Semi-automated data collection from electronic health records in a stroke unit in Brazil

ABSTRACT Background: There is a high demand for stroke patient data in the public health systems of middle and low-income countries. Objective: To develop a stroke databank for integrating clinical or functional data and benchmarks from stroke patients. Methods: This was an observational, cross-sectional, prospective study. A tool was developed to collect all clinical data during hospitalizations due to stroke, using an electronic editor of structured forms that was integrated with electronic medical records. Validation of fields in the electronic editor was programmed using a structured query language (SQL). To store the results from SQL, a virtual table was created and programmed to update daily. To develop an interface between the data and user, the Embarcadero Delphi software and the DevExpress component were used to generate the information displayed on the screen. The data were extracted from the fields of the form and also from cross-referencing of other information from the computerized system, including patients who were admitted to the stroke unit. Results: The database was created and integrated with the hospital electronic system, thus allowing daily data collection. Quality indicators (benchmarks) were created in the database for the system to track and perform decision-making in conjunction with healthcare service managers, which resulted in improved processes and patient care after a stroke. An intelligent portal was created, in which the information referring to the patients was accessible. Conclusions: Based on semi-automated data collection, it was possible to create a dynamic and optimized Brazilian stroke databank.

Absent Data: Engagements with Absence in a Twitter Collection Process

This paper considers the ways in which silences and absences are a central part of research that relies on automated data collection from social media or the internet. In recent years, automated data collection driven or supported research methods have gained popularity within the social sciences and humanities. With this increase in popularity, it becomes ever more pertinent to consider how to engage with digital data, and how both engagement and data are situated, messy, and contingent. Based on experiences with “missing” data, this paper mobilizes the framework of hauntology to make sense of what relationships may be built with missing data and how silences haunt research practices. Ultimately, we argue that it is possible to reimagine absent data not as a limitation but as an invitation to reflect on and establish new methods for working with automated data collections.

A database about the Members of European Parliament: Contributions and limitations of automated data collection in the study of European political elites

Over the past 25 years, a field of research concerning the careers of Members of the European Parliament (MEPs) has developed. Drawing on a massive amount of accessible open data, we have assembled an updated database including all MEPs from 1979 to September 2019. In this note, we describe the data collection processes and the construction of the database. Then, we propose an application concerning the turnover at the EP following the 2019 European elections. The longitudinal perspective provided by the database allows us to describe this turnover, which is important, but varies greatly according to nationality and political group, and does not fundamentally alter the division of parliamentary power. Finally, we identify some limitations: the lack of data in MEP profiles and difficulties both in the comparison between people from 27 countries and the comparison over a long period (1979–2019). As a result, the article shows that automated data collection can be very useful. However, in the case of individuals, as MEPs, it should be seen as a complementary source to other sources.

Automated and Controlled Data Collection Using Industrial IoT System for Smart Maintenance

Maintenance 4.0 is a concept that involves the use of IIoT (Industrial Internet of Things) technology to connect maintenance objects, which enables remote data collection, information exchange, analysis and potential improvement in productivity and efficiency, as well as planning maintenance activities. The purpose of this paper is to present the development of the Industrial Internet of Things data collection system, which relies on Tinkerforge IoT modules, that enables automated data collection alongside control of sensor and data collection parameters. To evaluate the ability of the system, an experiment was conducted where two equipment states were simulated using a rotational equipment failure simulator. The experiment determined that the presented IIoT system had successfully gathered information and that there is a clear distinction in acceleration patterns when simulating two different equipment states.