Patient Flow - OR in Healthcare

OR/MS Today recently published a good overview of an important Operations Research application in healthcare: Patient Flow: The new queueing theory for healthcare. The author, Randolph Hall, leads a progressive systems engineering group at USC, with research in queueing theory focusing on modeling, simulation, analysis, and optimization. Here is a general assessment from the article of how useful those techniques can be in a healthcare setting (large urban county hospital):

Healthcare systems can be changed for the better through a strategy that combines participation and creativity. But change cannot be sustained without vigilance and without analysis based on data. Herein lies the opportunity for the O.R. community. For instance, the O.R. community can work with hospital clinicians and administrators in these areas:

• Process modeling to ascertain how patients are currently served, to determine where inefficiencies exist and to prioritize future changes. Process modeling can reveal unnecessary repetition, miscommunication, and inconsistency in methods.
  
• Simulation modeling both to evaluate new processes and to understand and demonstrate the current causes of delay. For instance: simulating delays before and after, implementing a new appointment system, changing the methodology for assigning patients to beds, or implementing an electronic patient record system.
  
• Optimization can be used in many aspects of system design, such as scheduling nurses, scheduling operating rooms or facility layout.
  
• Queue analysis is invaluable when executed on a real-time basis to highlight the delays currently experienced throughout a hospital, and to make this information available to all key decision-makers, so that they can better understand delays both upstream and downstream, and act on these delays through reallocation of resources and appropriate prioritization of patients.

Queue analysis in this context is a springboard to other "downstream" optimization methods based on simulation, such as finite-horizon integer programming methods mentioned in the article for analyzing effects of surgery demand on hospital length-of-stay, or capacity analysis / revenue management methods and how these are specialized in the healthcare setting. The point is that these methods are now mainstream in a lot of service areas, and should be made so in healthcare analystics as well.