Patient Flow Analysis
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Patient Flow Analysis
Hospital overcrowding is a serious condition that threatens patient quality and a health system's bottom line. Overcrowding is not a new problem for hospitals, but in light of the Affordable Care Act, which expanded the insured base, health care leaders face a greater challenge than ever.
ProModel's predictive analytics for healthcare enables healthcare organizations to improve patient flow in high revenue patient care areas such as the emergency department and OR. Improved operational efficiencies include:
- Optimize patient throughput, LOS and resource utilization processes
- Smooth patient demand to avoid census peaks
- Synchronize work flow to optimize resources and reduce cost
- Enhance facility expansion/renovation – demand capacity planning for cost containment
"A significant value for healthcare is that simulation allows one to evaluate a problem in terms of whole-systems thinking, as it accounts for how actions in one part of a patient's pathway might impact other parts of that pathway and other patients' care.
What our customers say:
"Administration, the Nurse Manager and Front End Supervisor all agreed that the model was a very fair representation of how the center actually operated"
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Quickly and accurately evaluate the impact of various operational proposals to improve patient flow
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Experiment with the system behavior without experimenting with the actual system
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Length of Stay (LOS) reductions from their nursing unit discharge initiatives
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Avoided the extra cost of just building extra PCU units
Patient Flow
Hospital Patient Flow Optimization
Situation
The leadership team at a hospital in the Mid-Atlantic U.S. found themselves in a peak census situation several times a year where they did not have enough beds to accommodate the demand. With a daily average census of over 600 throughout the facilities, this issue would send the clinic into crisis mode which included crisis management committee meetings and micro-managing the transfers and discharges until the situation resolved itself.
They did not have a way to effectively proactively study patient flow behavior during these peak times in order to evaluate potential solutions to reduce or eliminate the peak census states. Because of the many variables such as volume of arrivals, timings of arrivals, service line specialty of admitting physician, acuity level of patient at time of admission, bed capacity among the nursing units, transfer center's distribution of patients, patients' length of stay on each unit and patients' movement between nursing units, the leadership team faced difficulty analyzing their patient flow.
Objectives
Results
Using ProModel's patient flow analysis solution they developed the capability to get answers to patient flow what if questions in minutes versus days, weeks or months. They now have a flexible environment that is adaptable to answer future patient flow questions. They are expecting to see the following improvements from their first phase:
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Reduced the Length of Stay in the main ED by 15%
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Reduced the Length of Stay in the GTA (Fast Track) by 33%
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Postponed the ED expansion indefinitely, avoiding a $1.2 million investment, and disruption to critical operations
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The estimated increase in patient admissions and throughput capabilities revealed the potential for an additional $900,000 in annual revenue.
Emergency Department
Emergency Department
Situation
Baystate is a 700-bed teaching hospital located in western Massachusetts that provides level one-trauma services. The ED manages over 96,000 patient visits a year through its 32-bed unit and a General Treatment Area (GTA) provides fast track services for non-acute, non-urgent care patients.
Patient volumes in the Emergency Department (ED) at Baystate Hospital had increased from their forecasted annual growth rate of 3% to almost 8% creating serious operational challenges. After comprehensive review, administration determined that they needed more beds and more staff to ensure the highest levels of patient care and satisfaction. Faced with numerous challenges, including a virtually land-locked facility, management was left to consider a complex $1.2 Million ED expansion.
Objectives
Conduct an Emergency Department simulation project to see if there were other more cost effective alternatives.
Results
The project team created a dynamic simulation model of the entire ED with MedModel to accurately replicate the existing department's operations. After the simulation model was validated and accepted by key hospital personnel, the project team utilized the model to identify areas for possible improvement, develop and evaluate potential solution scenarios, identify capabilities of the current facility to support effective operations and project the impact of continued growth on the department. They were able to achieve the following results:
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Identify and analyze system bottlenecks and performance metrics
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Understand room utilization statistics and Radiology wait times and queues
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Recommend room allocation and/or scheduling changes to improve room utilization in order to avoid facility expansion while maintaining a positive patient experience
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Dr. A – 4 rooms justified for majority of clinic days
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Dr. B – 3 rooms for each session (AM or PM)
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Dr. C – Schedule for entire day and share Pod with Dr. D (Wednesdays only)
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Dr. D – 3 Rooms sufficient on Mondays and Thursdays
Perioperative Services
Perioperative Services
Predictive Analytics Improves Surgical Division Room Utilization, Avoids Potential Expansion, While Maintaining Satisfactory Patient Wait Time
Situation
The Head of Emory Clinic Facility Operations received a request from one of Emory's surgical divisions' administrator for construction of additional exam rooms at the request of their physicians. In order to accommodate their patient volume, the physicians felt that they needed more than the current 3 or 4 rooms per physician. Before investing capital to expand the division, Facilities leadership requested an objective data driven analysis be performed.
Current policy states that while a patient is in radiology, the treatment room is held for the patient, even though there is not a live patient in the room, causing reduced room utilization. Lower utilization of rooms means more patients in the lobby, increasing wait times. The primary objective therefore was to analyze how many rooms each physician needed to best utilize available rooms while maintaining or reducing current patient wait times. Clinic Leadership needed to account for the business side of healthcare yet not compromise the patient experience.
Objectives
Results
Considering different room allocation numbers for physicians, it was found that assigning 2 rooms per physician resulted in a 120 % increase in wait times
while assigning 3 rooms per physician resulted in just a 28% increase in wait time from the current 4 rooms per physician setup.
With the addition of more detail and further analysis to the model, Clinic Operations support determined room allocations needed to be adjusted by physician by hour of the day. The optimum room allocation recommendations were:
This enabled the surgical division to increase patient volume enough in order to demonstrate to the physicians that additional rooms were not required, while still maintaining acceptable patient satisfaction standards.
Additionally, they gained insight into the impact on patient experience from having both doctors and residents interact with patients. As an academic, or teaching hospital, this was extremely important.
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Model the present state of patient and resource flow to identify and analyze system bottlenecks and performance metrics including lobby wait time and chair utilization to ultimately improve overall patient satisfaction.
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Obtain results from simulating operational scenarios including scheduling, staffing, and CPOE (computerized physician order entry), plus scenarios with estimated patient volumes over the next 3 years.
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Make operational recommendations to reduce patient waiting times and increase operational efficiency.
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Extend normal business hours from partial weekend hours (Sat-Sun 8am-2pm) to full weekend hours (Sat-Sun 7am-7:30pm)
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Run three bays on the weekend instead of two as initially planned
Ambulatory Care
Ambulatory Care
Predictive Analysis of Emory Healthcare's Infusion Center Scheduling, Staffing and Resource Utilization to Improve Patient Flow
"Administration, the Nurse Manager and Front End Supervisor all
agreed that the model was a very fair
representation of how the center actually operated."
Situation
For Emory's Infusion Center, issues with how resources were being utilized, schedule strategy, and a short staff led to long waiting times during busy days and peak hours. According to patients, spending more than 30-35 minutes in the waiting room was undesirable.
On top of that, their strategic plan forecasts a 9-10% increase in patient volume per year for the next three years. With such a complex and variable environment, they knew a simulation analysis would be the best way to find an optimal solution.
Objectives
Results
The first two recommendations implemented resulted in a 4% decrease in chair time and a 23.7% reduction of wait time in the lobby. They were as follows:
Patients prefer weekend appointments, so expanding the weekend schedule not only helped reduce the stress on the system during the week, but also increased patient satisfaction.
There are also plans to implement a re-organized schedule which would start the longer infusion appointments in the morning, and the 1, 2 and 3 hour appointments in the afternoon. Simulation results showed that chair times could be reduced by up to 13.7% and patient wait times by 35.76% from the current state.
Reducing patient wait times was the primary objective of the project. The simulation helped identify solutions that would reduce wait times while, at the same time, improving patient satisfaction scores.
-
Quickly and accurately evaluate the impact of various operational proposals to improve patient flow
-
Experiment with the system behavior without experimenting with the actual system
-
Length of Stay (LOS) reductions from their nursing unit discharge initiatives
-
Avoided the extra cost of just building extra PCU units.
Custom Solutions / Custom Development
Custom Solutions / Custom Development
Custom Hospital Patient Flow Analysis Technology
Situation
The leadership team at a hospital in the Mid-Atlantic U.S. found themselves in a peak census situation several times a year where they did not have enough beds to accommodate the demand. With a daily average census of over 600 throughout the facilities, this issue would send the clinic into crisis mode which included crisis management committee meetings and micro-managing the transfers and discharges until the situation resolved itself.
They did not have a way to effectively proactively study patient flow behavior during these peak times in order to evaluate potential solutions to reduce or eliminate the peak census states. Because of the many variables such as volume of arrivals, timings of arrivals, service line specialty of admitting physician, acuity level of patient at time of admission, bed capacity among the nursing units, transfer center's distribution of patients, patients' length of stay on each unit and patients' movement between nursing units, the leadership team faced difficulty analyzing their patient flow.
Objectives
Results
Using ProModel's custom patient flow analysis solution they developed the capability to get answers to patient flow what if questions in minutes versus days, weeks or months. They now have a flexible environment that is adaptable to answer future patient flow questions. They are expecting to see the following improvements from their first phase:
