Data Quality Assessment Methods Research Articles

A Survey of Data Quality Assessment Methods for Big Data

A Survey of Data Quality Assessment Methods for Big Data

A CLASSIFICATION MODEL FOR THE INFERENCE OF SPATIAL PRECISION OF OPENSTREETMAP BUILDINGS WITH INTRINSIC INDICATORS

Abstract. To evaluate the quality of OSM data, similarities between OSM features and their homologous features represented in a reference database are relevant metrics. However, reference databases do not exist everywhere or are not freely available. Thus, having data quality assessment methods that rely only on intrinsic indicators (i.e. based on data itself without considering external information) would be useful in these cases. This article specifically uses the radial distance as a target quality metric to measure the quality of shapes. Its aim is to build a random-forest based classification method that reconstructs whether this distance is higher or lower than a specified threshold, using only intrinsic indicators as inputs. The classification algorithm is evaluated on a first dataset by computing the ROC (Receiver Operating Characteristic) curve and using the AUC (Area Under Curve) as an evaluation metric. The transferability of the resulting algorithm is then evaluated by measuring its performance on a second, distinct dataset. The experiments show that the algorithm performs reasonably well on both the initial and the second dataset, and that intrinsic indicators give relevant information to infer comparison-based shape quality (i.e. the radial distance).

Frameworks, Dimensions, Definitions of Aspects, and Assessment Methods for the Appraisal of Quality of Health Data for Secondary Use: Comprehensive Overview of Reviews.

Health care has not reached the full potential of the secondary use of health data because of-among other issues-concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of health data. However, this increase in quantity has not been matched by a proportional improvement in the quality of health data. This review aims to offer a comprehensive overview of the existing frameworks for data quality dimensions and assessment methods for the secondary use of health data. In addition, it aims to consolidate the results into a unified framework. A review of reviews was conducted including reviews describing frameworks of data quality dimensions and their assessment methods, specifically from a secondary use perspective. Reviews were excluded if they were not related to the health care ecosystem, lacked relevant information related to our research objective, and were published in languages other than English. A total of 22 reviews were included, comprising 22 frameworks, with 23 different terms for dimensions, and 62 definitions of dimensions. All dimensions were mapped toward the data quality framework of the European Institute for Innovation through Health Data. In total, 8 reviews mentioned 38 different assessment methods, pertaining to 31 definitions of the dimensions. The findings in this review revealed a lack of consensus in the literature regarding the terminology, definitions, and assessment methods for data quality dimensions. This creates ambiguity and difficulties in developing specific assessment methods. This study goes a step further by assigning all observed definitions to a consolidated framework of 9 data quality dimensions.

MV2Net: Multi-Variate Multi-View Brain Network Comparison over Uncertain Data.

Visually identifying effective bio-markers from human brain networks poses non-trivial challenges to the field of data visualization and analysis. Existing methods in the literature and neuroscience practice are generally limited to the study of individual connectivity features in the brain (e.g., the strength of neural connection among brain regions). Pairwise comparisons between contrasting subject groups (e.g., the diseased and the healthy controls) are normally performed. The underlying neuroimaging and brain network construction process is assumed to have 100% fidelity. Yet, real-world user requirements on brain network visual comparison lean against these assumptions. In this work, we present MV^2Net, a visual analytics system that tightly integrates multi-variate multi-view visualization for brain network comparison with an interactive wrangling mechanism to deal with data uncertainty. On the analysis side, the system integrates multiple extraction methods on diffusion and geometric connectivity features of brain networks, an anomaly detection algorithm for data quality assessment, single- and multi-connection feature selection methods for bio-marker detection. On the visualization side, novel designs are introduced which optimize network comparisons among contrasting subject groups and related connectivity features. Our design provides level-of-detail comparisons, from juxtaposed and explicit-coding views for subject group comparisons, to high-order composite view for correlation of network comparisons, and to fiber tract detail view for voxel-level comparisons. The proposed techniques are inspired and evaluated in expert studies, as well as through case analyses on diffusion and geometric bio-markers of certain neurology diseases. Results in these experiments demonstrate the effectiveness and superiority of MV^2Net over state-of-the-art approaches.

Quality Assessment of Healthcare Databases


 The assessment of data quality and suitability plays an important role in improving the validity and generalisability of the results of studies based on secondary use of health databases. The availability of more and more updated and valid information on data quality and suitability provides data users and researchers an useful tool to optimize their activities.
 In this paper, we have summarized and synthesized the main aspects of Data Quality Assessment (DQA) applied in the field of secondary use of healthcare databases, with the aim of drawing attention to the critical aspects having to be considered and developed for improving the correct and effective use of secondary sources.
 Four developing features are identified: standardizing DQA methods, reporting DQA methods and results, synergy between data managers and data users, role of Institutions. Interdisciplinarity, multi-professionality and connection between government institutions, regulatory bodies, universities and the scientific community will provide the "toolbox" for i) developing standardized and shared DQA methods for health databases, ii) defining the best strategies for disseminating DQA information and results.

Comparison of Semirigorous and Empirical Models Derived Using Data Quality Assessment Methods

With the increase in available data and the stricter control requirements for mineral processes, the development of automated methods for data processing and model creation are becoming increasingly important. In this paper, the application of data quality assessment methods for the development of semirigorous and empirical models of a primary milling circuit in a platinum concentrator plant is investigated to determine their validity and how best to handle multivariate input data. The data set used consists of both routine operating data and planned step tests. Applying the data quality assessment method to this data set, it was seen that selecting the appropriate subset of variables for multivariate assessment was difficult. However, it was shown that it was possible to identify regions of sufficient value for modeling. Using the identified data, it was possible to fit empirical linear models and a semirigorous nonlinear model. As expected, models obtained from the routine operating data were, in general, worse than those obtained from the planned step tests. However, using the models obtained from routine operating data as the initial seed models for the automated advanced process control methods would be extremely helpful. Therefore, it can be concluded that the data quality assessment method was able to extract and identify regions sufficient and acceptable for modeling.

Two Approaches Provide Quality Control for NMR Measurement of Unconventional Rock

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201358, “Quality Control of NMR Measurements in Unconventional Rocks Using Hilbert Transforms and Semilogarithmic Derivatives,” by Pierre Aérens, SPE, David Medellin, and Carlos Torres-Verdin, SPE, The University of Texas at Austin, prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, 5-7 October. The paper has not been peer reviewed. Nuclear magnetic resonance (NMR) measurements are extremely valuable in the assessment of fluid-flow properties of rocks. However, inverted transverse-relaxation time (T2) distributions are often biased. The authors of the complete paper introduce and compare two quality-control approaches based on two different signal-processing practices: the semilogarithmic derivative and the Hilbert transform. This work provides the basis of effective quality-control methods for NMR measurements for the petrophysical interpretation of rocks with complex pore-size distributions. The fast and reliable quality control of estimated T2 distributions is not affected by inversion artifacts, relying only on unfiltered, raw data. Introduction Borehole NMR measurements are commonly riddled with excessive noise that precludes high-resolution assessments of rock and fluid properties, especially in unconventional rock. Inverted T2 distributions can be biased by positivity constraints and the regularization method used in the inversion, making it difficult to determine whether the estimated pore-size distributions are reliable or byproducts of the inversion or regularization. Quality control of inversion results is essential for the robust quantitative interpretation of proton magnetization decays. The complete paper presents a mathematical analysis for the two data-quality assessment methods and the work flow to estimate pseudo-T2 distributions on the basis of time-decay proton magnetizations. The estimation and assessment methods are then applied to both synthetic and experimental data sets to verify their validity and reliability. The laboratory examples are detailed in the complete paper. Validation With Synthetic Cases The authors apply the derivative and Hilbert methods to two synthetic examples for a total of three cases. Both synthetic examples comprise three exponential magnetization decays, with amplitudes equal to A1=0.5, A2 =0.2, and A3=0.3. The first example is referred to as ideal because it is not contaminated with noise and because the authors chose the T2 peak times separated by three orders of magnitude at 1 µs, 1 ms, and 1000 ms, respectively. The second example showcases a more-realistic choice of T2 peak values, namely 0.1, 10, and 200 ms, respectively.

The National Survey on Population and Family Health (NSPFH), Morocco-2018: a Data Quality Assessment

The National Survey on Population and Family Health (NSPFH) is an important source of data in Morocco. Its objective is to assess, periodically, the state of population health and the impact of policies and programs put in place, by updating the main demographic and health indicators. The report of the sixth NSPFH, conducted during the period October 2017-January 2018, illustrates updated socio-demographic data and the new prevalence of several diseases. As the NSPFH results are a reference for decision-makers, researchers and professionals, it seems necessary to promote them and give them more credibility. The objective of this paper is to verify the reliability of the NSPFH data and results using demographic data quality assessment methods (graphic methods: age-gender pyramid and distribution by gender and area of residency, and statistical methods: non-response rate and age accuracy indexes) to ensure if they are of good quality. This study showed that the NSPFH-2018 data were of good quality. Indeed, the non-response rate did not exceed 1.1% for all questionnaires (household 1.1%, woman 0.5% and elderly 1.1%). The age-gender pyramid confirmed the demographic transition towards ageing and the downward trend of fertility in Morocco. The distribution by area of residency confirmed the trend towards urbanization of the country (61% urban and 39% rural). The Whipple (1.05), Myers (4.73), Bachi (2.31) indexes and the United Nations Combined Index (31.21) were all within the standards for a good quality of age declaration. In addition, the results were consistent with each other and in line with the national and international health context.

Research on the management of seismic data quality assessment methods

The quality of real-time waveform data from seismic networks not only directly affects the accuracy of seismic positioning accuracy, but also has an irreversible impact on the credibility of in-depth research findings in seismic disciplines. The article introduces an engineering perspective evaluation method for seismic data quality assessment, and uses real-time waveform data from the seismic station network in Shandong to form a visualization report on the quality of seismic quasi-real-time data, so as to determine the data quality of the seismic station network more intuitively and accurately.

Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data.

ObjectiveTo synthesize data quality (DQ) dimensions and assessment methods of real-world data, especially electronic health records, through a systematic scoping review and to assess the practice of DQ assessment in the national Patient-centered Clinical Research Network (PCORnet).Materials and MethodsWe started with 3 widely cited DQ literature—2 reviews from Chan et al (2010) and Weiskopf et al (2013a) and 1 DQ framework from Kahn et al (2016)—and expanded our review systematically to cover relevant articles published up to February 2020. We extracted DQ dimensions and assessment methods from these studies, mapped their relationships, and organized a synthesized summarization of existing DQ dimensions and assessment methods. We reviewed the data checks employed by the PCORnet and mapped them to the synthesized DQ dimensions and methods.ResultsWe analyzed a total of 3 reviews, 20 DQ frameworks, and 226 DQ studies and extracted 14 DQ dimensions and 10 assessment methods. We found that completeness, concordance, and correctness/accuracy were commonly assessed. Element presence, validity check, and conformance were commonly used DQ assessment methods and were the main focuses of the PCORnet data checks.DiscussionDefinitions of DQ dimensions and methods were not consistent in the literature, and the DQ assessment practice was not evenly distributed (eg, usability and ease-of-use were rarely discussed). Challenges in DQ assessments, given the complex and heterogeneous nature of real-world data, exist.ConclusionThe practice of DQ assessment is still limited in scope. Future work is warranted to generate understandable, executable, and reusable DQ measures.

Extractable and Non-Extractable Antioxidants Composition in the eBASIS Database: A Key Tool for Dietary Assessment in Human Health and Disease Research.

The antioxidant properties of foods are crucial in nutrition, food chemistry, and medicine studies but are often underestimated, with significant amounts of bioactive compounds containing physiological and biochemical properties remaining in the residue from extraction as non-extractable antioxidants. Over the last decade, extractable and non-extractable compounds have become key in the evaluation/determination of the antioxidant properties of food matrices because of their relevance in human health. This has led to the need to include extractable and non-extractable antioxidants in comprehensive and harmonized food composition databases for a wide range of applications within research, food, pharmaceutical, nutraceutical, and cosmeceutical areas. Additionally, the databases are invaluable as part of the health claims application process. eBASIS, (Bioactive Substances in Food Information System) a comprehensive database containing quality-evaluated scientific data, covering the composition of bioactive compounds present in foods, has flexible structures, allowing it to be extended to include newly emerging data on extractable and non-extractable compounds. Search criteria were developed and defined for compiling suitable peer-reviewed literature. Data quality assessment methods were established for the addition of composition data and antioxidant activity, with a focus on various parameters including: the extraction procedure, the antioxidant measurements, the expression of results. A total of 437 quality-evaluated datapoints on the composition of extractable and/or non-extractable compounds were entered into the database. This database update represents one of the first examples of building a database dedicated to antioxidant properties. This expansion of eBASIS provides a novel and unique tool for nutritionists, dietitians, researchers to use for a wide range of applications, such as dietary assessment, exposure studies and epidemiological studies, and may contribute to an increase in high-bioactive food consumption by consumers.

No genetic signature of glacial refugia in current European fallow deer (Dama dama dama L., 1758) populations: a comment on Baker et al. (2017)

Genetic data are crucial for making inferences about the evolutionary history of species and deriving guidelines for conservation strategies. In conservation genetics studies, each step should be critically evaluated, from sampling strategy to data quality assessment and analytical methods: potential issues and biases should be considered, reproducibility should be guaranteed and the results of different analyses should be critically evaluated. By incorporating all these steps, we re-analysed data from Baker et al. (Heredity 119:16–26, 2017): our results led to conclusions that contradict the ones reached by the authors, which were based on weak support and biased analyses. European fallow deer current genetic makeup most likely derives from extensive human-mediated translocations. The genetic expectations of glacial refugia hypothesis cannot be found in current European fallow deer populations.

Pharmacists’ roles in supporting people living with severe and persistent mental illness: a systematic review protocol

IntroductionSevere and persistent mental illness (SPMI) can significantly impact a person’s social, personal and professional life. Previous studies have demonstrated pharmacists’ roles in mental healthcare; however, limited studies to date...

Clinical Research Informatics: Contributions from 2018.

Summary Objectives : To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2018. Method : A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting of the editorial team was organized to conclude on the selection of best papers. Results : Among the 1,469 retrieved papers published in 2018 in the various areas of CRI, the full review process selected four best papers. The first best paper describes a simple algorithm detecting co-morbidities in Electronic Healthcare Records (EHRs) using a clinical data warehouse and a knowledge base. The authors of the second best paper present a federated algorithm for predicting heart failure hospital admissions based on patients' medical history described in their distributed EHRs. The third best paper reports the evaluation of an open source, interoperable, and scalable data quality assessment tool measuring completeness of data items, which can be run on different architectures (EHRs and Clinical Data Warehouses (CDWs) based on PCORnet or OMOP data models). The fourth best paper reports a data quality program conducted across 37 hospitals addressing data quality Issues through the whole data life cycle from patient to researcher. Conclusions : Research efforts in the CRI field currently focus on consolidating promises of early Distributed Research Networks aimed at maximizing the potential of large-scale, harmonized data from diverse, quickly developing digital sources. Data quality assessment methods and tools as well as privacy-enhancing techniques are major concerns. It is also notable that, following examples in the US and Asia, ambitious regional or national plans in Europe are launched that aim at developing big data and new artificial intelligence technologies to contribute to the understanding of health and diseases in whole populations and whole health systems, and returning actionable feedback loops to improve existing models of research and care. The use of “real-world" data is continuously increasing but the ultimate role of this data in clinical research remains to be determined.

Integrated disease surveillance and response implementation in Liberia, findings from a data quality audit, 2017.

Introductionin spite of the efforts and resources committed by the division of infectious disease and epidemiology (DIDE) of the national public health institute of Liberia (NPHIL)/Ministry of health to strengthening integrated disease surveillance and response (IDSR) across the country, quality data management system remains a challenge to the Liberia NPHIL/MoH (Ministry of health), with incomplete and inconsistent data constantly being reported at different levels of the surveillance system. As part of the monitoring and evaluation strategy for IDSR continuous improvement, data quality assessment (DQA) of the IDSR system to identify successes and gaps in the disease surveillance information system (DSIS) with the aim of ensuring data accuracy, reliability and credibility of generated data at all levels of the health system; and to inform an operational plan to address data quality needs for IDSR activities is required.Methodsmulti-stage cluster sampling that included stage 1: simple random sample (SRS) of five counties, stage 2: simple random sample of two districts and stage 3: simple random sample of three health facilities was employed during the study pilot assessment done in Montserrado County with Liberia institute of bio medical research (LIBR) inclusive. A total of thirty (30) facilities was targeted, twenty nine (29) of the facilities were successfully audited: one hospital, two health centers, twenty clinics and respondents included: health facility surveillance focal persons (HFSFP), zonal surveillance officers (ZSOs), district surveillance officers (DSOs) and County surveillance officers (CSOs).Resultsthe assessment revealed that data use is limited to risk communication and sensitization, no examples of use of data for prioritization or decision making at the subnational level. The findings indicated the following: 23% (7/29) of health facilities having dedicated phone for reporting, 20% (6/29) reported no cell phone network, 17% (5/29) reported daily access to internet, 56.6% (17/29) reported a consistent supply of electricity, and no facility reported access to functional laptop. It was also established that 40% of health facilities have experienced a stock out of laboratory specimens packaging supplies in the past year. About half of the surveyed health facilities delivered specimens through riders and were assisted by the DSOs. There was a large variety in the reported packaging process, with many staff unable to give clear processes. The findings during the exercise also indicated that 91% of health facility staff were mentored on data quality check and data management including the importance of the timeliness and completeness of reporting through supportive supervision and mentorship; 65% of the health facility assessed received supervision on IDSR core performance indicator; and 58% of the health facility officer in charge gave feedback to the community level.Conclusionpublic health is a data-intensive field which needs high-quality data and authoritative information to support public health assessment, decision-making and to assure the health of communities. Data quality assessment is important for public health. In this review completeness, accuracy, and timeliness were the three most-assessed attributes. Quantitative data quality assessment primarily used descriptive surveys and data audits, while qualitative data quality assessment methods include primarily interviews, questionnaires administration, documentation reviews and field observations. We found that data-use and data-process have not been given adequate attention, although they were equally important factors which determine the quality of data. Other limitations of the previous studies were inconsistency in the definition of the attributes of data quality, failure to address data users’ concerns and a lack of triangulation of mixed methods for data quality assessment. The reliability and validity of the data quality assessment were rarely reported. These gaps suggest that in the future, data quality assessment for public health needs to consider equally the three dimensions of data quality, data use and data process. Measuring the perceptions of end users or consumers towards data quality will enrich our understanding of data quality issues. Data use is limited to risk communication and sensitization, no examples of use of data for prioritization or decision making at the sub national level.

The effects of data quality on the analysis of corporate board interlock networks

Nowadays, social network data of ever increasing size is gathered, stored and analyzed by researchers from a range of disciplines. This data is often automatically gathered from API’s, websites or existing databases. As a result, the quality of this data is typically not manually validated, and the resulting social networks may be based on false, biased or incomplete data. In this paper, we investigate the effect of data quality issues on the analysis of large networks. We focus on the global board interlock network, in which nodes represent firms across the globe, and edges model social ties between firms – shared board members holding a position at both firms. First, we demonstrate how we can automatically assess the completeness of a large dataset of 160 million firms, in which data is missing not at random. Second, we present a novel method to increase the accuracy of the entries in our data. By comparing the expected and empirical characteristics of the resulting network topology, we develop a technique that automatically prunes and merges duplicate nodes and edges. Third, we use a case study of the board interlock network of Sweden to show how poor quality data results in distorted network topologies, incorrect community division, biased centrality values and abnormal influence spread under a well-known diffusion model. Finally, we demonstrate how the proposed data quality assessment methods help restore the network structure, ultimately allowing us to derive meaningful and correct results from the analysis of the network.

Validating Data Quality Actions in Scoring Processes

Data quality has gained momentum among organizations upon the realization that poor data quality might cause failures and/or inefficiencies, thus compromising business processes and application results. However, enterprises often adopt data quality assessment and improvement methods based on practical and empirical approaches without conducting a rigorous analysis of the data quality issues and outcome of the enacted data quality improvement practices. In particular, data quality management, especially the identification of the data quality dimensions to be monitored and improved, is performed by knowledge workers on the basis of their skills and experience. Control methods are therefore designed on the basis of expected and evident quality problems; thus, these methods may not be effective in dealing with unknown and/or unexpected problems. This article aims to provide a methodology, based on fault injection, for validating the data quality actions used by organizations. We show how it is possible to check whether the adopted techniques properly monitor the real issues that may damage business processes. At this stage, we focus on scoring processes, i.e., those in which the output represents the evaluation or ranking of a specific object. We show the effectiveness of our proposal by means of a case study in the financial risk management area.

Pengembangan Metode Penilaian Mandiri Kualitas Data Rutin Di Dinas Kesehatan Kota Surakarta

Data are the basis to make a decision and policy. The quality of data is going to produce a better policy. The quality assessment methods nowadays do not include all indicators of data quality. If the indicators or assessment criteria in the quality assessment methods are more complete, the level of assessment methods of the data will be higher. The purpose of this study is to develop the method of independent assessment of routine data quality in Surakarta Health Department which is previously performed using the data quality assessment of PMKDR and HMN methods firstly.The design of this study is research and development (R&D) that has been modified into seven steps, namely formulating potential problems, collecting the data, designing the product, validating the design, fixing the design, testing the product, and fixing the product. The subjects consisted of 19 respondents who are managers of data in Surakarta Health Department. Data analysis method used is content analysis.The assessment results show that, in the pilot phase of the development of data quality assessment methods which have been developed, it is basically successful, or it can be used. The results of the assessment of the quality of the data by the developed method is the quality of data collection which is very adequate, the quality of data accuracy which is poor, the quality of data that consistency exists but is inadequate, the quality of the actuality of the data which is very adequate, the quality of periodicity data that is inadequate, the quality of the representation of the data that is very adequate, and sorting the data which is very adequate.It needs a commitment from Surakarta Health Department to take advantage of the development of these methods to assess the quality of data to support the availability of information, decision-making and planning of health programs. It also calls for the development of this research by conducting all stages of the steps of R&D so that the final result of the method development will be better.

A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data.

Objective:Harmonized data quality (DQ) assessment terms, methods, and reporting practices can establish a common understanding of the strengths and limitations of electronic health record (EHR) data for operational analytics, quality improvement, and research. Existing published DQ terms were harmonized to a comprehensive unified terminology with definitions and examples and organized into a conceptual framework to support a common approach to defining whether EHR data is ‘fit’ for specific uses.Materials and Methods:DQ publications, informatics and analytics experts, managers of established DQ programs, and operational manuals from several mature EHR-based research networks were reviewed to identify potential DQ terms and categories. Two face-to-face stakeholder meetings were used to vet an initial set of DQ terms and definitions that were grouped into an overall conceptual framework. Feedback received from data producers and users was used to construct a draft set of harmonized DQ terms and categories. Multiple rounds of iterative refinement resulted in a set of terms and organizing framework consisting of DQ categories, subcategories, terms, definitions, and examples. The harmonized terminology and logical framework’s inclusiveness was evaluated against ten published DQ terminologies.Results:Existing DQ terms were harmonized and organized into a framework by defining three DQ categories: (1) Conformance (2) Completeness and (3) Plausibility and two DQ assessment contexts: (1) Verification and (2) Validation. Conformance and Plausibility categories were further divided into subcategories. Each category and subcategory was defined with respect to whether the data may be verified with organizational data, or validated against an accepted gold standard, depending on proposed context and uses. The coverage of the harmonized DQ terminology was validated by successfully aligning to multiple published DQ terminologies.Discussion:Existing DQ concepts, community input, and expert review informed the development of a distinct set of terms, organized into categories and subcategories. The resulting DQ terms successfully encompassed a wide range of disparate DQ terminologies. Operational definitions were developed to provide guidance for implementing DQ assessment procedures. The resulting structure is an inclusive DQ framework for standardizing DQ assessment and reporting. While our analysis focused on the DQ issues often found in EHR data, the new terminology may be applicable to a wide range of electronic health data such as administrative, research, and patient-reported data.Conclusion:A consistent, common DQ terminology, organized into a logical framework, is an initial step in enabling data owners and users, patients, and policy makers to evaluate and communicate data quality findings in a well-defined manner with a shared vocabulary. Future work will leverage the framework and terminology to develop reusable data quality assessment and reporting methods.

Methods for assessing the quality of data in public health information systems: a critical review.

The quality of data in public health information systems can be ensured by effective data quality assessment. In order to conduct effective data quality assessment, measurable data attributes have to be precisely defined. Then reliable and valid measurement methods for data attributes have to be used to measure each attribute. We conducted a systematic review of data quality assessment methods for public health using major databases and well-known institutional websites. 35 studies were eligible for inclusion in the study. A total of 49 attributes of data quality were identified from the literature. Completeness, accuracy and timeliness were the three most frequently assessed attributes of data quality. Most studies directly examined data values. This is complemented by exploring either data users' perception or documentation quality. However, there are limitations of current data quality assessment methods: a lack of consensus on attributes measured; inconsistent definition of the data quality attributes; a lack of mixed methods for assessing data quality; and inadequate attention to reliability and validity. Removal of these limitations is an opportunity for further improvement.