Entity‐Coded Neutral‐File Format for Integrating Data Collectors

Introduction:Portable handheld computers and electronic data management systems have been used for national surveys in many high-income countries, however their use in developing countries has been challenging due to varying geographical, economic, climatic, political and cultural environments. In order to monitor and measure global adult tobacco use, the World Health Organization and the US Centers for Disease Control and Prevention initiated the Global Adult Tobacco Survey, a nationally representative household survey of adults, 15 years of age or older, using a standard core questionnaire, sample design, and data collection and management procedures. The Survey has been conducted in 14 low- and middle-income countries, using an electronic data collection and management system. This paper describes implementation of the electronic data collection system and associated findings.Methods:The Survey was based on a comprehensive data management protocol, to enable standardized, globally comparable high quality data collection and management. It included adaptation to specific country needs, selection of appropriate handheld hardware devices, use of open source software, and building country capacity and provide technical support.Results:In its first phase, the Global Adult Tobacco Survey was successfully conducted between 2008 and 2010, using an electronic data collection and management system for interviews in 302,800 households in 14 countries. More than 2,644 handheld computers were fielded and over 2,634 fieldworkers, supervisors and monitors were trained to use them. Questionnaires were developed and programmed in 38 languages and scripts. The global hardware failure rate was < 1% and data loss was almost 0%.Conclusion:Electronic data collection and management systems can be used effectively for conducting nationally representative surveys, particularly in low- and middle-income countries, irrespective of geographical, climatic, political and cultural environments, and capacity-building at the country level is an important vehicle for Health System Strengthening.

BackgroundUse of electronic data collection, management and analysis tools to support outbreak response is limited, especially in low income countries. This can hamper timely decision-making during outbreak response. Identifying available tools and assessing their functions in the context of outbreak response would support appropriate selection and use, and likely more timely data-driven decision-making during outbreaks.MethodsWe conducted a systematic review and a stakeholder survey of the Global Outbreak Alert and Response Network and other partners to identify and describe the use of, and technical characteristics of, electronic data tools used for outbreak response in low- and middle-income countries. Databases included were MEDLINE, EMBASE, Global Health, Web of Science and CINAHL with publications related to tools for outbreak response included from January 2010–May 2020. Software tool websites of identified tools were also reviewed. Inclusion and exclusion criteria were applied and counts, and proportions of data obtained from the review or stakeholder survey were calculated.ResultsWe identified 75 electronic tools including for data collection (33/75), management (13/75) and analysis (49/75) based on data from the review and survey. Twenty-eight tools integrated all three functionalities upon collection of additional information from the tool developer websites. The majority were open source, capable of offline data collection and data visualisation. EpiInfo, KoBoCollect and Open Data Kit had the broadest use, including for health promotion, infection prevention and control, and surveillance data capture. Survey participants highlighted harmonisation of data tools as a key challenge in outbreaks and the need for preparedness through training front-line responders on data tools. In partnership with the Global Health Network, we created an online interactive decision-making tool using data derived from the survey and review.ConclusionsMany electronic tools are available for data -collection, −management and -analysis in outbreak response, but appropriate tool selection depends on knowledge of tools’ functionalities and capabilities. The online decision-making tool created to assist selection of the most appropriate tool(s) for outbreak response helps by matching requirements with functionality. Applying the tool together with harmonisation of data formats, and training of front-line responders outside of epidemic periods can support more timely data-driven decision making in outbreaks.

Data Management for Quality Improvement: How to Collect and Manage Data.

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.

BackgroundLarge cross-sectional household surveys are common for measuring indicators of neglected tropical disease control programs. As an alternative to standard paper-based data collection, we utilized novel paperless technology to collect data electronically from over 12,000 households in Ethiopia.MethodologyWe conducted a needs assessment to design an Android-based electronic data collection and management system. We then evaluated the system by reporting results of a pilot trial and from comparisons of two, large-scale surveys; one with traditional paper questionnaires and the other with tablet computers, including accuracy, person-time days, and costs incurred.Principle FindingsThe electronic data collection system met core functions in household surveys and overcame constraints identified in the needs assessment. Pilot data recorders took 264 (standard deviation (SD) 152 sec) and 260 sec (SD 122 sec) per person registered to complete household surveys using paper and tablets, respectively (P = 0.77). Data recorders felt a lack of connection with the interviewee during the first days using electronic devices, but preferred to collect data electronically in future surveys. Electronic data collection saved time by giving results immediately, obviating the need for double data entry and cross-correcting. The proportion of identified data entry errors in disease classification did not differ between the two data collection methods. Geographic coordinates collected using the tablets were more accurate than coordinates transcribed on a paper form. Costs of the equipment required for electronic data collection was approximately the same cost incurred for data entry of questionnaires, whereas repeated use of the electronic equipment may increase cost savings.Conclusions/SignificanceConducting a needs assessment and pilot testing allowed the design to specifically match the functionality required for surveys. Electronic data collection using an Android-based technology was suitable for a large-scale health survey, saved time, provided more accurate geo-coordinates, and was preferred by recorders over standard paper-based questionnaires.

Background and Purpose: National AIDS indicator surveys generate large volumes of specimens and associated data, necessitating the preparedness of systems and processes in the reference laboratory. Specimen management is often more routine while management of data becomes secondary. However, complete and accurate laboratory results are essential to ensure data quality. Robust laboratory informatics solutions for managing data are needed to increase data accuracy and reduce turnaround time. Methods: During the 2012 Kenya AIDS Indicator Survey (KAIS), 62,435 samples were collected from respondent households and transferred to the National HIV Reference Laboratory (NHRL) for testing. The Association of Public Health Laboratories (APHL) collaborated with the Kenya Ministry of Health in laboratory process improvement, laboratory information system design and governance focusing on preparing the laboratory to manage the large volume of samples and associated data. This task included developing detailed work flow and data flow diagrams for the entire process, from field specimen and data collection, to receipt, testing and storage. These diagrams were used for training and for reference during the survey process. They were also used to define KAIS 2012 requirements for the existing laboratory information management system (LIMS). Laboratory processes were documented and matched up with the configurations developed in the LIMS. The team also designed specimen tracking and receiving forms based on KAIS 2012 protocol and LIMS requirements. They matched these forms with data elements in the LIMS for data harmonization. The KAIS survey team developed contingency plans for every KAIS 2012 laboratory activity to ensure NHRL implemented uniform procedures when normal operations did not occur. These plans focused on the management and standardization of human resources as appropriate personnel and associated procedures are integral to optimal information system performance. The team defined staff roles and responsibilities and developed procedures for managing forms, specimens and electronic data, including quality and use aligned with survey protocols. During the project, the KAIS team provided knowledge transfer to NHRL to ensure the development of local capacity and long-term sustainability. Results: Due to the informatics processes in place, NHRL was able to share laboratory test results from KAIS with other stakeholders within a day of being received. NHRL was also able to immediately access data on storage locations of all samples that were previously managed using paper systems – a significant challenge before the project. Visualization, mapping and contingency planning for each step of the process ensured that laboratory support to KAIS 2012 was timely, and of high quality, regardless of digressions from standard operating procedures caused by unexpected circumstances. While contingency plans are not routine, they can be critical to the success of the survey. Conclusions: Preparedness is key. Finalization of the survey protocol three months before the pilot, training and piloting all aspects of laboratory services a month prior to the survey ensured informatics solutions are appropriate and robust. Engaging all stakeholders -- particularly survey, laboratory and LIMS staff -- in laboratory informatics solutions planning, implementation, monitoring is critical to developing the most efficient systems and processes for a large national sero survey. During execution, constant and ongoing communication is essential to adapt to the evolving needs of the survey and its participants. Keywords: LIMS/LIS, Informatics, survey

BackgroundA population-level survey (PLS) is an essential and standard method used in public health research that supports the quantification of sociodemographic events, public health policy development, and intervention designs. Data collection mechanisms in PLS seem to be a significant determinant in avoiding mistakes. Using electronic devices such as smartphones and tablet computers improves the quality and cost-effectiveness of public health surveys. However, there is a lack of systematic evidence to show the potential impact of electronic data collection tools on data quality and cost reduction in interviewer-administered surveys compared with the standard paper-based data collection system.ObjectiveThis systematic review aims to evaluate the impact of the interviewer-administered electronic data collection methods on data quality and cost reduction in PLS compared with traditional methods.MethodsWe conducted a systematic search of MEDLINE, CINAHL, PsycINFO, the Web of Science, EconLit, Cochrane CENTRAL, and CDSR to identify relevant studies from 2008 to 2018. We included randomized and nonrandomized studies that examined data quality and cost reduction outcomes, as well as usability, user experience, and usage parameters. In total, 2 independent authors screened the title and abstract, and extracted data from selected papers. A third author mediated any disagreements. The review authors used EndNote for deduplication and Rayyan for screening.ResultsOur search produced 3817 papers. After deduplication, we screened 2533 papers, and 14 fulfilled the inclusion criteria. None of the studies were randomized controlled trials; most had a quasi-experimental design, for example, comparative experimental evaluation studies nested on other ongoing cross-sectional surveys. A total of 4 comparative evaluations, 2 pre-post intervention comparative evaluations, 2 retrospective comparative evaluations, and 4 one-arm noncomparative studies were included. Meta-analysis was not possible because of the heterogeneity in study designs, types, study settings, and level of outcome measurements. Individual paper synthesis showed that electronic data collection systems provided good quality data and delivered faster compared with paper-based data collection systems. Only 2 studies linked cost and data quality outcomes to describe the cost-effectiveness of electronic data collection systems. Field data collectors reported that an electronic data collection system was a feasible, acceptable, and preferable tool for their work. Onsite data error prevention, fast data submission, and easy-to-handle devices were the comparative advantages offered by electronic data collection systems. Challenges during implementation included technical difficulties, accidental data loss, device theft, security concerns, power surges, and internet connection problems.ConclusionsAlthough evidence exists of the comparative advantages of electronic data collection compared with paper-based methods, the included studies were not methodologically rigorous enough to combine. More rigorous studies are needed to compare paper and electronic data collection systems in public health surveys considering data quality, work efficiency, and cost reduction.International Registered Report Identifier (IRRID)RR2-10.2196/10678

With rapid development of modern society, the requirements for high precise temperature and humidity is growing rapidly, for example production, storage, transportation, environment etc., these all have strict requirements for temperature and humidity testing, so the real-time monitoring of temperature and humidity and alarm prompt is extremely important. In this paper, a temperature and humidity automatic alarm recorder is designed, which uses stm32 as control center, combines the technique of sensor technology, GSM technology and serial port to finish button control measurement of temperature and humidity or two road all the way. In the study, the system's hardware and software design are introduced in detail and testing results show that the system can realize the function of the temperature and humidity measurement record and alarm.

Effective data management plays a crucial role in optimizing the performance of educational institutions. This study aims to explore the implementation of a technology-based data management system for writing classes in the Student Preaching Institution of the State Islamic Institute (IAIN) Cirebon. The objective is to improve the efficiency and effectiveness of data collection, storage, and analysis, ultimately enhancing the overall quality of the writing classes. This research utilizes a mixed-methods approach, incorporating qualitative and quantitative data collection techniques. Firstly, a comprehensive review of the existing data management practices is conducted to identify the limitations and challenges faced by the institution. Secondly, a technology-based data management system is developed, integrating modern tools and techniques to streamline the data collection process. The system is designed to facilitate easy data entry, retrieval, and analysis, ensuring accurate and reliable information for decision-making purposes. The implementation of the technology-based data management system is evaluated through a series of case studies and interviews with relevant stakeholders. The findings reveal several significant benefits, including increased efficiency in data management, improved accuracy in reporting, and enhanced collaboration among the stakeholders involved in the writing classes. Moreover, the system enables the institution to track the progress of individual students, identify their strengths and weaknesses, and provide targeted interventions to support their learning and development. The results of this study contribute to the growing body of literature on the application of technology in educational institutions. The findings highlight the importance of leveraging technology to optimize data management processes, particularly in the context of writing classes in the Student Preaching Institution of IAIN Cirebon. The insights gained from this research can serve as a valuable resource for other educational institutions seeking to enhance their data management practices and improve the overall quality of their programs.

Combining the method of integration development of GIS,MIS and multi-technology, a GIS-based forest field management information system is developed aiming at solving the deficiency of traditional forest field management. The paper analyses and designs the system objectives, system hardware and software environment, system construction and system function etc. At the same time, several key technology problems such as the organization of the system data, the system integration of MapInfo and Delphi and the security design of the system are pointed out. At last, an application example is given.

Adding Centralized Electronic Patient-Reported Outcome Data Collection to an Established International Clinical Outcomes Registry

Validation of Electronic Systems to Collect Patient-Reported Outcome (PRO) Data—Recommendations for Clinical Trial Teams: Report of the ISPOR ePRO Systems Validation Good Research Practices Task Force

Background A production line is the basic unit of smart factories and smart manufacturing. However, owing to the development of the industrial Internet of Things, sensors, and other technologies, more data are being collected, leading to a data explosion, and the heterogeneous nature of multiple sources makes it difficult to manage data in a unified manner. Methods A production line data collection, storage, and management system based on cloud-fog-edge computing collaboration and a digital twin was designed. Multi-source heterogeneous data were collected and transmitted based on the OPC UA, and an information model of the production line was established. Modules for data mapping, publishing, and receiving were developed to achieve unified data collection and transmission. The data storage and management platform was constructed by front-end and back-end separation technologies. Results The developed data collection and management system was verified for functionality and performance on a digital twin production line. Functional tests show that the system has the functions of data acquisition and transmission, device addition and viewing, device data querying and downloading, data and model visualization, and user rights setting. The average time for edge data collection and transmission is 183.6ms. The average response time of the cloud for fog requests is less than 1s. This shows that the system can satisfy the real-time requirements of a digital twin production line. Conclusions The proposed system is real-time and stable, providing support for big data and virtual-reality interaction in digital twins.

Hardware techniques and environments underwent significant transformations in the field of information technology, represented by high-performance processors and hardware accelerators characterized by abundant heterogeneous parallelism, nonvolatile memory with hybrid storage hierarchies, and RDMA-enabled high-speed network. Recent hardware trends in these areas deeply affect data management and analysis applications. In this paper, we first introduce the development trend of the new hardware in computation, storage, and network dimensions. Then, the related research techniques which affect the upper data management system design are reviewed. Finally, challenges and opportunities are addressed for the key technologies of data management and analysis in new hardware environments.

There is a risk of introducing survey error at every stage of any study involving a survey: design, data collection, processing, and analysis. Effectively managing the survey sample from the time of sample selection through the survey lifecycle is essential to producing highquality data on schedule and within budget. Managing the survey lifecycle using software systems that are not fully integrated can result in error and cost inefficiencies. The development of an integrated data collection and management system that supports monitoring of survey error has the potential to reduce errors and improve operational efficiencies. This system, referred to as Nirvana, uses a standardized database, protocol, and terminology. It integrates case status and history information across modes of data collection and tracing as well as sample and contact information. Nirvana also centralizes questionnaire development, quality monitoring, and support for real-time survey management decisions.