The explosion of social networking websites, mobile apps and consumer devices for tracking personal information has created many new detailed data sources about health and wellbeing. This information has been demonstrated to promote positive health behaviours, such as encouraging a healthy diet (Brown, 2006), assisting in recovery of cancer patients (Jacobs, 2014), and reminding people to take their medication (Stawarz, 2014). These benefits have led calls to investigate the use of consumer technology within healthcare (Swan, 2009). It has been proposed that such personal information can form a body of patient data to support clinical decision making, in turn leading to more accurate diagnoses, improved patient outcomes, and reduced mortality (Rooksby, 2014).
Recent research has revealed that clinical decision making is often negatively affected by cognitive bias, leading to preventable adverse events, worsened patient outcomes, and higher mortality (Croskerry, 2013). Graber (2002) states that cognitive bias is a major cause of preventable diagnostic error, but is extremely challenging to study and reduce. This complements earlier discoveries by Kahneman (1982), in which people tend to use biases when making decisions under uncertainty, occasionally leading to severe errors. Many forms of cognitive bias have been identified - Wikipedia lists over 350  - however, the consequences of biases in clinical decision making remain largely unexplored (Croskerry, 2013). It is thus unknown how the provision of patient data may affect bias in clinical decisions.
For the purpose of this research, decisions which involve patient data are called evidence-based clinical decisions. The term patient data refers specifically to personal information which is collected by mobile apps and consumer devices, such as the number of steps taken, video lifelogs, location history and status updates on social networks. Patient data is subsequently interpreted by clinicians, which refers to the observation, analysis and sense-making of data.
This research will build on my earlier dissertation (West, 2014), in which a literature review was conducted of cognitive bias over the last 50 years. I presented a list of 11 cognitive biases which may affect clinical decisions made when patient data is used and, from this, identified that supplementing patient data may introduce further bias due to methods clinicians use to interpret patient data. Furthermore, it was found that these biases usually have a more significant effect when decisions are made in acute scenarios, such as emergency rooms, where decisions must be made quickly with limited resources.
My dissertation proposed that empirical research should be conducted in order to investigate the nature and effect of bias in evidence-based clinical decision making. The purpose of this research is thus threefold. First, in identifying biases which affect clinical decision making, this research will raise consciousness to the effect of bias in evidence-based clinical decisions. In turn, it is hoped that clinicians' increased understanding of bias may help reduce the frequency of adverse events, improve patient outcomes and reduce mortality. Second, in identifying the cases where biases are of high risk, this research will propose a number of steps which may be taken by data scientists and health scientists to avoid bias in evidence-based clinical decisions. Third, current research is limited in the area of bias in clinical decisions, and the effect of providing patient data has not yet been investigated. This research will thus involve conducting experiments in clinical environments in order to observe the effect of cognitive biases on data interpretation when time and resource constraints are present.
The disciplines involved in this research include Health Science, Cognitive Science and Computer Science. Each discipline has its own methodological and epistemological approaches, which limits how research from multiple disciplines can by synthesised (Repko, 2011). Therefore, this research will not attempt to take a standpoint from any one discipline, and will instead opt for an interdisciplinary perspective. The Web Science perspective is particularly useful in this case, as it concerns itself with the study of the World Wide Web within disciplines other than Computer Science. Hendler (2008) states that “despite the Web's great success as a technology and the significant amount of computing infrastructure on which it is built, it remains, as an entity, surprisingly unstudied.” It is hoped that the approach and findings in this dissertation will contribute to the growing field of Web Science.
 Wikipedia - List of cognitive biases - http://en.wikipedia.org/wiki/List_of_cognitive_biases
Brown, B., Chetty, M., Grimes, A., & Harmon, E. (2006). Reflecting on Health: A System for Students to Monitor Diet and Exercise. In CHI ’06 Extended Abstracts on Human Factors in Computing Systems (pp. 1807–1812). New York, NY, USA: ACM. Doi:10.1145/1125451.1125794
Croskerry, P. (2013). From Mindless to Mindful Practice — Cognitive Bias and Clinical Decision Making. New England Journal of Medicine, 368(26), 2445–2448. doi:10.1056/NEJMp1303712
Graber, M., Gordon, R., & Franklin, N. (2002). Reducing diagnostic errors in medicine: what’s the goal? Academic Medicine: Journal of the Association of American Medical Colleges, 77(10), 981–992.
Hendler, J., Shadbolt, N., Hall, W., Berners-Lee, T., & Weitzner, D. (2008). Web Science: An Interdisciplinary Approach to Understanding the Web. Commun. ACM, 51(7), 60–69. doi:10.1145/1364782.1364798
Jacobs, M. L., Clawson, J., & Mynatt, E. D. (2014). My Journey Compass: A Preliminary Investigation of a Mobile Tool for Cancer Patients. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 663–672). New York, NY, USA: ACM. Doi:10.1145/2556288.2557194
Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press.
Repko, A. F. (2011). Interdisciplinary Research: Process and Theory. SAGE.
Rooksby, J., Rost, M., Morrison, A., & Chalmers, M. C. (2014). Personal Tracking As Lived Informatics. In Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systems (pp. 1163–1172). New York, NY, USA: ACM. doi:10.1145/2556288.2557039
Stawarz, K., Cox, A. L., & Blandford, A. (2014). Don’T Forget Your Pill!: Designing Effective Medication Reminder Apps That Support Users’ Daily Routines. In Proceedings of the 32Nd Annual ACM Conference on Human Factors in Computing Systems (pp. 2269–2278). New York, NY, USA: ACM. doi:10.1145/2556288.2557079
Swan, M. (2009). Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking. International Journal of Environmental Research and Public Health, 6(2), 492–525. doi:10.3390/ijerph6020492
West, P. (2014, September 5). The Dangers of Patient Data in Clinical Decision Making. University of Southampton, Southampton.