The Quantified Patient

The Quantified Patient

The pervasiveness of wearable devices and apps for sensing and automatically capturing aspects relating to people's health and wellbeing have caused a rise in self-logging of such data. However, little work has considered the challenges of introducing this data into clinical workflows.

In the Quantified Self project, we worked with clinical cardiologists and other clinical care providers to understand the opportunities and challenges posed by use of self-logged data in clinical practice. Our work found that whereas self-logged data holds immediate promise for assessing and managing patients' symptomatic burdens, substantial challenges are present for diagnostic use: Self-logged data are currently highly non-standardised, which in combination with the sheer volume of data that can be collected presents makes interpretation of the data highly time-demanding.

Self-logged data also often represents dimensions of patient well-being that are different from those typically measured or considered in clinical workflows, which presents another barrier to effective interpretation of this data.

Investigators

Publications
West, P., Giordano R., Van Kleek M., & Shadbolt N. (2016).  The Quantified Patient in the Doctor's Office: Challenges and Opportunities (teaser). CHI'16.
West, P., Giordano R., Van Kleek M., & Shadbolt N. (2016).  The Quantified Patient in the Doctor's Office: Challenges & Opportunities. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 3066–3078.
Pagliari, C. (2016).  Digital Support for Childbirth in Developing Countries. {JAMA} Pediatrics. 170, 737.
West, P., Giordano R., & Van Kleek M. (2015).  The Quantified Self and Clinical Decision Making: Understanding and Reducing Clinical Decision Bias and Errors When Using Quantified Self..
Lee, S. Hwa, Nurmatov U. B., Nwaru B. I., Mukherjee M., Grant L., & Pagliari C. (2015).  Effectiveness of Health interventions for maternal, newborn and child health in low– and middle–income countries: Systematic review and meta–analysis. Journal of Global Health. 6,
Hanley, J., Fairbrother P., McCloughan L., Pagliari C., Paterson M., Pinnock H., et al. (2015).  Qualitative study of telemonitoring of blood glucose and blood pressure in type 2 diabetes. {BMJ} Open. 5, e008896.
Packer, H. S., Buzogany G., Smith D., Dragan L., Van Kleek M., & Shadbolt N. (2014).  The Editable Self: A Workbench for Personal Activity Data.
Smith, D. A. (2014).  iOS/Android (Cordova) Application for Self-Tracking.
Smith, D., Shadbolt N., & Van Kleek M. (2013).  Using Personal Activity Diaries to Enhance Electronic Health Records.