Author reference | Context | Aims of project | Methods/tools used in project | Results |
---|---|---|---|---|
(Ivanova et al. 2013) | Geo-spatial datasets in the national geo-information repositories in Netherlands | To suggest a system for guided search for spatial data resources called GUESS | -Use of popular search engines like OpenSearch to help in assessing fitness for purpose | Defined fitness for purpose of data based on users (experts and non-experts in geo-informatics) satisfaction from search results |
 |  |  | -Use metadata (information that helps users to assess the usefulness of a dataset relative to their problem) as a tool to evaluate fitness for purpose of datasets |  |
 |  |  | -Their approach is based on a 3-part data model (user profile, spatial data profiles and interaction profiles) | Allowed users without specific expertise to conduct free form search requests in their own language |
 |  |  | -Theoretical discussion on accuracy and completeness of data |  |
(Devillers et al. 2007) | Spatial On‒Line Analytical Processing (SOLAP) as a GIS data repository | To manage heterogeneous data quality and provide functions to support expert users in the assessment of the fitness for purpose of a given dataset | -Use the Quality Information Management Model = QIMM | Defined fitness for purpose as the closeness of the agreement between data characteristics and the explicit and/or implicit needs of a user for a given application in a given area |
 |  | -Focus on intrinsic data quality indicators such as completeness, correctness and accuracy underpins a prototype | ||
 |  |  | -Apply data quality analysis tool which is the Multidimensional User Manual (MUM) prototype | Researchers attempt to provide data quality indicators to help users determine a dataset’s fitness for purpose and better assess the fitness of data based on quality indicators/experts in GIS |
 |  |  | -Validate the QUMM of through demonstrations of the prototype to different users (GIS scientists, specialists in data quality issues, consultants in GIS, data producers, governmental agencies, typical GIS users, etc.) |  |
(Kahn et al. 2012) | Clinical dataset in US | To develop the efficacy of their data model in three large healthcare organizations | -Use a two-by-two conceptual model (PSP/IQ) for describing IQ | This is a well-grounded, logical approach and a case study to indicate health organizations need to use "fitness of use" to determine IQ (specifically soundness, dependable, useful and usable information) for analytical purposes |
 |  |  | -Focus on 8 dimensions of data quality (completeness, correctness, flexibility, etc.) |  |
 |  |  | -Surveyed 45 professionals to determine which IQ dimensions belong in each quadrant of the model | This assessment of DQ provides a reasonable baseline for determining what improvements should be made in DQ based on fitness for purpose for analytical purposes |
 |  |  | -Use case study method in 3 healthcare organizations that 75 people in each organization completed a 70-item questionnaire for assessing the quality of their patients information on the IQ dimensions |  |
(Chen 2009) | Infectious diseases dataset in US | To investigate the effect of 'quality’ of information and 'amount’ of information are used in the health behaviour | -Use mathematical modelling of infectious disease transmission, seeks to analyse how the amount of information about disease prevalence affects individuals’ incentives | Demonstrated "fitness for purpose" of data for agents to choose how much information to gather from others (personal communication from an anonymous reviewer) |
 |  |  | -More focus on data timeliness | This is a theoretical paper using several mathematical models to show that information quality affects health behaviour i.e. better information leads to better decision making |
 |  |  | -Use of mathematics software |  |
(Liaw et al. 2011) | An electronic Practice Based Research Network (ePBRN) with a data repository of routinely data from multiple EHRs | To develop a matrix for assessment and management the quality of data | Their methods include 3 phases: | They used a well-designed framework to describe the intrinsic DQ (correctness and consistency) and fitness for purpose (completeness) for research and clinical purposes |
 |  |  | (1) requirements specification based on the conceptual framework, |  |
 |  |  | (2) design and establishment of the ePBRN, and |  |
 |  |  | (3) evaluation of the data quality and fitness for research. |  |
 |  |  | -Use Microsoft Structured Query Language (SQL) to manage the extracted data and SAS used for datacleansing and analysis | 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 |
 |  |  | -Focus on correctness, completeness and consistency of clinical data |  |
(Hamilton et al. 2003) | Eighteen general practices in the Exeter Primary Care Trust in UK | To compare computer-only record keeping to paper-only and hybrid systems | -Use case control study of cancer patients aged over 40 years | Defined completeness as fitness for consultation in primary care |
 |  |  | -Classify records as paper, computer, or hybrid, depending on which medium stored the clinical information from consultations by descriptive statistics | Hybrid systems of primary care record keeping document higher numbers of consultations than computer-only or paper-only systems |
 |  |  | -Focus on completeness of data |  |