Healthcare and Medical Facility Analytics

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Healthcare and Medical Facility Analytics

The Affordable Care Act is putting increasing levels of pressure on a healthcare system which was already strained. Considering this new reality how do hospital and clinic decision makers best evaluate their current systems and future plans in order to comply with the ACA yet continue to meet patient care standards as well as community and financial performance objectives?

Healthcare needs an easier, more quantifiable and reusable way of visualizing current practices, analyzing potential process & policy changes and planning future service lines and strategic direction.

ProModel enables healthcare organizations to test their ideas and scenarios for radical cost reduction or other performance objectives in a virtual environment BEFORE they are implemented, reducing the risks of implementing ineffective initiatives.

Clinicians "simulate" their procedures before going live on patients, shouldn't healthcare business and operational practices be treated similarly?

Patient Flow
Emergency Department
Perioperative Services
Ambulatory Care
Patient Transfer
Custom Solution

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

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

Hospital System Model <

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:

Length of Stay (LOS) reductions from their nursing unit discharge initiatives
Avoided the extra cost of just building extra PCU units

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:

Reduced the Length of Stay in the main ED by 15%
Reduced the Length of Stay in the GTA (Fast Track) by 33%
Postponed the ED expansion indefinitely, avoiding a $1.2 million investment, and disruption to critical operations
The estimated increase in patient admissions and throughput capabilities revealed the potential for an additional $900,000 in annual revenue.

Perioperative Services

MedModel OR Case Cart Implementation Study

Situation

Hospitals face increasing pressure to reduce costs while continuing to provide quality care to patients. The operating room, one of the most difficult and expensive wings to manage, must run efficiently in order to avoid unnecessary costs. Hospital managers often implement case cart systems which create a centralized materials management system. Case carts carry medical supplies within an operating room. The case cart system ensures that the staff obtain the necessary materials and instruments in time for their upcoming procedures (1. Making a Case for a Case Cart System).

This study was undertaken to test the impact of implementing a case cart system on the OR process in a client’s newly configured OR Suite. The impact was determined by patient delays in any stage of the OR process that was attributable to case carts.

Objectives
The client wanted a predictive analytic model which would help them with the following:

Ability to reliably predict the impact of existing and increased demands within the OR on case cart capacity.
Evaluate impact of case cart number on OR throughput.
Examine impact of customized case cart lifts as a potential bottleneck on case cart utilization.
Determine Sterile Processing Department (SPD) resource requirements and their respective function on overall case cart utilization and OR utilization/throughput.
Predict optimal utilization strategies as case carts are introduced.
Identify bottleneck areas in the OR process with the introduction of case carts.
Examine the impact of future demand over a 5-10 year window with the ability to manipulate case cart number.
Ensure smooth work flow for patients and staff as case carts are introduced.

The model was designed to answer the following key questions:

Has the medical center acquired enough carts to satisfy the volume requirements?
Are there enough Sterile Processing Department resources to support the case cart process?
Will the case carts introduce any new delays in the patient process?
How many carts need to be staged prior to morning start to ensure smooth OR Suite flow?

Results
The model outputs suggest that maximum patient throughput could increase by 38% in 6 months with the implementation of a case cart system. The following additional insights were also gained from the study.

Determined that 55 small carts and 28 large carts are needed to ensure there are no delays due to case carts.
Determined that 6 SPD FTE’s are required to pack the morning case carts and 4-5 SPD FTE’s are required during normal OR operation hours.
Realized that cart picking must begin as soon as possible after midnight to ensure there are enough carts ready at the Dstart of the day. To maintain a steady flow, the carts must be available and ready for the first two procedures. The modelers found that maximum case cart use time occurs early for a maximum of 1 hour.
The implementation of case carts caused no significant delays in patient flow times.

Maximum System Volume

At these higher volumes both POCU and PACU spaces become limiting factors.

Solution Details

Defining the Process
A spreadsheet defines the “patient flow” process as it relates to patient type, location sequence, staffing utilized and task times. The spreadsheet “Staff” columns work together to schedule the first staff member required for each procedure step. Some procedure steps have the staffing flexibility of allowing an alternate position to “back up” the primary position. Times for each process step are defined in the Process spreadsheet using triangular distributions which account for work time as well as wait time.

Cases Defined by Historical Data
The medical center provided historical data such as original date of surgery, the service which performed the procedure, the surgeon assigned to the case, and the OR assignment.

Block Schedule
Operating room schedules are entered onto a spreadsheet. The model solution places the previously entered cases into schedule blocks and continues through the process until the patient completes the surgical experience.

Staffing
The simulation model uses the data on a worksheet to perform scheduling tasks by staff person, primary or secondary resource group, and times that shifts begin and end.

Sterile Processing Department Input Worksheet
Data entered into the Sterile Processing Department (SPD) worksheet is matched with the procedure from the “Cases” worksheet. The model solution will produce results indicating the turnaround time on the carts, and will predict the performance of SPD.

Location Assignments
A worksheet defines the primary and secondary uses of each location in the model.

Procedure Requirements
Three triangular time distributions are used on this model (Min / Mode / Max) to represent procedure times for all clinic procedures. The first triangular is used for the procedure itself. The second triangular is used for room turnover. The third and last triangular distribution is the set-up time occurring before the next procedure is performed.

Room Restrictions
“Special Restrictions” may apply for up to five ORs. These restrictions define the rooms that may be used by each service. An entry of “999” indicates that “any” OR may be used.

Services Using Case Carts Chart
Services using carts receive a “1” in corresponding column while services not using case carts remain blank.

References:
"1. Making a Case for a Case Cart System." Making a Case for a Case Cart System - Research - Herman Miller. Herman Miller Inc., n.d. Web. 14 June 2017.

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

Identify and analyze system bottlenecks and performance metrics
Understand room utilization statistics and Radiology wait times and queues
Recommend room allocation and/or scheduling changes to improve room utilization in order to avoid facility expansion while maintaining a positive patient experience

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:

Dr. A - 4 rooms justified for majority of clinic days
Dr. B - 3 rooms for each session (AM or PM)
Dr. C - Schedule for entire day and share Pod with Dr. D (Wednesdays only)
Dr. D - 3 Rooms sufficient on Mondays and Thursdays

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.

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

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.
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.
Make operational recommendations to reduce patient waiting times and increase operational efficiency.

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:

Extend normal business hours from partial weekend hours (Sat-Sun 8am-2pm) to full weekend hours (Sat-Sun 7am-7:30pm)
Run three bays on the weekend instead of two as initially planned

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.

Patient Transfer

Ensuring an Efficient Move into a Nine Story Inpatient Tower

Situation

Northwest Community Hospital, a 400 Bed hospital in suburban Chicago, is nationally recognized in Cardiac, Stroke and Gastrointestinal care. Their Existing Patient Tower dated back to 1958, all rooms were Semi-Private, in 38 Bed Nursing Units. They built a brand new 9 story patient tower called South Pavilion which added 200 beds, all of which are private. The challenge was how to most safely and effectively transfer the patients from the old tower to the new one.

Objectives

Of course, of utmost importance was that the patients be moved safely,
with no adverse medical complications.
They also wanted the move to be a positive experience for the patient
and their families.
To minimize the inconvenience to patients, the move would begin after patients
ate breakfast, at 8am, and should be completed in time for the last patients to be able
to eat lunch by 2pm.
Of secondary importance, was to conduct the move with efficient staffing.

Why Build a Simulation Model?

It was imperative that this move be successful. They only had one chance to do it right!
The only way they could "practice" was to do it via a simulation model.
The dynamic nature of the census required preparation for many different scenarios.
Moving patients lends itself very well to simulation. From a modeling standpoint, it is similar to a manufacturing or assembly process. The timing for the actual individual steps was very predictable.

Patient Flow Model

How the Model Influenced the Approach to the Move

Original Intended Plan: Move one floor at a time, to keep better control over the move.

What the model showed: This would take about 13 hours, compared to the 6 hour window the move was expected to take.
Final Decision: a shotgun start would be the most effective approach

Original Concern: Elevators were expected to be a significant bottleneck in the move process.

What the model showed: The elevators would NOT cause a bottleneck. The amount of time spent on the elevator was minimal, and so would be delays waiting for an elevator.
Final Decision: By designating elevators to specific floors, the amount of time spent waiting for elevators was minimized.

Results

The move was a success. It was completed by 12:30 pm (2 pm was the deadline) and had no significant issues.
The Model predicted the completion time within 15 minutes.
Management was very impressed with how much the simulation model helped, and by its accuracy â€“ future projects should have increased support.

Custom Solutions / Custom Development

Custom Hospital Patient Flow Analysis Technology

Situation

Objectives

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

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:

Length of Stay (LOS) reductions from their nursing unit discharge initiatives
Avoided the extra cost of just building extra PCU units.

Healthcare and Medical Facility Analytics