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BackgroundPeriodic demographic health surveillance and surveys are the main sources of health information in developing countries. Conducting a survey 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 (EDC) tools in interviewer-administered data collection processes. ObjectiveThis study aimed to compare data quality parameters in the data collected using mobile electronic and standard paper-based data capture tools in one of the health and demographic surveillance sites in northwest Ethiopia. MethodsA randomized controlled crossover health care information technology evaluation was conducted from May 10, 2016, to June 3, 2016, in a demographic and surveillance site. A total of 12 interviewers, as 2 individuals (one of them with a tablet computer and the other with a paper-based questionnaire) in 6 groups were assigned in the 6 towns of the surveillance premises. Data collectors switched the data collection method based on computer-generated random order. Data were cleaned using a MySQL program and transferred to SPSS (IBM SPSS Statistics for Windows, Version 24.0) and R statistical software (R version 3.4.3, the R Foundation for Statistical Computing Platform) for analysis. Descriptive and mixed ordinal logistic analyses were employed. The qualitative interview audio record from the system users was transcribed, coded, categorized, and linked to the International Organization for Standardization 9241-part 10 dialogue principles for system usability. The usability of this open data kit–based system was assessed using quantitative System Usability Scale (SUS) and matching of qualitative data with the isometric dialogue principles. ResultsFrom the submitted 1246 complete records of questionnaires in each tool, 41.89% (522/1246) of the paper and pen data capture (PPDC) and 30.89% (385/1246) of the EDC tool questionnaires had one or more types of data quality errors. The overall error rates were 1.67% and 0.60% for PPDC and EDC, respectively. The chances of more errors on the PPDC tool were multiplied by 1.015 for each additional question in the interview compared with EDC. The SUS score of the data collectors was 85.6. In the qualitative data response mapping, EDC had more positive suitability of task responses with few error tolerance characteristics. ConclusionsEDC possessed significantly better data quality and efficiency compared with PPDC, explained with fewer errors, instant data submission, and easy handling. The EDC proved to be a usable data collection tool in the rural study setting. Implementation organization needs to consider consistent power source, decent internet connection, standby technical support, and security assurance for the mobile device users for planning full-fledged implementation and integration of the system in the surveillance site.

Introduction: The configuration of electronic data capture (EDC) systems has a relevant impact on data quality in studies and patient registries. The objective was to develop a method to visualise the configuration of an EDC system to check the completeness and correctness of the data definition and rules. Methods: Step 1: transformation of the EDC data model into a graphical model, step 2: Checking the completeness and consistency of the data model, step 3: correction of identified findings. This process model was evaluated on the patient registry EpiReg. Results: Using the graphical visualisation as a basis, 21 problems in the EDC configuration were identified, discussed with an interdisciplinary team, and corrected. Conclusion: The tested methodological approach enables an improvement in data quality by optimising the underlying EDC configuration. [ABSTRACT FROM AUTHOR]

The ethical implications and regulatory requirements of AI applications and decision support systems are generally the subjects of interdisciplinary research. Case studies are a suitable means to prepare AI applications and clinical decision support systems for research. This paper proposes an approach that describes a procedure model and a categorization of the contents of cases for socio-technical systems. The developed methodology was applied to three cases and serve the researchers in the DESIREE research project as a basis for qualitative research and for ethical, social, and regulatory analyses. [ABSTRACT FROM AUTHOR]

The growing number of genes identified in relation to epilepsy represents a major breakthrough in diagnosis and treatment, but experts face the challenge of efficiently accessing and consolidating the vast amount of genetic data available. Therefore, we present the process of transforming data from different sources and formats into an Entity-Attribute-Value (EAV) model database. Combined with the use of standard coding systems, this approach will provide a scalable and adaptable database to present the data in a comprehensive way to experts via a dashboard. [ABSTRACT FROM AUTHOR]