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New types of patient health records aim to help physicians shift from a medical practice, often based on their personal experience, towards one of evidence based medicine, thus improving the communication among patients and care providers and increasing the availability of personal medical information. These new records, allowing patients and care providers to share medical data and clinical information, and access them whenever they need, can be considered enabling Ambient Assisted Living technologies. Furthermore, new personal disease monitoring tools support specialists in their tasks, as an example allowing acquisition, transmission and analysis of medical images. The growing interest around these new technologies poses serious questions regarding data integrity and transaction security. The huge amount of sensitive data stored in these new records surely attracts the interest of malicious hackers, therefore it is necessary to guarantee the integrity and the maximum security of servers and transactions. Blockchain technology can be an important turning point in the development of personal health records. This paper discusses some issues regarding the management and protection of health data exchanged through new medical or diagnostic devices.

Current medical practice for determining hemoglobin concentration (which is especially important for anemic patients in need of blood transfusion) involves frequent blood tests. In this work, we propose an alternative, non-invasive approach to hemoglobin estimation, based on image analysis of a specific conjunctival region. Our ultimate goal is to develop an easy-to-use wearable device that patients themselves can employ at home to autonomously assess their need of blood transfusion. In this paper, we detail the prototype of our device and the methodology for extracting key information from the color values of the acquired image. Tests conducted on 77 anemic and healthy patients show significant correlation between the real hemoglobin value obtained through blood sampling and the value estimated by our algorithm. A prototypical binary classification algorithm for assessing the need of blood transfusion yielded good results in terms of accuracy, specificity and sensitivity, thus making it possible to avoid a significant number of blood tests