Health care systems across the world generate large volumes of data about patients including information about their age, sex, and medical history. It also captures information on how patients interact across multiple points of care (e.g., hospitals, dentists, and general practice). Advances in data availability and computational power now mean that much of this data can be leveraged for social good. This ranges from the use of behavioral analytics to better predict service demand through to understanding the impact of behavior change interventions. In this project, we used patient data to explore the causes of low engagement in health care and the impact this has on patients and services. This also involved linking data sets from different organizations (e.g., health, death, and education). We observed that serially missing general practice appointments provided a risk marker for vulnerability and poorer health outcomes. While the project was administratively and methodologically challenging, the interdisciplinary background of the team ensured that the project was ultimately successful. This was particularly important when navigating a variety of different systems used to manage and distribute sensitive patient data. Our results have already started to inform debates concerning how best to reduce non-attendance and increase patient engagement within health care systems. Following a series of high-profile publications and associated impact events, non-academic beneficiaries have included governments, policymakers, and medical practitioners.