# Multi-Center Healthcare Data Quality Measurement Model and Assessment Using OMOP CDM
## 3 Results
### Multicenter OMOP CDM Data Quality Assessment Results
#### 3.1.1 NPR and WPR  
Without weighting the data error, the NPR was 96.58%, 90.08%, and 90.87% for institutions A, B, and C, respectively (Figure 1 and Table 4). The WPR result, which is an evaluation index that experts employed to weigh error and warning, was 98.52%, 94.26%, and 94.81% for institutions A, B, and C, respectively. Compared to that of the NPR, the WPR scores were higher; however, the difference in scores between the institutions was similar.  
The difference in quality between institutions is due to the influence of weights reflecting expert evaluation of verification rules, as classified into \"error\" and \"warning.\" For example, regarding the quality of patient information, the Patient ID rule that has a value of not null is an error. As this is a rule that has an important influence on quality, experts evaluated it with a weight of 0.64. However, the patient's racial classification was evaluated with a weight of 0.36, as it was identified as a rule that did not affect data quality. We confirm that the importance can be different even within tables that collect the same information.  
It was possible to verify the overall errors of healthcare data quality, and the following five types of errors were identified:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Total Error Rate** & **Error Rate** & **Warning Rate** & **NPR** & **WPR** \\ \hline A & 3.42\% & 0.89\% & 2.53\% & 96.58\% & 98.52\% \\ B & 9.92\% & 7.73\% & 2.19\% & 90.08\% & 94.26\% \\ C & 9.13\% & 6.79\% & 2.34\% & 90.87\% & 94.81\% \\ \(p\)-value & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multicenter OMOP CDM data quality summary results.  
Figure 1: Comparison of NPR and WPR according to error and warning weights.  
We identified a type of error in which the inspection result value of the inspection information table is not an integer greater than 0, but a negative number. Obviously, there cannot be a case where a test result value exists as a negative number. As a result of tracing the source data, it was confirmed that the unmeasurable value was defined as \(-\)9999, owing to an error in the inspection machine;
2. A type of error was revealed that is caused by a problem with the source data value (source_value), and it is a type of error that includes missing spelling such as \"Test Name (\"88888_Drug Name\", mi(missing spelling) minor salivary gland\")\". This type of error suggests that meaningless data can be loaded, and the reliability of the data can be reduced;
3. Error types that deviated from the standard term values owing to input errors between concept_id and code data values were found. In addition, the problem of mapping values to nonstandard values was also derived. The importance of mapping international standard terms is mentioned often in existing healthcare studies, suggesting that it may be a problem in multicenter studies that use OMOP CDM;
4. A type of error regarding chronological relationships was also identified. This violates the precedence relationship between the patient's date of birth and death and the observation period of each clinical information. This type of error draws attention to the implications of refining as errors that occur in the ETL process and errors that may occur in actual EHR systems;
5. A type of error that violates referential integrity was revealed. This is the type identified with most errors in this study. This error occurred because of the reference relationship between patient information and the treatment/diagnosis information table in the structure of the OMOP CDM. In other words, most of the data were loaded abnormally even though the data were updated, or the patient ID was present but could not be used for actual research because there was no examination history.