## 2 Literature Review
### Data Quality Dimensions Specific for the Healthcare Industry  
The _veracity_ characteristic of Big Data is argued to be of crucial importance for the healthcare industry [3]. The two main reasons forwarded are (i) inaccurate data could ultimately result in life or death decisions made with regard to patients and, therefore, _accuracy_ is a very important dimension; (ii) the high level of incorrectness usually present in doctors' prescriptions leads to a lot of wastages and inefficiencies, at the very least. Hence, _correctness_ could be derived to be another important dimension, according to the authors. Lastly, as the _veracity_ characteristic refers to the confidence in the use of data, _believability_ and _trust_ could be assumed to be also extremely prominent DQDs. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Velocity** & **Volume** & **Variety** \\ \hline \multirow{2}{*}{**Contextual**} & Consistency, Credibility, Confidentiality & Completeness, Credibility & Accuracy, Consistency, Understandability \\ \hline \multirow{2}{*}{**Temporal**} & Consistency, credibility, Currentness, Availability & Availability & Consistency, Currentness, Compliance \\ \hline \multirow{3}{*}{**Operational**} & Completeness, Accessibility, Efficiency, Traceability, Availability, Recoverability & Completeness, Accessibility, Efficiency, Availability, Recoverability, Precision \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Matrix of 3 Cs (contextual consistency, temporal consistency, operational consistency) relative to the 3 Vs (volume, variety, velocity) [23]. In the context of using clinical data in the US healthcare industry, some industry-based whitepapers point toward the importance of the following six DQDs, namely: _timeliness_, _equitability_, _care-safe_, _patient-centered_, _effective_, and _efficient_. Most of the above-mentioned dimensions are not part of the traditional list of DQDs normally discussed in data quality literature, and thus strengthen the rationale for the need for further research in this area. The six key attributes (dimensions) proposed by the UK audit commission in 2007 to improve data quality are: _accuracy_, _validity_, _reliability_, _timeliness_, _relevance_, and _completeness_. The Canadian Institute for Health Information (CIHI) Data Quality framework assessment tools updated in 2017 discusses the following groups of DQDs--_accuracy and reliability_, _timeliness and punctuality_, _comparability and coherence_, _accessibility and clarity_, and _relevance_[28]. The data quality framework of New Zealand Health Information Service (NZHIS) was adapted from the CIHI's data quality framework [28]. Furthermore, the NZHIS felt that there was a need for transparency in the quality dimensions, which brought the implantation of additional DQDs, such as privacy and security, by the ministry's senior advisors for the health sector. Those dimensions were generated basically to address privacy and security standards, legislation, policies, and processes, with the idea of ensuring the privacy and security of individual information. In conclusion, the DQDs given by NZHIS are _accuracy_, _timeliness_, _comparability_, _usability_, _relevance_, _privacy_, and _security_. There have been previous research studies conducted with the aim of determining the level of accuracy in international disease coding under the ICD-9 (International Classification of Diseases, ninth revision) standard, specifically in New Zealand [29]. The major result is that 18% of the first four digits of E-codes and 8% of the location codes were incorrect. No major differences were noted between small and large hospitals. This work tends to confirm the perception that accuracy is one of the very important DQDs, even for the very specific type of data in the health industry. This research again was carried out outside the Big Data context. Other authors conducted a research study within the specific domain of trauma-related registries [30].