Marika Cusick

Marika Cusick, Stanford Health Policy PhD candidate

Marika Cusick

  • PhD Student, Health Policy

Biography

Marika is a Health Policy PhD student in the Decision Sciences track. She holds a Bachelor of Arts in Statistical Science from Cornell University and a Master of Science in Information Science for Health Tech from Cornell Tech. Prior to joining Stanford in 2020, she worked at Weill Cornell Medicine, supporting the institution’s secondary use of electronic health record data for research.

Marika’s interests lie in the areas of health policy modeling, data science, and clinical policy interventions as applied to improve chronic disease healthcare delivery.

In The News

Software Developer
News

Policy Brief: The Complexities of Race Adjustment in Health Algorithms

As policymakers, health-care practitioners, and technologists pursue the application of AI and machine learning (ML) algorithms in health care, this policy brief underscores the need for health equity research and highlights the limitations of employing technical “fixes” to address deep-seated health inequities.
cover link Policy Brief: The Complexities of Race Adjustment in Health Algorithms
Getty Images Illustration kidneys
News

Removing Race Adjustment in Chronic Kidney Disease Care

A new study led by Stanford Health Policy researchers finds that algorithmic changes to a chronic kidney disease care equation are likely insufficient to achieve health equity as many other structural inequities remain.
cover link Removing Race Adjustment in Chronic Kidney Disease Care
Getty Images Kidneys Illustration
News

Screening Adults 35 and Older for Chronic Kidney Disease Would Increase Life Expectancy in Cost-effective Way

Many people don’t know they have chronic kidney disease until it progresses. A new study led by Stanford Health Policy researchers finds that screening would increase life expectancy in a cost-effective way.
cover link Screening Adults 35 and Older for Chronic Kidney Disease Would Increase Life Expectancy in Cost-effective Way