Timothy Pluard, MD

Medical Director of Saint Luke’s Cancer Center and Director of Saint Luke’s Koontz Center for Advanced Breast Cancer
Saint Luke's Cancer Institute


Timothy J. Pluard, MD, is medical director at Saint Luke’s Cancer Institute. Under his leadership, Saint Luke’s has launched two new signature centers, offering a level of cancer expertise not found within 450 miles of Kansas City.

In 2016, Saint Luke’s Hospital launched its innovative Koontz Center for Advanced Breast Cancer, one of the few centers nationally, and the only in the Midwest, dedicated exclusively to the care of patients with metastatic breast cancer.

In 2017, Dr. Pluard led the creation of Saint Luke’s Center for Precision Oncology, the only center within a 450-mile radius that brings together experts in clinical oncology, tumor genomics, and computational biology. This unique multidisciplinary team has the expertise to identify the exact genomic alteration causing a patient’s tumor, and find the best possible treatment—even if it has never been used on that kind of cancer before.

Prior to joining Saint Luke’s, Dr. Pluard served as associate professor of medicine at Washington University in Saint Louis, and clinical director of breast oncology at Siteman Center, a National Cancer Institute designated Comprehensive Cancer Center.


Utilizing Machine Learning/AI of Wearable Data in Patients with Advanced Cancer to Improve Patient Outcomes and Reduce Health Care Costs

Emergency room visits and hospitalizations are frequent events in patients with advanced cancer. These untoward events negatively impact patient quality of life, may reduce patient survival, and increase health care costs. Reducing the frequency of these events remains a challenge. A previous study in patients with advanced lung cancer demonstrated that patients who were able to report their symptoms directly to the care team via a web-based platform had improved survival compared to the standard of care group. Wearable devices that can monitor biometric data, activity, and sleep patterns are widely available and in use. We hypothesized that using machine learning/AI to analyze this data we could develop a predictive model that could identify patients at high risk of an ER visit or hospitalization. This would generate an alert to the patient care team allowing intervention that might avoid such untoward events. Outcome measures include patient quality of life, number of untoward events, health care costs and overall patient survival. The study is ongoing and progress to date will be presented.

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