Applications for data collection: AppDatCol

Health surveillance or routine health surveys are the main sources of health-related information in developing countries. The need to support the paper process and the recent advanced popularity of mobile devices fortified the development and use of electronic data collection methods in community health and clinical research works. Data collection apps are mobile applications that make it possible to collect data from a smartphone, tablet, or iPad. The main objective of this article is to explore different type of applications easily available for using as a tool for data collection purpose. This article will brief about software’s that are easily available to be customized and can be used for data collection. Mobile data collection apps are becoming integral to secure, reliable, and scalable research. The efficiency and dependability of these mobile survey apps, even in offline settings, open doors to new research possibilities. It begins with the freedom and adaptability of designing research-specific forms that work even in the most challenging environments. Sharing experiences of the barriers and distinct benefits of this technology will help future users to be better informed and allow for the swifter adoption of these and similar technologies. Although any digital form may suffice for the purpose of data gathering, not every data collection system may be used for sensitive, clinical or research data. We believe that Teamscope and CSPro stands out in the mobile data collection landscape and is the best choice for research purposes. No other application combines data encryption, passcode lock, cross-device compatibility with iOS and android, support for both cross sectional and longitudinal studies, like these applications does.

Waiting Time and Patient Satisfaction in a Subspecialty Eye Hospital in Cameroon Using a Mobile Data Collection Kit: Pre and Post Quality Improvement Intervention (Preprint)

BACKGROUND Waiting time could considerably increase the cost to both the clinic and the patient, as well as be a major predictor of the satisfaction of eye care users. Efficiently managing waiting time remains a challenge in hospitals. Waiting time management will become even more crucial in the post-pandemic era. A key consideration when improving waiting time is the involvement of eye care users. This study aimed at improving patient waiting time and satisfaction through the use of Plan-Do-Study-Act quality improvement cycles. OBJECTIVE The study’s objectives were to (1) determine the waiting time and patient satisfaction, (2) measure the association between waiting time and patient satisfaction, and (3) determine the effectiveness of the Plan-Do-Study-Act model in improving waiting time and satisfaction. METHODS This was a pre and post-quality improvement study among patients consulting with the Magrabi ICO Cameroon Eye Institute, aged 19-80 years. We made use of Plan-Do-Study-Act (PDSA) cycles to carry out improvement audits of waiting time and satisfaction over 6 weeks. A mobile data collection kit (ODK) was used for real-time tracking of waiting, service, and idling times at each service point. Subjects were also asked whether or not they were satisfied with waiting time at the point of exit. Data from 25 pre-intervention and 24 post-intervention subjects were analyzed in Stata14. An unpaired t-test was used to assess the statistical significance of observed differences in times pre and post-intervention. Logistic regression was used to examine the association between satisfaction and waiting time. RESULTS Forty-nine subjects were recruited with mean(SD) age 49(15.7) years. The pre-intervention mean(SD) waiting, service, and idling times were 449.6(96.6) minutes, 111.9(47.0) minutes, and 337.7(98.1) minutes respectively. There was no significant association between patient wait time and satisfaction (Odds Ratio=1.0; 95% CI: 0.99 to 1.0, P=.26; Chi2=.25). The use of Plan-Do-Study-Act led to a 14.5% (65.4/449.6) improvement in waiting time (t=2.0, df=47, P=.05) and a non-significant increase in patient satisfaction from 32% (8/25) to 33.3% (8/24) (z=0.1, P=.9). CONCLUSIONS The use of PDSA led to a borderline statistically significant reduction of 65.4 minutes in waiting time over 6 weeks and an insignificant improvement in satisfaction, suggesting that quality improvement efforts have to be done over a considerable period to be able to produce significant changes. The study provides a good basis for quality improvement in limited-resource settings making use of block appointment systems, with comprehensive subspecialty eye care services. We recommend shortening the patient pathway and other measures including a phasic appointment system, automated patient time monitor, robust ticketing, patient pathway supervision, standard triaging, task shifting, doctor consultation planning, patient education, and additional registration staff.

Mobile data collection apps

The expansion of the mobile consumer market in the last decade has resulted in the widespread availability of affordable, multifunctional tablets, and smartphones with a range of uses. Whether for scientific research or conservation practice, these devices provide a means of digital data collection that is an increasingly time- and cost-effective alternative to traditional methods. This chapter discusses recent advances in mobile data collection, especially with cloud storage, including the advantages and limitations of this emerging approach. It will also review current hardware and software options for conservation data collection, focusing on devices and apps with high customisability, and provide an overview of how these systems may be applied in conservation science. As a case study, it will examine the transition from paper to digital data collection at a primate conservation project at the Issa Valley, Tanzania. And finally, it will identify gaps and precautions in current applications of mobile data collection and suggest what lies ahead for digital data collection in conservation.

ODK-X: From A Classic Process To A Smart Data Collection Process

Data collection is one of the first and main phases of the data life cycle. It enables improvements to be made across all phases of the data lifecycle. In this sense, we have proposed a data collection process qualified as Smart. For our smart data collection process, we have adopted the principles of the smart data approach allowing less data to be transmitted to the analysis and storage processes, while maintaining better data quality. In addition, we also used Edge computing since it provides services with faster response and better quality, compared to cloud computing. To experiment this process on mobile data, we propose to extend a mobile data collection software solution and adopt one of the key data collection methods. In this paper, we tested our smart data collection process via the ODK-X software suite and were able to identify the added value of our process compared to the one used by default during collection.

Supporting Medical Staff from Psycho-Oncology with Smart Mobile Devices: Insights into the Development Process and First Results

Cancer is a very distressing disease, not only for the patients themselves, but also for their family members and relatives. Therefore, patients are regularly monitored to decide whether psychological treatment is necessary and applicable. However, such monitoring processes are costly in terms of required staff and time. Mobile data collection is an emerging trend in various domains. The medical and psychological field benefits from such an approach, which enables experts to quickly collect a large amount of individual health data. Mobile data collection applications enable a more holistic view of patients and assist psychologists in taking proper actions. We developed a mobile application, FeelBack, which is designed to support data collection that is based on well-known and approved psychological instruments. A controlled pilot evaluation with 60 participants provides insights into the feasibility of the developed platform and it shows the initial results. 31 of these participants received paper-based questionnaire and 29 followed the digital approach. The results reveal an increase of the overall acceptance by 58.5% in the mean when using a digital screening as compared to the paper-based. We believe that such a platform may significantly improve cancer patients’ and relatives’ psychological treatment, as available data can be used to optimize treatment.

Tailoring Mobile Data Collection for Intervention Research in a Challenging Context: Development and Implementation in the Malakit Study (Preprint)

BACKGROUND An interventional study named Malakit was implemented between 2018 and 2020 to address malaria on gold mining areas in French Guiana, in collaboration with Suriname and Brazil. This innovative intervention relied on the distribution of kits for self-diagnosis and self-treatment to gold miners after training by health mediators, named “facilitators” in the project. OBJECTIVE This paper aims to describe the process by which the information system was designed, developed and implemented to achieve the monitoring and evaluation of the Malakit intervention. METHODS The intervention was implemented in challenging conditions in five cross-border distribution sites which imposed strong logistical constraints for the design of the information system: isolation in the Amazon forest, tropical climate, lack of reliable electricity supply and Internet connection. Additional constraints originated from the interaction of the multicultural players involved in the study. The Malakit information system was developed as a patchwork of existing open-source, commercial services and tools developed in-house. Facilitators collected data from participants using Android tablets with ODK Collect, and sent encrypted form records to Ona when Internet was available. A custom R package (MalakitR) and a dashboard web app were developed to retrieve, decrypt, aggregate, monitor and clean data according to the feedback of facilitators and supervision visits on the field. RESULTS Between April 2018 and March 2020, nine facilitators generated a total of 4,863 form records, corresponding to an average of 202 records per month. Facilitators’ feedback was essential to adapt and improve mobile data collection and monitoring. Few technical issues were reported. The median duration of data capture was five minutes, suggesting that EDC was not overtaking time from participants, and it decreased over the course of the study as facilitators become more experienced. The quality of data collected by facilitators was satisfactory with only 3% of form records requiring correction. CONCLUSIONS The development of the information system for the Malakit project was a source of innovation that mirrored the inventiveness of the intervention itself. Our experience confirms that, even in a challenging environment, it is possible to produce good quality data and evaluate a complex health intervention by carefully adapting tools to field constraints and health mediators’ experience. CLINICALTRIAL ClinicalTrials.gov NCT03695770