Aeetes: An App Generator for Sustainable and Secure Health Data Collection
Background and Purpose: Aeetes represents an approach to patient data collection in LMIC that is intended to be scalable, sustainable and secure.
Methods: Existing approaches to data collection are typically either personal-computer-based or enterprise-based. Aeetes occupies a third space, that of peer-to-peer devices. It is intended to occupy a spot between the PC-based and enterprise-based approaches, addressing issues that make these approaches a poor match with some deployment scenarios.
Results: The Aeetes approach is characterized by a compiler-based approach to data collection app generation: A new data collection app is generated from an input specification of the data to be collected and the user interface for data collection. The benefit of this is greater software reliability, since the data model is made explicit in the code, and eventually greater security through analysis of the code for information flows. Aeetes also takes measures to protect patient confidentiality against loss of devices.
Conclusions: Aeetes represents an approach to moving away from PC-based approaches to data collection with poor security characteristics, to an approach that is more reliable and secure, while also being sustainable. It is hoped that some of the approach taken by Aeetes may eventually influence other systems such as OpenMRS.
Keywords: Health Impact Assessment, Public Health Informatics, Health Information Management, Software Design, Computer Security, Database Management Systems
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