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 and quality in health care delivery. Her 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 is currently a scholar with 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 her training in learning health system research, Dr. Coley is studying current barriers to implementing evidence-based predictive analytics tools to help develop prediction tools that can be deployed and sustained in clinical care. Her research plan also focuses 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, she worked with urologists to develop a prediction model that enables personalized management of low-risk prostate cancer. She designed an interactive decision support tool that calculates and communicates patients’ predictions in real-time and is currently building the statistical structure necessary to support a continuously learning model. The resulting prediction tool will be integrated into the clinical workflow so that new observations will be automatically incorporated into the existing model, improving both patient-level predictions as well as researchers’ understanding of risk in the population.

Dr. Coley completed her PhD in biostatistics at the University of Washington. Her 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, bariatric surgery, 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

Courcoulas A, Coley RY, Clark JM, McBride CL, Cirelli E, McTigue K, Arterburn D, Coleman KJ, Wellman R, Anau J, Toh S, Janning CD, Cook AJ, Williams N, Sturtevant JL, Horgan C, Tavakkoli A. Interventions and operations 5 years after bariatric surgery in a cohort from US National Patient-Centered Clinical Research Network Bariatric Study. JAMA Surg. 2020 Jan 15. pii: 2758646. doi: 10.1001/jamasurg.2019.5470. [Epub ahead of print]. PubMed

Coley RY, Boggs JM, Beck A, Hartzler AL, Simon GE. Defining success in measurement-based care for depression: a comparison of common metrics. Psychiatr Serv. 2019 Dec 18:appips201900295. doi: 10.1176/appi.ps.201900295. [Epub ahead of print]. PubMed

Simon GE, Shortreed SM, Coley RY. Positive predictive values and potential success of suicide prediction models. JAMA Psychiatry. 2019 Jun 26. pii: 2737196. doi: 10.1001/jamapsychiatry.2019.1516. [Epub ahead of print]. PubMed

Shortreed SM, Cook AJ, Coley RY, Bobb JF, Nelson JC. Challenges and opportunities for using big health care data to advance medical science and public health. Am J Epidemiol. 2019 May 1;188(5):851-861. doi: 10.1093/aje/kwy292. PubMed

Huntley JH, Coley RY, Carter HB, Radhakrishnan A, Krakow M, Pollack CE. Clinical evaluation of an individualized risk prediction tool for men on active surveillance for prostate cancer. LID - S0090-4295(18)30903-8 [pii] LID - 10.1016/j.urology.2018.08.021 [doi] Urology. 2018 Aug 29. pii: S0090-4295(18)30903-8. doi: 10.1016/j.urology.2018.08.021 [Epub ahead of print] PubMed

 

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