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.
Bayesian analysis, causal inference, data visualization, hierarchical models, longitudinal data analysis, missing data, prediction, survival analysis
Suicide risk, depression treatment, measurement-based care, antipsychotic use in adolescents
Biostatistics, prostate cancer, risk stratification, stakeholder engagement, surveillance
Biostatistics, data visualization, interactive decision-support tools, learning health systems, stakeholder engagement
Biostatistics, clinical decision-support, learning health systems, patient-centeredness, shared decision-making, stakeholder engagement
Ulloa-Pérez E, Blasi PR, Westbrook EO, Lozano P, Coleman KF, Coley RY. Pragmatic randomized study of targeted text message reminders to reduce missed clinic visits. Perm J. 2022 Apr 5;26(1):64-72. doi: 10.7812/TPP/21.078. PubMed
Coughlin JW, Nauman E, Wellman R, Coley RY, McTigue KM, Coleman KJ, Jones DB, Lewis K, Tobin JN, Wee CC, Fitzpatrick SL, Desai JR, Murali S, Morrow EH, Rogers AM, Wood GC, Schlundt DG, Apovian CM, Duke MC, McClay JC, Soans R, Nemr R, Williams N, Courcoulas A, Holmes JH, Anau J, Toh S, Sturtevant JL, Horgan CE, Cook AJ, Arterburn DE; PCORnet Bariatric Study Collaborative. Preoperative depression status and five year metabolic and bariatric surgery outcomes in the PCORNET bariatric study cohort. Ann Surg. 2022 Jan 19. doi: 10.1097/SLA.0000000000005364. [Epub ahead of print]. PubMed
Coley RY, Walker RL, Cruz M, Simon GE, Shortreed SM. Clinical risk prediction models and informative cluster size: assessing the performance of a suicide risk prediction algorithm. Biom J. 2021 Oct;63(7):1375-1388. doi: 10.1002/bimj.202000199. Epub 2021 May 24. PubMed
Coley RY, Johnson E, Simon GE, Cruz M, Shortreed SM. Racial/ethnic disparities in the performance of prediction models for death by suicide after mental health visits. JAMA Psychiatry. 2021 Apr 28:e210493. doi: 10.1001/jamapsychiatry.2021.0493. [Epub ahead of print]. PubMed
Simon GE, Matarazzo BB, Walsh CG, Smoller JW, Boudreaux ED, Yarborough BJH, Shortreed SM, Coley RY, Ahmedani BK, Doshi RP, Harris LI, Schoenbaum M. Reconciling statistical and clinicians' predictions of suicide risk. Psychiatr Serv. 2021 Mar 11:appips202000214. doi: 10.1176/appi.ps.202000214. [Epub ahead of print]. PubMed
Biostatistician Yates Coley reports on new predictive analytics work that’s decreasing missed visits at KP Washington.
Kaiser Permanente researchers stress need to test how prediction models perform in all racial, ethnic groups.
But for most women, digital breast tomosynthesis improves cancer detection and reduces recalls.
Watch video on latest results from PCORnet Bariatric Study. (Spoiler alert: Bypass, not sleeve.)
VeryWell Health, May 6, 2021