The Healthcare Systems Engineering Master Program uses systems modeling and analytics tools coupled with a broad overview of systems of healthcare to enable students to address complex operational challenges. Students will encounter a variety of tools, including: project management, engineering economics, statistics and stochastic modeling, operations research and optimization, process flow and queuing, simulation and information systems analysis and design. The program also places a strong emphasis on applied learning and professional development, with relevant projects and assignments woven throughout the curriculum.

Mathematics Prerequisites

Introduction to Industrial Engineering Mathematics

A review of linear algebra and an introduction to quantitative analysis, manipulation of matrices, core concepts associated with systems of linear equations and linear optimization, algebraic and geometric models. The credits for this course cannot be applied to any undergraduate or graduate degree offered by the Industrial & Systems Engineering Department or the Healthcare Systems Engineering master’s program.

Engineering Probability and Statistics

Random variables, probability functions, expected values, statistical inference, hypothesis testing, regression and correlation, analysis of variance, introduction to design of experiments, and fundamentals of quality control.

Lehigh's Healthcare Systems Engineering Master program is unique in that the students come from a variety of academic and professional backgrounds, their common denominator being a collective frustration with the state of the healthcare system and a desire to use innovative and analytical approaches to improve it. This variety in backgrounds ensures a vibrant ecosystem and enhanced learning, but also creates a need to ensure that all the students will be successful in the rigorous engineering program they go through. To this end, two courses are offered for students who need to build or refresh quantitative knowledge, particularly in the areas of probability, statistics, and linear algebra, which are the areas of mathematics relevant to the problems and tools applicable in healthcare.


Healthcare Core Courses (12 credits)

Introduction to Healthcare Systems

The state of healthcare from economic, systems, quality, and historical perspectives. Components of the healthcare system including, facilities, delivery and treatment systems, and personnel. System costs, reimbursement methods and financial aspects in healthcare. Healthcare policy, laws and ethics. System performance measures including access, cost effectiveness and quality of care.

Quality and Process Improvement in Healthcare

The dimensions of healthcare quality and their definitions, quality metrics, accreditation and other benchmarking and evaluation methods. Change management, project planning and team management. Continuous improvement tools including “lean”, “six-sigma”, and “TQM”.

Financial Management in Healthcare

Engineering economics in healthcare; value metrics (net present value, return on investment, etc.), cost-benefit analysis, capital projects and improvements. Accounting methods in healthcare systems. Reimbursement methods, organizations, and alternatives. Financial strategy, planning, pricing and capital formation in “for”, and “not for” profit settings.

Information Technology in Healthcare

Introduction to information systems in healthcare. Components of the system; electronic medical records, patient monitoring and data collection (clinical information systems), ancillaries (lab, pharmacy, radiology), imaging and digital technology, financial, inventory and management information systems. Enterprise systems in healthcare, IT driven cost, efficiency and treatment quality metrics. Data warehousing, sharing, mining, protection and privacy issues.

Students are required to take all the Healthcare Core courses. These provide students with the necessary background, specialized knowledge, and management skills required to identify inefficiencies in healthcare systems and propose appropriate alternatives to reduce cost and improve the overall quality of our health care system. Students with significant professional or academic experience covering some of the areas above can work with their advisor to make relevant substitutions, thus ensuring they will take the classes that will benefit them the most.


Systems Engineering Foundation Courses (9 credits)


Applications of discrete and continuous simulation techniques in modeling industrial systems. Simulation using a high-level simulation language. Design of simulation experiments.

Design of Experiments

Experimental procedures for sorting out important causal variables, finding optimum conditions, continuously improving processes, and trouble-shooting. Applications to laboratory, pilot plant and factory.

Optimization Models and Applications

Modeling and analysis of operations research problems using techniques from mathematical programming. Linear programming, integer programming, multi-criteria optimization, stochastic programming and nonlinear programming using an algebraic modeling language.

Stochastic Models and Applications

Introduction to stochastic process modeling and analysis techniques and applications. Generalization of the Poisson process; renewal theory, queuing, and reliability; Brownian motion and stationary processes.

Nine credit hours are dedicated to the systems engineering foundation courses, covering systems thinking skills and analytical tools. Students must select three of the four options listed. Students with professional or academic experience covering one or more of the areas above can work with their advisor to make relevant substitutions and delve deeper into systems engineering, data science, or optimization modeling coursework.


Electives (3 - 6 credits)

Engineering Elective

Generally, students select their engineering elective from the Industrial and Systems Engineering, Computer Science and Engineering, or Bioengineering departments.

Free Elective

The free elective can be taken in any area. Popular choices include additional project work, quantitative courses such as data science or machine learning, and upper level and graduate courses in public health, economics, business, management, or science.

Six credit hours are drawn from an approved pool of cross-disciplinary courses; these courses have been selected to allow students to tailor the program to their particular interests. Elective courses come from various areas of systems engineering, analytics, and data science, as well as science, business, economics, public health, and pharmaceutical affairs. Students will select electives based on their professional interests and background, with the support and approval of their advisor. Some areas students have taken electives from in the past include: management, finance, economics, data mining and machine learning, business strategy, management, information systems, scheduling and operations, supply chain management.


Capstone Project (3 - 6 credits)

Healthcare Systems Engineering Capstone Project

Students work individually or in small groups on healthcare delivery projects that are applicable to their professional interests and the broader needs of the industry, in topics that range from operational to clinical management.

Students must complete an industry-relevant healthcare systems engineering capstone project, under the supervision and guidance of Lehigh faculty and industry partners. Recent capstone projects have focused on projects such as quality management, patient flow capacity optimization, operating room scheduling, hospital occupancy planning, healthcare supply chains, data mining and health informatics, ER patient throughput optimization, optimal patient care delivery, hospital acquired infections (HAI), therapeutic optimization, medical decision making, pharmaceutical applications, chronic care modeling.