Healthcare Staffing: One of the key challenges facing clinics and hospitals today is their ability to control the number of care provider available based on the varying number of patients. With a variable patient arrival rate and treatment requirement, these organizations are faced with one critical decision; how many staff members do we need to keep patient wait time low and minimize the Left without being seen patients?
Increasing efficiency while minimizing risk, Simulation and live tracking are the main tools required to implement a solution that can dynamically adjust staffing levels based on historical data and current conditions constraints.
Simulation in general has always been a tool to predict the future of the operation and analyze a multitude of what-if scenarios across several industry types. Traditionally, a simulation model is built to represent the current operation (clinic, ED, OR area, outpatient, etc.) and a set of distributions are applied to the model in order to identify potential improvements. The validity of the solution provided is fully dependent on the type of distribution being used and the time span of the simulation. In other words, if the wrong distribution is used, the simulation results will be inaccurate and may not represent the true future state.
Hospitals and clinics are also making the transition to Electronic Medical Records, and RFID tracking. EMR provides a true current state of the patient and the patient condition in addition to staff interaction with the patient. RFID on the other hand is purely used for equipment tracking and monitoring to determine equipment locations and maintenance requirements. While both implementations store key data of the patients and operations, they both run independently of each other and do not communicate to improve the overall efficiency of the hospital.
In order to provide a complete solution to the staffing and efficiency problem, a solution needs to capture information from both EMR and RFID, along with any other tracking data available, and play it in real-time to forecast the future of the operation. The forecast needs to take into consideration historical data in order to determine the future state along with a variability percentage. Therefore, any system capable of performing the analysis should be able to learn from the past, add variability to it, and perform a set of monte carlo simulations before suggesting a solution. The final results should be geared toward lower patient wait time, increase operational efficiency, and eliminate the Left without being seen patients.
Real-time Dynamic Simulators have three key capabilities that can be used to enable hospital and clinic administrators to better manage their operation and improvement overall efficiency. The ability to connect to multiple systems at the same time and provide a live view of the operation, a replay with analysis, and forecasting capability.
The live view of the current operation retrieves data from both EMR and RFID and provides a graphical representation in 2D or 3D of the operation. In addition, using the 3D view, real-time dynamic simulators can provide an entity-follow view that allows any one monitoring the operation to view what the entity is seeing as it progresses through the system.
Replay and analysis can be generated from the historically tracked data. As an example, a process improvement person might decide to analyze the efficiency of the operation on Mondays. This is done by rewinding the data to the date in question and replaying, in fast mode, every operation that was tracked. During the replay (and when a potential problem is identified), the process improvement person switches the model to simulation mode. Switching to simulation mode triggers 2 main events; the first forces the model to learn the constraints based on historical performance, while the second enables the analysis to identify how the process can be improved.
Forecasting the staffing requirement can be automatically adjusted based on the outcome of the predictive behavior of the simulator. By loading the current state scenario from EMR and RFID, reading the historical constraints and performance criteria, and introducing percentage variability into the model, the dynamic simulation environment can forecast staffing requirements; dynamically modify the flow to improve efficiency, and alert key personnel of potential bottlenecks and delays.
Remote Monitoring: SimTrack enables multiple remote views of the operation. In addition, a web based interface is provided for data monitoring and reporting.
By properly configuring a dynamic simulator, the model can be set to automatically adjust if a station is added, a bed is moved, or the number of staff members is modified. On-the-fly dynamic simulators have the ability to modify their constraints and system flow as the simulation is running and without the need to restart.
Dynamic, on-the-fly Simulation has come a long way since its initial inception, to the point where it can interact in real time with external systems and provide users with unprecedented analysis of past events, and insight into future performance of the systems. Simulation models that can learn and adapt from collected data and apply the learned constraints to predict the future are now available. The key issue of keeping the model updated and in sync with current environment can now be done dynamically using the live interaction feature to live systems (EMR, RFID, barcodes, etc.). The SimTrack dynamic visibility tool was designed specifically to enable organizations analyze the past, visualize the present, and forecast and optimize the future in a simple integrated environment.
SimTrack is a dynamic visibility and analysis tool that utilizes Simcad Pro patented technology to provide real-time operational visibility, dynamic replay, proactive forecasting, and customization reports. SimTrack open architecture enables it to interact with a multitude of live data systems RFID, Barcode, GPS, ERP, MRP, EMR/EHR, etc. enabling organizations to better manage the daily activities, improve system efficiency and provide historical replay and analysis to help in understanding the past and improving the future.