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Table 5 Methodologies used to specify data quality for implementation

From: Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review

Study types 1 2 3 4 5 Summary and results of methodologies Contexts
Reference        
(Gillies 2000a)      Represent a tool to assist with continuous improvement of the use of information systems in general practice based on their requirements which is accurate information Health information
       Shows how the model can be practically used to improving the use of coding (external consistency of data) and accurate information (data correctness) within a general practice in a systematic way  
(Kahn et al. 2012)     This is a well-grounded, logical approach and a case study to indicate health organizations need sound, dependable, useful and usable information for analytical purposes. Clinical data
       However, there is need to some details of their participants, sampling and why focus on only 16 dimensions of Information Quality (IQ).  
       This approach could be applicable way for the assessment of DQ in CDM because such an assessment provides a reasonable baseline for determining what improvements should be made in DQ based on fitness for purpose for analytical purposes  
(Liaw et al. 2011)    They used a well-designed framework to describe the intrinsic DQ (correctness and consistency) and fitness for purpose (completeness) for research and clinical purposes Clinical data
       However, this study raised the theoretical dependence of the SQL/SAS approach on the lack of a transparent and explicit data model, metadata and process within proprietary EHRs  
(Arts et al. 2003)     Their approach demonstrates that after physicians’ training, completeness, correctness and adherence to data definitions increased in ICUs significantly Clinical data
(Arts et al. 2002b)     Demonstrate a list of procedures for high data quality assurance in medical registry based on causes of insufficient data quality Health information
(Arts et al. 2002a)    Show that the overall DQ of medical registries has good quality (focusing on accuracy and completeness) and also explain their positive results as compared with earlier reports from the literature. Clinical data
       However, they did not compare data quality before and after the implementation of procedures to improve the accuracy of data  
(Stvilia et al. 2009)     Use a mixed methodology with multiple data sources: 1. The analysis of 150 Web pages and related web sites identified the major approaches the providers use to define their Health web pages
       IQ criteria set: a. centrally defined, b. community constructed, and c. outsourced to third-party raters. 2. The researchers surveyed a convenience sample of 108 healthcare information consumers to gain better  
       insight into the health IQ evaluation behaviour of consumers. 3. Semi structured in-depth interviews with a sample of 20 survey participants  
       Use a sample of the IPL’s Q&A communication archives to identify the healthcare IQ criteria used by consumers and information intermediaries  
       Results show that consumers may lack the motivation or literacy skills to evaluate the information quality of health  
(Kahn et al. 2002)      Developing a two-by-two conceptual model for describing IQ (PSP/IQ) Health information
       Mapping the 16 IQ dimensions into their model  
       Survey 45 professionals to determine which IQ dimensions belong in each quadrant of the model  
       Case study in 3 healthcare organizations that 75 people in each organization completed a 70-item questionnaire (a 10-point Likert scale) for assessing the quality of their patients information on  
       Provide a reasonable baseline for determining what improvements should be made in DQ (soundness, dependable useful and usable information) based on fitness for purpose for professionals analytical purposes.  
       Demonstrating the efficacy of the PSP/IQ model in three large healthcare organizations  
(Britt et al. 2007)     Use statistical methods to manage data quality using SAS as a computer program in statistical package Clinical data
       Measure representativeness, reliability, validity and accuracy of BEACH data eg. Reliability of coding of reasons for encounters and issues validity of ICPC to categorizing data. Accuracy of problem labels recorded by GPs (About 1000 GPs participate yearly)  
(Chen 2009)     Focus on a full mathematical analysis (mathematical software) Infectious diseases
       Investigate the effect of quality of information and amount of information are used interchangeably in the health behaviour e.g. decision making  
(Choquet et al. 2010)      Use Talend Open Studio open source software as well as developed stored procedures in SQL for the object quality criteria Hospital dataset
       Use the 6 HL7 information models for modelizing their domain  
       Apply the TDQM 4 steps approach to score quality of each vertex of IQT  
       Use two consensual resources to standardize the EHR vocabulary, include: 1) ATC: The WHO drugs and substances international classification and 2) NEWT: organisms taxonomy database  
       Propose methods and measures to assess data quality (focus on data accuracy)  
       Propose 3 dimensions to classify the quality measures proposed (objects, concepts, and terms) as vertexes of their model Information Quality Triangle = IQT)  
       Measure the distance between standardized information models and reference terminologies against its CIS  
       Allow building pertinent and coherent monitoring trends  
       Present that controlled vocabularies are a necessity to share data  
(Cunningham-Myrie et al. 2008)      Use ICD-10 for coding various collected data and to facilitate comparability of standardized data Health information
       Use Two broad categories of information were sought: a) epidemiological data and b) health service utilization data  
       Show that data management systems in hospitals were not linked to facilitate generation of cost-effectiveness estimates and other information required to compare options for health investment  
       Show methodological way for improvement health information quality for the economic analysis  
(Huaman et al. 2009)     Timeliness and data quality were assessed by calculating the percentage of reports sent on time and percentage of errors per total number of reports, respectively Infectious disease surveillance
       Use training program: 12 week prospective study with training program for reporting personnel.  
       Randomised selection to phone, visit or control for their supervisions  
       The training improved report timeliness but did not have such impact on data quality.  
(Kiragga et al. 2011)    Use the Research Cohort database as the reference "gold standard" for the assessment of data accuracy Infectious diseases
       Use statistical test e.g.: Categorical variables were compared using Chi-square test, the Mann–Whitney test was used for the continuous variables  
       Compare 2 databases, one from a clinic and one from a research team to assess the quality of data (completeness and accuracy)  
       Results show that there is a high rate of underreporting of OIs in a routine HIV clinic database and demonstrate high rates differences between clinic and research databases  
       Their findings have important implications for the use and interpretation of data derived from routine HIV observational databases for research and audit, and they highlight the need for ongoing regular validation of key data items in these databases  
(Lima et al. 2010)     Use a decision support example around a hypothetical patient called John who experiences an exacerbation of his COPD Clinical Guidelines (CG) for COPD
       Use the Clinical Guideline for COPD that there are 16 criteria that suggest the patient should be admitted and the model takes into account answers to each criterion  
       Present a model for the prediction and evaluation of quality of information to a multi criteria decision making process  
       Model describes a decision support tool for use in the management of COPD  
  1. Notes for study types: See Table 2 for legend.