Artykuły w czasopismach: „Data quality indicators”

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Matute, Jorge, i A. P. Gupta. "Data Quality and Indicators". American Journal of Agricultural and Biological Sciences 2, nr 1 (1.01.2007): 23–30. http://dx.doi.org/10.3844/ajabssp.2007.23.30.

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Johnson, Barry L., T. Damstra, Chris Derosa, C. Elmer i M. Gilbert. "Workshop on Toxicological Data Quality Indicators". Toxicology and Industrial Health 9, nr 4 (lipiec 1993): 577–604. http://dx.doi.org/10.1177/074823379300900402.

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Iseni, Jeton, i Olaf Jacob. "I33 Data quality indicators for huntington’s disease observational studies; data quality indicators framework – an explorative study". Journal of Neurology, Neurosurgery & Psychiatry 87, Suppl 1 (wrzesień 2016): A70.2—A70. http://dx.doi.org/10.1136/jnnp-2016-314597.198.

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Weiss, Manfred S. "Global indicators of X-ray data quality". Journal of Applied Crystallography 34, nr 2 (1.04.2001): 130–35. http://dx.doi.org/10.1107/s0021889800018227.

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Global indicators of the quality of diffraction data are presented and discussed, and are evaluated in terms of their performance with respect to various tasks. Based on the results obtained, it is suggested that some of the conventional indicators still in use in the crystallographic community should be abandoned, such as the nominal resolutiondminor the mergingRfactorRmerge, and replaced by more objective and more meaningful numbers, such as the effective optical resolutiondeff,optand the redundancy-independent mergingRfactorRr.i.m.. Furthermore, it is recommended that the precision-indicating mergingRfactorRp.i.m.should be reported with every diffraction data set published, because it describes the precision of the averaged measurements, which are the quantities normally used in crystallography as observables.

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Cadarette, S. M., S. B. Jaglal, L. Raman-Wilms, D. E. Beaton i J. M. Paterson. "Osteoporosis quality indicators using healthcare utilization data". Osteoporosis International 22, nr 5 (25.06.2010): 1335–42. http://dx.doi.org/10.1007/s00198-010-1329-8.

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Weiss, M. S., i R. Hilgenfeld. "Global Indicators of X-ray Data Quality". Acta Crystallographica Section A Foundations of Crystallography 56, s1 (25.08.2000): s105. http://dx.doi.org/10.1107/s0108767300022789.

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Pulido Moncada, Mansonia, Donald Gabriels i Wim M. Cornelis. "Data-driven analysis of soil quality indicators using limited data". Geoderma 235-236 (grudzień 2014): 271–78. http://dx.doi.org/10.1016/j.geoderma.2014.07.014.

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Pickering, Ashley E., Petrus Malherbe, Joan Nambuba, Corey B. Bills, Emilie Calvello Hynes i Brian Rice. "Clinical emergency care quality indicators in Africa: a scoping review and data summary". BMJ Open 13, nr 5 (maj 2023): e069494. http://dx.doi.org/10.1136/bmjopen-2022-069494.

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ObjectivesEmergency care services are rapidly expanding in Africa; however, development must focus on quality. The African Federation of Emergency Medicine consensus conference (AFEM-CC)-based quality indicators were published in 2018. This study sought to increase knowledge of quality through identifying all publications from Africa containing data relevant to the AFEM-CC process clinical and outcome quality indicators.DesignWe conducted searches for general quality of emergency care in Africa and for each of 28 AFEM-CC process clinical and five outcome clinical quality indicators individually in the medical and grey literature.Data sourcesPubMed (1964—2 January 2022), Embase (1947—2 January 2022) and CINAHL (1982—3 January 2022) and various forms of grey literature were queried.Eligibility criteriaStudies published in English, addressing the African emergency care population as a whole or large subsegment of this population (eg, trauma, paediatrics), and matching AFEM-CC process quality indicator parameters exactly were included. Studies with similar, but not exact match, data were collected separately as ‘AFEM-CC quality indicators near match’.Data extraction and synthesisDocument screening was done in duplicate by two authors, using Covidence, and conflicts were adjudicated by a third. Simple descriptive statistics were calculated.ResultsOne thousand three hundred and fourteen documents were reviewed, 314 in full text. 41 studies met a priori criteria and were included, yielding 59 unique quality indicator data points. Documentation and assessment quality indicators accounted for 64% of data points identified, clinical care for 25% and outcomes for 10%. An additional 53 ‘AFEM-CC quality indicators near match’ publications were identified (38 new publications and 15 previously identified studies that contained additional ‘near match’ data), yielding 87 data points.ConclusionsData relevant to African emergency care facility-based quality indicators are highly limited. Future publications on emergency care in Africa should be aware of, and conform with, AFEM-CC quality indicators to strengthen understanding of quality.

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Kolozsvári, László Róbert, i Imre Rurik. "Quality improvement in primary care. Financial incentives related to quality indicators in Europe". Orvosi Hetilap 154, nr 28 (lipiec 2013): 1096–101. http://dx.doi.org/10.1556/oh.2013.29631.

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Quality improvement in primary care has been an important issue worldwide for decades. Quality indicators are increasingly used quantitative tools for quality measurement. One of the possible motivational methods for doctors to provide better medical care is the implementation of financial incentives, however, there is no sufficient evidence to support or contradict their effect in quality improvement. Quality indicators and financial incentives are used in the primary care in more and more European countries. The authors provide a brief update on the primary care quality indicator systems of the United Kingdom, Hungary and other European countries, where financial incentives and quality indicators were introduced. There are eight countries where quality indicators linked to financial incentives are used which can influence the finances/salary of family physicians with a bonus of 1–25%. Reliable data are essential for quality indicators, although such data are lacking in primary care of most countries. Further, improvement of indicator systems should be based on broad professional consensus. Orv. Hetil., 2013, 154, 1096–1101.

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Schnelle, John F., Mary P. Cadogan, June Yoshii, Nahla R. Al-Samarrai, Dan Osterweil, Barbara M. Bates-Jensen i Sandra F. Simmons. "The Minimum Data Set Urinary Incontinence Quality Indicators". Medical Care 41, nr 8 (sierpień 2003): 909–22. http://dx.doi.org/10.1097/00005650-200308000-00005.

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Raffelsberger, Wolfgang, Doulaye Dembélé, Mike G. Neubauer, Marco M. Gottardis i Hinrich Gronemeyer. "Quality Indicators Increase the Reliability of Microarray Data". Genomics 80, nr 4 (październik 2002): 385–94. http://dx.doi.org/10.1006/geno.2002.6848.

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Kollmann, Nils Patrick, Benedikt Langenberger, Reinhard Busse i Christoph Pross. "Stability of hospital quality indicators over time: A multi-year observational study of German hospital data". PLOS ONE 18, nr 11 (7.11.2023): e0293723. http://dx.doi.org/10.1371/journal.pone.0293723.

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Background Retrospective hospital quality indicators can only be useful if they are trustworthy signals of current or future quality. Despite extensive longitudinal quality indicator data and many hospital quality public reporting initiatives, research on quality indicator stability over time is scarce and skepticism about their usefulness widespread. Objective Based on aggregated, widely available hospital-level quality indicators, this paper sought to determine whether quality indicators are stable over time. Implications for health policy were drawn and the limited methodological foundation for stability assessments of hospital-level quality indicators enhanced. Methods Two longitudinal datasets (self-reported and routine data), including all hospitals in Germany and covering the period from 2004 to 2017, were analysed. A logistic regression using Generalized Estimating Equations, a time-dependent, graphic quintile representation of risk-adjusted rates and Spearman’s rank correlation coefficient were used. Results For a total of eight German quality indicators significant stability over time was demonstrated. The probability of remaining in the best quality cluster in the future across all hospitals reached from 46.9% (CI: 42.4–51.6%) for hip replacement reoperations to 80.4% (CI: 76.4–83.8%) for decubitus. Furthermore, graphical descriptive analysis showed that the difference in adverse event rates for the 20% top performing compared to the 20% worst performing hospitals in the two following years is on average between 30% for stroke and AMI and 79% for decubitus. Stability over time has been shown to vary strongly between indicators and treatment areas. Conclusion Quality indicators were found to have sufficient stability over time for public reporting. Potentially, increasing case volumes per hospital, centralisation of medical services and minimum-quantity regulations may lead to more stable and reliable quality of care indicators. Finally, more robust policy interventions such as outcome-based payment, should only be applied to outcome indicators with a higher level of stability over time. This should be subject to future research.

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Weidema, Bo Pedersen, i Marianne Suhr Wesnæs. "Data quality management for life cycle inventories—an example of using data quality indicators". Journal of Cleaner Production 4, nr 3-4 (styczeń 1996): 167–74. http://dx.doi.org/10.1016/s0959-6526(96)00043-1.

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Krzyzanowska, Monika K., Melanie Lynn Powis, Nathan Taback, Christina Diong, Katherine Enright, Christopher M. Booth i Maureen E. Trudeau. "Reliability of administrative data for evaluating quality of systemic cancer treatment." Journal of Clinical Oncology 35, nr 15_suppl (20.05.2017): e18269-e18269. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e18269.

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e18269 Background: There is ongoing interest in leveraging administrative data to examine quality but methodological concerns persist. We evaluated the reliability of a previously established panel of administrative data derived quality measures for systemic treatment. Methods: The cohort consisted of women diagnosed with early stage (stage I-III) breast cancer (ESBC) in Ontario, Canada, in 2010. Performance on 11 quality indicators evaluated using deterministically linked healthcare administrative databases has been reported previously. Sensitivity and specificity were examined using the chart as the gold standard. Results: The administrative cohort consisted of 6,795 women with ESBC from which a validation cohort of 705 patients was randomly selected from among patients who underwent cancer surgery at one of five hospitals chosen to balance feasibility and institutional characteristics.Sensitivity and specificity varied by indicator (Table 1). Reliability of some indicators may have been affected by suboptimal chart documentation in instances where care spanned multiple settings or the medical record was fragmented, or where the number of eligible patients for that indicator was low. Conclusions: Administrative data can be used to evaluate quality of systemic cancer therapy but understanding the reliability characteristics of individual indicators is essential to inform their appropriate use and interpretation. [Table: see text]

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Powis, Melanie Lynn, Nathan Taback, Christina Diong, Katherine Enright, Christopher M. Booth, Maureen E. Trudeau i Monika K. Krzyzanowska. "Reliability of administrative data for evaluating the quality of systemic treatment for cancer." Journal of Clinical Oncology 35, nr 8_suppl (10.03.2017): 208. http://dx.doi.org/10.1200/jco.2017.35.8_suppl.208.

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208 Background: There is ongoing interest in leveraging administrative data to examine quality but methodological concerns persist. We evaluated the reliability of a previously established panel of administrative data derived quality measures for systemic cancer treatment. Methods: The study cohort consisted of women diagnosed with early stage (stage I-III) breast cancer (ESBC) in Ontario, Canada, in 2010. Performance on 11 quality indicators evaluated using deterministically linked healthcare administrative databases has been reported previously. The sensitivity and specificity of these 11 indicators were examined using the chart as the gold standard. Results: The administrative cohort consisted of 6,795 women with ESBC from which a validation cohort of 705 patients was randomly selected from among patients who underwent cancer surgery at one of five hospitals chosen to balance feasibility and institutional characteristics.Sensitivity and specificity varied by indicator (Table). Reliability of some indicators may have been affected by suboptimal chart documentation in instances where care spanned multiple settings or the medical record was fragmented, or where the number of eligible patients for that indicator was low. Conclusions: Administrative data can be used to evaluate quality of systemic cancer therapy but understanding the reliability characteristics of individual indicators is essential to inform their appropriate use and interpretation. [Table: see text]

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Safitri, Risa Hani, I. Gusti Agung Bagus Mataram i I. Putu Krisna Arta Widana. "Analysis of Receptionist Service Quality to Increase Guest Satisfaction at Hotel Yusro Jombang". International Journal of Green Tourism Research and Applications 3, nr 2 (28.12.2021): 58. http://dx.doi.org/10.31940/ijogtra.v3i2.58-68.

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This study aimed to analyze the level of receptionist service quality in increasing guest satisfaction at Hotel Yusro Jombang, East Java. The number of samples used was 60 respondents, with a purposive sampling data collection technique. The primary data collection method for service quality variables is using a questionnaire that has been tested for its validity and reliability. The analysis technique used is Customer Satisfaction Index, Servqual, and Importance Performance Analysis, presented in a Cartesian diagram. This study indicates that there is a negative, positive and neutral gap between guest perceptions and expectations. Customers are satisfied with the services provided because the value of the positive gap is more than the negative. The quality level of receptionist service in improving guest satisfaction at Hotel Yusro Jombang has been good, because the value of customer satisfaction index (CSI) of 90.22% is in the range of 81%-100%, meaning, in general, the guest satisfaction index at Hotel Yusro Jombang is on the "very satisfied" criteria. Next, based on the result of importance-performance analysis, each indicator's position in the cartesius diagram found an indicator that is a priority to be fixed, i.e., indicator in the A quadrant and an indicator that is an achievement to be maintained indicator which is in the B quadrant. As for indicators that are considered most satisfying by guests, they are X3 indicators, X5 indicators, and X10 indicators, each of them has a score of 0.04.

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Windhager, Florian, Saminu Salisu i Eva Mayr. "Exhibiting Uncertainty: Visualizing Data Quality Indicators for Cultural Collections". Informatics 6, nr 3 (31.07.2019): 29. http://dx.doi.org/10.3390/informatics6030029.

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Uncertainty is a standard condition under which large parts of art-historical and curatorial knowledge creation and communication are operating. In contrast to standard levels of data quality in non-historical research domains, historical object and knowledge collections contain substantial amounts of uncertain, ambiguous, contested, or plainly missing data. Visualization approaches and interfaces to cultural collections have started to represent data quality and uncertainty metrics, yet all existing work is limited to representations for isolated metadata dimensions only. With this article, we advocate for a more systematic, synoptic and self-conscious approach to uncertainty visualization for cultural collections. We introduce omnipresent types of data uncertainty and discuss reasons for their frequent omission by interfaces for galleries, libraries, archives and museums. On this basis we argue for a coordinated counter strategy for uncertainty visualization in this field, which will also raise the efforts going into complex interface design and conceptualization. Building on the PolyCube framework for collection visualization, we showcase how multiple uncertainty representation techniques can be assessed and coordinated in a multi-perspective environment. As for an outlook, we reflect on both the strengths and limitations of making the actual wealth of data quality questions transparent with regard to different target and user groups.

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van Hintum, Theo, Frank Menting i Elisabeth van Strien. "Quality indicators for passport data in ex situ genebanks". Plant Genetic Resources 9, nr 3 (11.05.2011): 478–85. http://dx.doi.org/10.1017/s1479262111000682.

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Given the increasing importance of data quality for the ex situ conservation and utilization of plant genetic resources (PGR), an indicator was created that quantifies the level of completeness of passport data. This passport data completeness index (PDCI) uses the presence or absence of data points in the documentation of a genebank accession, taking into account the presence or value of other data points. For example, a wild accession should have a well-defined collection site but no variety name. Any type of accession, wild, landrace, breeding material or modern variety, can attain a maximal score of ten for this index. The applicability of this index was tested on the complete contents of EURISCO, the European catalogue of ex situ maintained PGR containing over one million records. Analysis of the PDCI of the material in EURISCO provided valuable insight in the data quality of European collections. The PDCI can be used to identify datasets that might need additional attention and improvement or datasets that need more careful interpretation than others.

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Güdemann, Laura, i Ralf Münnich. "Quality and Sensitivity of Composite Indicators for Sustainable Development". Austrian Journal of Statistics 52, nr 5 (11.09.2023): 82–100. http://dx.doi.org/10.17713/ajs.v52i5.1539.

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Composite indicators can be understood as a summary of well-chosen and relevant sub-indicators which are combined into a single number. Their aim is to represent a multi-dimensional construct and map the performance of entities such as countries or companies. These multidimensional constructs are for example sustainability, poverty or well-being. Composite indicators are widely applied in various disciplines such as social or economic research and benefit from their apparent ease of interpretation. In the context of the Sustainable Development Framework a composite indicator over all 17 sustainable development goals, as been proposed. As composite indicators are commonly applied in highly sensitive areas this, urges the need to discuss methodical advantages and disadvantages as well as their adequacy for performance comparisons. In this paper we discuss and illustrate quality issues with regard to aspects of the subjective choices made in the construction process of composite indicators, imputation of missing data and the survey design. As an example we construct a composite indicator on sustainable economic development using data of the Sustainable Development framework. Furthermore, we exemplify and discuss strategies and methods for the quality assessment of a composite indicator.

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Correia, Rebecca H., Darly Dash, Aaron Jones, Meredith Vanstone, Komal Aryal, Henry Yu-Hin Siu, Aquila Gopaul i Andrew P. Costa. "Primary care quality for older adults: Practice-based quality measures derived from a RAND/UCLA appropriateness method study". PLOS ONE 19, nr 1 (19.01.2024): e0297505. http://dx.doi.org/10.1371/journal.pone.0297505.

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We established consensus on practice-based metrics that characterize quality of care for older primary care patients and can be examined using secondary health administrative data. We conducted a two-round RAND/UCLA Appropriateness Method (RAM) study and recruited 10 Canadian clinicians and researchers with expertise relevant to the primary care of elderly patients. Informed by a literature review, the first RAM round evaluated the appropriateness and importance of candidate quality measures in an online questionnaire. Technical definitions were developed for each endorsed indicator to specify how the indicator could be operationalized using health administrative data. In a virtual synchronous meeting, the expert panel offered feedback on the technical specifications for the endorsed indicators. Panelists then completed a second (final) questionnaire to rate each indicator and corresponding technical definition on the same criteria (appropriateness and importance). We used statistical integration to combine technical expert panelists’ judgements and content analysis of open-ended survey responses. Our literature search and internal screening resulted in 61 practice-based quality indicators for rating. We developed technical definitions for indicators endorsed in the first questionnaire (n = 55). Following the virtual synchronous meeting and second questionnaire, we achieved consensus on 12 practice-based quality measures across four Priority Topics in Care of the Elderly. The endorsed indicators provide a framework to characterize practice- and population-level encounters of family physicians delivering care to older patients and will offer insights into the outcomes of their care provision. This study presented a case of soliciting expert feedback to develop measurable practice-based quality indicators that can be examined using administrative data to understand quality of care within population-based data holdings. Future work will refine and operationalize the technical definitions established through this process to examine primary care provision for older adults in a particular context (Ontario, Canada).

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Li, Hao, Ziwen Yu, Lingyu Song, Xia Chen, WenWu Tan, Peilun Li, Jianjun Zhang, Ke Xiao, Li Wang i Haijun Lin. "Index Correlation Analysis in Water Quality Monitoring Big Data". Journal of Physics: Conference Series 2504, nr 1 (1.05.2023): 012059. http://dx.doi.org/10.1088/1742-6596/2504/1/012059.

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Abstract Among the water quality indicators, permanganate and turbidity are important indicators to reflect the pollution status of water bodies. In order to study the correlation between the two, the water quality monitoring data of relevant water areas were obtained by designing a web crawler, and the water quality monitoring data set was constructed. After the data was cleaned, the correlation analysis was carried out. The experimental results show that there is a big difference in the correlation coefficient between the two indicators at different periods of the same monitoring point. The correlation between the two indicators in the abundant-water season is greater than that in the flat-water season, and the correlation between them in the flat-water season is greater than that in the poor-water season. Among them, there was a high positive correlation between the two indicators during the abundant-water season, and there is little correlation between them during the poor-water season.

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Barnsley, Jan, Louise Lemieux-Charles i G. Ross Baker. "Selecting Clinical Outcome Indicators for Monitoring Quality of Care". Healthcare Management Forum 9, nr 1 (kwiecień 1996): 5–12. http://dx.doi.org/10.1016/s0840-4704(10)60938-6.

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Clinical outcome indicators are used to identify opportunities for improvement in patient care processes. This paper focuses on issues specific to the selection of clinical outcome indicators for use in assessing performance within and between hospitals. The issues and examples are based on the experiences of a university research team that worked in collaboration with a group of teaching hospitals to develop and monitor clinical outcome indicators. Four sets of issues are discussed: the intended use, and end users of indicator information; aspects of indicator validity; data quality; and dissemination and use of indicator information. Recommendations are made that apply to individual hospitals, groups of hospitals and health care systems.

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Putrenko, V. V., i N. M. Pashynska. "THE USE OF REMOTE SENSING DATA FOR MODELING AIR QUALITY IN THE CITIES". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5/W1 (13.12.2017): 57–62. http://dx.doi.org/10.5194/isprs-annals-iv-5-w1-57-2017.

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Monitoring of environmental pollution in the cities by the methods of remote sensing of the Earth is actual area of research for sustainable development. Ukraine has a poorly developed network of monitoring stations for air quality, the technical condition of which is deteriorating in recent years. Therefore, the possibility of obtaining data about the condition of air by remote sensing methods is of great importance. The paper considers the possibility of using the data about condition of atmosphere of the project AERONET to assess the air quality in Ukraine. The main pollution indicators were used data on fine particulate matter (PM2.5) and nitrogen dioxide (NO2) content in the atmosphere. The main indicator of air quality in Ukraine is the air pollution index (API). We have built regression models the relationship between indicators of NO2, which are measured by remote sensing methods and ground-based measurements of indicators. There have also been built regression models, the relationship between the data given to the land of NO2 and API. To simulate the relationship between the API and PM2.5 were used geographically weighted regression model, which allows to take into account the territorial differentiation between these indicators. As a result, the maps that show the distribution of the main types of pollution in the territory of Ukraine, were constructed. PM2.5 data modeling is complicated with using existing indicators, which requires a separate organization observation network for PM2.5 content in the atmosphere for sustainable development in cities of Ukraine.

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Rantz, Marilyn J., Lori Popejoy, David R. Mehr, Mary Zwygart-Stauffacher, Lanis L. Hicks, Victoria Grando, Vicki S. Conn, Rose Porter, Jill Scott i Meridean Maas. "Verifying Nursing Home Care Quality Using Minimum Data Set Quality Indicators and Other Quality Measures". Journal of Nursing Care Quality 12, nr 2 (grudzień 1997): 54–62. http://dx.doi.org/10.1097/00001786-199712000-00011.

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Zhao, Jing, i Qi Guo. "Intelligent Assessment for Visual Quality of Streets: Exploration Based on Machine Learning and Large-Scale Street View Data". Sustainability 14, nr 13 (4.07.2022): 8166. http://dx.doi.org/10.3390/su14138166.

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At present, the collection and analysis of large amounts of key data for the visual quality assessment of streets are performed manually. The assessment efficiency is not high, and the effective information is not fully explored. This study aims to establish an intelligent method for assessing the visual quality of streets. Taking the Hexi District of Tianjin as an example and using street view images as the assessment medium, an assessment model of objective physical indicators is established based on PaddleSeg, an assessment model of subjective perceptual indicators is established based on neural image assessment, and a visual quality assessment model of streets is established based on a random forest. The above models can intelligently evaluate the visual quality of streets and key indicators affecting visual quality. The influence of each key indicator on the visual quality of streets and the relationship between objective physical indicators and subjective perceptual indicators are analyzed. Through a combination of subjective and objective as well as qualitative and quantitative methods, the results show satisfactory assessment accuracy. In short, this study uses machine-learning techniques to improve the scientific rigor and efficiency of visual quality assessment and expand the scale of visual quality assessment data.

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Zuniga, F., C. Blatter i M. Simon. "DATA QUALITY OF NATIONAL QUALITY INDICATORS AND THEIR ACCEPTANCE IN SWISS NURSING HOMES". Innovation in Aging 2, suppl_1 (1.11.2018): 421. http://dx.doi.org/10.1093/geroni/igy023.1575.

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Zimmerman, David R. "Improving nursing home quality of care through outcomes data: the MDS quality indicators". International Journal of Geriatric Psychiatry 18, nr 3 (2003): 250–57. http://dx.doi.org/10.1002/gps.820.

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Song, Longhao. "University Employment Quality Evaluation System Based on Multicriteria Decision and Data Analysis". Scientific Programming 2021 (28.12.2021): 1–7. http://dx.doi.org/10.1155/2021/3838140.

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In the educational sector, an evaluation index is required to draw up planning. The establishment of an evaluation index is useful to properly predict the employment quality of graduates. Such valuable indices help educational administrative departments to formulate talent training standards. Multicriteria decision making is a decision-making tool that can be used in the formulation of the evaluation index. This research work proposes an effective evaluation model to assess the employment quality of graduate students. The model uses 10 evaluation indicators which are considered to be the standard employment quality. The proposed evaluation method utilizes the entropy method and fuzzy comprehensive evaluation. Correlation between the employment quality evaluation index and employment quality is computed. The analytic hierarchy model is used to solve the weight of each employment quality evaluation index to the employment quality evaluation coefficient. According to the value characteristics of the 14 employment indicators, the expert method is used to assign scores to the sample data on each indicator. Thus, the indicator scores of the sample corresponding to the item are obtained. Through the evaluation of the employment quality of a certain university, the evaluation results are consistent with the actual employment quality of graduates. The employment quality evaluation model of college graduates established in this paper provides effective means and applications.

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Owen, J. B., i J. F. Wilson. "Improving quality of care: Challenges to implementing quality indicators". Journal of Clinical Oncology 24, nr 18_suppl (20.06.2006): 16030. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.16030.

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16030 Background: To assess the overall quality of cancer care, efforts seek consensus quality indicators that crosscut health services. Although pilot programs have collected and analyzed relevant clinical data, they have been unable to collect radiation oncology (RO) data detailed enough to assess the quality of services or to inform key decision makers. Quality Research in Radiation Oncology (QRRO) conducted retrospective surveys of national practice since 1973 with major positive impact on the quality of practice through recursive processes. Methods: From Donabedian’s model of quality assessment, QRRO analyzes crucial quality components by conducting Facilities, Process, and Outcomes Surveys. Survey design allows calculation of national averages for patients treated with RO and comparisons by key factors. Evolving data collection methods allow assessment of modern technologies. Methods start with definition and measurement of evidence-based quality indicators but allow greater detail and specification than most other quality measurement efforts. Results: QRRO showed that radiation dose affected outcomes for prostate cancer patients. Higher radiation doses were associated with improved local tumor control rates and treatment techniques affected toxicity rates. These results, presented widely in numerous venues, stimulated dose escalation clinical trials. Trials conducted in the USA all used QRRO results as critical data, providing the major impetus to test new directions in dose escalation and new methods to target delivery more precisely. National practice shifted to higher doses and use of conformal techniques. The dataset is unique in providing cross-sectional information on practice patterns with a wide variety of treatment approaches from many institutions and sufficient details of treatment delivery to allow examination of questions about quality and effects of techniques. Conclusions: Mounting societal demands for improvement in the quality of care, ever increasing complexity of radiation therapy, and escalating use of multi-modality treatment make continuing to measure, report, and improve quality of care in RO crucial to patients and the profession. Methods must keep pace with new technologies and techniques in radiation therapy. [Supported by NCI grant CA 65435]. No significant financial relationships to disclose.

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Telles, Tiago S., Ana J. Righetto, Marco A. P. Lourenço i Graziela M. C. Barbosa. "No-tillage system participatory quality index". Revista Brasileira de Engenharia Agrícola e Ambiental 24, nr 2 (luty 2020): 128–33. http://dx.doi.org/10.1590/1807-1929/agriambi.v24n2p128-133.

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ABSTRACT The no-tillage system participatory quality index aims to evaluate the quality and efficiency of soil management under no-tillage systems and consists of a weighted sum of eight indicators: intensity of crop rotation, diversity of crop rotation, persistence of crop residues in the soil surface, frequency of soil tillage, use of agricultural terraces, evaluation of soil conservation, balance of soil fertilization and time of adoption of the no-tillage system. The aim of this study was to assess the extent to which these indicators correlate with the no-tillage system participatory quality index and to characterize the farmers who participated in the research. The data used were provided by ITAIPU Binacional for the indicators of the no-tillage system participatory quality index II. Descriptive analyses were performed, and the Pearson correlation coefficient between the index and each indicator was calculated. To assess the relationship between the indicators and the farmers’ behavior toward the indicators, principal component analysis and cluster analysis were performed. Although all correlations are significant at p-value ≤ 0.05, some correlations are weak, indicating a need for improvement of the index. The principal component analysis identified three principal components, which explained 66% of the variability of the data, and the cluster analysis separated the 121 farmers into five groups. It was verified that the no-tillage system participatory quality index II has some limitations and should therefore be reevaluated to increase its efficiency as an indicator of the quality of the no-tillage system.

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Petzold, Thomas, Stefanie Deckert, Paula R. Williamson i Jochen Schmitt. "Quality Measurement Recommendations Relevant to Clinical Guidelines in Germany and the United Kingdom: (What) Can We Learn From Each Other?" INQUIRY: The Journal of Health Care Organization, Provision, and Financing 55 (1.01.2018): 004695801876149. http://dx.doi.org/10.1177/0046958018761495.

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We conducted a systematic review of clinical guidelines (CGs) to examine the methodological approaches of quality indicator derivation in CGs, the frequency of quality indicators to check CG recommendations in routine care, and clinimetric properties of quality indicators. We analyzed the publicly available CG databases of the Association of the Scientific Medical Societies in Germany (AWMF) and National Institute for Health and Care Excellence (NICE). Data on the methodology of subsequent quality indicator derivation, the content and definition of recommended quality indicators, and clinimetric properties of measurement instruments were extracted. In Germany, no explicit methodological guidance exists, but 3 different approaches are used. For NICE, a general approach is used for the derivation of quality indicators out of quality standards. Quality indicators were defined in 34 out of 87 CGs (39%) in Germany and for 58 out of 133 (43%) NICE CGs. Statements regarding measurement properties of instruments for quality indicator assessment were missing in German and NICE documents. Thirteen pairs of CGs (32%) have associated quality indicators. Thirty-four quality indicators refer to the same aspect of the quality of care, which corresponds to 27% of the German and 7% of NICE quality indicators. The development of a standardized and internationally accepted methodology for the derivation of quality indicators relevant to CGs is needed to measure and compare quality of care in health care systems.

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Earle, Craig C., Elyse R. Park, Bonnie Lai, Jane C. Weeks, John Z. Ayanian i Susan Block. "Identifying Potential Indicators of the Quality of End-of-Life Cancer Care From Administrative Data". Journal of Clinical Oncology 21, nr 6 (15.03.2003): 1133–38. http://dx.doi.org/10.1200/jco.2003.03.059.

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Purpose: To explore potential indicators of the quality of end-of-life services for cancer patients that could be monitored using existing administrative data. Methods: Quality indicators were identified and assessed by literature review for proposed indicators, focus groups with cancer patients and family members to assess candidate indicators and generate new ideas, and an expert panel ranking the meaningfulness and importance of each potential indicator using a modified Delphi approach. Results: There were three major concepts of poor quality of end-of-life cancer care that could be examined using currently-available administrative data (such as Medicare claims): institution of new anticancer therapies or continuation of ongoing treatments very near death; a high number of emergency room visits, inpatient hospital admissions, or intensive care unit days near the end of life; and a high proportion of patients never enrolled in hospice, only admitted in the last few days of life, or dying in an acute-care setting. Concepts such as access to psychosocial and other multidisciplinary services and pain and symptom control are important and may eventually be feasible, but they cannot currently be applied in most data systems. Indicators based on limiting the use of treatments with low probability of benefit or indicators based on economic efficiency were not acceptable to patients, family members, or physicians. Conclusion: Several promising claims-based quality indicators were identified that, if found to be valid and reliable within data systems, could be useful in identifying health-care systems in need of improving end-of-life services.

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Vink, Karina, Md Nasif Ahsan, Hisaya Sawano i Miho Ohara. "Global Water-Related Risk Indicators: Meta-Analysis of Indicator Requirements". Journal of Disaster Research 12, nr 2 (16.03.2017): 355–67. http://dx.doi.org/10.20965/jdr.2017.p0355.

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Despite a long developmental history of water-related disaster risk indicators, there is still no consensus or reliable system for selecting objective data, no methodological system for choosing and verifying the relevancy of water-related disaster risk indicators, and no linking results back to root causes or addressing possible impacts on policies or actors to instigate change.Global policy documents such as the Sendai Framework for Disaster Risk Reduction (DRR) 2015–2013 [1] emphasize the urgent need for indicators capable of measuring risk reduction. However, developing and determining risk indicators faces many issues. Most disaster risk indices published do not yet include a basic overview of what data was used and how it was collected, let alone provide a systematic explanation of why each indicator was included, and why others were not. This consequently complicates linking the findings to their potential policy impacts. It also complicates the providing of clear-cut recommendations for improving resilience, which is a common intent of disaster risk indices.This study, which focuses on water-related hazards, aims to provide disaster managers with a set of criteria for evaluating existing datasets used in disaster risk indices, index construction methods, and the links back to policy impacts. So far, there has been no comprehensive overview of indicator requirements or scoring systems. Previous studies concerning indicator evaluating metrics [2] have fewer metrics and have not yet addressed the different tiers of requirements, namely objective indicator data quality, methodological/epistemological aspects of index composition, and, most importantly, policy and actors of change (impact requirements). Further testing of these metrics in local studies can lead to the greatly needed scientific justification for indicator selection and can enhance index robustness.The results aid in developing an evaluation system to address issues of data availability and the comparability of commonly used indicator sources, such as the World Bank. Once indicators can be scientifically linked to impacts through policy devices, national governments or other actors can become more likely to claim ownership of the data management of indicators. Future studies should expand this evaluation system to other natural hazards and focus on investigating the links between indicators and DRR in order to further validate indicator selection robustly.

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Korytnikova, Nadezhda V. "Paradata as Indicators of Online Survey Data Quality: Classification Experience". Sotsiologicheskie issledovaniya, nr 3 (marzec 2021): 111–20. http://dx.doi.org/10.31857/s013216250010298-0.

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Guthrie, Bruce, Melanie Inkster i Tom Fahey. "Tackling therapeutic inertia: role of treatment data in quality indicators". BMJ 335, nr 7619 (13.09.2007): 542–44. http://dx.doi.org/10.1136/bmj.39259.400069.ad.

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Johantgen, Meg, Anne Elixhauser, Judy K. Ball, Marsha Goldfarb i D. Robert Harris. "Quality Indicators Using Hospital Discharge Data: State and National Applications". Joint Commission Journal on Quality Improvement 24, nr 2 (luty 1998): 88–105. http://dx.doi.org/10.1016/s1070-3241(16)30364-9.

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Ciofoaia, Victor, Mark J. Metwally i Stephen O'Mahony. "Data Mining Electronic Medical Records to Determine Colonoscopy Quality Indicators". Gastroenterology 140, nr 5 (maj 2011): S—568. http://dx.doi.org/10.1016/s0016-5085(11)62354-8.

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Fogliaroni, Paolo, Fausto D’Antonio i Eliseo Clementini. "Data trustworthiness and user reputation as indicators of VGI quality". Geo-spatial Information Science 21, nr 3 (3.07.2018): 213–33. http://dx.doi.org/10.1080/10095020.2018.1496556.

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Bright, Jonathan, Stefano De Sabbata, Sumin Lee, Bharath Ganesh i David K. Humphreys. "OpenStreetMap data for alcohol research: Reliability assessment and quality indicators". Health & Place 50 (marzec 2018): 130–36. http://dx.doi.org/10.1016/j.healthplace.2018.01.009.

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Jeanne, M., A. Lamer, B. Tavernier, R. Logier i B. Vallet. "Automatic acquisition of anaesthesia quality indicators in a data warehouse". European Journal of Anaesthesiology 30 (czerwiec 2013): 54. http://dx.doi.org/10.1097/00003643-201306001-00166.

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Zhou, Zhengjie, Leon D. Wegner i Bruce F. Sparling. "Data quality indicators for vibration-based damage detection and localization". Engineering Structures 230 (marzec 2021): 111703. http://dx.doi.org/10.1016/j.engstruct.2020.111703.

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Rybalova, O., O. Bryhada i O. Ilinskyi. "METHOD OF DETERMINING THE ECOLOGICAL COMPONENT QUALITY OF LIFE". Municipal economy of cities 4, nr 164 (1.10.2021): 220–33. http://dx.doi.org/10.33042/2522-1809-2021-4-164-220-233.

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The work examines modern methodological approaches to assessing the quality of life using environmental indicators. Analysis of the existing methods in Ukraine for determining the environmental component when measuring the quality of life showed the need to develop a new method for a comprehensive assessment of the state of the environment. This is due to the fact that the existing methodology for assessing the quality of life does not take into account the ecological component as a separate block of indicators, and also contains some inaccuracies in the formulas for calculating the final indicator. In this regard, a new method is proposed for determining the ecological component in the general system for assessing the quality of life of the population, which is the scientific novelty of the work. Based on the analysis of monitoring data on the quality of air, surface waters and soils of Ukraine, statistical reporting on environmental indicators of the development of regions of Ukraine, intermediate indicator indicators are calculated, and then the final complex indicator of the state of the environment is determined. Calculation formulas and assessment scales in points of the state of environmental components are proposed. The proposed method is based on the processing of data from official state statistics and environmental monitoring, which determines the reliability of the initial data. The proposed technique can easily be used as an algorithm for computerized calculations of the indicator of a comprehensive assessment of the state of the environment. The calculation of the indicator of the ecological state was made on the basis of current statistical data, which showed the need for immediate environmental protection measures in the industrially developed regions of Ukraine: Donetsk, Zaporozhye, Dnepropetrovsk and Lugansk regions.

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Yang, Yide. "Rapid Patent Quality Evaluation Method Based on Big Data Analysis: Chinese Invention Patentsas Sample". Advances in Economics, Management and Political Sciences 7, nr 1 (13.09.2023): 343–49. http://dx.doi.org/10.54254/2754-1169/7/20230253.

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High-quality patents have high technical value and market competitive advantage. Faced with the huge number of patent data, how to rapidly and efficiently identify the quality of patents from the patent announcement is a crucial research issue at present. Therefore, it is reasonable to predict that, big data based techniques will be the best method to exploit this kind of data. The patents authorized by CNIPA (China National Intellectual Property Administration) are taken as the research object. This study chooses several types of patent evaluation indicators and uses EWM (The Entropy Weight Method) to calculate the weight of each indicator. The study determines a correction coefficient to enhance the usability and provides the final quality score of each patent. The evaluating formula is provided. In this study, easily accessible patent indicators are used, which makes it easier to evaluate the quality of patents. By this method, rapidly evaluating the patent quality only by its basic announcement data is feasible, which solves the limitation that laborious access to advanced indicators.

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Courtney, Mary, Maria T. O'Reilly, Helen Edwards i Stacey Hassall. "Benchmarking clinical indicators of quality for Australian residential aged care facilities". Australian Health Review 34, nr 1 (2010): 93. http://dx.doi.org/10.1071/ah09663.

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To undertake exploratory benchmarking of a set of clinical indicators of quality care in residential care in Australia, data were collected from 107 residents within four medium-sized facilities (40–80 beds) in Brisbane, Australia. The proportion of residents in each sample facility with a particular clinical problem was compared with US Minimum Data Set quality indicator thresholds. Results demonstrated variability within and between clinical indicators, suggesting breadth of assessment using various clinical indicators of quality is an important factor when monitoring quality of care. More comprehensive and objective measures of quality of care would be of great assistance in determining and monitoring the effectiveness of residential aged care provision in Australia, particularly as demands for accountability by consumers and their families increase. What is known about the topic?The key to quality improvement is effective quality assessment, and one means of evaluating quality of care is through clinical outcomes. The Minimum Data Set quality indicators have been credited with improving quality in United States nursing homes. What does this paper add?The Clinical Care Indicators Tool was used to collect data on clinical outcomes, enabling comparison of data from a small Australian sample with American quality benchmarks to illustrate the utility of providing guidelines for interpretation. What are the implications for practitioners?Collecting and comparing clinical outcome data would enable practitioners to better understand the quality of care being provided and whether practices required review. The Clinical Care Indicator Tool could provide a comprehensive and systematic means of doing this, thus filling a gap in quality monitoring within Australian residential aged care.

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Sowińska-Świerkosz, Barbara, i Malwina Michalik-Śnieżek. "The Methodology of Landscape Quality (LQ) Indicators Analysis Based on Remote Sensing Data: Polish National Parks Case Study". Sustainability 12, nr 7 (2.04.2020): 2810. http://dx.doi.org/10.3390/su12072810.

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Landscape quality (LQ) encompasses diverse characteristic of the natural and cultural environment. The most effective tool to analyze LQ is the use of indicators. The main problem in the assessment of LQ is not the lack of indicators, but its multitude. That is why, the indicators’ categorization is a problematic issue. The paper aims to introduce and test the methodology for selecting the suitable indicators based on the example of two national parks located in the south-east part of Poland. The method composed of the following stages: (1) Selection of spatial units being analyzed; (2) selection of indicators type(s); (3) selection of specific indicators; (4) calculation of indicator set no 1; (5) analysis of the correlation between indicators’ pairs; (6) selection of a final set of indicators; (7) analysis of effectiveness. The latter stage, refers to the statistical analysis of significance between results obtained dependently on the data sources, a spatial unit of analysis and analyzed regions. The results showed that the categorization composed of ten, mainly composite indicators, can be applied to conclude on different levels of LQ of protected areas. The differences between two analyzed data sources, different spatial units, as well as diverse regions, occurred to be statistically insignificant. Generally, the results of the effectiveness analysis showed that a final categorization of LQ indicators is adequate to conclude on the diverse dimensions of LQ of analyzed protected areas.

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Novis, David A., Stephen Renner, Richard Friedberg, Molly K. Walsh i Andrew J. Saladino. "Quality Indicators of Blood Utilization". Archives of Pathology & Laboratory Medicine 126, nr 2 (1.02.2002): 150–56. http://dx.doi.org/10.5858/2002-126-0150-qiobu.

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Abstract Objectives.—To determine the normative rates of blood unit crossmatched to transfused (C:T) ratios, red blood cell (RBC) unit wastage, and RBC unit expiration that exist in hospital communities throughout the United States, and to examine hospital blood bank practices associated with more desirable (lower) rates. Design.—In 3 separate studies, participants in the College of American Pathologists Q-Probes laboratory quality improvement program collected data retrospectively on the number of transfusion crossmatches performed in their institutions and the number of RBC-containing units that were transfused into patients, the number of units that expired (outdated) prior to being utilized, and the number that were wasted due to mishandling. Participants also completed questionnaires describing their hospitals' and blood banks' laboratory and transfusion practices. Setting and Participants.—One thousand six hundred thirty-nine public and private institutions, well more than 80% of which were known to be located in the United States. Main Outcome Measures.—Quality indicators of blood utilization (namely, the C:T ratio, the rate of RBC unit expiration, and the rate of RBC unit wastage). Results.—Participants submitted data on 12 288 404 RBC unit transfusions. The C:T ratios were 1.5 or less in the top-performing 10% of participating institutions (90th percentile and above), 1.8 to 1.9 in the midrange of participating institutions (50th percentile), and 2.4 or greater in the bottom-performing 10% of participating institutions (10th percentile and below). Red blood cell unit expiration rates were 0.1% or less at the 90th percentile and above, 0.3% to 0.9% at the 50th percentile, and 3.5% or greater at the 10th percentile and below. Red blood cell unit wastage rates were 0.1% or less at the 90th percentile and above, 0.1% to 0.4% at the 50th percentile, and 0.7% or greater at the 10th percentile and below. Depending on which quality indicator was examined, lower values (ie, better performances) were found in institutions that had fewer than 200 hospital beds, no teaching programs, no on-site full-time medical directors of transfusion services, did not utilize maximum surgical blood order schedules, set C:T threshold goals of 2.0 or less, monitored categories of health care workers responsible for RBC wastage, monitored requests for RBC components by transfusion indication, did not accept short-dated units from blood distribution centers, and if they did accept short-dated units, were allowed to return those units to the distribution centers. Conclusions.—Hospital blood bank personnel can achieve C:T ratios below 2.0, RBC unit expiration rates below 1.0%, and RBC unit wastage rates below 0.5%. Lower C:T ratios and/or RBC unit expiration rates were associated with blood bank personnel setting C:T thresholds of 2.0 or less, monitoring requests for blood components by transfusion indication criteria, monitoring categories of health care workers responsible for blood wastage, not accepting short-dated units from blood distribution centers, and if short-dated units were accepted, being allowed to return those units to the blood distribution center. These practices were not associated with lower blood wastage rates.

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Shen, Yongjun, Da Ruan, Elke Hermans, Tom Brijs, Geert Wets i Koen Vanhoof. "Modeling qualitative data in data envelopment analysis for composite indicators". International Journal of System Assurance Engineering and Management 2, nr 1 (marzec 2011): 21–30. http://dx.doi.org/10.1007/s13198-011-0051-z.

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Kronenberg, Christoph, Tim Doran, Maria Goddard, Tony Kendrick, Simon Gilbody, Ceri R. Dare, Lauren Aylott i Rowena Jacobs. "Identifying primary care quality indicators for people with serious mental illness: a systematic review". British Journal of General Practice 67, nr 661 (3.07.2017): e519-e530. http://dx.doi.org/10.3399/bjgp17x691721.

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BackgroundSerious mental illness (SMI) — which comprises long-term conditions such as schizophrenia, bipolar disorder, and other psychoses — has enormous costs for patients and society. In many countries, people with SMI are treated solely in primary care, and have particular needs for physical care.AimThe objective of this study was to systematically review the literature to create a list of quality indicators relevant to patients with SMI that could be captured using routine data, and which could be used to monitor or incentivise better-quality primary care.Design and settingA systematic literature review, combined with a search of quality indicator databases and guidelines.MethodThe authors assessed whether indicators could be measured from routine data and the quality of the evidence.ResultsOut of 1847 papers and quality indicator databases identified, 27 were included, from which 59 quality indicators were identified, covering six domains. Of the 59 indicators, 52 could be assessed using routine data. The evidence base underpinning these indicators was relatively weak, and was primarily based on expert opinion rather than trial evidence.ConclusionWith appropriate adaptation for different contexts, and in line with the relative responsibilities of primary and secondary care, use of the quality indicators has the potential to improve care and to improve the physical and mental health of people with SMI. However, before the indicators can be used to monitor or incentivise primary care quality, more robust links need to be established, with improved patient outcomes.

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Nurhaida, Ida, Inge Dhamanti, Vina Ayumi, Fitri Yakub i Benny Tjahjono. "Hospital quality classification based on quality indicator data during the COVID-19 pandemic". International Journal of Electrical and Computer Engineering (IJECE) 14, nr 4 (1.08.2024): 4365. http://dx.doi.org/10.11591/ijece.v14i4.pp4365-4375.

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This research aim is to propose a machine learning approach to automatically evaluate or categories hospital quality status using quality indicator data. This research was divided into six stages: data collection, pre-processing, feature engineering, data training, data testing, and evaluation. In 2020, we collected 5,542 data values for quality indicators from 658 Indonesian hospitals. However, we analyzed data from only 275 hospitals due to inadequate submission. We employed methods of machine learning such as decision tree (DT), gaussian naïve Bayes (GNB), logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), linear discriminant analysis (LDA) and neural network (NN) for research archive purposes. Logistic regression achieved a 70% accuracy rate, SVM a 68% accuracy rate, and neural network a 59.34% of accuracy. Moreover, K-nearest neighbors achieved a 54% of accuracy and decision tree a 41% accuracy. Gaussian-NB achieved a 32% accuracy rate. The linear discriminant analysis achieved the highest accuracy with 71%. It can be concluded that linear discriminant analysis is the algorithm suitable for hospital quality data in this research.

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Zhou, Na, Chengchuan Chen, Yubei Liu, Zhaolan Yu, Aminu K. Bello, Yanhua Chen i Ping Liu. "Assessing the quality of CKD care using process quality indicators: A scoping review". PLOS ONE 19, nr 12 (10.12.2024): e0309973. https://doi.org/10.1371/journal.pone.0309973.

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Introduction Assessing the quality of chronic kidney disease (CKD) management is crucial for optimal care and identifying care gaps. It is largely unknown which quality indicators have been widely used and the potential variations in the quality of CKD care. We sought to summarize process quality indicators for CKD and assess the quality of CKD care. Methods We searched databases including Medline (Ovid), PubMed, Cochrane Library, Web of Science, CINAHL, and Scopus from inception to June 20, 2024. Two reviewers screened the identified records, extracted relevant data, and classified categories and themes of quality indicators. Results We included 24 studies, extracted 30 quality indicators, and classified them into three categories with nine themes. The three categories included laboratory measures and monitoring of CKD progression and/or complications (monitoring of kidney markers, CKD mineral and bone disorder, anemia and malnutrition, electrolytes, and volume), use of guideline-recommended therapeutic agents (use of medications), and attainment of therapeutic targets (blood pressure, glycemia, and lipids). Among the frequently reported quality indicators (in five or more studies), the following have a median proportion of study participants achieving that quality indicator exceeding 50%: monitoring of kidney markers (Scr/eGFR), use of medications (ACEIs/ARBs, avoiding non-steroidal anti-inflammatory drugs (NSAIDs)), management of blood pressure (with a target of ≤140/90, or without specific targets), and monitoring for glycated hemoglobin A1c (HbA1c)). The presence of diabetes, hypertension, cardiovascular disease, or proteinuria was associated with higher achievement in indicators of monitoring of kidney markers, use of recommended medications, and management of blood pressure and glycemia. Conclusion The quality of CKD management varies with quality indicators. A more consistent and complete reporting of key quality indicators is needed for future studies assessing CKD care quality.

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