Evaluation of Electronic and Paper-Pen Data Capturing Tools for Data Quality in a Public Health Survey in a Health and Demographic Surveillance Site, Ethiopia: Randomized Controlled Crossover Health Care Information Technology Evaluation (Preprint)

Abstract

Background: Periodic demographic health surveillance and surveys are the main sources of health information in developing countries. Conducting survey in requires extensive use of paper-pen and manual work and lengthy processes to generate the required information. Despite the rise of popularity in using electronic data collection systems to alleviate the problems, sufficient evidence is not available to support the use of electronic data capture tools in interviewer administered data collection processes.Objective: The purpose of this study was to compare data quality parameters in the data collected using mobile electronic and standard paper-based data capture tools in one of health and demographic surveillance sites in northwest Ethiopia Methods: A randomized controlled crossover Healthcare IT-Evaluation was conducted from May 10 to June 3 rd , 2016 in demographic and surveillance site. Twelve interview administrator, as two individuals in six groups (one with a tablet computer and the other with paper-based questionnaire), were assigned in the six towns of the surveillance premises. Data collectors switch data collection method based on computer generated random order. Data was cleaned using Mysql program and transferred to SPSS and R statistical software for analysis. Descriptive and mixed ordinal logistic analyses employed. The qualitative interview audio record was transcribed, and the usability of this open data kit (ODK) based system was assessed using system usability scale (SUS) and interview mapping in the isometric dialogue principles for system usability.Result: From the submitted 1,251 complete records/questionnaires in each tools, 42 % (522) of the paper and pen data capture (PPDC) and 29 % (285) of the electronic data capture (EDC) tool questionnaires had one or more types of data quality errors. The overall error rates were 1.67% and 0.6% for PPDC and EDC respectively. The chances of more errors on PPDC tool were multiplied by 1.015 for each additional question in the interview compared to EDC. The SUS score of the data collectors was 85.6. In qualitative data response mapping, EDC had more positive suitability of task responses with few error tolerance characteristics. Conclusion:EDC possessed significantly better data quality and efficient compared with PPDC, explained in fewer errors, instant data submission, and easy handling. The EDC proved to be a usable data collection tool in the rural study setting. The consistent power source and decent 2 internet connection, standby technical support, and security assurance for the device users suggested by the data collectors before full-fledged the implementation of the system.