Sexy job, sense of humor, slogan
By Peter Horner
They already have the sexiest job of the 21st century according to a Harvard Business Review article by Tom Davenport and D.J. Patil. Now, it turns out, data scientists also have a great sense of humor. Who knew?
The Institute for Operations Research and the Management Sciences (INFORMS) will hold exams for its Certified Analytics Professional program according to the following schedule:
Campus of University of MD, Baltimore County
University of Colorado Denver
University of Washington
Eastside Leadership Center
Bellevue, Wash. (suburb of Seattle)
Queens School of Business
Toronto, Ontario, Canada
Jan. 11, 2014
ITPG Education Center
Vienna, Va. (suburb of Washington, D.C.)
Jan. 29, 2014
University of Alabama
Business Analytics Symposium
March 6, 2014
Drexel University James E. Marks Intercultural Center
March 29, 2014
INFORMS Conference on Business Analytics and O.R.
Westin Boston Waterfront
March 30, 2014
Gartner BI & Analytics Summit
Venetian Resort Hotel & Casino
June 21, 2014
INFORMS Conference on The Business of Big Data
San Jose Marriott
San Jose, Calif.
To apply, click on https://www.informs.org/Certification-Continuing-Ed/Analytics-Certification/Apply-for-Certification
For more information, click on https://www.informs.org/Certification-Continuing-Ed/Analytics-Certification
March 30 - April 1, 2013
2014 INFORMS Conference on Business Analytics & Operations Research
June 22-24, 2014
2014 INFORMS Conference on the Business of Big Data
San Jose, CA
December 15, 2013
AnyLogic Conference: Multimethod Simulation & Modeling
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Healthcare management engineering
Its scope, fundamental principles and potential and what it means for operations research professionals.
By Alexander Kolker
Modern medicine has achieved great progress in treating individual patients. However, according to the highly publicized report “Building a Better Delivery System: A New Engineering/Healthcare Partnership” , relatively little material resources and technical talent have been devoted to the proper functioning of the overall healthcare delivery as an integrated and economically sustainable system. This report provides strong convincing arguments that a real impact on quality, efficiency and sustainability of the healthcare system can be achieved by the systematic and widespread use of methods and principles of system engineering.
The system boundaries can be defined at different levels (scales). For example, a healthcare system can be defined at the nationwide level; in this case, the main interdependent and connected elements of the system are separate hospitals and large clinics, insurance companies, government bodies, etc.
At a lower level, a system can be defined as a stand-alone hospital; in this case the main interdependent and connected elements of the system are hospital departments, such as emergency, surgical, intensive care, etc.
Management/system engineering methodology can be applied at all system levels. However, the specific method can be different depending on the system scale and complexity. For example, system dynamics that operate mostly with macro-level aggregated patient categories and large financial flows can be an appropriate method for analyzing the nationwide healthcare system and policy issues. On the other hand, such a powerful method as discrete event simulation that operates mostly with individual patients as entities can be more appropriate for analyzing operations of the lower-scale systems such as an individual hospital. The separate hospital is itself a complex system, comprised of many interdependent departments and units.
The scope of healthcare management engineering (ME) can broadly be defined as developing managerial decisions for efficient allocation of material, human and financial resources needed for delivery of high-quality care using various mathematical and computer simulation methods. The term “management engineering” is sometimes substituted by other near-equivalent terms such as “operations research,” “system engineering,” “industrial engineering,” “management science” or “operations management.”
Given variable patient volumes and variable service/procedure time, ME methodology is indispensable in addressing hospital management issues, such as:
- Capacity: How many beds, operating rooms or pieces of equipment are needed for different services?
- Staffing: How many nurses and other providers are needed for a particular shift in a unit?
- Scheduling: How to optimally schedule the minimally required staff for the particular shifts?
- Patient flow: What maximal patient delays are acceptable in order to achieve the system throughput goals?
- Resource allocation: What minimal amount of resources is required for different patient service lines?
- Forecasting: How do you forecast the future patient demand or transaction volumes?
This list can easily be extended to include any other area of operational management that requires quantitative analysis to justify managerial decision-making.
Using an analogy, the entire hospital is a patient; ME is a medical field with different specialties; and the management engineer is a doctor who diagnoses the operational diseases and develops treatment plans for an ailing hospital and its operations.
Traditional Management and Management Engineering
There are many possible definitions of management. Here management is defined as controlling and leveraging available resources (material, financial and human) aimed at achieving system performance objectives.
Traditional healthcare management is based on past experience, intuition, educated guesses – simple linear projections based on the average values of input variables.
In contrast, ME is based on comparative outcomes of validated mathematical models of system operations.
As noted in , “Currently, management lacks the proper decision support for determining the consequences of their decisions and therefore for making good choices.” Decisions based on ME methodology are often different compared to traditional management decisions. Sometimes, they even look counterintuitive. Several factors contribute to this difference.
First, most managerial decisions in healthcare settings are being made in highly variable and random environments. Avoiding the complications of incorporating uncertainty into decision-making by ignoring it or turning it into certainty is a general human tendency. For example, an average procedure time is typically treated as if it is a fixed value, ignoring the effect of non-symmetrical variability around this average. Such a practice usually results in significantly inaccurate conclusions.
Another factor is that healthcare systems usually contain internal interconnections and interdependencies of units and staff. These multiple interconnections make healthcare systems truly complex. Traditional management lacks a means of capturing such interconnections and predicting the response of one unit to change in other units. This is a root cause of the frequently observed unintended consequences of managerial decisions that look reasonable on the surface.
One more factor is a non-linear scaling effect (size effect) of healthcare systems. Larger systems can function at a higher utilization level and lower patient waiting time compared to smaller systems with the same ratio of patient volumes to their size. Such non-linear relationships are not easy to incorporate into traditional decision-making.
Only analytic models (if applicable) or simulation models offer a means of capturing all these factors into the efficient managerial decision-making. Multiple examples of this approach are presented in  and .
Bridging the Gap Between ME Professionals and Hospital Administrators
The already referenced report  states in an unusually blunt way that “relatively few healthcare professionals or administrators are equipped to think analytically about healthcare delivery as a system or to appreciate the relevance of engineering tools. Even fewer are equipped to work with engineers to apply these tools.”
Thus, it is often difficult for many administrators to appreciate the role of management engineering methodology for the healthcare delivery process analysis. Many of them simply do not see “what’s in it for me.”
As Butler  states, “…it is imperative for administrators to familiarize themselves with the array of quantitative decision techniques provided by management science/operations research (MS/OR).” Vissers  argues, “Modeling-based health care management ought to become just as popular as evidence-based medicine.” Carter  summarizes, “… ailing healthcare systems desperately needs a dose of operations research.” Fabri  supports this assessment saying, “… fixing healthcare will require individuals who are ‘bilingual’ in healthcare and in systems engineering principles.” Kopach-Konrad et. al  also support this view: “… medical professionals and managers need to understand and appreciate the power that systems engineering concepts and tools can bring to re-designing and improving healthcare environments and practices.”
Berwick , the administrator of the Centers for Medicare and Medicaid Services, adds, “Healthcare leaders tend not to be aware of the engineering disciplines or to be suspicious of their applicability. … Bridge building here will be expensive, and it will take time, but it will pay off.”
Compton and Reid  argue along the same line: “… most healthcare professionals do not even know what questions to ask system engineers nor what to do with the answers, and vice versa. … Few system engineers understand the constraints under which healthcare providers operate. In short, these two groups of professionals often talk to each other but seldom understand each other.”
Story  states in his strongly articulated article, “Local hospital leadership must play a role, either becoming educated and passionate about systems [management] engineering or stepping aside to allow progress. …Leaders who remain unfamiliar with the concepts of system engineering often slow or even prevent change through a lack of passion, education and vision.” While this view might look extreme, it nonetheless reflects the depth of frustration and one of the root causes of the problem with practical implementation of ME methodology.
At the same time, for the last few years, some positive signs have been observed as ME slowly makes its way into healthcare settings. For example, a 2011 workshop  held in Washington, D.C., drew together participants from healthcare and engineering disciplines to identify challenges in healthcare that might benefit from a system engineering perspective.
To address the challenge of transforming the system of care delivery in practice, some leading healthcare organizations have adopted this area as a strategic priority. For example, the Mayo Clinic, one of the largest integrated medical centers in the United States, has defined the “science of healthcare delivery” as one of its four strategic directions. The Mayo Clinic has also created the Center for the Science of Healthcare Delivery, a new initiative that will focus on creating improved approaches to how healthcare is delivered .
Hospital administrators and physicians do not need to know all the technical details of management engineering methodology. This is another profession and a separate area of expertise. However, in order to be truly effective leaders, hospital administrators at all levels must understand why traditional management approaches are often not accurate, short-lived or unsustainable; which quantitative technique is more appropriate for addressing a particular managerial problem; and what can be expected from a particular technique and what are its strength and limitations.
Some Fundamental ME Principles
Several fundamental ME principles are summarized below. A more complete list and detailed illustrations of these principles are provided in  and  using multiple examples adapted from hospital and clinic practice. In a healthcare setting, these general management principles play a role similar to the laws of physics in natural sciences.
- For systems with a similar type of service, mutually interchangeable (pooled) resources are more efficient than specialized resources with the same total ratio capacity/workload.
- Specialized resources (staff, operating/ procedure rooms, beds, etc) typically cost more than mutually interchangeable (pooled) resources.
- Size matters. Large hospitals (units) have better comparative operational performance characteristics than small hospitals (units) with the same ratio patient volume/ size.
- Process improvement based on simple linear proportional adjustments of input values and direct benchmarking could be misguided and short-lived if the scale effect (organization’s size) is not taken into account.
- Workload leveling (smoothing) of scheduled procedures is an effective strategy for reducing wait time and improving patient flow.
- Improvement performance of separate subsystems or units/departments (local improvement) does not usually result in the improvement of the entire hospital system performance.
- Capacity, staffing and financial estimations based on average input values without taking into account the variability around the averages usually result in significant miscalculation of the required resources. This is called the flaw (deception) of averages.
Opportunities for OR/MS Professionals
Three categories of OR/MS professionals are involved in healthcare ME: university researchers, consulting firms and hospital staff members/employees.
University researchers (typically professors from industrial engineering departments) are involved in academic research that is funded through extramural grants and/or seek collaboration with local hospitals to perform projects on a consulting basis. The projects are funded through contracts with hospitals or local health authorities.
Consulting firms perform projects and other services for hospitals on a commercial basis. Typically, these firms are staffed with highly qualified OR/MS professionals in the areas of process simulation and data analysis. Because of the commercial basis of their work, the costs of consulting firms are typically much higher for hospitals than the costs of collaboration with universities.
OR/MS professionals hired by hospitals as staff members are potentially the most effective. They typically serve as dedicated internal consultants. However, many hospitals tend to hire entry-level MEs and place them in quality departments along with quality/process improvement specialists. The latter get used to the traditional approach. If the support and understanding of ME principles at the upper level is lacking, this mix only creates confusion. Only a handful of large hospitals have the vision of forming dedicated management engineering departments. However, staffing these departments with ME professionals who have hospital experience is a challenge because of their acute shortage.
Thus, on one hand OR/MS professionals have a lot of opportunities to apply their sorely needed expertise in healthcare settings. On the other hand, despite some encouraging signs, getting there is not easy because healthcare as a whole is not ready yet to widely let these type of professionals in, especially if it assumes significant change in traditional hospital relationships.
Hopefully, relentless demonstration of practically relevant examples of ME methodology along with the inevitable external pressure for cutting hospital costs by much more efficient use of available resources will result in a much wider demand for ME unique skills and expertise.
Alexander Kolker (firstname.lastname@example.org), Ph.D., is the outcomes operations project manager with Children’s Hospital and Health System, Wisconsin. He has more than 10 years of experience in practical application of quantitative methods for healthcare management, including hospital capacity expansion planning, system-wide patient flow analysis, forecasting, staffing and scheduling. The author of two books, he has presented at many international and national conferences on the topic of healthcare management engineering, operations management and simulation modeling. The views expressed in this article are the author’s own. They do not represent in any way the views of the author’s current affiliation with Children’s Hospital and Health System, Wisconsin.
- Reid, P., Compton, W, Grossman, J., Fanjiang, G., Eds., 2005, “Building a Better Delivery System: A New Engineering/Healthcare Partnership,” National Academy of Engineering and Institute of Medicine, Washington, D.C., National Academy Press.
- Joustra, P., de Witt, J., Van Dijk, N., Bakker, P., 2011, “How to juggle priorities? An interactive tool to provide quantitative support for strategic patient-mix decisions: an ophthalmology case,” Health Care Management Science, Vol. 14, No. 4.
- Kolker, A., 2011, “Healthcare Management Engineering: What Does This Fancy Term Really Mean?” Springer, N.Y.
- Kolker, A., 2011, “Efficient Managerial Decision-Making in Healthcare Settings: Examples and Fundamental Principles” in “Management Engineering for Effective Healthcare Delivery: Principles and Applications,” A. Kolker, P. Story (Eds), IGI-press Global, pp. 1-45.
- Butler, T., 1995, “Management Science/ Operations Research Projects in Health Care: The Administrator’s Perspective,” Health Care Management Review, Vol. 20, No. 1, pp. 19-25.
- Vissers, J M.H., 1998, “Health Care Management Modeling: A Process Perspective,” Health Care Management Science, Vol. 1, No. 2, pp. 77-85.
- Carter, M., 2002, “Health Care Management. Diagnosis: Mismanagement of Resources,” OR/MS Today, Vol. 29, No. 2 (April), pp. 26-32.
- Fabri, P., 2008, “Can Health Care Engineering Fix Health Care?” American Medical Association Journal of Ethics, Virtual Mentor, Vol. 10, No. 5, pp. 317-319.
- Kopach-Konrad, R., Lawley, M., Criswell, M., Hasan, I., Chakraborty, S., Pekny, J., Doebbeling, B., 2007, “Applying Systems Engineering Principles in Improving Health Care Delivery,” Journal of General Internal Medicine, Vol. 22, No. 3, pp. 431-437.
- Berwick, D., 2011, “Observations on Initiating Systems Change in Healthcare: Challenges to Overcome” in “Engineering a Learning Healthcare System: A Look at the Future,” workshop summary, Washington, D.C., The National Academies Press, p.58.
- Compton, W. Dale, Reid, P., 2008 (Spring), “Engineering and Health Care Delivery System (Editorial), The Bridge, Vol. 38, No. 1, pp. 3-5 (National Academy of Engineering)
- Story, P., 2009, “Are We Thinking Systems Yet ?” American Society for Quality (ASQ), January 2009.
- Mayo Clinic, 2011, “Mayo Clinic Launches New Center to Focus on How Healthcare is Delivered,” Mayo Clinic News, Jan. 25.