The increasing ubiquity of personal tracking devices is leading to the possibility of using health and wellbeing data to support clinical decisions. Hundreds of devices, mobile apps and social networking websites exist to record personal information related to health, including weight, diet and activity. Such data has been demonstrated as providing self-insight and promoting positive health behaviours, such as maintaining a healthy diet. As such, there has been interest in its use by clinicians to support decision making. However, clinicians' ability to interpret data may be prone to cognitive bias and poor judgement.
Through reviewing literature on the use of personal data and making clinical decisions within time and resource constraints, this dissertation synthesises a series of cognitive biases pertaining to a number of clinical scenarios. From this, the dangers and consequences of their use in healthcare are assessed. In agreement with previous research, the biases identified pose a greater risk within scenarios where there is limited time and resources. Drawing from these results, this dissertation forms framework for future research into data use in clinical scenarios.