Management Science for Healthcare Applications

Management Science for Healthcare Applications

Alexander Kolker (API Healthcare, USA)
Copyright: © 2014 |Pages: 11
DOI: 10.4018/978-1-4666-5202-6.ch131
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Introduction

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” (Reid et al., 2005), relatively little material resources and technical talent have been devoted to the proper functioning of the overall health care delivery as an integrated and economically sustainable system. This report provides strong convincing arguments that a big impact on quality, efficiency and sustainability of the health care system can be achieved by the systematic and widespread use of methods and principles of system engineering. The term ‘system engineering’ is frequently substituted by the terms: ‘management science’, ‘operations research’, ‘management engineering’, ‘industrial engineering’, ‘business analytics’ or ‘operations management’.

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 clinics, diagnostic imaging centers, insurance companies, and government bodies.

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, among others.

Management science methodology can be applied at all system levels (scales). However the specific method can be different depending on the system scale and complexity. For example, system dynamics that operates mostly with macro-level aggregated patient categories and large financial flows can be appropriate to analyzing the nationwide healthcare system and policy issues. On the other hand, a powerful method such as discrete event simulation that operates mostly with individual patients or documents, as entities, can be more appropriate to analyzing operations of the lower scale systems such as a separate hospital. Nonetheless, the separate hospital is itself a complex system, comprised of many interdependent departments and units.

The scope of healthcare management science/engineering can broadly be defined as developing managerial decisions for efficient allocating of material, human and financial resources needed for delivery of high quality care using various mathematical and computer simulation methods.

Given variable patient volumes and variable service/procedure time, management science methodology is indispensable in addressing 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 at the various points of care 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 to predict the future patient demand or transaction volumes?

  • Comparing Productivity of Different Units with Multiple Inputs and Outputs: How to combine different productivity metrics into one total score for each unit?

  • Optimizing a Supply Chain and Inventory Management: How to manage the supply chain to minimize the total procurement cost?

  • Performing Predictive Statistical Data Analysis for Marketing and Budget Planning: What business analytics and data mining technique to use?

This list can easily be extended to include any other area of operational management that requires quantitative analysis to justify managerial decision-making. Some quantitative techniques are summarized in this chapter in Table 1 ‘Summary of some quantitative methods used for various applications of management science’.

Key Terms in this Chapter

Management: Activities for controlling and leveraging the limited amount of available resources (material, financial and human) aimed at the best possible way of achieving system performance objectives.

Management Science (MS): Is a systematic way of developing managerial decisions for efficient allocating of material, human and financial resources needed for developing optimized business decisions or delivery high quality care using analytic mathematical and computer simulation methods. It is based on Operations research. MS methodology is practically equivalent to business analytics, management engineering, operations management, system engineering, industrial engineering.

Discrete Event Simulation: One of the most powerful methodologies for using computer models of the real systems to analyze their performance by tracking system changes (events) at discrete points in time.

Flow Bottleneck / Constraint: A resource (material or human) whose capacity is less than or equal to demand for its use.

Flaw (Deception) of Averages: Capacity, staffing and financial estimations based on average input values without taking into account the variability around the averages. This typically results in significant underestimation or, sometimes, overestimation of required resources (except for a strictly linear relationship between the input and output).

Operations Research: The discipline of applying mathematical models of complex systems with random variability aimed at developing optimized operational business decisions.

Complex System: A system that exhibits a mutual interdependency of components and for which a change in the input parameter(s) can result in a non-proportional large or small change of the system output.

Queuing Theory: Mathematical methods for analyzing the properties of waiting lines (queues) in simple systems without interdependency. Typically uses analytic formulas that must meet some rather stringent assumptions to be valid.

Management Principle: An insight that is both general (applicable to many settings) and stable (relevant now and in the future).

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