scholarly journals Implementing Real-Time Data Suicide Surveillance Systems

Crisis ◽  
2021 ◽  
Vol 42 (5) ◽  
pp. 321-327
Author(s):  
Anna Baran ◽  
Rebekka Gerstner ◽  
Michiko Ueda ◽  
Agnieszka Gmitrowicz
Keyword(s):  
Real Time ◽  
Surveillance Systems ◽  
Time Data ◽  
Real Time Data

scholarly journals Mobile technologies for disease surveillance in humans and animals

2014 ◽  
Vol 81 (2) ◽  
Author(s):  
Mpoki Mwabukusi ◽  
Esron D. Karimuribo ◽  
Mark M. Rweyemamu ◽  
Eric Beda
Keyword(s):  
Developing Countries ◽  
Human Health ◽  
Real Time ◽  
Disease Surveillance ◽  
Open Data ◽  
Weather Conditions ◽  
Mobile Technologies ◽  
Surveillance Systems ◽  
Time Data ◽  
Real Time Data

A paper-based disease reporting system has been associated with a number of challenges. These include difficulties to submit hard copies of the disease surveillance forms because of poor road infrastructure, weather conditions or challenging terrain, particularly in the developing countries. The system demands re-entry of the data at data processing and analysis points, thus making it prone to introduction of errors during this process. All these challenges contribute to delayed acquisition, processing and response to disease events occurring in remote hard to reach areas. Our study piloted the use of mobile phones in order to transmit near to real-time data from remote districts in Tanzania (Ngorongoro and Ngara), Burundi (Muyinga) and Zambia (Kazungula and Sesheke). Two technologies namely, digital and short messaging services were used to capture and transmit disease event data in the animal and human health sectors in the study areas based on a server–client model. Smart phones running the Android operating system (minimum required version: Android 1.6), and which supported open source application, Epicollect, as well as the Open Data Kit application, were used in the study. These phones allowed collection of geo-tagged data, with the opportunity of including static and moving images related to disease events. The project supported routine disease surveillance systems in the ministries responsible for animal and human health in Burundi, Tanzania and Zambia, as well as data collection for researchers at the Sokoine University of Agriculture, Tanzania. During the project implementation period between 2011 and 2013, a total number of 1651 diseases event-related forms were submitted, which allowed reporters to include GPS coordinates and photographs related to the events captured. It was concluded that the new technology-based surveillance system is useful in providing near to real-time data, with potential for enhancing timely response in rural remote areas of Africa. We recommended adoption of the proven technologies to improve disease surveillance, particularly in the developing countries.

Big data analytics—lessons learnt from global E&P operators

2015 ◽  
Vol 55 (2) ◽  
pp. 409
Author(s):  
Kevin Kalish
Keyword(s):  
Real Time ◽  
Predictive Models ◽  
Big Data Analytics ◽  
Surveillance Systems ◽  
Well Performance ◽  
Time Data ◽  
Quality Of Data ◽  
Real Time Data ◽  
Exploration And Production ◽  
Intelligent Wells

Exploration and production operators are striving to attain the hidden knowledge in their key asset: data. Data and real-time data from intelligent wells supplement historical interpretations and generated datasets. It is paramount to gain insight from these multiple datasets, which enable engineers and stakeholders to make faster and more accurate decisions under uncertainty. By combining the traditional deterministic and interpretive workflows with a data-driven probabilistic set of analyses, it is possible to predict events that result in poor reservoir or well performance or facility failures. By building predictive models based on cleansed historical data and by analysing them in real-time data streams, it is now feasible to optimise production. Controlling costs and ensuring efficient processes that impact positively on health, safety and environment and resource usage are key benefits that fall out of analytical methodologies. This extended abstract provides recent examples of global exploration and production operators using an analytics oilfield framework to: improve the quality of data by integrating relevant sources from multiple monitoring and surveillance systems across all geology, geophysics and reservoir engineering (GGRE) disciplines into a unified view; predict unplanned events so that mitigation can be planned in advance; use predictive models to avoid frequent and unnecessary preventive maintenance that interferes with production schedules, strains maintenance staff and increases costs; and, increase decision support across disparate upstream disciplines by using data mining to create accurate predictive and descriptive models.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data

MHA releases largest real‐time data from MH help‐seekers

2021 ◽  
Vol 31 (6) ◽  
pp. 7-7
Author(s):  
Valerie A. Canady
Keyword(s):  
Real Time ◽  
Time Data ◽  
Real Time Data

Why Ecological Momentary Assessment Surveys Go Incomplete?: When It Happens and How It Impacts Data

2020 ◽  
Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  
Keyword(s):  
Real Time ◽  
Self Report ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
The Impact ◽  
Momentary Assessment

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

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