Claudia Nau, PhD

Research Scientist I - Department of Research & Evaluation (KPSC)


Dr. Claudia Nau is a sociologist and demographer by training. She's an Investigator at the Kaiser Permananente Southern Californa (KPSC) Department of Research & Evaluation. Her work focuses on addressing social determinants and the social needs of patients. One of her particular interests is devising strategies to support patients in addressing the web of challenges that social determinants and chronic illness bring about. To meet that purpose, she is involving community, patient, and clinical stakeholders in developing approaches for whole-person care.

Dr. Nau also applies predictive modeling to identify high-risk patients and communities. She is interested in improving the measurement and modeling of the effects of social determinants on obesity and obesity-related outcomes. She uses a range of quantitative methods, including machine learning, micro-simulation, spatial analysis, Geographic Information Systems, and demographic methods. She is also interested in collaborating with public health agencies and community associations to develop synergies around research and prevention.

Areas of Focus

  • Social determinants and patient social needs
  • Obesity and obesity-related diseases
  • Stakeholder engagement
  • Predictive modeling

Current Work

My work focuses on both stakeholder engagement and the exploration of new data and methods for informing interventions that address the social determinants of health. I'm the lead Principal Investigator (PI) on several projects:

  • American Heart Association: We're exploring how we can capitalize on the wealth of data from Electronic Health Records while keeping our patient data safe. Our goal is to help public health departments and community organizations with decision making and advocacy through a mapping tool.
  • Women Infants and Children (WIC) Program: On this grant project, my collaborators and I have safely merged and de-identified data from the Women Infants and Children (WIC) Program to assess the short and long-term benefits of WIC.
  • Archstone Foundation & Healthy African American Families: We've partnered to address the non-medical needs of elderly patients in our KPSC-depression care program.

On several projects, I use machine learning and natural language processing to identify patients at high risk for asthma exacerbations, firearm injury, social needs, and patients who would benefit from timely palliative care referrals.