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Healthcare Analytics: Matching Scarce Resources: Linear Optimization Models for Resource Allocation and Service Lines
- General Linear Optimization:
- The Concept
- Structure of a Linear Optimization Model
- Model Formulation
- The concept of binding constraints, elevating constraints, surplus/ slack, shadow price, reduced price/cost
- Excel templates examples of setting up and solving the following problems:
- Staffing nursing personnel with a skill mix for an outpatient clinic
- Comparison of integer and fractional optimal solutions
- Optimization of patient service volumes: keep or drop a service line?
- Diagnostic tests offered by a Lab: which tests and in what volumes should be offered to minimize the total cost?
Overview of the webinar
Practical application of Linear Optimization method of finding the lowest cost (or maximal revenue) solutions for the units with limited amount of available resources, such as nursing or lab work hours or length of stay. Setting up the LO problems using the Excel solver will be demonstrated.
The examples are based on real staffing and scheduling problems taken from the hospital practice.
Who should attend?
Nursing Managers, Chief Nursing Officers, Directors and VP of quality and operations improvements of healthcare organizations interested in learning practical methods of data analytics for optimal resource allocation, and pieces of equipment.
Why should you attend?
Linear Optimization methodology was awarded the Nobel Prize in Economics in 1975 to L. Kantorovich (Russia), C. Koopmans (Netherlands) for the development methodology of Efficient Allocation of Resources. Linear Optimization (LO) is the methodology for finding the optimal solutions for resource allocation with limited amount of resources (constraints), equipment types or service lines that provide maximal revenue; the best purchase-mix; minimal cost staffing & scheduling, and many others.
You should attend this webinar if you are interested in learning a practical application of a method of finding the lowest cost (or maximal revenue) solutions for the units with limited amount of available resources, such as nursing or lab work hours or length of stay. Setting up the LO problems using the Excel solver will be demonstrated.
The instructor’s hospital experience, in particular with linear optimization, was presented in his widely cited book with more than 10,000 sales worldwide: Kolker, A., “Healthcare Management Engineering in Action” 2nd Ed., Springer, 2024. This book was used as a main text for the training course by the National Health System, the UK, as well as by the Lubar School of business at the University of Wisconsin-Milwaukee (USA) for the graduate course on Healthcare delivery system and data analytics.
Faculty - Mr.Alexander Kolker
Alexander Kolker holds a Ph.D. in applied mathematics and statistics. He is an expert in advanced data analytics for operations management, computer simulation modelling and staffing optimization with the main focus on healthcare applications. Alexander is the lead editor and author of 2 books, 8 book chapters, 10 journal papers, and a speaker at 18 international conferences & webinars in the area of operations management and data analytics.
As an adjunct faculty at the UW-Milwaukee Lubar School of Business, he developed and taught a graduate course Business 755-Healthcare Delivery Systems-Data Analytics.
He worked 12 years for GE (General Electric) Healthcare as a Data Scientist and CT Detector design engineer, 3 years for Froedtert Hospital, the largest healthcare facility in Southern state of Wisconsin, and 5 years for Children’s Hospital of Wisconsin as a lead computer simulation and system improvement consultant.
Currently he is teaching a 12-sessions online course “Healthcare Operations Research and Management Science” for the UK, National Health System (NHS)-Midland & Lancashire.
Alexander has also completed four business consulting projects using simulation modelling for the optimal staffing and capacity analysis for: Boston Consulting Group, Children’s Hospital of Wisconsin, Ohio Hospital Association, and US Bank Corporation.