The Apogee simulation [was] correct. Our average Door to Doctor time decreased from 45 minutes to 15 minutes. The RN staff was thrilled. The patients are happier with how fast they are being seen. This process improvement initiative cost us much less than the proposed addition of 10 new ED beds.
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Why are modeling and simulation relevant in healthcare informatics?
Healthcare informatics has progressed from workflow automation to using the information applications collect to support and improve productivity and customer value. The next generation of problem solving in healthcare will be process systems and simulations that use the data generated by the many healthcare information applications to direct change to the workflow and operational processes in real time. The challenge is knowing what works best, where to change, and when to initiate the change. Modeling and simulation offers the ability to carry out complex changes on the computer to produce a virtual replicated environment to test alternative solutions before we go live with changes. Modeling and simulation allow a richer depiction of the actual problem space by including not just structural issues and business rules, but the clinical logic that is inherent in the flow of patients.
Specific Patient Flow Realizations
- Modeling of patient processing using continuous equations generates similar results to reality.
- Patient waiting areas replicate water filling reservoirs and first and second order equations applied to outflow of these reservoirs replicates patient processing.
- Single nodes of the entire patient process can significantly decrease patient flow, but single nodes cannot increase patient flow by themselves.
- By measuring the fill and drain characteristics of synthetically plugged or opened systems, critical patient flow parameters for a specific department can be approximated.
Hospital Transformation
Case Study: Staffing Optimization
Prior to the onset of the recession, hospitals were struggling to increase facility space, staffing, and ancillary support. One early effect of the recession was that the relative over-staffing and budget impact because of volume and revenue shortfalls.
Operational Problem
Apogee has studied and simulated many times at our clients' requests. We have looked at physician, midlevel, nursing and support staff levels, schedules, mixes (MD + PA?NP), models (functional vs. team nursing) and target metrics (RN/Pt ratios).
Our clients are now looking for more dynamic tools to optimize staffing levels. They are telling us that the finer tuning and more frequent adjustment lead to better financial performance and clinical care.
The real time staffing window ties back to the budget forecast and looks to integrate trend analysis to improve decision making.
Consistent use of the output of the staffing matrix has resulted in a significant budget turn around through staffing level adjustments and no decrease in service outcomes.
Hospital Transformation
Case Study: Laboratory TAT
Several hospital clients have observed laboratory TAT is affecting departmental level LOS, through put and bed turn over. In several critical care pathways (ACS, stroke) there has been concern about impact on clinical outcome.
Operational Problem
The effects of delayed lab TAT are universally recognized and can be measured as increased LOS, delays in treatment, and inappropriate work-around strategies — i.e., abuse of STAT lab ordering, or by surveys of staff morale, etc.
The ideal solution to the problem has been elusive but includes changes in lab processing strategies — from batch to continuous processing — duplication of expensive lab facilities — i.e., free standing lab in the ED dapartment — or other innovations such as POC lab testing and results reporting.
Apogee has done an extensive number of simulations for different facilities looking a the impact of POC testing when varied by time of day, types of tests being done and sensitivity to central lab performance.
Results were driven by client actual data after specific test level analysis.
Simulation output led to several short term pilots of POC lab testing whose configurations were the result of department level simulations
"Decreasing Lab Turnaround Time Improves Emergency Department Throughput and Decreases Emergency Medical Services Diversion: A Simulation Model", Alan B. Storrow, MD, Chuan Zhou, PHD, Gary Gaddis, MD, PhD, Jin H. Han, MD, MS, Karen Miller, RN, David Klubert, MD, Andy Laidig, Dominik Aronsky, MD, PhD. ACADEMIC EMERGENCY MEDICINE. 2008; 15:1-6.
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