Industrial engineers analyze and design healthcare delivery process by addressing process efficiency, productivity and patient access resulting in safer and more cost-effective systems.
- Healthcare Process Improvement using real-time location tracking (RTLS)
- Diabetes Management at community health centers: Examining associations with patient and regional characteristics, efficiency, and staffing patterns
- Inventory Management of Blood Supply Chain
- Tracking Alzheimer’s Disease Progression Path
- Smart Dynamic Appointment Scheduling Rules
Healthcare Process Improvement using RTLS
The goal of this study is to develop a real-time location tracking protocol that will improve the healthcare delivery process by minimizing missed opportunities caused by work delays.
- Use the RTLS-based nursing activity analysis system at an ICU at the University Hospital
- Validate location system performance against manual observation of nursing activity.
- Correlate nursing activity metrics against patient outcomes as measured by Sequential Organ Failure Assessment (SOFA) score
Inventory Management of Blood Supply Chain
- Reduce total cost along blood supply chain
- Total cost = Fixed transportation cost + variable purchasing cost + shortage cost + holding cost + outdating cost
- Developed four heuristic policies (Order-up-to-level, Modified order-up-to-level policy, Last value method and Weighted mean variance policy) that can be implemented easily using an Excel spreadsheet
- Policies were able to reduce blood shortage by 17% and blood outdating by 23%
- Can act as decision support systems to hospital administrators
Tracking Alzheimer’s Disease Progression Path
- To use neuroimaging technique to track AD progression especially for serving as biomarkers in clinical trials, it is desirable to have global indices which can summarize numerous complicated imaging features.
- In this research, we propose a new global index, derived from non-linear dimension reduction of brain MRI features, to track AD progression over time.
Smart Dynamic Appointment Scheduling Rules
- Motivated by real-life case of a family medicine clinic in the US
- Objectives are to improve resource utilization and patient satisfaction
- Stacking approach achieved the best classification accuracy for no-show risk prediction followed by gradient boosting
- Proposed smart appointment scheduling rules outperformed the existing approach significantly in terms of patient waiting time, nurse utilization and provider utilization