Flexible Data Management on Mobile Systems

Author(s):  
Sharath Chandrashekhara
2019 ◽  
Vol 19 (S1) ◽  
Author(s):  
Yages Singh ◽  
Debra Jackson ◽  
Sanjana Bhardwaj ◽  
Natasha Titus ◽  
Ameena Goga

Abstract Background Although the use of technology viz. mobile phones, personalised digital assistants, smartphones, notebook and tablets to monitor health and health care (mHealth) is mushrooming, only small, localised studies have described their use as a data collection tool. This paper describes the complexity, functionality and feasibility of mHealth for large scale surveillance at national and sub-national levels in South Africa, a high HIV-prevalence setting. Methods In 2010, 2011–12 and 2012–13 three nationally representative surveys were conducted amongst infants attending 580 facilities across all 51 districts, within all nine provinces of South Africa, to monitor the effectiveness of the programme to prevent mother-to-child transmission of HIV (PMTCT). In all three surveys a technical protocol and iterative system for mobile data collection was developed. In 2012–13 the system included automated folders to store information about upcoming interviews. Paper questionnaires were used as a back-up, in case of mHealth failure. These included written instructions per question on limits, skips and compulsory questions. Data collectors were trained on both systems. Results In the 2010, 2011–12 and 2012–2013 surveys respectively, data from 10,554, 10,071, and 10,536 interviews, and approximately 186 variables per survey were successfully uploaded to 151 mobile phones collecting data from 580 health facilities in 51 districts, across all nine provinces of South Africa. A technician, costing approximately U$D20 000 p.a. was appointed to support field-based staff. Two percent of data were gathered using paper- questionnaires. The time needed for mHealth interviews was approximately 1,5 times less than the time needed for paper questionnaires 30–45 min versus approximately 120 min (including 60–70 min for the interview with an additional 45 min for data capture). In 2012–13, 1172 data errors were identified via the web-based console. There was a four-week delay in resolving data errors from paper-based surveys compared with a 3-day turnaround time following direct capture on mobile phones. Conclusion Our experiences demonstrate the feasibility of using mHealth during large-scale national surveys, in the presence of a supportive data management team. mHealth systems reduced data collection time by almost 1.5 times, thus reduced data collector costs and time needed for data management.


2014 ◽  
Vol 63 (10) ◽  
pp. 2549-2562 ◽  
Author(s):  
Mario Fanelli ◽  
Luca Foschini ◽  
Antonio Corradi ◽  
Azzedine Boukerche

1997 ◽  
Vol 36 (02) ◽  
pp. 79-81
Author(s):  
V. Leroy ◽  
S. Maurice-Tison ◽  
B. Le Blanc ◽  
R. Salamon

Abstract:The increased use of computers is a response to the considerable growth in information in all fields of activities. Related to this, in the field of medicine a new component appeared about 40 years ago: Medical Informatics. Its goals are to assist health care professionals in the choice of data to manage and in the choice of applications of such data. These possibilities for data management must be well understood and, related to this, two major dangers must be emphasized. One concerns data security, and the other concerns the processing of these data. This paper discusses these items and warns of the inappropriate use of medical informatics.


1980 ◽  
Vol 19 (01) ◽  
pp. 37-41
Author(s):  
R. F. Woolson ◽  
M. T. Tsuang ◽  
L. R. Urban

We are now conducting a forty-year follow-up and family study of 200 schizophrenics, 325 manic-depressives and 160 surgical controls. This study began in 1973 and has continued to the present date. Numerous data handling and data management decisions were made in the course of collecting the data for the project. In this report some of the practical difficulties in the data handling and computer management of such large and bulky data sets are enumerated.


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