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

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

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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

Coley RY, Duan KI, Hoopes AJ, Lapham GT, Liljenquist K, Marcotte LM, Ramirez M, Schuttner L. A call to integrate health equity into learning health system research training. Learn Health Syst. 2022 Jul 24;6(4):e10330. doi: 10.1002/lrh2.10330. eCollection 2022. PubMed

Coleman KJ, Wellman R, Fitzpatrick SL, Conroy MB, Hlavin C, Lewis KH, Coley RY, McTigue KM, Tobin JN, McBride CL, Desai JR, Clark JM, Toh S, Sturtevant JL, Horgan CE, Duke MC, Williams N, Anau J, Horberg MA, Michalsky MP, Cook AJ, Arterburn DE, Apovian CM. Comparative safety and effectiveness of Roux-en-Y gastric bypass and sleeve gastrectomy for weight loss and type 2 diabetes across race and ethnicity in the PCORnet bariatric study cohort. JAMA Surg. 2022 Oct 1;157(10):897-906. doi: 10.1001/jamasurg.2022.3714. PubMed

Cruz M, Shortreed SM, Richards JE, Coley RY, Yarborough BJ, Walker RL, Johnson E, Ahmedani BK, Rossom R, Coleman KJ, Boggs JM, Beck AL, Simon GE. Machine learning prediction of suicide risk does not identify patients without traditional risk factors. J Clin Psychiatry. 2022 Aug 31;83(5):21m14178. doi: 10.4088/JCP.21m14178. PubMed

Simon GE, Shortreed SM, Boggs JM, Clarke GN, Rossom RC, Richards JE, Beck A, Ahmedani BK, Coleman KJ, Bhakta B, Stewart CC, Sterling S, Schoenbaum M, Coley RY, Stone M, Mosholder AD, Yaseen ZS. Accuracy of ICD-10-CM encounter diagnoses from health records for identifying self-harm events. J Am Med Inform Assoc. 2022 Aug 26:ocac144. doi: 10.1093/jamia/ocac144. [Epub ahead of print]. PubMed

Coley RY, Smith JJ, Karliner L, Idu AE, Lee SJ, Fuller S, Lam R, Barnes DE, Dublin S. External validation of the eRADAR risk score for detecting undiagnosed dementia in two real-world healthcare systems. J Gen Intern Med. 2022 Jul 29. doi: 10.1007/s11606-022-07736-6. Online ahead of print. PubMed

 

Research

Female doctor and senior female patient sitting around desk

Dementia risk screening tool shows promise

In a new study, a tool to help discover undiagnosed dementia performed well in 2 separate health systems.

From research to practice

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Predicting and preventing missed clinic visits

Biostatistician Yates Coley reports on new predictive analytics work that’s decreasing missed visits at KP Washington.

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.

KPWHRI in the Media

Assistant Biostatistics Investigator Yates Coley, PhD, talks about a new tool that shows promise for identifying risk of undiagnosed dementia

New tool helps gauge risk of undiagnosed dementia

Medscape, Aug. 9, 2022