Abstract: People living with type 1 diabetes generate data as a byproduct of diabetes management. The development of decision support technologies can be enabled by harnessing these patient-generated data, but a major challenge is for these technologies to provide meaningful and highly personalized guidance to support individual patients’ decision-making processes. In this paper, results from a year-long qualitative study were reported. Twenty-six people with type 1 diabetes were interviewed regarding the types of self-generated data they use for decision-making, their decision-making processes using self-generated data, and the difficulties they experience when attempting to use this data for decision-making. These patients’ behaviors and difficulties point to new approaches to designing decision support technologies for personal use, including patient-centered and automated data entry, automated and individualized data analysis, and humanized output.

Learning Objective 1: Understand the four patterns of decision-making processes undertaken by patients that rely on self-generated health data.

Learning Objective 2: Learn the different types of self-generated health information that patients use for medical decision-making support.

Learning Objective 3: Ideas for redesigning decision support technologies for patients based on the characteristics they desire from their self-generated health information management systems


Si Sun (Presenter)

Kaitlin Costello, Rutgers University

Presentation Materials: