## 4 Discussion  
This paper compares paper-based and technology-assisted data management processes and tries to extrapolate the value of mHealth solutions for capturing remote health data. In particular, the present study assesses the usability and technological aspects of the ODK Scan supported by the perspective of the field workers in relation to their work settings. However, it is also noted that in the developing country context, a prerequisite to the success of mHealth interventions is the consideration of developers and implementers about the field workers' expectations, needs and work settings [29; 30]. Moreover, a gap between the work practices and use of the new technologies is attributed by the poor implementation design of the technology-based interventions [31]. In the Public-Private Mix model of the TB control program of Pakistan, despite available direct-to-digital data collection solutions, the ODK Scan paper-to-digital system was chosen because of the current healthcare system requirement. Largely, the healthcare system relies on paper-based records for recording, reporting, verification, validation and other purposes. This setting informed the selection of ODK Scan paper-to-digital system. In other mHealth interventions where Android-based smartphones are used primarily for data collection, the ability of smartphones to transfer data in real time from remote areas to centrally located offices is achieved by exploiting the internet connectivity [15; 32]. Similarly, in this field test, internet connectivity benefited field workers in transferring the data and labor involved in sharing the data in the form of hard copies was avoided. However, the ability of smartphones to transfer data by using 3G internet connectivity instead of WiFi would be highly beneficial. More than just digitization of patient's records, technology also supports report generation, case management and patient follow-up [33]. Data quality achieved through the ODK Scan process is not impressive if compared with its counter process. This drop in quality can be explained through imperfect computer vision algorithms, and the change in the field workers roles; previously field workers were not solely responsible for ensuring data quality. In terms of time, the routine process of data review takes much longer than the new process of the data review and verification. Therefore, an alternative approach to improve the data quality would be to carry out supportive supervision, so that acceptable level of quality of the final set of data could be achieved [34]. However, supportive supervision role can be strengthened by providing appropriate training and timely feedback to the field workers on the data quality aspects. Additionally, effective data verification approaches require determination of the data quality characteristics and the application of quality standards [35]. However, the benefits of the mHealth technologies are improved with increased validity of the digital data [36]. Data quality could also be enhanced by improving ODK Scan's algorithms, but this is an expensive and difficult proposition. The worst of the accuracy problems were associated with poor form alignment due to bending pages. This might be helped by improving the form alignment algorithm or training the classifiers to handle misaligned snippets. Classification accuracy might also be improved by using context information, such as specified valid numeric or date ranges, though this would increase form design complexity. In the ODK Scan pilot test, unrestricted mobile and internet usage was authorized in good faith to generate ownership and responsibility among the field workers. It is also reported that the free use policy has generated a sense of ownership and empowerment among the health workers by recognizing the significance and convenience of the smartphone usage in their practices [37]. The most challenging aspect of the use of ODK Scan was filling in Scan-compatible forms that included fill-in bubbles, number boxes and text fields.