026ndash;NPC Good Practice recommendations (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), and by evaluating the risk of bias of key model inputs (disease prevalence, surgical parameters, waiting list parameters, and priority score distributions). Certainty of evidence was assessed using the GRADE framework, applying specific guidances (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eData synthesis was structured according to the type of surgical procedure (cataract surgery, knee replacement, or inguinal hernia repair), the type of prioritisation tool, and the outcome measures assessed. Considering the heterogeneity of the reporting of the results quantitative synthesis was not feasible and results were summarized using a narrative synthesis. To synthesize psychometric performance, correlation coefficients were interpreted using predefined thresholds: values\u0026thinsp;\u0026lt;\u0026thinsp;0.40 were classified as weak correlations; values between 0.40 and 0.69 as moderate correlations; values between 0.70 and 0.89 as strong correlations; and values between 0.90 and 1.00 as very strong correlations (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). For the second objective, visual representations of the forest plots were used to illustrate waiting-time outcomes when it was possible. These were conducted using R. In addition, the main findings of the review were summarized in a Summary of Findings Table.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSystematic search\u003c/h2\u003e \u003cp\u003eThe systematic search was conducted separately for each of the three surgical procedures, and the selection process is summarised in the flow diagrams \u003cb\u003e(Additional file 2)\u003c/b\u003e. In total, 25, 18, and two studies were included for cataract surgery, knee replacement, and inguinal hernia repair, respectively. Full-text exclusions and reasons are reported in \u003cb\u003eAdditional file 3\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eAmong the included studies, 15 identified or evaluated the psychometric performance of prioritisation tools for cataract surgery (\u003cspan additionalcitationids=\"CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), 13 for knee replacement (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39 CR40 CR41 CR42\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), and two for inguinal hernia repair (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). However, only seven studies assessed their impact for cataract surgery (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan additionalcitationids=\"CR47 CR48 CR49 CR50\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) and four studies for knee replacement (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of prioritisation tools\u003c/h2\u003e \u003cp\u003eA total of nine prioritisation tools were identified for cataract surgery, six for knee replacement, and two for inguinal hernia repair. The characteristics of these tools are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrioritisation tools identified and their characteristics for waiting lists for cataract surgery, knee replacement, and inguinal hernia repair\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElective surgery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrioritisation tool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e\u003cb\u003eCataract surgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCatalan Agency for Health Technology Assessment and Research cataract priority system (CPPS)\u003c/em\u003e (\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Visual Impairment\u003c/p\u003e \u003cp\u003e2. Probability of recovery\u003c/p\u003e \u003cp\u003e3. Difficulty performing activities of daily living\u003c/p\u003e \u003cp\u003e4. Ability to work\u003c/p\u003e \u003cp\u003e5. Having someone to care for the patient/ being caregiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall score, by sum of the scores of each criterion. Scores can range from 0 (lowest priority) to 100 (highest priority)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIRYSS-Cataract Priority Score (ICPS)\u003c/em\u003e (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Relevance\u003c/p\u003e \u003cp\u003e2. Ocular comorbidities\u003c/p\u003e \u003cp\u003e3. Pre-intervention visual acuity in the eye with cataract\u003c/p\u003e \u003cp\u003e4. Visual function reported by the patient before surgery\u003c/p\u003e \u003cp\u003e5. Visual acuity in the contralateral eye.\u003c/p\u003e \u003cp\u003e6. Type of cataract (laterality)\u003c/p\u003e \u003cp\u003e7. Social dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall score, by sum of the scores of each criterion. Scores can range from 0 (lowest priority) to 100 (highest priority)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eManitoba Cataract Waiting List Program\u003c/em\u003e (MCWLP) \u003cem\u003eprioritisation system\u003c/em\u003e (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Functional impairment\u003c/p\u003e \u003cp\u003e2. Waiting time\u003c/p\u003e \u003cp\u003e3. Work impairment\u003c/p\u003e \u003cp\u003e4. Impairment in work performance\u003c/p\u003e \u003cp\u003e5. Possible loss of driver\u0026rsquo;s license\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall score by the sum of each criteria. The higher the score, the higher the priority.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWestern Canada\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eWaiting List Project tool (WCWL)\u003c/em\u003e (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Corrected visual acuity in the non-operated eye\u003c/p\u003e \u003cp\u003e2. Corrected visual acuity in the eye to be operated\u003c/p\u003e \u003cp\u003e3. Glare\u003c/p\u003e \u003cp\u003e4. Age-related macular degeneration and other ocular comorbidities\u003c/p\u003e \u003cp\u003e5. Impaired visual function\u003c/p\u003e \u003cp\u003e6. Other significant disabilities\u003c/p\u003e \u003cp\u003e7. Ability to work, live independently, or care for dependents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall score, by sum of the scores of each criterion. Scores can range from 0 (lowest priority) to 100 (highest priority)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWCWL modified*\u003c/em\u003e (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Corrected visual acuity in the non-operated eye\u003c/p\u003e \u003cp\u003e2. Anticipated visual acuity in the eye to be operated\u003c/p\u003e \u003cp\u003e3. Complexity of the case\u003c/p\u003e \u003cp\u003e4. Condition of the contralateral eye\u003c/p\u003e \u003cp\u003e5. Vision impairments\u003c/p\u003e \u003cp\u003e6. Impact of comorbidity on improvement after cataract removal\u003c/p\u003e \u003cp\u003e7. Impact of cataracts on the ability to monitor or treat comorbidity\u003c/p\u003e \u003cp\u003e8. Quality of Life questionnaire\u003c/p\u003e \u003cp\u003e9. Ability to work, care for dependents, or live independently.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCataract Clinical Priority Assessment Criteria (Cataract CPAC)\u003c/em\u003e (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Corrected visual acuity (Snellen chart)\u003c/p\u003e \u003cp\u003e2. Glare\u003c/p\u003e \u003cp\u003e3. Ocular comorbidity\u003c/p\u003e \u003cp\u003e4. Visual function impairment (VF-14)\u003c/p\u003e \u003cp\u003e5. Social/occupational/educational problems\u003c/p\u003e \u003cp\u003e6. Patient disability\u003c/p\u003e \u003cp\u003e7. Discretional clinical points (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall score, by sum of the scores of each criterion. Scores can range from 0 (lowest priority) to 100 (highest priority)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eThe national priority project\u003c/em\u003e (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Visual acuity\u003c/p\u003e \u003cp\u003e2. Glare\u003c/p\u003e \u003cp\u003e3. Ocular comorbidity\u003c/p\u003e \u003cp\u003e4. Ability to work, care for dependents or function independently\u003c/p\u003e \u003cp\u003e5. Degree of visual function impairment\u003c/p\u003e \u003cp\u003e6. Other significant disabilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall score, by sum of the scores of each criterion. Scores can range from 0 (lowest priority) to 100 (highest priority)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFantini et. al tool\u003c/em\u003e (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Visual acuity in the eye to be operated on\u003c/p\u003e \u003cp\u003e2. Visual acuity in the contralateral eye\u003c/p\u003e \u003cp\u003e3. Visual Function\u003c/p\u003e \u003cp\u003e4. Patient's ability to work or live independently.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall score, by sum of the scores of each criterion. Scores can range from 0 (lowest priority) to 100 (highest priority)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNationell Indikationsmodell for Kataraktextraktion\u003c/em\u003e (NIKE) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Visual acuity of the operated eye\u003c/p\u003e \u003cp\u003e2. Visual acuity of the contralateral eye\u003c/p\u003e \u003cp\u003e3. Difficulty perceived by the patient in performing daily activities\u003c/p\u003e \u003cp\u003e4. Cataract symptoms (glare, difference between eyes)\u003c/p\u003e \u003cp\u003e5. Ability to live independently (work, driving, help at home, caring for family members, etc.)\u003c/p\u003e \u003cp\u003e6. Medical/ophthalmological reasons for urgent surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSum of the scores for each criterion. Scores can range from 0 (lowest priority) to 18 (highest priority).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eKnee replacement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCatalan Hip and Knee Priority Score\u003c/em\u003e (CHKPS) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Severity of the illness (clinical and radiological examination)\u003c/p\u003e \u003cp\u003e2. Probability of recovery\u003c/p\u003e \u003cp\u003e3. Difficulty performing daily activities\u003c/p\u003e \u003cp\u003e4. Limited ability to work\u003c/p\u003e \u003cp\u003e5. Does the patient have a caregiver?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall score, by sum of the scores of each criterion. Scores can range from 0 (lowest priority) to 100 (highest priority)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eThe hip and knee replacement priority criteria tool\u003c/em\u003e (HKPT) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Pain on movement\u003c/p\u003e \u003cp\u003e2. Pain at rest\u003c/p\u003e \u003cp\u003e3. Ability to walk without significant pain\u003c/p\u003e \u003cp\u003e4. Other functional limitations\u003c/p\u003e \u003cp\u003e5. Abnormal findings on physical examination related to the affected joint\u003c/p\u003e \u003cp\u003e6. Potential for disease progression documented by radiographic findings\u003c/p\u003e \u003cp\u003e7. Threat to the patient's role and independence in society\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSum of priority criteria scores. Maximum score 100; the higher the score, the higher the priority.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIRYSS Hip and Knee Priority Score (IHKPS)\u003c/em\u003e (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Pain on movement\u003c/p\u003e \u003cp\u003e2. Functional limitations when walking\u003c/p\u003e \u003cp\u003e3. Abnormal findings on physical examination\u003c/p\u003e \u003cp\u003e4. Pain at rest\u003c/p\u003e \u003cp\u003e5. Social role (impact on the ability to care for dependents and to work or live independently)\u003c/p\u003e \u003cp\u003e6. Other conditions (which could improve with joint replacement)\u003c/p\u003e \u003cp\u003e7. Other functional limitations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSum of priority criteria scores. Maximum score 100; the higher the score, the higher the priority.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOntario tool\u003c/em\u003e (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Pain at rest\u003c/p\u003e \u003cp\u003e2. Pain during daily activities\u003c/p\u003e \u003cp\u003e3. Difficulty working or caring for others\u003c/p\u003e \u003cp\u003e4. Expected improvement in functional status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSum of the scores for the criteria (divided into functional classes)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePATHWAY tool\u003c/em\u003e (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Pain\u003c/p\u003e \u003cp\u003e2. Mobility/function\u003c/p\u003e \u003cp\u003e3. Activities of daily living\u003c/p\u003e \u003cp\u003e4. Inability to work/care for others\u003c/p\u003e \u003cp\u003e5. Waiting time\u003c/p\u003e \u003cp\u003e6. Radiological severity\u003c/p\u003e \u003cp\u003e7. Mental well-being\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSum of scores by criterion. Scores for each level are not clear.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNew Zealand Orthopaedic Association Score (NZOA)\u003c/em\u003e (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Pain\u003c/p\u003e \u003cp\u003e2. Personal functional limitation (due to an orthopaedic condition of the hip or knee)\u003c/p\u003e \u003cp\u003e3. Social limitation (due to an orthopaedic condition of the hip or knee)\u003c/p\u003e \u003cp\u003e4. Potential benefit of the operation\u003c/p\u003e \u003cp\u003e5. Consequence of delay\u0026thinsp;\u0026gt;\u0026thinsp;6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSum of scores per criterion. Scores can range from 0 (best condition) to 100 (worst condition).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eInguinal hernia repair\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDynamic Priority Scoring for hernia (DPS)\u003c/em\u003e (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Pain\u003c/p\u003e \u003cp\u003e2. Reducibility\u003c/p\u003e \u003cp\u003e3. Risk of complications\u003c/p\u003e \u003cp\u003e4. Functional consequences\u003c/p\u003e \u003cp\u003e5. Other relevant factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEach clinical criterion has weighted values. The more severe the condition, the higher the score assigned to that criterion.\u003c/p\u003e \u003cp\u003eThe scores corresponding to all the conditions presented by the patient are added together. The total score reflects the overall clinical priority. (Category 1 - highest priority; 2 - intermediate priority; and 3 - lowest priority)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOudhoff et. al tool\u003c/em\u003e (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1. Physical symptoms\u003c/p\u003e \u003cp\u003e2. Psychological distress\u003c/p\u003e \u003cp\u003e3. Social limitations\u003c/p\u003e \u003cp\u003e4. Work impairments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIt starts with a baseline score that varies depending on the perspective (7 to 16.3), to which the values of the criteria categories (depending on the perspective) are added.\u003c/p\u003e \u003cp\u003eThe higher the score, the lower the priority; conversely, the lower the score, the higher the priority.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e*Changes: 1) Each comorbidity is included separately and its severity is assessed; 2) visual acuity was grouped into 7 categories; 3) glare was clinically measured as none, mild, moderate or severe; 4) questions about quality of life were grouped as none, mild, moderate or severe, and 3 additional items were included.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe heat map showed marked differences in the distribution of prioritisation criteria across domains by surgical procedure. In cataract surgery, criteria were predominantly concentrated in the physical or organ-specific function domain (33%), followed by daily life and occupational impact (25%) and comorbidities and expected benefit (19%). In contrast, prioritisation tools for inguinal hernia repair placed greater emphasis on clinical severity and symptoms (44%), followed by daily life and occupational impact and contextual and social factors (22% each). For knee replacement, the highest proportions were observed for clinical severity and symptoms (37%) and daily life and occupational impact (23%), with physical or organ-specific function also well represented (17%). The least represented domains were contextual and social factors in cataract surgery (9%), comorbidities and expected benefit in inguinal hernia repair (0%), and comorbidities and expected benefit together with contextual and social factors (11% each) in knee replacement. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrioritisation tools for cataract surgery\u003c/h2\u003e \u003cp\u003eFor cataract surgery, the prioritisation tools most frequently evaluated for psychometric performance were the Western Canada Waiting List (WCWL) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and the Catalan Agency for Health Technology Assessment and Research cataract priority system (CCPS) (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), mainly in the Spanish context. These tools were validated against visual function, visual acuity, quality-of-life measures, and other prioritisation tools. The CPPS showed weak correlations with quality-of-life measures, visual function (VF-14), and visual acuity, whereas the WCWL demonstrated moderate correlations with pain (VAS) and weak correlations with visual function and visual acuity. Only a few studies compared tools directly, reporting moderate correlations between CPPS and WCWL. Reported reliability measures varied across tools. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePsychometric performance of the prioritisation tools identified\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElective surgery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrioritisation tool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReliability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e\u003cb\u003eCataract surgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCCPS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Medical opinion: Moderate correlation (r 0.65; CI95% 0.61\u0026ndash;0.69) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)*\u003c/p\u003e \u003cp\u003e\u0026bull; Patient perception: Weak correlation (r 0.31; CI95% 0.26\u0026ndash;0,36) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)*\u003c/p\u003e \u003cp\u003e\u0026bull; HUI3: Weak correlation (r 0.16; CI95% 0.07\u0026ndash;0.24) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)*\u003c/p\u003e \u003cp\u003e\u0026bull; EQ-5D: Weak correlation (r 0.36; CI95% 0.26\u0026ndash;0.45) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)*\u003c/p\u003e \u003cp\u003e\u0026bull; Assigns higher scores than WCWL; however, the scoring pattern was similar(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; VF-14: Weak correlation (r 0.11) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Post-operative VA: Weak correlation (r 0.18) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; High scores on the tool were considered appropriate for surgery (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; ICPS: Moderate correlation (r 0.56; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; WCWL: Moderate correlation (r 0.62; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026bull; Inter-observer agreement: 0.79 (0.63\u0026ndash;0.95) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)*\u003c/p\u003e \u003cp\u003e\u0026bull; ICPS vs CCPS vs WCWL: Kappa ranged from 0.13 to 0.40 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eICPS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Pre-intervention values\u0026thinsp;\u0026le;\u0026thinsp;40 and 0.40 for VF-14 and VA, respectively, were associated with low ICPS priority values (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; CCPS: Moderate correlation (r 0.56; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; WCWL: Moderate correlation (r 0.71; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026bull; ICPS vs CPPS vs WCWL: Kappa ranged from 0.13 to 0.40 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eWCWL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; VF-14: High correlation with criterion 4 (r 0.71) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; EVA: Moderate correlation (r 0.65) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; VFA-14: Weak correlation(r 0.35) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026bull; Interrater agreement on the ratings of urgency of ophthalmologists: 0.44 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Excellent agreement for four criteria items (\u0026gt;\u0026thinsp;0.75), fair to good agreement for one item, and low agreement for three items (\u0026lt;\u0026thinsp;0.40) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026bull; Assigns lower scores than CCPS assigns higher scores; however, the scoring pattern was similar (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; VF-14: Weak correlation(r 0.11) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Post-operative VA: Weak correlation (r 0.07) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; High scores on the tool were considered appropriate for surgery (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; ICPS: Strong correlation (r 0.71; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; CCPS: Moderate correlation (r 0.62; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026bull; ICPS vs CPPS vs WCWL: Kappa ranged between 0.13 y 0.40 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eWCWL modified\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026bull; Generalisation coefficient: 0.852 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Low variance and good agreement between assessors and over time(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Generalisation coefficient of the quality-of-life scale: 0.941 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Agreement between assessors: 0.979 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e\u0026bull; Reliability per item: 0.948 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCataract CPAC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNew Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● EQ-5D: Weak correlation (r 0.22) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● SF-12: Weak correlation (r 0.01) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● VF-14: Moderate correlation (r 0.45) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eThe national priority project\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNew Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● EVA: Moderate correlation (r 0.41) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● Correlations with EVA varied among assessors, with weak to moderate correlations estimated (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFantini et. al tool\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● Inter-criterion: Weak correlation (\u0026lt;\u0026thinsp;0.41) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNIKE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● Inter-criterion: Weak correlation (r 0.28; CI95% 0.231 a 0.573) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e● Re-test: Coefficient of 0.53 (CI95% 0.32\u0026ndash;0.68; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● Inter-observer agreement: Coefficient 0.92 (CI95% 0.88\u0026ndash;0.95; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eKnee replacement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCHKPS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● Construct validity: 0.93 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● Predictive validity: 0.99 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● Medical opinion: Moderate correlation (r 0.64; CI95% 0.60\u0026ndash;0.68) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)*\u003c/p\u003e \u003cp\u003e● Patient perception: Weak correlation (r 0.31; CI95% 0.24\u0026ndash;0.38) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)*\u003c/p\u003e \u003cp\u003e● HUI3: Weak correlation (r 0.23; CI95% 0.11\u0026ndash;0.36) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)*\u003c/p\u003e \u003cp\u003e● EQ-5D: Weak correlation (r 0.36; CI 95% 0.26\u0026ndash;0.45) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)*\u003c/p\u003e \u003cp\u003e● WOMAC: Weak correlation (r 0.39; CI 95% 0.33\u0026ndash;0.45) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● Good discrimination for classification of urgent vs. other and ordinary vs. other (AUC of 0.91 and 0.94) (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● Lower AUC than IHKPS (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● IHKPS: Moderate correlation (0.43 to 0.64) with WOMAC, lower than IHKPS (0.50 a 0.74) (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e● Agreement between observers: 0.79 (0.64\u0026ndash;0.94) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHKPT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● EVA: correlation between 0.36 and 0.67 (lowest for criterion 3 and highest for criterion 4) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● EVA: No statistical differences in the assessment of the HKPT and the EVA. Similar correlation. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● WOMAC: Results only for hip arthroplasty (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● WOMAC: Weak correlation (r 0.33), variables between items (r 0.08 to 0.32) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● EQ-VAS: Weak correlation (r 0.26) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● EQ-5D: Weak correlation (r 0.33) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● MAWT: Weak correlation (r 0.38) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e● Inter-observer agreement: 0.25 to 0.85 (lowest criterion value 6). Criteria 2, 3, 4, 5, and 7 had values\u0026thinsp;\u0026ge;\u0026thinsp;0.70 (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIHKPS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● WOMAC: Strong correlation (0.78, function; 0.69, pain; and 0.50, stiffness) (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● Overall convergent validity: between 0.45 and 0,68 (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● IHKPS greater AUC than CHKPS\u003c/p\u003e \u003cp\u003e● WOMAC: Moderate to strong correlation (0,50 a 0,74) (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNZOA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNew Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e● Oxford: Moderate correlation (r 0.43) (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● RWS: Moderate correlation (r 0.34) (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e● Agreement: 88.3% (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e● Kappa 0.71 (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eVF-14: 14-item visual function scale, VA: visual acuity, VAS: visual analogue scale; HUI3: Health Utilities Index\u0026thinsp;\u0026minus;\u0026thinsp;3; AUC: area under the curve; VAS: visual analogue scale; HUI3: Health Utilities Index\u0026thinsp;\u0026minus;\u0026thinsp;3; AUC: area under the curve; RWS: reduced WOMAC score; MAWT: maximum acceptable waiting time; r: correlation coefficient\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*The values correspond to general psychometric estimates of the tool and not to specific estimates for the disease-specific tool.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEvidence on the effect of prioritisation tools on waiting times was available only for the CPPS, WCWL, NIKE (\u003cem\u003eNationell Indikationsmodell f\u0026ouml;r Kataraktextraktion\u003c/em\u003e), and the National Priority Project tools. Results from individual studies are reported in \u003cb\u003eAdditional file 4\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eCompared with status quo and regardless of the type of prioritisation tool, estimates derived from non-randomised studies suggested a weak association between standardised differences in surgical entry-exit order and priority scores in one setting, and overall increases in waiting times following the implementation of prioritisation strategies in another context (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). These increases were driven exclusively by longer waiting times among patients assigned lower priority scores, with delays exceeding the status quo by 0.29 to 0.69 months and progressively increasing as priority decreased. In contrast, among higher-priority patients, reductions in waiting times compared with the status quo were observed (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). In contrast, most estimates derived from simulation modelling studies indicated reductions in mean waiting times compared with the status quo, including first-in, first-out systems (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). \u003cb\u003e(Additional file 7)\u003c/b\u003e. However, when results were stratified by priority category, modelling studies consistently showed that waiting-time reductions were concentrated among patients in the highest priority groups, with relative reductions ranging from 9% to 27%, while waiting times increased among lower-priority categories (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of finding table of the impact in waiting list of the priorization tools\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInter.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComp.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e№ of studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCertainty of the evidence\u003c/p\u003e \u003cp\u003e(GRADE)*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eImpact\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCataract surgery\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime differences in waiting times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePriorization tool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatus quo**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 non-randomized studies (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e⨁◯◯◯\u003c/p\u003e \u003cp\u003eVery low \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOne study reported a weak correlation (r 0.25) between standardized differences in entry\u0026ndash;exit order for surgery and priority scores, whereas a very strong correlation was observed with the status quo (FIFO) system (r 0.99). Another study found that the probability of first-eye cataract surgery within three months was higher among patients with the highest prioritisation and progressively lower among groups with lower prioritisation. Differences between prioritized groups and the status quo were statistically significant across all comparisons; however, a reduction in waiting time was observed only in the highest priority category (-0.45 months), while longer waiting times were observed in lower priority categories (0.29 to 0.69 months). The same pattern was observed when analyses were stratified by sex (female-only and male-only cohorts).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime differences in waiting times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePriorization tool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatus quo**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 modelling study (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e⨁◯◯◯\u003c/p\u003e \u003cp\u003eVery low \u003csup\u003eb,c,d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOne study reported that a prioritized waiting list system was associated with greater effectiveness, achieving an approximate 60% reduction in delays to access surgery. Other studies showed that differences in waiting times varied across regions; however, consistently shorter waiting times were observed among patients prioritized using the tool, with reductions ranging from \u0026minus;\u0026thinsp;0.65 to -2.73 months. Another study found that, while prioritisation reduced waiting times among prioritized patients, it was associated with increased waiting times among non-prioritized groups. Relative reductions in waiting time among patients with the highest priority ranged from 9% to 27%, depending on the time function modeled, whereas relative increases among patients with the lowest priority ranged from 14% to 57%.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime differences in waiting times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePriorization tool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 modelling study (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e⨁◯◯◯\u003c/p\u003e \u003cp\u003eVery low \u003csup\u003ee,f\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe study reported that delays were greater under the triage system compared with the prioritized waiting list system.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKnee replacement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime differences in waiting times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePriorization tool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatus quo**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 non-randomized studies (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e⨁◯◯◯\u003c/p\u003e \u003cp\u003eVery low \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOne study reported in two publications identified a weak correlation (r 0.09) between standardized differences in entry\u0026ndash;exit order for surgery and priority scores, whereas a very strong correlation was observed with the status quo (FIFO) system (r 0.99).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime differences in waiting times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePriorization tool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatus quo**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 modelling study (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e⨁⨁◯◯\u003c/p\u003e \u003cp\u003eLow \u003csup\u003ed,h,i\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOne study reported that, on average, 9.3% more patients received surgery within their maximum recommended waiting time per year when a prioritisation tool was used. Overall, shorter waiting times were observed among patients prioritized using the tool compared with the status quo (FIFO) system. However, another study reported that waiting time differences varied by priority score and that, overall, waiting times were longer when the prioritisation tool was applied, with an adjusted difference of 4.49 months (95% CI 4.23 to 4.75).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime differences in waiting times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePriorization tool\u0026thinsp;+\u0026thinsp;other policy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatus quo**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 modelling study (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e⨁⨁⨁⨁\u003c/p\u003e \u003cp\u003eHigh \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdding waiting time guarantee policies to the prioritisation system yielded heterogeneous results compared with the status quo. Compared with status quo, the addition of a waiting time guarantee of 3 months for high-priority patients and 6 months for all patients was associated with a slightly lower proportion of patients receiving surgery within their maximum recommended waiting time (-0.6% and \u0026minus;\u0026thinsp;1.1%, respectively). In contrast, adding a waiting time guarantee of 3 months for both high- and medium-priority patients was associated with a higher proportion of patients receiving surgery within the recommended time (+\u0026thinsp;6.1%).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003e*Certainty of evidence: Modelling studies and non-randomized studies were initially rated as high certainty of evidence and downgraded\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003e**Status quo is defined as the baseline waiting list management system, including a first-in, first-out (FIFO) approach.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eExplanations\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ea. Downgraded by one level for risk of bias due to some concerns in most of the included studies, mainly related to residual confounding, insufficient adjustment for baseline differences, and difficulties in isolating the effect of the intervention from concurrent system-level changes.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eb. Half of the modelling studies had good credibility and half had poor credibility. For this reason, downgraded by one level for risk of bias because this could potentially affect the simulation and the model outputs.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ec. Downgraded by two levels for risk of bias due to cumulative concerns related to key model inputs that were either insufficiently reported or derived from potentially non-representative sources, raising concerns about selection bias and applicability to the target population.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ed. Downgraded by one level due to imprecision, as effect estimates and trends varied depending on the type of prioritisation tool, prioritisation category, and the measure used to estimate waiting time differences.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ee. The model credibility was poor; for this reason, downgraded by one level for risk of bias because this could potentially affect the simulation and the model outputs.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ef. Downgraded by two levels for high risk of bias due to lack of reporting of crucial surgery and waiting list parameters used as model inputs.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eg. Downgraded by two levels for high risk of bias due to potential confounding from concurrent regional health policies, lack of adjustment for important confounders, and risk of attrition bias related to non-random withdrawal from the waiting list.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eh. Models had good credibility.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ei. Downgraded by one level for risk of bias due to some concerns regarding the representativeness of key model inputs, particularly the use of priority score distributions derived from a pilot sample from a single community.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen it was compared with a triage one study (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) reported that delays were greater under the triage system compared with the prioritised waiting list system (no value reported). \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrioritisation tools for knee replacement\u003c/h2\u003e \u003cp\u003eFor knee replacement, the prioritisation tools most frequently evaluated for psychometric performance were the CHKPS (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) and the HKPT (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), mainly in the Spanish and Canadian contexts. These tools were validated against functional measures, such as the WOMAC, and generic quality-of-life instruments, including HUI3, EQ-5D, and EQ-VAS, consistently showing weak correlations. In contrast, the HKPT exhibited variable correlations with pain measured using a visual analogue scale, depending on the specific criterion assessed. High agreement between observers were identified in CHKPS but variable in the case of HKPT. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCompared with the status quo, and regardless of the type of prioritisation tool, estimates derived from non-randomized studies indicated a weak association between standardized differences in surgical entry\u0026ndash;exit order and priority scores in one setting (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Findings from modelling studies showed heterogeneous effects. One study reported that the implementation of a prioritisation tool increased the proportion of patients receiving surgery within the maximum recommended waiting time (9.3% per year) and reduced waiting times compared with a first-in, first-out system (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). In contrast, another study found that the effects varied by priority category and that, overall, waiting times increased following implementation of the tool (4.49 months) (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhen prioritisation tools were combined with additional waiting-list management policies, such as waiting time guarantees, their impact on timely access to surgery was heterogeneous (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). The direction and magnitude of the effects varied according to how the guarantees were defined and which priority groups they targeted. In some scenarios, the introduction of waiting time guarantees was associated with a slight reduction in the proportion of patients treated within their recommended maximum waiting times, whereas in others, extending guarantees to both high- and medium-priority patients led to an overall improvement in timely surgical access. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePrioritisation tools for inguinal hernia repair\u003c/h2\u003e \u003cp\u003eFor inguinal hernia repair, evidence on the psychometric performance and effect of prioritisation tools was extremely limited, and no studies directly assessing these outcomes were identified. One study reported a modelling simulation based on the DPS system; however, the results were presented at a general level and were not specific to hernia repair (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRisk of bias and certainty of the evidence\u003c/h2\u003e \u003cp\u003eThe risk of bias of the studies included is reported in \u003cb\u003eAdditional file 6\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eFor cataract surgery, the certainty of evidence on the impact of prioritisation tools on waiting lists was rated as very low, downgraded for risk of bias in most non-randomized studies and for cumulative risk of bias in key model inputs, imprecision, and, in some cases, poor model credibility in modelling studies. Similarly, for knee replacement, certainty ranged from high to very low: non-randomized studies were downgraded for high risk of bias, and modelling studies for some concerns regarding model input risk of bias and imprecision. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings highlight substantial heterogeneity across cataract surgery, knee replacement, and inguinal hernia repair in the availability, composition, psychometric performance, and effects of prioritisation tools. Across procedures, these tools do not assess priority in the same way. Instead, they include different domains and criteria depending on what is considered most relevant for each clinical context.\u003c/p\u003e \u003cp\u003eIn cataract surgery, prioritisation criteria extend beyond functional limitations to include social and occupational impact and expected benefit, aiming to capture the broader consequences of vision loss for independence and social participation. This is particularly relevant because cataract surgery, although not life-saving, is highly effective in improving vision-related functioning and quality of life (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), especially in older adults (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e), in whom visual impairment often interacts with functional decline. However, factors not always captured by prioritisation tools, such as social support and other contextual circumstances, may reduce the likelihood of meaningful improvement even when expected benefit appears favourable, as poor postoperative care and unmanaged comorbidities can compromise visual recovery (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKnee replacement prioritisation tools emphasise pain, physical function, and social and occupational impact, reflecting the burden of knee osteoarthritis in older adults, in whom disability is strongly associated with loss of independence and increased care needs beyond clinical severity alone (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Unlike cataract surgery, expected benefit is less frequently specified, likely because knee replacement is commonly considered a treatment of last resort after failed conservative management (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). As a result, prioritisation tends to focus on symptom burden, functional limitation, and their consequences for everyday life and social roles. However, this implicit assumption of benefit may overlook patients whose outcomes are attenuated by frailty, comorbidities, or limited rehabilitation capacity (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). As in cataract surgery, social support and other contextual circumstances are less frequently captured by prioritisation tools, despite their potential to influence postoperative recovery and, consequently, the extent of improvement achieved after surgery. This suggests that even when prioritisation frameworks are oriented towards need, they may remain insufficiently sensitive to factors that modify surgical benefit in routine practice.\u003c/p\u003e \u003cp\u003eIn contrast, prioritisation tools for inguinal hernia repair place greater emphasis on clinical severity and symptoms, reflecting an explicit risk-based logic (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). This is expected, as inguinal hernia carries a risk of acute complications such as incarceration or strangulation, which may require emergency surgery and can be life-threatening (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). This also helps to explain why comorbidities and expected benefit were not represented in the identified tools, since the anticipated benefit of surgery may be treated as relatively uniform once indication has been established.\u003c/p\u003e \u003cp\u003eDespite the inclusion of broader domains in several prioritisation tools, our review found that, for cataract surgery and knee replacement, psychometric performance was mainly characterised by weak, and less frequently moderate, correlations with quality-of-life and functional scales, together with variable reliability. These findings are consistent with previous systematic reviews reporting acceptable face validity but limited construct validity, with heterogeneous associations between prioritisation scores and external outcome measures across patient subgroups (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). From a measurement perspective, this raises concerns about whether existing tools adequately capture the constructs they are intended to operationalise for priority setting and whether they can reliably identify patients with the greatest need and potential to benefit from surgery (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). Taken together, this evidence suggests that current frameworks may need to be re-examined, not necessarily through the development of entirely new instruments, but through the integration or adaptation of existing validated quality-of-life and functional scales to better capture relevant domains while avoiding duplication of prior methodological efforts, as previously suggested (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEvidence on the effects of prioritisation tools on waiting times was limited and heterogeneous, with few validated tools reporting effect estimates, mainly for cataract surgery and, to a lesser extent, for knee replacement, and similar patterns were observed in both cases. We identified divergent findings in overall waiting-time differences across study designs. This discrepancy is likely driven, at least in part, by differences in adherence to prioritisation strategies. In modelling studies, all patients are prioritised strictly according to the tool, whereas empirical evidence suggests that this assumption may not hold in practice (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). This gap between theoretical allocation and real-world implementation is likely to be critical for understanding why apparently promising prioritisation strategies may yield attenuated or inconsistent effects in practice.\u003c/p\u003e \u003cp\u003eFor these tools to be implemented effectively, services need clear responsibility for oversight, explicit criteria for their use in practice, support from clinicians and managers, and regular assessment of their effects on prioritisation decisions and patient outcomes (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). One important challenge is that prioritisation tools may encounter resistance from clinical staff, thereby limiting adherence and weakening their intended effects, and underscoring the need to actively de-implement pre-existing prioritisation practices, such as reliance on subjective judgement or informal criteria, which may persist despite the formal introduction of structured prioritisation tools (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). However, implementation challenges are not limited to clinician acceptance. Even when clinical staff agree with prioritisation strategies, their routine use may be constrained by the organisational conditions under which care is delivered, particularly in publicly funded systems where demand is high and consultation time is limited. For example, if clinicians have only a short consultation in which to assess the patient, make clinical decisions, and complete an additional prioritisation instrument, the consistent use of complex tools may be unrealistic, which makes administration time an important, yet rarely reported, consideration for feasibility and system integration.\u003c/p\u003e \u003cp\u003eAn important finding was that empirical and some modelling studies assessed waiting-time effects by prioritisation category. These studies consistently showed that prioritisation tools reduced waiting times for patients in the highest-priority category, while waiting times increased for lower-priority groups compared with the status quo. From an equity perspective, this pattern may be desirable, as it reallocates limited surgical capacity towards patients with greater need and supports more timely access for those most likely to benefit within constrained health system resources. Accordingly, the effects of prioritisation should not be judged solely on the basis of average waiting-time reductions, but also on their distributive consequences across priority groups. For this reason, studies reporting only overall waiting-time differences should be interpreted with caution, as subgroup analyses are needed to determine whether increases in waiting times reflect the prioritisation policy itself or are concentrated among lower-priority groups.\u003c/p\u003e \u003cp\u003eEvidence evaluating prioritisation tools in combination with other waiting-list management policies, such as maximum waiting-time guarantees, or against active comparators, was scarce. This is an important limitation, given that most countries already operate multiple waiting-time management policies (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), making it difficult to disentangle the effects of prioritisation tools from those of coexisting strategies. Future evaluations should therefore account more explicitly for the broader policy mix within which these tools are implemented.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and strengths\u003c/h2\u003e \u003cp\u003eThis review has several limitations. First, the available evidence was limited and unevenly distributed across procedures, with most studies identified for cataract surgery and, to a lesser extent, knee replacement, while evidence for inguinal hernia repair was extremely scarce. Second, substantial heterogeneity in intervention characteristics, outcome definitions, and analytical approaches precluded quantitative synthesis and limited direct comparability across studies and tools. Third, as the effects of prioritisation policies are likely to be context dependent, transferability should be interpreted with caution, particularly in settings other than Spain, from which most effect estimates were derived. Fourth, the included studies captured only the period from entry onto the surgical waiting list onwards, without accounting for earlier delays in primary care, referral, and specialist assessment, which may be relevant for patient-centred outcomes. Fifth, some empirical and modelling studies appeared to reflect pathways in which patients used the private sector to accelerate access, potentially influencing both entry onto the waiting list and subsequent waiting-time estimates. Finally, this review focused on tools specifically developed for prioritisation purposes and did not include other instruments that, although not originally designed for this purpose, might also be relevant to waiting-list management.\u003c/p\u003e \u003cp\u003eThis review also has important strengths. To our knowledge, it is the first to synthesise evidence on the characteristics, psychometric performance, and waiting-time effects of prioritisation tools across elective surgical procedures. By combining these dimensions, it can support decision-making by identifying which tools exist, how they have been validated, whether they may be transferable to other contexts, and whether they are associated with measurable effects on waiting times. In addition, it is the only review to include modelling studies, which provide a useful perspective on the potential effects of prioritisation under ideal implementation assumptions and on what might be achievable if some real-world implementation challenges were overcome.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe identified several multidimensional prioritisation tools designed to reflect differences in clinical risk, functional impairment, and social impact across cataract surgery, knee replacement, and inguinal hernia repair. However, evidence on their psychometric performance and impact was available only for cataract surgery and knee replacement. Overall, validation studies showed weak associations with commonly used quality-of-life and functional scales, variable reliability, and limited and inconsistent evidence on effects on waiting times.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cem\u003eCCPS: Catalan Agency for Health Technology Assessment and Research cataract priority system\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCHKPS: Catalan Hip and Knee Priority Scoring system\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCPPS: Cataract Priority Scoring System\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDPS: Dutch Priority Score\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEQ-5D: EuroQol 5-Dimensions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEQ-VAS: EuroQol Visual Analogue Scale\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGRADE: Grading of Recommendations Assessment, Development and Evaluation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHKPT: Hip and Knee Priority Tool\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHUI3: Health Utilities Index Mark 3\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eISPOR: International Society for Pharmacoeconomics and Outcomes Research\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNIKE: Nationell Indikationsmodell f\u0026ouml;r Kataraktextraktion\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNPC: National Pharmaceutical Council\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eO1: Objective 1\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eO2: Objective 2\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOSF: Open Science Framework\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePICO: Population, Intervention, Comparison, Outcome\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVAS: Visual Analogue Scale\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVF-14: Visual Function Index-14\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWCWL: Western Canada Waiting List\u003c/em\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eAll data analysed in this study were derived from publicly available published studies. No new datasets were generated. The data extracted and analysed are included in this published article and its supplementary materials.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was commissioned and funded by the Spanish Ministry of Health in the framework of activities carried out by the Spanish Network of Agencies for Assessing National Health System Technologies and Performance (RedETS). The study was conducted independently of study sponsors. There was no sponsor involvement in the study design; collection, analysis and interpretation of the data; in writing of the manuscript; or in the decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eWNG and SIV conceived and designed the study and developed the review protocol. WNG and MMA conducted the literature search, screened studies, extracted data, analysed and synthesised the results, and drafted the first version of the manuscript. SIV provided methodological guidance and critically revised the manuscript for important intellectual content. All authors reviewed and approved the final manuscript and its content.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Maria Pilar Blas Diez for developing and validating the search strategy. They also thank Silvia Moler-Zapata and Anal\u0026iacute;a Abt Sacks for their valuable review of, and comments on, the manuscript. Finally, they are grateful to all members of the National Working Group on Waiting Lists of the Spanish National Health System for their contributions and valuable discussions. The views expressed in this article are those of the authors and do not necessarily reflect the official position of their affiliated institutions.\u0026nbsp;\u003cbr clear=\"all\"\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcIntyre D, Chow CK. Waiting Time as an Indicator for Health Services Under Strain: A Narrative Review. Inquiry. 2020;57:46958020910305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganisation for Economic Co-operation and Development. Waiting times for health services: next in line. Paris: OECD Publishing; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOudhoff JP, Timmermans DR, Knol DL, Bijnen AB, van der Wal G. Waiting for elective general surgery: impact on health related quality of life and psychosocial consequences. 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Healthc (Amst). 2018;6(2):104\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"waiting lists, cataract extraction, arthroplasty, replacement, knee, herniorrhaphy (source: MeSH)","lastPublishedDoi":"10.21203/rs.3.rs-9447805/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9447805/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis review identifies and describes clinical prioritisation tools used to rank patients on surgical waiting lists for cataract surgery, knee replacement, and inguinal hernia repair, and evaluate the effect of these tools on elective surgery waiting times.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a systematic review following Cochrane Rapid Review methods and PRISMA guidelines. Searches were performed in PubMed, Embase, and Google Scholar to identify studies evaluating prioritisation tools for cataract surgery, knee replacement, and inguinal hernia repair. We described the tools, their criteria and domains, assessed psychometric performance, and synthesised evidence on waiting-time outcomes. The certainty of the evidence was evaluated using GRADE methodology.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eForty-six studies were included: 25 on cataract surgery, 19 on knee replacement, and 2 on inguinal hernia repair. Nine prioritisation tools were identified for cataract surgery, six for knee replacement, and two for inguinal hernia repair. Across the three procedures, identified tools incorporated multiple domains reflecting differences in clinical characteristics and disease burden. Evidence on psychometric performance and waiting-time effects was available only for cataract surgery and knee replacement and showed weak to moderate correlations with other tools. Evidence on the impact of prioritisation on waiting times was heterogeneous. Non-randomised studies showed weak associations between priority scores and surgical order, with longer waits for lower-priority patients in some settings. Modelling studies suggested either overall reductions in waiting times or reductions confined to high-priority patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePrioritisation tools adopt procedure-specific, multidimensional approaches, but evidence supporting their effectiveness in reducing waiting times is heterogeneous and, in some cases, uncertain.\u003c/p\u003e","manuscriptTitle":"Prioritisation tools for cataract surgery, knee replacement, and inguinal hernia repair waiting lists and their effectiveness in reducing elective surgery waiting times: A rapid systematic review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:41:22","doi":"10.21203/rs.3.rs-9447805/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f9ce6a9a-f03f-4d0e-afc9-87ea19a5eb22","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-05T08:37:54+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T08:56:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:41:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9447805","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9447805","identity":"rs-9447805","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}