Optimal timing of clinical interventions for chronic kidney disease

Using data-driven tools for decision-making

With Dr. Steven Shechter

Operations Research can help solve many health care challenges. During a joint CHCM Seminar and C2E2 Rounds, Dr. Steven Shechter, an Assistant Professor at the Sauder School of Business, presented an example of an Operations Research based solution for optimizing the timing of the stages of treatment necessary for chronic kidney disease.

Periodic laboratory tests help clinicians measure the progress of a disease and forecast when a treatment should start. Forecast accuracy is particularly important when there is a lead time needed to prepare patients for treatment. For example, chronic kidney disease patients who will need hemodialysis should ideally have a type of vascular access (called an arteriovenous fistula) created months in advance so that it matures by the time dialysis is needed. It is undesirable to have the fistula ready too early or too late, which may occur due to the uncertainty in fistula maturation and dialysis start times.

There are many crucial timing points in the treatment of chronic kidney disease. By creating a data-driven tool to help clinicians decide when to begin an arteriovenous fistula, Dr. Shechter and his team are aiming to reduce the frequency of complications relating to vascular access for hemodialysis.

About the Speaker

Dr. Steven Shechter
Steven Shechter is an Assistant Professor in the Operations and Logistics Division of the Sauder School of Business at the University of British Columbia. He is also a faculty affiliate of the Centre for Health Care Management at UBC.

Steven’s research interests lie at the intersection of operations research (OR) and health care. He has collaborated with clinicians, biostatisticians, health policy researchers, and other colleagues from OR to consider optimal decision making for organ allocation and HIV therapy planning. Methodologically, Steven is interested in applying dynamic programming and simulation modeling techniques to inform important problems in medical decision making.