## 3 Findings  
### Data Quality  
App-level quality measure: During an intervention period of three months, 122 patients' paper-based records were scanned, digitized and checked for quality. All of these data were synced and available on the cloud-based ODK aggregate server for access and remote analysis. The first task was to assess ODK Scan's ability to correctly parse the user filled machine-readable data fields, the structured number boxes and the fill-in bubbles. Accuracy rates for these fields were calculated by manually reviewing the 122 records, consisting of 10,600 digits and 8000 fill-in bubbles, and comparing Scan's raw predicted value against the ground truth value of the image snippet. The recognition accuracy for the fill-in bubbles was found to be much higher than that of the number boxes. Including blank fields(fields that users did not fill in), the Scan app classified 99.2% fill-in bubbles and 79.4% digits correctly (Table 1).  
The misclassified number fields were further investigated by mapping accuracy measures of the number boxes on the TB form to specific fields on the physical paper. It was found that fields located on the right side of the page, the side farthest from the register book's binding, suffered from lower accuracy measures than the number boxes which were located closer to the center or left-hand side (Figure 4). Inspection of the captured images showed that the pages were bending away from the binding. This bending warped the point-to-point mapping of the captured image to the template file, which caused the expected image snippet coordinates to mismatch. The resulting image snippets were misaligned, meaning that there would be missing edges and other important information. The structured number classifier's accuracy suffered when attempting to classify misaligned image snippets.  
User-level quality measure: The ODK Scan workflow includes viewing, editing and verifying data before it is synced with the server. Field workers were therefore given complete responsibility for the quality assessment and improvement. However, in the existing manual data entry process, this responsibility is shared between a field worker and a data management officer (DMO). The DMO is responsible for data entry and quality at the next level of management. When data quality (accuracy) of synced records were checked and compared with the quality of manually entered records, it was found that the existing manual data entry process resulted in a data accuracy rate of 94%, while the ODK Scan process only reached 86%. As described in the methodology section, both of these accuracy  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Field** & **All Fields** & **Blank Fields Excluded** \\ \hline Number & 79.4\% & 77.6\% \\ Fill-in Bubbles & 99.2\% & 97.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measurement of accuracy rates for recognition of fill-in bubbles and number boxes.  
Figure 4: Mapping of the accuracy measures of the number boxes.  
rates were calculated by comparing values observed from the paper records with the values that were saved to the digital record in each process. While it is understandable that human accuracy for data entry did not reach a perfect 100% for either processes, the discrepancy between the two rates shows that the task-dependence of field-worker for data verification with ODK Scan did not meet the quality of the existing manual data entry. This discrepancy between the manual data entry and verified ODK Scan data accuracy rates is further explored in the discussion section.