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PubMed2002

Benchmarking in emergency health systems.

This paper discusses the role of benchmarking as a component of quality management. It describes the historical background of benchmarking, its competitive origin and the requirement in today's health environment for a more collaborative approach. The classical 'functional and generic' types of benchmarking are discussed with a suggestion to adopt a different terminology that describes the purpose and practicalities of benchmarking. Benchmarking is not without risks. The consequence of inappropriate focus and the need for a balanced overview of process is explored. The competition that is intrinsic to benchmarking is questioned and the negative impact it may have on improvement strategies in poorly performing organizations is recognized. The difficulty in achieving cross-organizational validity in benchmarking is emphasized, as is the need to scrutinize benchmarking measures. The cost effectiveness of benchmarking projects is questioned and the concept of 'best value, best practice' in an environment of fixed resources is examined.

Benchmarking↗
PubMed2000

Benchmarking in health-system pharmacy: current research and practical applications.

The application of benchmarking techniques to hospital pharmacy practice is discussed. Benchmarking is a process designed to discover best practices through a comparison of various competing methods aimed at achieving a particular goal. Benchmarking antimicrobial drug utilization and rates of bacterial resistance through comparison with a multitude of similar hospitals can be used by an institution both to identify potential problem areas in its pharmacy practice and to aid in establishing appropriate and attainable goals. The effectiveness of various activities targeted at reducing appropriate drug use can also be benchmarked. In 1993, the Benchmarking Program was established at Millard Fillmore Hospital. This program consists of a network of hospital pharmacists who supply data on antimicrobial use, antimicrobial management activities, and rates of antimicrobial resistance. The program was designed both to serve hospital pharmacies in optimizing antimicrobial management and to create a national database for evaluating relationships among antimicrobial use, management, and resistance. Hospitals participating in the Benchmarking Program receive an annual report that allows them to compare themselves with peer groups and with best-performing "benchmark hospitals." All data from U.S. hospitals contained in the Benchmarking Program database are pooled and analyzed to identify meaningful trends. However, information gained from the institutionwide data must be supplemented by studies at the patient level. Benchmarking antimicrobial drug use in an institutional setting can identify successes as well as potential problem areas in pharmacy practice and aid in establishing appropriate and attainable goals.

Anti-Bacterial Agents↗
PubMed2001

Benchmarking patient outcomes.

PURPOSE: To examine the usefulness of three types of benchmarking for interpreting patient outcome data. DESIGN: This study was part of a multiyear, multihospital longitudinal survey of 10 patient outcomes. The patient outcome used for this methodologic presentation was central line infections (CLI). The sample included eight hospitals in an integrated healthcare system, with a range in size from 144 to 861 beds. The unit of analysis for CLI was the number of line days, with the CLI rate defined as the number of infections per 1,000 patient-line days per month. METHODS: Data on each outcome were collected at the unit level according to standardized protocols. Results were submitted via standardized electronic forms to a central data management center. Data for this presentation were analyzed using a Bayesian hierarchical Poisson model. Results are presented for each hospital and the system as a whole. FINDINGS: In comparison to published benchmarks, hospital performances were mixed with regard to CLI. Five of the 8 hospitals exceeded 2.2 infections per 1,000 patient-line days. When benchmarks were established for each hospital using 95% credible intervals, hospitals did reasonably well with only isolated months reaching or going beyond the benchmark limits. When the entire system was used to establish benchmarks with the 95% credible intervals, the hospitals that reached or exceeded the benchmark limits remained the same, but some hospitals had CLI rates more frequently in the upper 50% of the benchmarking limits. CONCLUSIONS: Benchmarking of quality indicators can be accomplished in a variety of ways as a means to quantify patient care and identify areas needing attention and improvement. Hospital-specific and system-wide benchmarks provide relevant feedback for improving performance at individual hospitals.

Benchmarking↗
PubMed2000

Foodservice benchmarking: practices, attitudes, and beliefs of foodservice directors.

OBJECTIVES: To identify foodservice directors' use of performance measures and to determine their current practices of, and attitudes and beliefs about, benchmarking. DESIGN: A survey was conducted using a researcher-developed questionnaire that had been validated in a pilot-test. The questionnaire was mailed to 600 randomly selected foodservice directors; 247 (41%) responses were analyzed. SUBJECTS/SETTING: Subjects were foodservice directors in the United States from 4 categories of foodservice operations: college/university, correctional, health care, and school. STATISTICAL ANALYSES: Results were analyzed using descriptive statistics and chi 2 tests to investigate associations between variables of interest. RESULTS: The most common performance measures used by foodservice directors were food cost percentage, cost per unit or area of service, and meals per labor hour. Internal benchmarking had been used by 71% of the respondents, external benchmarking by 60%, and functional/generic by 25%. Seventy-seven percent of the respondents thought benchmarking had some or great importance in their jobs. Category of foodservice operation was associated with type of benchmarking partner and was related to certain performance measures. Sixty-one percent of respondents reported needing knowledge and skills about benchmarking. APPLICATIONS/CONCLUSIONS: Foodservice directors, regardless of category of foodservice operation, perceive benchmarking as a useful management tool to improve processes, products and services. Foodservice directors can use benchmarking to compare their financial performance with that of other organizations and learn how to improve their facility by examining best-practice processes of successful organizations.

Administrative Personnel↗
PubMed2003

Defining the need for radiotherapy for lung cancer in the general population: a criterion-based, benchmarking approach.

BACKGROUND: We have previously used an evidence-based, epidemiologic approach to estimate the proportion of incident cases that should be treated with radiotherapy (RT) for lung cancer. The first objective of the present study was to compare this evidence-based estimate of the appropriate rate of use of RT with the rates observed in selected "benchmark" communities where there are no barriers to the appropriate use of RT and no incentives to the unnecessary use of RT. The second objective of the study was to compare the rates of use of RT in the general populations in the United States and Canada with the estimated appropriate rate. METHODS: We established benchmark rates for the use of RT for lung cancer in Ontario, Canada, where: 1) residents make no direct payments for RT; 2) all RT is provided by site-specialized radiation oncologists in multidisciplinary cancer centers, and 3) radiation oncologists receive a salary in lieu of technical fees. Communities located close to cancer centers without long waiting lists for RT were selected to serve as benchmarks. Prospectively gathered electronic treatment records from all RT cancer centers were linked to the provincial cancer registry to describe the rate of use of RT in Ontario. The public use file of Surveillance, Epidemiology and End Results Registries (SEER) was used to describe the use of RT in the United States. RESULTS: Overall, 41.3% (95% confidence interval [CI], 39.9%, 42.7%) of incident cases of lung cancer received RT as part of their initial management in the benchmark communities compared with the evidence-based estimate of 41.6% (95% CI, 39.2%, 44.1%). The rate of use of RT in the initial management of nonsmall cell lung cancer (NSCLC) in the benchmark communities was 49.3% (95% CI, 47.5%, 51.1%) compared with the evidence-based estimate of 45.9% (95% CI, 41.6%, 50.2%). The use of RT in the initial management of small-cell lung cancer (SCLC) in the benchmark communities was 47.0% (95% CI, 43.3%, 50.7%) compared with the evidence-based estimate of 45.4% (95% CI, 42.4%, 48.4%). In many counties of Ontario, the observed rates of RT use in the initial management of lung cancer were significantly lower than either the benchmark rate or the evidence-based estimate of the appropriate rate. In contrast, rates of use of RT in most counties in the SEER regions of the United States were close to, or higher than, the estimated appropriate rate. CONCLUSIONS: The observed benchmark rate converged on the evidence-based estimate of the appropriate rate of use of RT for lung cancer, suggesting that either measure might reasonably be used as a "standard" against which to compare rates observed in similar populations elsewhere.

Aged↗
PubMed1996

Benchmarking the US physician workforce. An alternative to needs-based or demand-based planning.

OBJECTIVE: To propose population-based benchmarking as an alternative to needs- or demand-based planning for estimating a reasonably sized, clinically active physician workforce for the United States and its regional health care markets. DESIGN: Cross-sectional analysis of 1993 American Medical Association and American Osteopathic Association physician masterfiles. POPULATION: The resident population of the 306 hospital referral regions in the United States. MAIN OUTCOME MEASURES: Per capita number of clinically active physicians by specialty adjusted for age and sex population differences and out-of-region health care utilization. The measured physician workforce was compared with 4 benchmarks: the staffing within a large (2.4 million members) health maintenance organization (HMO), a hospital referral region dominated by managed care (Minneapolis, Minn), a hospital referral region dominated by fee-for-service (Wichita, Kan), and the proposed "balanced" physician supply (50% generalists). RESULTS: The proportion of the US population residing in hospital referral regions with a higher per capita generalist workforce than the benchmark was 96% for the HMO benchmark, 60% for Wichita, and 27% for Minneapolis. The specialist workforce exceeded all 3 benchmarks for 74% of the population. The per capita workforce of generalists was not related to the proportion of generalists among regions (Pearson correlation coefficient=0.06; P=.26). CONCLUSIONS: Population-based benchmarking offers practical advantages to needs- or demand-based planning for estimating a reasonably sized per capita workforce of clinically active physicians. The physician workforce within the benchmarks of an HMO and health care markets indicates the varying opportunities for regional physician employment and services. The ratio of generalists to specialists does not measure the adequacy of the supply of the generalist workforce either nationally or for specific regions. Research measuring the relationship between physician workforces of different sizes and population outcomes will guide the selection of future regional benchmarks.

Catchment Area, Health↗
PubMed1996

Quantalization of continuous data for benchmark dose estimation.

Benchmark doses corresponding to low levels of noncancer disease risk have been proposed to replace the no-observed-adverse-effect level for establishing allowable daily intakes or reference doses. For quantal data each animal is classified with or without a disease. The proportion of animals with an adverse effect (risk) is observed as a function of dose of a toxic substance. The calculation of a benchmark dose is relatively straightforward. For continuous data a somewhat more complicated designation of risk is required. Because of the more direct procedures with quantal data, consideration could be given to converting continuous data to quantal data before estimating benchmark doses. The purpose of this paper is to compare the precision of the two approaches (use of continuous or quantalized data) for a number of sublinear dose-response curves ranging from low to high probabilities of risk at the highest dose. In these studies, five animals per dose were generally satisfactory to estimate the benchmark dose for continuous data, whereas the corresponding quantalized data generally do not perform as well even with 10 to 20 animals per dose. For quantalized data, the lower 95% confidence limits on the estimates of the benchmark dose were generally a factor of 3 to 4 below the true benchmark dose, whereas the confidence limits using the continuous data were generally within a factor of 2 of the true benchmark dose. Although the use of quantalized data for the estimation of risk is more direct, estimates of benchmark doses using the continuous data were more precise. Based on this study, converting continuous data to quantal data is not recommended.

Animals↗
PubMedDate not supplied

Benchmarking in healthcare: evaluating data and transforming it into action.

After the benchmarking team has accumulated data for the development of comparisons, it must be validated for completeness and consistency. Various factors can skew the analysis and should be watched: Subjective interpretations of survey questions. Lack of common definitions. Composition of input data. External factors and extraordinary events. After verifying consistency of data gathered from benchmarking partners, calculate appropriate statistics for the performance metric. Typical data tabulations are: mean, median, ratio, minimum and maximum value, normal operating range, standard deviation and correlation coefficient. Statistics derived from the data produce the benchmark against which you will measure your institution's performance. Gap analysis establishes the difference between your internal operation's performance and that of the benchmark. Information developed from properly collected data will help you determine reasons for the gap between your performance and the benchmark and project future trends. The next step is to develop an action plan based on what has been learned from benchmarking and targeted to improving performance in areas that further the strategic goals of the institution. In addition to determining performance goals, you must analyze the decision-making process involved in making changes that will move you toward those goals. Since the goal of benchmarking is to improve the organization, the team must present its analysis to those members of management who can approve an action plan. Changes resulting from benchmarking can range from incremental improvement of existing practices all the way to reenginering. Action plans are designed to effect change at levels that will vary according to the goals that have been set. The more incremental the change, the easier the implementation. The more radical the change, the greater the reward.

Data Display↗
PubMed2001

Improving quality improvement using achievable benchmarks for physician feedback: a randomized controlled trial.

CONTEXT: Performance feedback and benchmarking, common tools for health care improvement, are rarely studied in randomized trials. Achievable Benchmarks of Care (ABCs) are standards of excellence attained by top performers in a peer group and are easily and reproducibly calculated from existing performance data. OBJECTIVE: To evaluate the effectiveness of using achievable benchmarks to enhance typical physician performance feedback and improve care. DESIGN: Group-randomized controlled trial conducted in December 1996, with follow-up through 1998. SETTING AND PARTICIPANTS: Seventy community physicians and 2978 fee-for-service Medicare patients with diabetes mellitus who were part of the Ambulatory Care Quality Improvement Project in Alabama. INTERVENTION: Physicians were randomly assigned to receive a multimodal improvement intervention, including chart review and physician-specific feedback (comparison group; n = 35) or an identical intervention plus achievable benchmark feedback (experimental group; n = 35). MAIN OUTCOME MEASURE: Preintervention (1994-1995) to postintervention (1997-1998) changes in the proportion of patients receiving influenza vaccination; foot examination; and each of 3 blood tests measuring glucose control, cholesterol level, and triglyceride level, compared between the 2 groups. RESULTS: The proportion of patients who received influenza vaccine improved from 40% to 58% in the experimental group (P<.001) vs from 40% to 46% in the comparison group (P =.02). Odds ratios (ORs) for patients of achievable benchmark physicians vs comparison physicians who received appropriate care after the intervention, adjusted for preintervention care and nesting of patients within physicians, were 1.57 (95% confidence interval [CI], 1.26-1.96) for influenza vaccination, 1.33 (95% CI, 1.05-1.69) for foot examination, and 1.33 (95% CI, 1.04-1.69) for long-term glucose control measurement. For serum cholesterol and triglycerides, the achievable benchmark effect was statistically significant only after additional adjustment for physician characteristics (OR, 1.40 [95% CI, 1.08-1.82] and OR, 1.40 [95% CI, 1.09-1.79], respectively). CONCLUSION: Use of achievable benchmarks significantly enhances the effectiveness of physician performance feedback in the setting of a multimodal quality improvement intervention.

Aged↗
PubMed1999

Application of legal and regulatory rules and policies to benchmarking systems.

Although there are few federal or state laws that address benchmarking systems, there are legal issues of which those who develop or use benchmarking system should be aware. Benchmarks may be used as standards of care in tort or professional discipline actions. Use of benchmark data to set fees or exclude providers may raise antitrust concerns. Payers may use benchmark data to set coverage policies or payment rates. Which data are included in benchmarking systems and who has access to those data raise issues of confidentiality. Lastly, benchmarking system developers may want to protect their intellectual property rights and may need to consider medical device laws if they claim that the systems may be used in patient management.

Antitrust Laws↗
PubMed2006

Statistical benchmarks for process measures of quality of care for mental and substance use disorders.

OBJECTIVE: Benchmarks, representing the level of performance achieved by the best-performing providers, can be used to set achievable goals for improving care, but they have not heretofore been available for mental health care. This article describes the application of a method for developing statistical benchmarks for 12 process measures of quality of care for mental and substance use disorders. METHODS: Twelve quality measures--taken from a core measure set selected by a multistakeholder panel through a formal consensus process--were constructed from 1994-1995 administrative data on care received by Medicaid beneficiaries in six states. Conformance rates were calculated at the provider level and presented as means, 90th-percentile results, and statistical benchmarks. Sample sizes for each measure ranged from 356 to 4,494 providers and from 1,205 to 78,627 cases. Three measures involved antidepressant treatment, two involved antipsychotic treatment, and one involved mood stabilizers for bipolar disorder. Six other measures involved follow-up treatment visits. RESULTS: Benchmarks for provider-level performance ranged from 59.7 percent to 97.7 percent, markedly higher than the mean results, which ranged from 9.4 percent to 65.4 percent. Benchmark results varied widely-in contrast to results for these measures at the 90th percentile of providers and in contrast to performance standards that apply the same numerical goal across varied clinical processes. CONCLUSIONS: Statistical benchmarks can be applied to results from quality assessment of mental health care. Further research should examine whether incorporating benchmarks into quality improvement activities leads to better mental health care and substance-related care and improved outcomes.

Benchmarking↗
PubMed1995

Application of the benchmark method to risk assessment of trichloroethene.

An alternative approach for risk assessment of nongenotoxic substances, the benchmark method, has been evaluated and applied to trichloroethene as a test case. The benchmark dose is the dose that corresponds to a specific increase in risk, normally 1 or 10%. Experimental data from the literature on trichloroethene were used for these calculations. Eighty sets of data on effects on liver, kidney, the central nervous system, and tumors were analyzed. All non-observed-effect levels (NOELs) were higher than the benchmark dose corresponding to 1% extra risk, and 42% of the NOELs and 93% of the lowest-observed-effect levels (LOELs) were higher than the benchmark dose corresponding to 10% extra risk. The present study confirms that the benchmark methodology gives a more detailed picture of dose-response relationships than risk assessment using the NOEL/LOEL approach and facilitates comparison between various toxicity studies. However, the polynomial regression models used in the present study quite often failed to fit the experimental data. Despite the advantages with the benchmark approach, several factors must be considered in the risk assessment process. In the case of trichloroethene, a revised risk assessment using the benchmark approach would lead to a similar guideline value as the traditional NOEL/LOEL approach.

Animals↗
PubMed2025

A complete diploid human genome benchmark for personalized genomics.

Human genome resequencing typically involves mapping reads to a reference genome to call variants; however, this approach suffers from both technical and reference biases, leaving many duplicated and structurally polymorphic regions of the genome unmapped. Consequently, existing variant benchmarks, generated by the same methods, fail to assess these complex regions. To address this limitation, we present a telomere-to-telomere genome benchmark that achieves near-perfect accuracy (i.e. no detectable errors) across 99.4% of the complete, diploid HG002 genome. This benchmark adds 701.4 Mb of autosomal sequence and both sex chromosomes (216.8 Mb), totaling 15.3% of the genome that was absent from prior benchmarks. We also provide a diploid annotation of genes, transposable elements, segmental duplications, and satellite repeats, including 39,144 protein-coding genes across both haplotypes. To facilitate application of the benchmark, we developed tools for measuring the accuracy of sequencing reads, phased variant call sets, and genome assemblies against a diploid reference. Genome-wide analyses show that state-of-the-art de novo assembly methods resolve 2-7% more sequence and outperform variant calling accuracy by an order of magnitude, yielding just one error per 100 kb across 99.9% of the benchmark regions. Adoption of genome-based benchmarking is expected to accelerate the development of cost-effective methods for complete genome sequencing, expanding the reach of genomic medicine to the entire genome and enabling a new era of personalized genomics.

Journal Article↗
PubMed1995

Benchmarking reference services: an introduction.

Benchmarking is based on the common sense idea that someone else, either inside or outside of libraries, has found a better way of doing certain things and that your own library's performance can be improved by finding out how others do things and adopting the best practices you find. Benchmarking is one of the tools used for achieving continuous improvement in Total Quality Management (TQM) programs. Although benchmarking can be done on an informal basis, TQM puts considerable emphasis on formal data collection and performance measurement. Used to its full potential, benchmarking can provide a common measuring stick to evaluate process performance. This article introduces the general concept of benchmarking, linking it whenever possible to reference services in health sciences libraries. Data collection instruments that have potential application in benchmarking studies are discussed and the need to develop common measurement tools to facilitate benchmarking is emphasized.

Efficiency, Organizational↗
PubMed1995

Where does benchmarking fit? Crafting a role for sharing best practices.

Benchmarking strategy alone may get you started, but the development of a sustainable program becomes the key to success. That development involves characteristics such as educating and nurturing appropriate benchmarking behaviors, building a best practice network to share learning and developing a continuous updating mechanism. With clear direction and these supporting systems in place, benchmarking can be a powerful tool to help you innovate today and stay on the right track for tomorrow. A thorough understanding of the benchmarking can avoid the "Benchmarks are great," "More benchmarks are always better," and "Why do I have so much trouble innovating?" cycle that has plagued many who embraced this idea without first seeing its risks to culture and improvement. With the proper program, benchmarks can add the critical shared learning and best practice role that successful change initiatives need.

Creativity↗
PubMed2004

An evaluation of benchmark dose methodology for non-cancer continuous-data health effects in animals due to exposures to dioxin (TCDD).

The U.S. Environmental Protection Agency (EPA) has conducted extensive reviews and analyses of health effects associated with exposures to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and related compounds. Because the carcinogenicity of TCDD has received considerable attention from EPA and others, this paper focuses on animal data for non-cancer health effects that sometimes appear to be almost as sensitive as cancer to TCDD exposures. Benchmark dose (BMD) methodology can be used to identify point-of-departure (POD) estimates for use in derivation of reference doses or evaluation of margins of exposure. However, selection of an appropriate BMD methodology for assessment of non-cancer data, which are usually continuous (non-quantal), needs to be considered. One option available for a benchmark dose is to use a small percentage change in the mean response relative to the estimated maximum effect of TCDD at large doses. The benchmark based on a change estimated to equal 1% of the estimated maximum change from background to the asymptotic response at large doses (denoted as the relative ED01) was used by EPA in a reassessment of TCDD health risks. A lower confidence limit (LED01) could serve as a point of departure for setting a reference dose (RfD). This is a somewhat arbitrary effect level, generally within the background range of variation among unexposed animals, with an unknown risk. An alternative approach is recommended in which the risk of abnormal levels can be estimated. For continuous-data effects, a low and/or high percentile (e.g., 1st and/or 99th) in unexposed control animals can be used to define abnormal (not necessarily adverse) levels. From a dose-response curve and the standard deviation, it is possible to estimate the excess risk (proportion) of animals with abnormal levels as a function of dose for normally distributed levels. With this approach, the risk-based benchmark dose (BMD01) represents the dose with an estimated excess risk of 1% of the animals in the abnormal range rather than an arbitrary change in the value of the measured endpoint. Values for the relative and risk-based benchmark doses are computed from published data for a variety of non-cancer health effects associated with exposure to TCDD. For the 30 cases investigated, the BMD01 tended to vary around the lowest experimental dose tested, whereas the relative ED01 tended to be about a factor of three below the lowest dose, and the BMD01 was more precisely estimated than the ED01 as reflected by narrower confidence intervals. The BMDL01 values were on average more than fivefold higher than the corresponding LED01 values. However, these values still provide a conservative assessment for POD assessment, because the BMDL01 tends to be about an order of magnitude lower (more conservative) than the no-observed-adverse-effect level. This analysis demonstrates the potential impact of alternative choices in benchmark dose methodology. In combination with selection of appropriate adverse health effect endpoint(s) and studies, use of the risk-based BMD results in identification of more valid and meaningful POD estimates for non-cancer effects compared to the use of the relative ED approach.

Animals↗
PubMed1999

Benchmarking pathology services: implementing a longitudinal study.

This paper details the benchmarking process and its application to the activities of pathology laboratories participating in a benchmark pilot study [the Royal College of Pathologists of Australasian (RCPA) Benchmarking Project]. The discussion highlights the primary issues confronted in collecting, processing, analysing and comparing benchmark data. The paper outlines the benefits of engaging in a benchmarking exercise and provides a framework which can be applied across a range of public health settings. This information is then applied to a review of the development of the RCPA Benchmarking Project. Consideration is also given to the nature of the preliminary results of the project and the implications of these results to the on-going conduct of the study.

Australia↗
PubMed2005

Benchmarking of Monte Carlo based shutdown dose rate calculations for applications to JET.

The calculation of dose rates after shutdown is an important issue for operating nuclear reactors. A validated computational tool is needed for reliable dose rate calculations. In fusion reactors neutrons induce high levels of radioactivity and presumably high doses. The complex geometries of the devices require the use of sophisticated geometry modelling and computational tools for transport calculations. Simple rule of thumb laws do not always apply well. Two computational procedures have been developed recently and applied to fusion machines. Comparisons between the two methods showed some inherent discrepancies when applied to calculation for the ITER while good agreement was found for a 14 MeV point source neutron benchmark experiment. Further benchmarks were considered necessary to investigate in more detail the reasons for the different results in different cases. In this frame the application to the Joint European Torus JET machine has been considered as a useful benchmark exercise. In a first calculational benchmark with a representative D-T irradiation history of JET the two methods differed by no more than 25%. In another, more realistic benchmark exercise, which is the subject of this paper, the real irradiation history of D-T and D-D campaigns conducted at JET in 1997-98 were used to calculate the shut-down doses at different locations, irradiation and decay times. Experimental dose data recorded at JET for the same conditions offer the possibility to check the prediction capability of the calculations and thus show the applicability (and the constraints) of the procedures and data to the rather complex shutdown dose rate analysis of real fusion devices. Calculation results obtained by the two methods are reported below, comparison with experimental results give discrepancies ranging between 2 and 10. The reasons of that can be ascribed to the high uncertainty on the experimental data and the unsatisfactory JET model used in the calculation. A new dedicated JET benchmark experiment will be performed trying to solve these issues.

Algorithms↗