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. 2023 Feb 13;47(1):23. doi: 10.1007/s10916-022-01892-2

Table 2.

Mapping of EHR DQA programs to concepts identified

SN Main category Low-level concepts Description Example instances
1 DQ-Task DQ-Task Specifications for the DQA activity
2 DQ-Task DQ-Dimension Data error to investigate, quality properties determining how well data are fit for use, or label for grouping measurements Completeness [6466, 69, 71, 74, 75, 77, 7981, 83, 84], conformance,[64, 65, 68, 70, 71, 77, 82] plausibility [48, 65, 70, 71, 73, 77, 83], consistency [74, 75, 79, 83], accuracy [69, 84], timeliness [75], out of range [73, 83], representation completeness [78, 79], domain completeness [78, 79], domain constraints [78, 79], syntax accuracy [69], duplicate [83], domain consistency [79], precision [74], violations of logical order [83], redundancy [84], readability [84].
3 DQ-Task Data-Element An individual unit of an observation Data elements determined at runtime [73, 78, 81, 84] pre-defined data elements, e.g. growth measurements [48], discharge summaries [74], and emergency records [83].
4 DQ-Task DQ-Metric An aggregate measure for assessing defined DQ-Dimensions Simple ratio [69, 73, 75, 79, 80], counts [66, 70, 73, 77], weighted scores [81, 84], and Boolean values[73].
5 DQ-Task Baseline A threshold for judging the level of a DQ-Dimension in a dataset for a particular use case Greater than 99.9% [67] and 90%[79], user-defined [73, 84], previous DQ-Metric score [65, 74].
6 DQ-Task Periodicity The type of execution and frequency supported On-demand [68, 81, 83] scheduled e.g. every 24 h [72], quarter [66] and other specified intervals [65, 67, 71].
7 DQ-Task Application area The point in the EHR data cycle where the DQA program or tool would be applicable Directly on EHRs data stores [72, 74], EHR data exchanged via health information exchange frameworks [64, 75]
8 DQ-Task Priority The rationale for focusing on selected dimensions and data elements Data elements type supported by available measurement [71, 84], data elements are necessary for intended use cases [71, 72], dimensions prevalent in previous records and literature [65, 84], dimensions for which measurements and required data are available [65], demands of internal and external data consumers [71].
9 Target-Data Target-Data One or more tuples containing observations
10 Target-Data Data-Source The range of sources or datasets that the program can be applied to Single [72, 74, 75], multiple[6870, 78]
11 Target-Data Data connection The method for accessing data sources. DQA program can support more than one type of connection CSV files [70, 84], database scripts or connections [7072, 74, 80, 81], REST API [69], Health Level Seven (HL7) document [75], XML[77].
12 Target-Data Data-Integrator The method for consolidating data from different sources into a single model or view. Extract, transform and load (ETL) [68, 69, 71, 76, 77]
13 Target-Data Data-Model Logical representation of data elements, their relationships, and constraints that is used to enable other components to operate and share the same data uniformly Observational Medical Outcomes Partnership (OMOP) [68, 69, 80], extended OMOP [71], Clinical Research Document [77], openEHR,[78], PCORnet [65, 66, 80] Informatics for Integrating Biology & the Bedside (i2b2) [70], Digital Imaging and Communications in Medicine (DICOM) [72, 82], National Summary Care Record Format [76], locally defined standards [69, 81].
14 Target-Data Data-Location The physical location of the Target-Data Users’ repository [68, 69, 77], central server [71, 76]
15 Target-Data Size The amount of data the program can support or has been validated with. Small (0-100k) [74, 77], medium (100k to 1 M) [79, 80], large (1 M+) [68].
16 Target-Data Data-Transformer Functions for converting data from one format, structure and value to another Vocabulary crosswalks [71, 75]
17 DQ-Measurement DQ-Measurement Criteria for measuring DQ-Dimension
18 DQ-Measurement Data-Level This refers to the data level considered in the DQ measurement. Cell level [69], field level [65, 67, 70, 84], record level [74, 81, 83], table level [65, 67, 71].
19 DQ-Measurement Measurement-Source Method for creating measurements and accompanying reference items Domain experts [6872, 79, 80], crowdsourcing [68, 71], data standards or dictionaries [71, 77, 78], national guidelines [76], literature review [71], statistical analysis [83, 84].
20 DQ-Measurement Representation Format for representing measurements Natural text [68, 72, 80], conditional logic statements [75, 78, 79], database queries [67, 69, 70, 73, 78], metadata repository[67, 69], programming language scripts [71, 73, 83], mathematical and computational models [48, 74, 81].
21 DQ-Report DQ-Report The content of reports and type of analysis Summary metrics [69], DQA metadata [67, 79], date and time the result was obtained [67, 71], severity warnings or comments [64, 65, 68, 82], error message to display [68, 71, 73], data profile of source data [68, 80], records returned per dataset or site [77], records returned linked to assessment metadata [67, 69, 70, 72, 73, 83, 84], aggregate results from multiple assessments or sites [66, 70, 77], results grouped by data element [6668, 71, 83], suggestions on improvements [64], information to exclude [69].
22 DQ-Report Dissemination-Method Techniques or tools for communicating assessment methods Store results in a repository [66, 67, 69, 70, 80], file export [71, 76, 77], Tables [68, 70, 73], charts [66, 68, 79, 80, 84], longitudinal views [66], collaborative workspace, e.g. Github [71]
23 DQ-Mechanism DQ-Mechanism The mechanism for operationalising DQA components Visualisation tool [84], dedicated tool [48, 68, 71, 80, 83]
24 DQ-Mechanism Feature Functions that enable a DQ-Mechanism to perform satisfactorily and meet Stakeholder requirements See Table 3.