Yates Coley, PhD

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“Learning health systems promise to improve medical decision-making in the era of big data by making up-to-date analyses of patient information and scientific knowledge available to physicians and patients in real time.”

Yates Coley, PhD

Assistant Investigator, Kaiser Permanente Washington Health Research Institute

Biography

Yates Coley, PhD, is a biostatistician whose research promotes predictive analytics and learning health systems as a way to improve value quality, and equity in health care delivery. Their statistical research focuses on developing clinical prediction models that are accurate, actionable, and fair. This work spans several statistical domains including repeated measurements, missing data, and machine learning.

Dr. Coley’s paper examining racial and ethnic inequity in two suicide prediction models was awarded Paper of the Year at the Healthcare Systems Research Network 2021 Annual Conference. The two models performed well for visits by patients who were White, Hispanic, and Asian but did not accurately identify high-risk visits for patients who were Black, American Indian, and Alaskan Native, likely due to persistent structural barriers limiting access to affordable, high-quality, and culturally competent mental health care. The study emphasized the importance of assessing performance within racial and ethnic subgroups of all prediction models before clinical implementation to ensure that prediction models ameliorate, rather than exacerbate, existing health disparities.

Dr. Coley is a recent graduate of  the CATALyST K12 Washington Learning Health System Program funded by the Agency for Healthcare Research and Quality and the Patient-Centered Outcomes Research Institute. As part of their training in learning health system research, Dr. Coley studied current barriers to implementing evidence-based predictive analytics tools to help develop prediction tools that can be deployed and sustained in clinical care. Their research plan also focused on statistical methods to address racial bias in clinical prediction algorithms.     

Before starting as an assistant investigator at Kaiser Permanente Washington Health Research Institute (KPWHRI) in 2016, Dr. Coley was a postdoctoral research fellow at Johns Hopkins Bloomberg School of Public Health. There, they worked with urologists to develop a prediction model that enables personalized management of low-risk prostate cancer.

Dr. Coley completed their PhD in biostatistics at the University of Washington. Their dissertation research proposed methods to improve effectiveness estimates in HIV prevention trials by accounting for unobserved variability in risk.

At KPWHRI, Dr. Coley collaborates on projects across a range of research areas including mental health, breast cancer imaging, aging, and health services.

Research interests and experience

  • Biostatistics

    Bayesian analysis, causal inference, data visualization, hierarchical models, longitudinal data analysis, missing data, prediction, survival analysis

  • Mental Health

    Suicide risk, depression treatment, measurement-based care, antipsychotic use in adolescents

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    Cancer

    Biostatistics, prostate cancer, risk stratification, stakeholder engagement, surveillance

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    Health Informatics

    Biostatistics, data visualization, interactive decision-support tools, learning health systems, stakeholder engagement

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    Health Services & Economics

    Biostatistics, clinical decision-support, learning health systems, patient-centeredness, shared decision-making, stakeholder engagement

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Recent publications

Delaney SS, Coley RY, Brown Z. 1,5-Anhydroglucitol: a new predictor of neonatal birth weight in diabetic pregnancies. Eur J Obstet Gynecol Reprod Biol. 2015 Jun;18:55-8. doi: 10.1016/j.ejogrb.2015.03.021. Epub 2015 Mar 28. PubMed

Fisher AJ, Coley RY, Zeger SL. Fast out-of-sample predictions from Bayesian hier-archical models of latent health states. arxiv: 1510.08802.

 

News

Crowd of ethnic diverse people gathering. Illustration, dark blue tones, hand drawn style.

Examining racial inequity in suicide prediction models

Kaiser Permanente researchers stress need to test how prediction models perform in all racial, ethnic groups.

news

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Benefit of 3D mammogram less with very dense breasts

But for most women, digital breast tomosynthesis improves cancer detection and reduces recalls.

healthy findings blog

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Best weight-loss surgery for diabetes and severe obesity?

Watch video on latest results from PCORnet Bariatric Study. (Spoiler alert: Bypass, not sleeve.)

KPWHRI In the Media

KPWHRI researchers’ study examines equity and risk assessment in mental health

Suicide prediction models exacerbate racial disparities in health care

VeryWell Health, May 6, 2021