GeoHealth: A Georeferenced System for Health Data Analysis in Primary Care

G., J. H.; Rebelo, M.S.; Brentani, Alexandra; Grisi, Sandra; Gutierrez, Marco Antônio

doi:10.1109/tla.2012.6142483

Cited by 11 publications

(2 citation statements)

References 4 publications

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“…The GeoHealth has been the target of many studies over the last years, so that further information can be found in the original material [3,4,8,29], as well as in a comprehensive description in Multimedia Appendix 1 [40,41]. For the readers’ convenience, the data flow diagram presented in Figure 3 shows how personal information is handled by the different subprocesses.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, the privacy analysis is particularly grounded on the GeoHealth system. GeoHealth has been targeted in various scientific publications over the years, including work about the design process [8,29], large-scale deployment [3], and CHWs’ experience with the technology [4], which enables us to perform the PIA on the basis of published material, as well as previous first-hand experience with the system.…”

Section: Introductionmentioning

confidence: 99%

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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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