Tag Archives: prescriptive analytics

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.

Simulating Impatient Customers

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ProModel Guest Blogger: Dr. Farhad Moeeni, Professor of Computer & Information Technology, Arkansas State University

Dr. Farhed Moeeni - Prof. of Computer & Information Technology, Arkansas State University

Dr. Farhad Moeeni 

Simulation is one of the required courses for the MBA degree with MIS concentration at Arkansas State University.  The course was developed a few years ago with the help of a colleague (Dr. John Seydel).  We use Simulation Using Promodel, Third Ed. (Harrell, et al., Ghosh and Bowden, McGraw-Hill) for the course.  In addition, students have access to the full-version of the Promodel software in our Data Automation Laboratory. The course has attracted graduate students from other areas including arts and sciences, social sciences and engineering technology who took the course as elective or for enhancing research capability.  Students experience the entire cycle of simulation modeling and analysis through                                          comprehensive group projects with a focus on business decision making.

Most elements of waiting lines are shared by various queuing systems regardless of entity types such as human, inanimate, or intangible.  However, a few features are unique to human entities and service systems, two of which are balking and reneging.  One of the fairly recent class projects included modeling the university’s main cafeteria with various food islands. Teams were directed to also model balking and reneging, which was challenging. The project led to studying various balking and reneging scenarios and their modeling implications, which was very informative.

Disregarding the simple case of balking caused by queue capacity, balking and reneging happens because of impatience.  Balking means customers evaluate the waiting line, anticipate the required waiting time upon arrival (most likely by observing the queue length) and decide whether to join the queue or leave. In short, balking happens when the person’s tolerance for waiting is less than the anticipated waiting time at arrival.  Reneging happens after a person joins the queue but later leaves because he/she feels waiting no longer is tolerable or has utility.  Literature indicates that both decisions can be the result of complex behavioral traits, criticality of the service and service environment (servicescape). Therefore, acquiring information about and modeling balking or reneging can be hard.  However, it offers additional information on service effectiveness that is hard to derive from analyzing waiting times and queue length.

For modeling purposes, the balking and reneging behavior is usually converted into some probability distributions or rules to trigger them. To alleviate complexity, simplified approaches have been suggested in the literature.  Each treatment is based on simplifying assumptions and only approximates the behavior of customers. This article addresses some approaches to simulate balking.  Reneging will be covered in future articles.  Scenarios to model balking behavior include:

  1. On arrival, the entity joins the queue only if the queue length is less than a specified number but balks otherwise.
  2. On arrival, the entity joins the queue if the queue length is less than or equal to a specified number. However, if the length of the queue exceeds, the entity joins the queue with probability  and balks with probability  (Bernoulli distribution).
  3. The same as Model 2 but several (Bernoulli) conditional probability distribution is constructed for various queue lengths (see the Example).
  4. On arrival, a maximum tolerable length of queue is determined from a discrete probability distribution for each entity. The maximum number is then compared with the queue length at the moment of arrival to determine whether or not the entity balks.

The first three approaches model the underlying tolerance for waiting implicitly.  Model 4 allows tolerance variation among customers to be modeled explicitly.

A simulation example of Model 3 is presented. The purpose is to demonstrate the structure of the model and not model efficiency and compactness.  The model includes a single server, FCFS discipline, random arrival and service.  The conditional probability distributions of balking behavior are presented in the table. The data must be extracted from the field.  The simulation model is also presented below. After running the models for 10 hours, 55 (10% of) customers balked. Balking information can be very useful in designing or fine-tuning queuing systems in addition to other statistics such as average/maximum waiting time or queue length, etc.

Condition

Conditional Probability Distribution
Probability of Joining the Queue (p) Probability of Balking (1-p)
Queue Length <= 4 1.00 0
5<=Queue Length <= 10 0.7 0.3
Queue Length > 10 0.2 0.8

Prof Mooeini Sim Chart

About Dr. Moeeni:

Dr. Farhad Moeeni is professor of Computer and Information Technology and the Founder of the Laboratory for the Study of Automatic Identification at Arkansas State University. He holds a M.S. degree in industrial engineering and a Ph.D. in operations management and information systems, both from the University of Arizona.

His articles have been published in various scholarly outlets including Decision Sciences Journal, International journal of Production Economics, International Journal of Production Research, International Transactions in Operational Research, Decision Line, and several others. He has also co-authored two book chapters on the subject of automatic identification with applications in cyber logistics and e-supply chain management along with several study books in support of various textbooks. .

He is a frequent guest lecturer on the subject of information systems at the “Centre Franco Americain”, University of Caen, France.

Current research interests are primarily in the design, analysis and implementation of automatic identification for data quality and efficiency, RFID-based real-time location sensing with warehousing applications, and supply chain management. Methodological interests include design of experiments, simulation modeling and analysis, and other operations research techniques. He is one of the pioneers in instructional design and the teaching of automatic identification concepts within MIS programs and also is RFID+ certified.

Dr. Moeeni is currently the principle investigator of a multi-university research project funded by Arkansas Science and Technology Authority, Co-founder of Consortium for Identity Systems Research and Education (CISRE), and on the Editorial Board of the International Journal of RF Technologies: Research and Applications.

Contact Information

moeeni@astate.edu

ProModel Solutions Presented at the 2015 Patient Flow Summit

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In May ProModel joined a diverse and talented group of healthcare professionals in Las Vegas to share best practices for improving process and positively impacting the quality of patient care.  Presenters provided views on a wide variety of patient flow issues including population health management, RTLS systems, healthcare reform, readmissions, surgical variability and ED processes.

Not only did ProModel have an exhibit at the event where we were able to officially unveil our new Patient Flow RX solution, but we were also very lucky to have ProModel client and user David Fernandez MHA there to give an insightful and informative presentation on his successful use of simulation in the healthcare world.  Fernandez is VP of Cancer Hospital, Neuroscience and Perioperative Services at Robert Wood Johnson University Hospital and his presentation “Let My Patients Flow! Streamlining the OR Suite” described his use of lean management principles and simulation modeling to improve patient flow in the OR.

David Fernandez MHA, Robert Wood Johnson University Hospital discusses his use of simulation for improving patient flow in the OR

David Fernandez MHA, Robert Wood Johnson University Hospital discusses his use of simulation for improving patient flow in the OR

Among numerous other presentations, keynote speaker Eugene Litvak PhD, President & CEO of Institute for Healthcare Optimization was there to address the application of queuing theory to healthcare processes, as he believes it is a methodology that will correctly address the challenge of hospitals to match random patient demand to fixed capacity.

The Patient Flow Summit helped hospital leaders from all over the world learn the latest about optimizing capacity, streamlining operations, improving patient care, and increasing fiscal performance.

Presenters provided views on a wide variety of patient flow issues

Presenters provided views on a wide variety of patient flow issues

ProModels (L) Kurt Shampine, VP and (R) Dan Hickman, CTO – unveiling Pateint Flow Rx!

ProModels (L) Kurt Shampine, VP and (R) Dan Hickman, CTO – unveiling Pateint Flow Rx!

Power of Predictive Analytics for Healthcare System Improvement and Patient Flow

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Hospitals are currently under intense pressure to simultaneously improve the effectiveness and efficiency of healthcare delivery in an environment where operating costs are being reduced, downsizing and consolidation is the norm, and cost for care is increasing while revenue is decreasing.  At the same time the systemic effects of peak census and varying demand on patient LOS are creating capacity issues and unacceptable patient wait times…leading to a major decline in patient satisfaction.

The amount of proposals to enhance a hospitals quality care are as numerous as the healthcare professionals dedicated to the cause.  What hospitals need however is the ability to quickly and accurately evaluate the impact of those various operational proposals and to experiment with system behavior without disrupting the actual system – and ProModel’s simulation technology is allowing them to do just that.

The predictive analytic capability of ProModel simulation will allow healthcare professionals to test assumptions and answer those patient flow “what if” questions in a matter of minutes and days, not weeks and months.  Simply put, it’s providing a decision support system to assist healthcare leaders in making critical decisions quickly with a higher degree of accuracy and confidence.

Simulation will also help healthcare staff quickly identify room availability and recognize high risk patient flow bottlenecks before extreme problems occur.  This invaluable knowledge will then lead to reductions in patient wait times and LOS, avoid unnecessary re-admissions and costly expansions, and most importantly – increase the overall quality of service and patient satisfaction.

Flanagan Industries Brings New Facility Online Thanks To ProModel Solution

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Flanagan Industries is a major contract manufacturer of aerospace hardware specializing in highly engineered and high value machined components and assemblies.  Over the years their manufacturing operations had been growing steadily to the point where they absolutely needed additional capacity . The original space was not conducive to a manufacturing environment and had become an impediment to taking on more business and staying competitive in the global economy.  So Flanagan decided to expand by opening a new facility that could house bigger and better machinery, however they needed to ensure that the move to the new location would not disrupt their current operations and customer orders.

In the video below, see how Flanagan used a ProModel Simulation Solution to successfully bring their new facility online:

 

 

 

FREE ProModel Webinar: Predictive vs. Prescriptive Analytics

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Join ProModel’s CTO, Dan Hickman, and Product Manager, Kevin Jacobson (KJ), on Wednesday November 5, 2014 – 2:00 PM EST for an informative webinar on predictive vs. prescriptive analytics. 

With over 15 years in the industry, Dan has an uncanny understanding of how important both types of analyses are to the success of your business. KJ, with ProModel for over 11 years, manages the Project and Portfolio Simulation product development group. He works closely with our clients on the development of advanced PPM (Project Portfolio Management) predictive and prescriptive analytic tools. He has the hands-on experience to best illustrate how the tool works and how it can help you with your predictive and prescriptive analytic needs.

Together they will show you how ProModel’s Enterprise Portfolio Simulator with Portfolio Scheduler provides the benefits prescriptive analysis can bring to resource capacity planning and project selection. Gain an understanding of the difference between applying predictive and prescriptive analytics to your PPM data, with specific examples focusing on scenario experimentation and portfolio optimization.  KJ will demo some of the newer features of EPS that provide logical recipes for modeling  and show how these tools can help you represent your unique PPM business rules.  The new business rules capabilities of EPS provide portfolio simulation like never before.

CLICK BELOW TO REGISTER FOR THIS WEBINAR NOW!

https://www150.livemeeting.com/lrs/8002083257/Registration.aspx?pageName=k09m7ldp55z3t048&FromPublicUrl=1

 

 

Same Venue, Different Challenges

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Weeds Pic

Rob Wedertz – Director, Navy Programs

Just a few weeks ago, I had the privilege of attending the Tail Hook Association’s annual conference in Reno, Nevada.  It is the first time I attended the conference not as an active duty member of the Naval Aviation community, but as a vendor supporting the enterprise through our role as the software application provider of the Naval Synchronization Toolset.  Surprisingly, other than keeping much different hours and standing on the opposite side of the booth table, the conference felt much like it did every year I have attended in the past.  There were many “so what are you doing these days?” conversations with old friends and the ever-present aura of “Naval Aviation is special because…” throughout the exhibit hall.

In fact, had I not taken the opportunity to attend some of the panels and engage some of our key stakeholders in pointed conversations it would have been extremely difficult to differentiate this year’s conference from any other I had attended over the last 2 decades.  There was a new vernacular that weaved its way into this year’s conference.  Words like “sequestration”, “draw-down”, and “budget constraints” permeated the Rose A ballroom, and for the first time in many years, I sensed a palpable uncertainty among the leadership of Naval Aviation as they extolled the virtues of tail hook aviation’s role in the world theatre against the backdrop of future shoe string budgets and unknown war fighting requirements.  (Ironically, the Air Boss told a poignant story of a “nugget” strike fighter pilot from CVW-8 expertly delivering ordnance in the fight against ISIS the same day the morning news detailed the withdrawal of forces from Afghanistan as “hostilities in the Middle East come to a close”.)

Given the environment we’re in and the abundance of questions marks hovering over the next several years, it should come as no surprise that many attendees, including most of the NAE leadership took a great deal of interest in the “little” ProModel booth nestled among missile mock-ups, Joint Strike Fighter simulators, and high-tech defense hardware displays.  In fact, as one of the very few (if not the only) predictive/prescriptive analytics software vendors in attendance at Hook ’14, we were an anomaly.

Tailhook '14

ProModel’s Keith Vadas and Carl Napoletano speak with VADM Dunaway, Commander, Naval Air Systems Command

 

A common theme emerged during our discussions with visitors and through comments made during the various panel discussions – decisions must be made via actionable data, courses of action must be modeled and validated, and technology-enabled decision support applications must be agile enough to get an answer in short order.  Thus the interest in ProModel.

While the Naval Synchronization Toolset is in its infancy from a relative viewpoint (we achieved initial operating capability just a year ago), ProModel has been delivering enterprise-wide decision support tool capabilities to its customers (both private and DoD) for over 25 years.  As industries have evolved (adopted Lean Six Sigma methodologies, harnessed data collection and aggregation, and leveraged emerging technologies) so has ProModel.  We have learned, alongside our customers, that there is significant “power” in diminishing uncertainties through “what-if” analysis and exploration of alternatives via technology-enabled decision support tools like the NST.  The questions the NAE gets asked have answers and it is discovering that getting there is a matter of adopting a philosophy that centers around modeling the behavior of the system, deciding on dials (variables), and exploring the alternatives.

The NST is that system.  Through our integration efforts with Veracity Forecasting and Analysis, we have delivered a software application that establishes the demand signal (the Master Aviation Plan module), models the behavior of the system (Carrier Strike Group Schedule, Air Wing Schedules, and Squadron Schedules), models the behavior of elements (the Airframe Inventory Management module) the utilization of the FA-18 A-F inventory over time, and provides a “sandbox” environment that facilitates optimal disposition of assets in order to meet the requirements of the NAE over time.

We heard, during our attendance at Hook ’14, that the optimal management of the FA-18 inventory was one of the focal points of the NAE leadership.  And although we’ve been involved in the development efforts of the NST for more than 2 years, it is the first time that the challenges of inventory management have taken center stage at a venue that has long been unchanged and timeless.  We felt privileged to be among the professionals in attendance at Hook ’14 and even more proud to be an integral part of the solution set to Naval Aviation’s challenges going forward.  We’ll be back next year and hope that the NAE is no longer talking about it.

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