Past Research


Open-angle Glaucoma: 

This research is focused on developing innovative research models to personalize monitoring and management of patients with Open-Angle Glaucoma (OAG), the second leading cause of blindness in the world.

The modeling framework can be used to forecast future disease progression specifically for the patient and to improve the quality of care while avoiding wasteful expenditures. The model is able to (1) optimize the time interval between sequential monitoring tests; (2) specify the best set of tests to take during each patient's office visit (e.g. tonometry and perimetry) taking into account the value and cost of each test; and (3) provide patient-specific target levels for key disease risk factors (e.g., target intraocular pressure) to control the disease progression over time, so the clinician can use this in choosing the intervention. This research harnesses linear quadratic Gaussian (LQG) systems modeling and the Kalman filter to achieve these critical goals simultaneously. 

Coordinated Care Delivery:

Providing timely access to surgery is crucial for patients with high acuity diseases like cancer. This research presents a methodological framework to make efficient use of scarce resources including surgeons, operating rooms, and clinic appointment slots with a goal of coordinating clinic and surgery appointments so that patients with different acuity levels can see a surgeon in the clinic and schedule their surgery within a maximum wait time target that is clinically safe for them. We propose six heuristic scheduling policies with two underlying ideas behind them: (1) proactively book a tentative surgery day along with the clinic appointment at the time appointment request is received, and (2) intelligently space out clinic and surgery appointments such that if the patient does not need his/her surgery appointment there is sufficient time to offer it to another patient. Validation is done using data from division of colorectal surgery at the Mayo Clinic. 


Patient Handoffs:

This research focuses on reducing medical errors in hospital, thereby improving patient outcomes. In collaboration with Mayo Clinic, I developed a new patient-centered decision support system that leverages integer programming methods to design work shift schedules for residents and fellows which minimize the number of patient handoffs, therefore reduce medical errors due to communication breakdown, while complying with all mandatory ACGME duty-hour standards, providing required coverage, and maintaining physician quality of life. The result of this work is published in the journal of Health Care Management Science [Kazemian et al. 2013].