Georeferenced and secure mobile health system for large scale data collection in primary care

Joao, H. G.; Rebelo, M.S.; Brentani, Alexandra; Grisi, Sandra; Iwaya, Leonardo Horn; Simplício, Marcos A.; Carvalho, Tereza Cristina Melo de Brito; Gutierrez, Marco Antônio

doi:10.1016/j.ijmedinf.2016.06.013

Cited by 17 publications

(18 citation statements)

References 15 publications

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“…According to the World Health Organization [1], mHealth refers to the deployment and application of mobile technologies for the delivery of public health. mHealth encompasses a wide range of mobile technologies that address people's health care needs such as access to health information, medical advice, health-care services, and medical follow-up [2,3]. Specifically, mHealth views patients as consumers and empowers them to manage their own health [4].…”

Section: Introductionmentioning

confidence: 99%

What Motivates Chinese Young Adults to Use mHealth?

Lee

et al. 2019

Healthcare

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mHealth is one of China’s national strategies that brings affordable, accessible, and convenient health care to its entire population, may they be in cities or rural areas. Although Chinese young adults are among the first to adopt mHealth, the factors influencing Chinese young adults to use mHealth are yet to be studied both empirically and in depth. This study explores the mechanism that determines Chinese young adults’ intention to use mHealth, based on an extended Technology Acceptance Model (TAM). The extended TAM was tested using responses from 486 Chinese young adults. The results showed that perceived usefulness strongly and significantly influenced people’s intention to use mHealth. Additionally, communication effectiveness, health consciousness, and perceived ease of use were found as significant factors influencing people’s intention to use mHealth through perceived usefulness. Distrust was not found to significantly influence people’s intention to use mHealth.

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Section: Introductionmentioning

confidence: 99%

What Motivates Chinese Young Adults to Use mHealth?

Lee

et al. 2019

Healthcare

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“…Mobile health (mHealth) apps for health surveys and surveillance play a crucial role in creating rich data repositories for public health decision-making [1,2]. Apps for health surveys are usually known as mHealth Data Collection Systems (MDCSs), used by Community Health Workers (CHWs), replacing less efficient and less reliable paper-based approaches [3,4]. The CHWs’ main task is to visit families at their homes to provide primary care, but they also carry out surveys, collect the family’s data, and report it to the government.…”

Section: Introductionmentioning

confidence: 99%

“…Each stage of the PIA process builds up on each other, offering not only the risk assessment but also a solid strategy for risk management regarding privacy. In this paper, a PIA is presented using the GeoHealth MDCS [3] as a case study to ground our analysis. As our methodology, the PIA framework proposed by Oetzel et al [17,18] is adopted in this study.…”

Section: Introductionmentioning

confidence: 99%

Mobile Health Systems for Community-Based Primary Care: Identifying Controls and Mitigating Privacy Threats

Iwaya¹,

Fischer‐Hübner²,

Åhlfeldt³

et al. 2019

JMIR Mhealth Uhealth

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Background Community-based primary care focuses on health promotion, awareness raising, and illnesses treatment and prevention in individuals, groups, and communities. Community Health Workers (CHWs) are the leading actors in such programs, helping to bridge the gap between the population and the health system. Many mobile health (mHealth) initiatives have been undertaken to empower CHWs and improve the data collection process in the primary care, replacing archaic paper-based approaches. A special category of mHealth apps, known as mHealth Data Collection Systems (MDCSs), is often used for such tasks. These systems process highly sensitive personal health data of entire communities so that a careful consideration about privacy is paramount for any successful deployment. However, the mHealth literature still lacks methodologically rigorous analyses for privacy and data protection. Objective In this paper, a Privacy Impact Assessment (PIA) for MDCSs is presented, providing a systematic identification and evaluation of potential privacy risks, particularly emphasizing controls and mitigation strategies to handle negative privacy impacts. Methods The privacy analysis follows a systematic methodology for PIAs. As a case study, we adopt the GeoHealth system, a large-scale MDCS used by CHWs in the Family Health Strategy, the Brazilian program for delivering community-based primary care. All the PIA steps were taken on the basis of discussions among the researchers (privacy and security experts). The identification of threats and controls was decided particularly on the basis of literature reviews and working group meetings among the group. Moreover, we also received feedback from specialists in primary care and software developers of other similar MDCSs in Brazil. Results The GeoHealth PIA is based on 8 Privacy Principles and 26 Privacy Targets derived from the European General Data Protection Regulation. Associated with that, 22 threat groups with a total of 97 subthreats and 41 recommended controls were identified. Among the main findings, we observed that privacy principles can be enhanced on existing MDCSs with controls for managing consent, transparency, intervenability, and data minimization. Conclusions Although there has been significant research that deals with data security issues, attention to privacy in its multiple dimensions is still lacking for MDCSs in general. New systems have the opportunity to incorporate privacy and data protection by design. Existing systems will have to address their privacy issues to comply with new and upcoming data protection regulations. However, further research is still needed to identify feasible and cost-effective solutions.

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“…Increasingly, mobile health (mHealth) interventions are grabbing the attention of the public health professionals for their ability to improve the public health situation [10]. mHealth refers to the application of portable hand-held devices and communication capabilities in the health care programs [11]. During the past decade mHealth has been continuously evolving within the eHealth domain, where it has offered great potential to overcome many traditional barriers in delivering care and reducing the distance, time and cost [12].…”

Section: Introductionmentioning

confidence: 99%

ODK Scan: Digitizing Data Collection and Impacting Data Management Processes in the Tuberculosis Control Program of Pakistan

Ali¹,

Powers²,

Beorse³

et al. 2016

Preprint

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Abstract:The present grievous situation of the tuberculosis disease can be improved by efficient case management and timely follow-up evaluations. With the advent of digital technology this can be achieved by quick summarization of the patient-centric data. The aim of our study was to assess the effectiveness of the ODK Scan paper-to-digital system during testing period of three months. A sequential, explanatory mixed-method research approach was employed to elucidate technology use. Training, smartphones, application and 3G enabled SIMs were provided to the four field workers. At the beginning, baseline measures of the data management aspects were recorded and compared with endline measures to see the impact of ODK Scan. Additionally, at the end, users' feedback was collected regarding app usability, user interface design and workflow changes. 122 patients' records were retrieved from the server and analysed for quality. It was found that ODK Scan recognized 99.2% of multiple choice bubble responses and 79.4% of numerical digit responses correctly. However, the overall quality of the digital data was decreased in comparison to manually entered data. Using ODK Scan, a significant time reduction is observed in data aggregation and data transfer activities, however, data verification and form filling activities took more time. Interviews revealed that field workers saw value in using ODK Scan, however, they were more concerned about the time consuming aspects of the use of ODK Scan. Therefore, it is concluded that minimal disturbance in the existing workflow, continuous feedback and value additions are the important considerations for the implementing organization to ensure technology adoption and workflow improvements.

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Georeferenced and secure mobile health system for large scale data collection in primary care

Cited by 17 publications

References 15 publications

What Motivates Chinese Young Adults to Use mHealth?

What Motivates Chinese Young Adults to Use mHealth?

Mobile Health Systems for Community-Based Primary Care: Identifying Controls and Mitigating Privacy Threats

ODK Scan: Digitizing Data Collection and Impacting Data Management Processes in the Tuberculosis Control Program of Pakistan

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