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

Abstract

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.

Cyber Risk in Health Facilities: A Systematic Literature Review

“…Although the results highlight the use of the most important reactive risk assessment tools in the healthcare sector such as Incident Reporting [80,81,88], Root Cause Analysis [31,69], and Failure Mode and Effect Analysis [16], it describes not enough studies useful to investigate this topic. Furthermore, the results describe the main cyber risks applied at the management of electronic medical records [33,41,62], electronic health records [26,55,71,89], telemedicine devices [16], and mobile health [38,40,62,83]. The literature analyzed calls for studies to other subject areas such as Business, Management and Accounting; Social Science; and Mathematics.…”

Security and Privacy of mHealth Applications: A Scoping Review

“…Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2020.2999934, IEEE Access Nurgalieva et al: Security and Privacy Evaluation Review and the GeoHealth Privacy Impact Assessment (PIA) framework [90]. Table 2 provides information on the validation of both new and existing evaluation frameworks.…”

“…Only 40% (4/10) of frameworks were specific to the evaluation of security and/or privacy of mHealth applications. While one study focused solely on the privacy of mHealth apps by applying the Privacy Impact Assessment (PIA) framework [90], the remainder targeted both security and privacy at the same time.…”

“…Intermediate stage, testing. At the intermediate stage between the creation of mHealth apps and their adoption ( Table 3, yellow colour), studies address the following objectives: systematic identification and evaluation of potential privacy risks in mHealth Data Collection Systems [90], improving the quality of mHealth apps [91], and e-mental health assessment at organisational level or as a self-assessment for app developers [84]. Target users here are more mixed: app developers, health care providers, mental health advocates, app users, policy makers, and researchers.…”

“…The final type of framework consists of checklists or sets of evaluation criteria or principles [5], [87]- [90]. More specifically, some of the frameworks go into more detail on evaluation suggestions and, in addition to more general objectives and principles, recommend evaluation tools and guidelines:…”