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 [64–66, 69, 71, 74, 75, 77, 79–81, 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[68–70, 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 [70–72, 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 [68–72, 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 [66–68, 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.