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Muge Capan

Assistant Professor

My current research utilizes data science, stochastic modeling and decision analysis focused on the secondary use of EHR data to inform acute care decisions in the hospital setting. Example research studies focus on: i) diagnosis decisions for hospitalized sepsis patients informed by simultaneous organ system failures using network models, ii) treatment decisions regarding he type and timing of first treatment using a Markov Decision Process model, and iii) display decisions to effectively communicate the clinical risk of adverse outcomes utilizing usability testing and statistical analysis.

The number of older individuals with complex healthcare needs discharged from hospital to home healthcare (HHC) is rising. HHC refers to community-based services to address complex clinical needs (e.g., skilled nursing) through home visits. During HHC, patient assessment data that are not captured in the hospital Electronic Health Records (EHR) are collected. However, HHC data are rarely integrated with hospital EHR, resulting in missed care planning opportunities in the home setting. For example, patient functional impairment identified during HHC combined with hospital EHR data (e.g., diagnoses associated related to functional decline) could inform HHC nursing interventions and visit frequency. In addition to informing HHC care planning, analysis of HHC data in combination with hospital EHR data could identify patient-level features associated with rehospitalization risk.

Moving forward, I am interested in better understanding the continuum of care, learning from health data in the community / home setting (such as sensor and wearables data collected during HHC) for older and/or medically complex patients, informing care planning and resource allocation decisions, and ultimately supporting healthy aging in place.

Academic Background

  • BS, Industrial Engineering, Technical University of Berlin, Berlin, Germany, 2005
  • MSc, Industrial Engineering, Technical University of Berlin, Berlin, Germany, 2008
  • PhD, Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, 2014
Capan, M., Schubel, L., Pradhan, I., Catchpole, K., Shara, N., Arnold, R., Schwartz, J., Seagull, F., Miller, K. (2022). Display and Perception of Risk: Analysis of Decision Support System: Display and its Impact on Perceived Clinical Risk of Sepsis-induced Health Deterioration. Health Informatics Journal. 28(1):14604582211073075. doi: 10.1177/14604582211073075
Rosenstrom, E., Meshkinfam, S., Ivy, J. S., Goodarzi, S. H., Capan, M., Huddleston, J., & Romero-Brufau, S. (2022). Optimizing the First Response to Sepsis: An Electronic Health Record-Based Markov Decision Process Model. Decision Analysis.
Jazayeri, A., Capan, M., Ivy, J., Arnold, R., & Yang, C. C., (2021). Proximity of Cellular and Physiological Response Failures in Sepsis. IEEE Journal of Biomedical and Health Informatics (J-BHI).
Agor, J., Ozaltin, O.Y., Ivy, J.S., Capan, M., Arnold, R., & Romero, S (2019). The value of missing information in severity of illness score development. Journal of Biomedical Informatics, 97, 103255. doi: 10.1016/j.jbi.2019.103255.
Prints, M., Fishbein, D., Arnold, R., Stander, E., Miller, K., Kim, T., & Capan, M. (2019). Understanding the perception of workload in the emergency department and its impact on medical decision making. American Journal of Emergency Medicine. doi: 10.1016/j.ajem.2019.07.021.
Jazayeri, A., Capan, M., Yang, C., Khoshnevisan, F., Chi, M., & Arnold, R. (2019). Network-Based Modeling of Sepsis: Quantification and Evaluation of Simultaneity of Organ Dysfunctions. In the 10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB). doi:10.1145/3307339.3342160.
Capan, M., Hoover, S., Ivy, J., Miller, K., & Arnold, R. (2018). Not all organ dysfunctions are created equal – prevalence and mortality in sepsis. Journal of Critical Care, 48:257-262. doi: 10.1016/j.jcrc.2018.08.021.
Capan, M., Hoover, S., Miller, K., Pal, C., Glasgow, J., Jackson, E.V., & Arnold, R. (2018). A data-driven approach to early warning score-based alert management. BMJ Open Qual, 7(3), e000088. doi: 10.1136/bmjoq-2017-000088.
Contact Info

Department of Mechanical and Industrial Engineering
College of Engineering
Engineering Lab/213D
Amherst, MA 01003-9292

(413) 545-5735