Author Archives: stevehaekler

Dr. Linda Ann Riley’s (Univ. New Haven) Innovative Teaching Approach for ProModel and Process Simulator (2 of 2)

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Headshot_Linda Riley UNH

Dr. Linda Ann Riley Ph.D. Univ New Haven

As mentioned previously in the first post of this series, students in this final two-course sequence complete a technical capstone project. For the first time teaching in this program, I allowed the students to choose either ProModel or Process Simulator as the analysis program for their individual technical project.  In the Six Sigma class, the first sections of the technical paper were completed and involved identifying a process improvement opportunity in the student’s workplace, creating a project charter, value stream map, process flow chart, and undertaking extensive lean/six sigma statistical and qualitative analysis of the as-is process.  Once complete, students then propose process improvement scenarios.

The ultimate goal of the final class in the MSEOM program, Simulation Techniques and Applications, is to develop a moderate level of expertise using ProModel and then simulate both the as-is and the initial improved process design identified in the Six Sigma class.  Through iterations on process improvement using ProModel, the end result is an improved process that meets stakeholder requirements. One of the benefits of the students’ familiarity with Process Simulator was the number of common features between the two programs such as the Output Viewer environment, shared statements and functions and the Minitab interface.  But students did exhibit some push back when it came to building a model in ProModel.  To them, the Process Simulator environment was far easier to construct a model.

From my perspective, in Process Simulator, they relied too heavily on default values that are automatically inserted when simulation properties are applied, as well as the input and output buffers associated with each activity.  These features, e.g. input buffers, became the solution to any bottleneck in a process.  On the other hand, compared to learning ProModel, these built-in defaults caused far less frustration for the students when first running the model.  The models never became “gridlocked” because there were virtually unlimited buffers for each activity.

My intent in the simulation class was that every lab model and exercise undertaken in ProModel would be also completed in Process Simulator and vice versa.  This seemed at first to be a good reinforcement for using both programs.  Unfortunately, this strategy failed.  Relatively new to Process Simulator myself, I didn’t initially realize that several of the basic ProModel statements such as Group, Move With, Graphic and View, which had been required for my ProModel labs, were not available in Process Simulator.  In addition, the class used the student version of ProModel.  Consequently, when opening the Process Simulator exercises in ProModel, activities in Process Simulator, which correlate to locations in ProModel exceeded the student version limits.  One of the reasons this occurred was because a single activity in Process Simulator translated to three different locations in ProModel because of the input and output buffers.  The next time I teach the two-course sequence, I have a much clearer perspective of how to seamlessly construct the labs and exercises so they are interchangeable between the two programs.

After attempting this experiment, it is now far more evident to me that Process Simulator is a superior product for modeling processes defined using process flow charts as shown below.  It is ideal for use in a Quality, Six Sigma/Lean Process Optimization scenario.  I will definitely continue to use the product moving forward.

2018-10-17 14_48_01-Electronics Manufacturing.vsd [Compatibility Mode] - Visio Professional

ProModel best models highly dynamic scenarios where for example, detailed “scoreboards,” visualization of movement and external file reading and writing is required. In my opinion, it is best used for modeling large-scale systems with simulation as portrayed in the model screen shot below:

PM commuter transit model

I will also continue to use ProModel but I most likely won’t be using both Process Simulator and ProModel concurrently.  Process Simulator is ideal for an introduction to modeling while ProModel allows for far more complexity in all aspects of modeling.

In the end, approximately 20% of my students used Process Simulator as the modeling tool for their capstone project while the remainder used ProModel.  For most of my students, CAD drawings of workplace layouts and GIS mapping files that accurately reflect scale and travel times were used as background graphics for their technical models.  In addition, many of the students used external arrival files containing multiple attributes associated with each entity arrival.  Thus, ProModel was the software product of choice in these instances.

My final assessment is that Process Simulator is a phenomenal product.  With an expert level background in ProModel and Visio, the learning curve for me was practically non-existent.  For students, their familiarity with the Visio environment made the introduction to Process Simulator both fast and challenge-free.

About Dr. Linda Ann Riley Contact Information: linda.ann.riley@gmail.com

Linda Ann Riley, Ph.D. presently serves as an Adjunct Professor of Engineering for the University of New Haven’s graduate program in Engineering and Operations Management. She retired from full time teaching and administration in 2015.  Dr. Riley worked for 12 years at Roger Williams University (RWU) where she held the positions of Associate Dean, Engineering Program Coordinator and Professor of Engineering. Prior to RWU, she was a Professor and Program Director at New Mexico State University for 18 years.  Her teaching experience includes both engineering and business courses and she is the recipient of a number of corporate, university and national excellence in teaching awards. Dr. Riley is the author/co-author of over 100 articles, technical and research reports, and book contributions. Her area of scholarly interest involves stochastic system optimization using simulation and evolutionary algorithms.

Real-World Simulation Examples for Student Learning

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Prof Hassan Rajaei Ph.D. – Bowling Green State University; Department of Computer Science

Objectives

Simulation is a powerful tool for teaching students about the techniques as well as providing deeper understanding of courses such as networking, operating systems, operational research, just to name a few. Simulation is a well-known technique for evaluating what-if scenarios and decision making in industry, defense, finance, and many others. Students quickly realize these values and want to learn how to master this technique.

Teaching simulation techniques often requires attractive problem assignments. Real-world has numerous examples that excite students to study and motivate them focusing on their learning objectives. Further, it challenges them to develop models to reflect the reality. Clear examples can teach students how to collect data, develop the base model, improve it to advanced models, analyze the obtained results, and think about the usability of their simulation results. These learning outcomes can clearly demonstrate valuable educational objectives.

A simulation tool like ProModel has numerous example models in its library, but the educational objectives can be best achieved through step-by-step experimental development of useful samples. ProModel can be a great help by exploring the details of similar examples.

This article, presents an example where a group of students developed a simulation model for the Bowling Green State University (BGSU) Students Union Cafeteria. Managing a university dining hall often exhibits challenges for the food services located in it. This study focused on reducing the average waiting time of the diners, while increasing overall efficiency of the services.

Simulating the Nest Cafeteria

This project focused on finding solutions for the Falcon’s Nest Cafeteria to increase the efficiency and decrease the average time of the customer spent in the system.

Overview of the Nest: Students cafeteria at BGSU functions as an important part of the University’s dining service. This cafeteria serves thousands of students every day. During the rush-hours of lunch and dinner, this place gets really congested with long queues contributing to long waiting times. In this simulation, the Nest model consists of five main components: Customers, Servers, Locations, Queues, and Cashiers.

Using ProModel: This tool was selected for multiple reasons: a) the availability; b) the course used the tool and trained students; c) the tool supports discrete-event systems; d) large number of library models; e) statistical analysis and output results; f) animations.

Problems Encountered: The main problem faced was lack of statistics and accurate information. Other barriers included project time limit and lack of deeper familiarity of ProModel.

Possible Solutions: Based on primary analysis, two potential solutions were feasible:

1) Increase the attractiveness of other food stations which have lower waiting time;

2) Increase number of food servers.

Three approaches to reach the goals:

a) Ask the SME to provide all data and statistics;

b) Make a very detailed model over the actual system;

c) Combination of (a) & (b) methods.

Approach c was adopted for the study.

Simulation Models

Four models were developed: 1) base, 2) intermediate, 3) advanced, 4) final

Base Model: The base model had very basic setups with one food station and one cashier. The objective was to test the station service and the customers’ arrival, and their flow in the system.

Intermediate Model: All food stations were added according to the Nest along with the logic for entities to move through the system with a shared queue.

 Advanced Model: The advanced model includes all queues targeting to obtain realistic statistics using several scenarios (Figure 1).

Figure 1

Figure 1:  The Advanced Model improved from the intermediate one

Final Model: After developing three scenarios, obtaining good confidence, making sure they were on the right track, students moved towards developing the final model shown in Figure 2. It was implemented using a time schedule to simulate the rush hour and normal operating hours.

Figure 2

Figure 2:  Final simulation model for the Falcon’s Nest Cafeteria

Results and Analysis

In this simulation, students first aimed to find an ideal solution to demonstrate how to reduce the waiting time. It turned out that such a scenario would need more implementation time. Instead, students focused on two solutions:

1) To make other food stations more attractive;

2) Adding additional workers to the top three food stations. Test cases were developed for each solution.

The result shown in Figure 3 demonstrate a reduction in the average time compared to the baseline, except Case 3. The figure suggests an 11.1% decrease in average time spent in the Nest.

Figure 3

Figure 3: Solution 1 demonstrating reduction of Average Time in the system

Next method focused on improving the waiting time by adding food runners to 3 populated stations. This method was simulated and tested with 4 scenarios, and was compared with the baseline.

Figure 4

Figure 4: Solution 2, advocating one additional worker at each food station

As was expected, by adding a food-runner to each station the average time of the customers would decrease, however, certain stations would benefit most. If case 4 is adopted, there would be a 12.6% reduction in time spent by customers. If only 1 food runner is added, then the result yields only to 6.1% decrease in average time spent in the system by customers.

Concluding Remarks

This article presents an example of a real-world case study conducted by a group of students as a term project in a simulation techniques course shared by senior undergraduate students as well as graduate students. An important result of this study demonstrates how deeply the students were engaged in their learning objectives of the course. In a short period of time, they conducted a complete case study including: observation, gathering data, analyzing the problem at hand, developing models, confirming with the subject matter expert, documenting, and delivering the results. The full article is published in ASEE 2017 Annual Conference.

Professor Hassan Rajaei Ph.D.  

Hassan Rajaei is a Professor of Computer Science at Bowling Green State University, Ohio.  His research interests include Distributed Systems & IoT, Cloud Computing, High Performance Computing (HPC), Computer Simulation, Distributed Simulation, with applications focus on communications & wireless networks. Dr. Rajaei has been active in simulation conferences (e.g. SCS SprintSim, WSC) as organizer as well as research contributor. Dr. Rajaei received his Ph.D. from Royal Institute of Technologies, KTH, Stockholm, Sweden and he holds a MSEE from Univ. of Utah.

Decision Support Tool Promotes Army’s Supply Chain Readiness

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Two Department of Defense publications recently ran articles on the Army’s continuous pursuit of  supply chain excellence.  Our men and women in the military cannot do their jobs without the right materiel at the right place, at the right time, in the right quantity, and the right condition. ProModel Corporation is the software developer behind the Decision Support Tool (DST) featured in both articles.

Learn more about ProModel and our products, in booth #200 at the upcoming AUSA Global Force Symposium and Exposition on Mar 26-28 at the Von Braun Center in Huntsville, AL.

Decision Support Tool Promotes Army’s Supply Chain Readiness

“DST gives materiel managers the capability to realign equipment on their property books to maximize readiness or fulfill high-priority requirements. A plan that once took days to create can now be completed in minutes. With just a few clicks of the mouse, property book officers can optimize their formation, synchronizing existing equipment on-hand against authorization.”

– Lt. Col. Rodney Smith, chief, Distribution Integration Division, DMC.

Read full Army.mil article

AMC (Army Materiel Command) Restoring its Atrophied Repair Parts Inventory

Using a new system called the Decision Support Tool that Army Sustainment Command runs out of Rock Island Arsenal, Ill., “for the first time in my career, we can really see ourselves, and so we know where every single piece of equipment in the Army is, whether you are in the active component, the Reserve component or the National Guard. It’s never been that way; it’s very powerful.”

Army Materiel Command – General Gus Perna

Read full Defense News article

Huntsville Utilities Knows How to Win Back Customers

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Watch this presentation from the 2017 E Source Forum Conference given by Huntsville Utilities on how they rebuilt customer relationships and prioritized customer experience (CX). ProModel played a role in this turnaround as mentioned starting at the 6:40 mark.

Speaking of Huntsville Alabama – We will be at the annual AUSA Global Force Symposium and Expo at the Von Braun Center in Huntsville, AL Mar 26-28. Stop by ProModel booth #200 and check out our latest products and releases.

Oilfield Equipment Manufacturer Optimizes New Facility Design

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CHALLENGES

A leader in the design, manufacture, and supply of oilfield equipment had recently purchased land to build a world class manufacturing facility.  The new location would be designed to capture future growth but needed to be sized correctly; not a wasteful over-construction yet not too small at the same time.

The senior executive team thought simulation modeling would allow them to analyze their manufacturing processes, identify bottlenecks, capture productivity improvements, and properly size the new facility.  After a lengthy vendor sourcing exercise, ProModel Corporation was selected as the best provider to answer this modeling challenge.

OBJECTIVES

  • Model the existing manufacturing processes
  • Identify current process constraints using various customer demand scenarios
  • Simulate maximum throughput potential with the current processes and equipment layout
  • Using LEAN process improvement skills, simulate a more productive manufacturing process and scale that upward to capture growth
  • Simulate the new manufacturing facility and validate the desired growth rates. Upon completion of this step, the layout would be given to the architects for structural design.

VALUE PROVIDED

  • Immediate identification of a critical bottleneck that once resolved, increased cell throughput by 53% and overall production by 19%
  • Throughput has grown 45% since the launch of the initiative due to a much better understanding of their manufacturing methods and related constraints
  • Manufacturing standards used by the production planning team were far from accurate thus creating a workflow imbalance
  • Equipment previously slated for purchase was determined to add no throughput benefit thus saving several hundred thousand in capital expenditures
  • Numerous future state layouts were modeled thus allowing the team to ultimately select the most productive equipment arrangements
  • The simulation model became a powerful sales tool with customers; understanding the flow in the facility and how it could absorb their incremental orders
  • Even during a severe industry downturn, the company continued to capture market share due to improved manufacturing methods.

SOLUTION

A ProModel senior consultant worked with the engineering staff to build dynamic models of their current production facility and planned future construction.

First, a dynamic flexible model of the existing facility was created and validated.  That model was used to define the true capacity of the existing facility, analyze current constraints, evaluate capital improvement options, and test new LEAN concepts that were under consideration for the current and future facility.

A major challenge to creating the model was accommodating the tremendous variety of products manufactured.  A user friendly interface for running the model was developed to provide the ability to run any variation of mix/demand against several operational configurations.

The key learnings from the existing facility model were then applied to the new facility design.  Alternate facility layouts and new material handling concepts were evaluated to ensure the plant of the future would meet all capacity targets.

3D Animation of a Portion of the Plant

3D Animation of a Portion of the Plant

 

An Intern Walks into ProModel

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jenn-ross-headshotInauguration Day

There it is. I can see it. Sitting there, small and shiny on an oversized executive style desk is an ominous call bell. Do I ring the bell?  Maybe that is a little weird. Maybe I should cough loudly. Maybe I should shuffle forward quietly. It is my first day as an intern at ProModel and I am nervous. I am relieved from my quandary and greeted warmly by the head of marketing. We tour the office and I shake hands with my new associates. My nerves simultaneously fade as each new face brings kindness and words of welcome.

My first day brings many small challenges and small victories. The nine seasons of office experience I had previously acquired did not have me completely prepared for this new journey. Apparently, many things go into working in an office. Things I did not learn from watching “The Office” on NBC. I learned to push the little red tab in on the hot water spout at the water dispenser and learned the number to dial before you dial the number on the office phones.

I receive a briefing from two passionate and experienced model builders. Discrete event simulation’s ability to predict outcomes under uncertainty leaves me feeling impressed and hopeful. Modeling can aid in reducing patient wait time, improving various military operations, increasing throughput in a warehouse, and solving real world problems. There appears to be no limit to the industry or circumstance in which modeling can provide clarity. There was even one story about a model built for pigs.

jenn-ross-in-her-office

Looking Ahead to My First 100 Days in My Not-So-Oval-Office

This internship will allow me to dabble in many fields. I am looking to launch my career and discover the route that best fits my strengths and interests. I will explore and experiment in model building, finance, marketing, and customer relations. The friendly, team-oriented work environment encourages me to find ways to add value to the company. There is some grunt work involved in being an intern, but there is also the opportunity to play with cardboard boxes and organize filing cabinets.

When the head of marketing first came to me and asked me to write a blog post, it scared me a little. My apprehension quickly turned to excitement as I reflected on my first few weeks experience. This post has been easy to write, as the experience thus far has been superb and the employees could not be kinder.  I anticipate writing future blog posts as my internship progresses!

Jenn Rosscat-1999679_1280

Healthcare IT: Top Trends and Innovations in 2016

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This post was originally published by Christine Slocumb on the Clarity Quest Marketing blog and has been re-posted with permission. 

As the President of one of the top healthcare marketing agencies, I’m continually fascinated at the wide array of technologies emerging in the space. Every week we get lead calls from companies with new products or services addressing pressing healthcare technology issues.

Here are some of the trends we hear lots of buzz around in 2016.

Prescriptive Analytics

Health systems are getting more sophisticated at understanding their current state using descriptive analytics of their data, however, knowing what’s going right or wrong is only a small step in fixing the issues. Now companies offer predictive and even prescriptive analytics to forecast the future and to offer corrective suggestions.

One example is FutureFlow Rx by ProModel, which not only predicts patient flow across a health system, but also gives corrective actions and likely outcomes for each.

Prescriptive Analytics versus Predictive Analytics versus Diagnostic Analytics graphic from FutureFlowRx
The move from descriptive to prescriptive analytics. Courtesy: ProModel Corporation

Data Migration Tools

Whether it’s moving DICOM images from one system to another or migrating from one EHR system to another, tools to transfer data are here to stay.

Interconnectivity

Connecting records and information systems is still in its infancy. We’re seeing more and more demand for tools that provide interfaces, such as eMedApps’ Care Connectivity Platform™, which maintains continuity and uptime while establishing bridges.

eMedApps CareBridge Interoperability Platform Diagram

Niche Practice Systems by Specialty

Epic, Allscripts, and the other big EHR dogs have trouble breaking into specialty areas such as dermatology, ENT and more notably behavioral health. Smaller companies, such as Logik Solutions, which sells billing software for behavioral health, are growing by selling into practices in specialty areas.

Consolidation in Imaging IT

IBM bought Merge; Fuji acquired TeraMedica; and Hitachi left the VNA business. PACS is a tough replacement sale and vendor neutral archives are often seen as a “nice to have” versus a “must have”. Expect to see more shakeout and consolidation in this area.

More Data Integration Between Payers, Providers and Pharmaceutical Companies

Clinicians need a better way to understand which drugs are covered under specific payer plans, at what levels, and if policies and restrictions are attached to a drug. Payers need to keep costs under control. Pharmaceutical companies want to promote their drugs as quickly and efficiently as possible. Expect to see systems such as MMIT’s Mobile Search Formulary App that offer an accurate display of drug coverage to all parties by validating data from multiple payer and pharma sources.

No doubt these are exciting times for health IT. Stay tuned for our next post on this topic after HIMSS 2017.

About the Author:
Chris is the founder and president of Clarity Quest Marketing, where she leads a talented group of marketers and designers helping healthcare and technology companies achieve marketing and business goals. To learn more about Chris’ experiences and qualifications, visit our Meet Our Executive Team page.

Top 8 Benefits of Proactive Patient Flow Optimization

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DanH avatar_34902-300x300

Dan Hickman ProModel CTO

Unpredictably high numbers of scheduled admissions and an uncertain number of available beds.

Stressed staff due to ED boarding, long patient wait times, and off-service placements.

Length of stay and cost per case metrics exceed CMS value-based care efficiency measures.

Sound familiar? 

Patient flow optimization is one of the most cost-effective ways to improve operational effectiveness, the patient stay experience and your hospital’s bottom line. Here’s how.

Top 8 Reasons to Implement Patient Flow Optimization Today

  1. Decrease the Length of Stay (LOS). Find “hidden discharges” (potential candidates for discharge based on diagnosis codes and average LOS metrics) in your current census.
  2. Improve Bottleneck and ADT Issue Visibility. Simply having data does not empower decision makers. In fact, too much data can cause clinical operations staff to ignore it altogether. A patient flow optimization system delivers visual data all hospital staff can easily digest and use to make informed decisions that benefit the hospital and the patients.
  3. Right-size Staffing. By coupling accurate census predictions with staff needs, your health system will experience lower labor costs based on predictable admit, discharge and transfer (ADT) cycles, optimal staffing sizes and diminished demand for expensive nursing agency personnel.
  4. Enhance the Patient Journey. Minimize patient frustration by admitting the vast majority of inpatients to on-service units, even during peak periods.
  5. Capture Additional Revenue. Decreasing length of stay increases bed capacity, so fewer patients leave the hospital without being seen.
  6. Increase Access to Care. Patient flow optimization decreases ED boarding duration, speeds up admissions, and lowers left without being seen (LWBS) rates.
  7. Lower Infrastructure Costs. With patient flow optimization, health systems make optimal use of the existing hospital’s physical footprint, avoiding unnecessary costly build outs.
  8. Staff Satisfaction. Welcome to the stress-free huddle. FutureFlow Rx gives your staff a personal heads-up on issues affecting admissions, discharges and transfers, so they can be addressed at huddle meetings. Prescriptive corrective actions from the patient flow optimization system further empower staff with recommendations based on data and simulation.

 

About FutureFlow Rx™ Patient Flow Optimization

FutureFlow Rx by ProModel uses historical patient flow patterns, real-time clinical data, and discrete event simulation to reveal key trends, provide operational insights, and deliver specific corrective action recommendations to enhance the patient stay experience, lower costs and drive additional revenues. Our platform accurately predicts future events, helping hospitals make the right operational decisions to reduce risk, decrease LOS and improve operational margins. Schedule a demo.

dashboard 300 dpi

 FutureFlow Rx’s dashboard consists of  key performance indicator (KPI) “cards”. The left side of each card shows the last 24 hours; the right side predicts the “Next 24”; and clicking the upper right “light bulbs” provides prescriptive actions to improve the predicted future.

 

Improved Bottom Line and Better Care? Start with A-D-T

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kurt-shampine-avatar-150x150w-whitebackground

Kurt Shampine ProModel Corp Sr. VP LifeSciences

By solving issues related to admission, transfer, and discharge (ADT), patient flow optimization is one of the most cost effective and simplest ways to improve quality of care and increase revenues. Patient flow analytics systems can safely accelerate the patient journey through your health system, so you can make the best choices for your patients and your hospital’s bottom line.

Let’s take a look at how patient flow optimization can address each element of ADT.

modules_adm_dschrgewhitebackgroundA: ADMIT Patients Effectively

Efficient patient flow through hospital departments leads to satisfying healthcare experiences for everyone involved in the patient’s care. Increase safety and satisfaction by always having an admit bed in the right unit at the right time.

Emergency Department (ED) boarding and overcrowding issues are often at the center of admission challenges. 91% of hospital ED directors experience issues with overcrowding and its associated complications. When EDs try to function above their capacity limits, patients suffer from long wait times, increased lengths of stay, and improper care and attention from overloaded staff.

The key to preventing overcrowding and boarding is to address patient flow hospital-wide so admissions can be made quickly to the correct departments 24/7.

modules_adm_dschrgewhitebackground D: Solve Slow DISCHARGE Dilemmas

When admissions outpace discharges, hospitals face mounting issues such as lack of available beds, overcrowded waiting rooms, and staff shortages. Every hour of discharge delay costs your hospital up to $2,500 per patient.

Patient flow analytics can help your hospital solve the discharge puzzle to reduce length of stay, prioritize morning discharges and improve patient throughput.

A quality prescriptive patient flow system can even find hidden discharges in your current census. With one glance at a dashboard you can see which patients are approaching discharge by diagnosis, past-due discharges, and those with co-morbidity issues requiring a possible diagnosis-related group (DRG) transfer.

modules_adm_dschrgewhitebackground T: TRANSFER Smarter & Faster

Effective patient transfers play a major role in keeping patients safe and satisfied. Patients transitioning from one provider or healthcare setting to another too often get lost in the shuffle or delayed.

Patient flow optimization helps hospital staff facilitate the movement of patients from one facility, unit, or department to another using analytics and foresight to avoid any hurdles.
If a patient is initially placed in a suboptimal unit, a patient flow optimization system can prescribe a transfer to the correct unit as soon as a bed and staff are available in the correct service, ensuring that patient receives optimal care.

How FutureFlow Rx™ Solves ADT Challenges

Our real-time, prescriptive analytics system provides hospital staff with current patient flow conditions, future projections, and specific tips for improving patient flow over the entire ADT continuum.

FutureFlow Rx’s dashboard consists of key performance indicator (KPI) “cards”. The left side of each card shows the last 24 hours; the right side predicts the “Next 24”; and clicking the upper right “light bulbs” provides prescriptive actions to improve the predicted future.

Optimizing ADT effectiveness improves patient health and satisfaction, streamlines patient flow throughout the health system, and helps clinical teams work together as a cohesive unit leading to higher job satisfaction. With intelligent patient placement, unnecessary risks and costs can be more easily avoided.

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