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

Associate Biostatistics Investigator, Kaiser Permanente Washington Health Research Institute
Affiliate Assistant Professor, Department of Biostatistics, University of Washington

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. They also lead predictive analytics work and direct biostatistical support for KPWHRI’s Center for Accelerating Care Transformation.

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

  • Cancer

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

  • Health Informatics

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

  • Health Services & Economics

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

Recent publications

Simon GE, Shortreed SM, Johnson E, Yaseen ZS, Stone M, Mosholder AD, Ahmedani BK, Coleman KJ, Coley RY, Penfold RB, Toh S. Predicting risk of suicidal behavior from insurance claims data vs. linked data from insurance claims and electronic health records. Pharmacoepidemiol Drug Saf. 2023 Dec 19. doi: 10.1002/pds.5734. [Epub ahead of print]. PubMed

Simon GE, Cruz M, Boggs JM, Beck A, Shortreed SM, Coley RY. Predicting outcomes of antidepressant treatment in community practice settings.  Psychiatr Serv. 2023 Dec 5:appips20230380. doi: 10.1176/appi.ps.20230380. [Epub ahead of print]. PubMed

Dublin S, Greenwood-Hickman MA, Karliner L, Hsu C, Coley RY, Colemon L, Carrasco A, King D, Grace A, Lee SJ, Walsh JME, Barrett T, Broussard J, Singh U, Idu A, Yaffe K, Boustani M, Barnes DE. The electronic health record Risk of Alzheimer's and Dementia Assessment Rule (eRADAR) Brain Health Trial: Protocol for an embedded, pragmatic clinical trial of a low-cost dementia detection algorithm.  Contemp Clin Trials. 2023;135:107356. doi: 10.1016/j.cct.2023.107356. Epub 2023 Oct 17.  PubMed

Williamson BD, Coley RY, Hsu C, McCracken CE, Cook AJ. Considerations for subgroup analyses in cluster-randomized trials based on aggregated individual-level predictors. Prev Sci. 2023 Oct 28. doi: 10.1007/s11121-023-01606-1. [Epub ahead of print]. PubMed

Hoopes AJ, Cushing-Haugen KL, Coley RY, Fuller S, White C, Ralston JD, Mangione-Smith R. Characteristics of adolescents who use secure messaging on a health system's patient portal. Pediatrics. 2023 Jul 1;152(1):e2022060271. doi: 10.1542/peds.2022-060271. PubMed

 

Healthy findings blog

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What motivates Yates Coley’s work on prediction models?

Their work contributes to improved quality of care and better understanding of patients’ needs.

New findings

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Some adolescents are more likely to use patient portal than others

A new study aims to understand trends in digital care communication among teens.

Research

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Improving and advancing mental health care

KPWHRI researchers are contributing to better mental health care for people nationwide.

KPWHRI in the media

Informed use of machine learning tools

AI tools in hematology: With thorough assessment, ‘informed decisions’ are possible

Healio, Dec. 12, 2023