Healthcare Analytics Laboratory
Introduction | Course Goals | Sample Project Descriptions
The Healthcare Analytics Laboratory at Chicago Booth brings the real-world business problems of the modern healthcare industry into the classroom. Under the guidance and supervision of Professor Dan Adelman and a team of doctoral mentors, teams of Booth MBA candidates learn to apply sophisticated quantitative analytical methods to improve healthcare delivery. The class culminates in presentations to executives at the sponsoring firms.
The course is an innovative application of action learning to the instruction of complex data analytics. The projects are varied in size and scope, reflecting the heterogeneity of the healthcare industry today. In past years, students have:
- designed an auction-based staff scheduling tool for hospitals;
- created a framework to optimize the mix of accepted referral patients to meet a hospital's social, financial and regulatory objectives;
- evaluated the financial viability of a managed care program;
- benchmarked clinical outcomes at a tertiary hospital against national standards.
By necessity, students work with sensitive, proprietary data provided by project sponsors, which is often subject to HIPAA regulations. All course participants undergo HIPAA training prior to the beginning of class. All data cleaning, manipulation, and analysis is conducted on a secure, high-performance virtual machine maintained by Booth IT.
Course projects are carefully scoped so that students can make steady progress towards clear, attainable goals over the course of the 10-week winter quarter. Please note that this course will not be offered during the 2022-2023 academic year.
To learn more about the Healthcare Analytics Laboratory and to discuss opportunities to sponsor projects please contact:
Dan Adelman, Professor of Operations Management
Kristin Hitchcock, Associate Director, Healthcare Initiative
- To provide deep exposure to healthcare management issues from inside the firm.
- To develop a toolkit for data analytics projects.
- To grow as individuals functioning within teams.
- To improve business communication skills.
- To improve healthcare systems through discipline-based management thinking.
Data Analytics Project Laboratory
Prerequisites: Statistics (Bus 41100),
Optimization/Simulation (Bus 36106), OM (Bus 40000)
Assessing a New Care Management Program for High-Risk Patients
Frequently hospitalized people, colloquially known as super-utilizers or hot spots, require a disproportionate amount of healthcare resources. Furthermore, reimbursement for their care is declining under health care reform. A sponsoring healthcare organization has experimented with assigning these patients to a new care program, in which each patient's care is managed globally by a single point of contact, or care manager. The intention is to reduce per-patient costs while simultaneously improving the quality of care.
One of the main questions to be answered in this project is how to predict (from detailed patient-level data collected on enrollees thus far) what the effect of the managed care program will be, in terms of health outcomes and demand for care of a given patient as well as the program's financial sustainability. This requires careful thought about how to learn in a "big data"-type of environment. Another question arises: for any patient that is selected into the program, there exists a tradeoff between an expected reduction in costs of care versus the optimal collection of information in order to learn about the impactability of similar patients. How can this organization optimize the selection of patients into the program in order to best handle that tradeoff? The primary deliverables of this project are 1) an analysis of the program's effectiveness in terms of both health and cost outcomes, 2) the identification of recommended improvements to the patient selection process, and 3) and assessment of the program's ability to manage care for the hot spotter population in a cost-effective, financially sustainable fashion.
Comparing Clinical Return-on-Investment Across Knee Surgeries
Value-based care, which rewards quality and outcomes, is now widely seen as the replacement for traditional procedure based fee-for-service systems. In a survey of 200 C-suite executives, 30% "agreed completely" that providers should immediately shift their focus from volume to value. Furthermore, the Affordable Care Act will introduce a value-based modifier to Medicare physician fee schedule payments.
In light of this growing trend towards value-based care, it has become increasingly important to demonstrate the care that is provided adds value. Orthopedics is one area where technological innovation has inarguably improved the lives of millions of Americans, but the comparative value of care across different procedures from the patient's perspective has not been quantified. Project members will use a large national claims database to deliver (1) models that estimate the returns to select knee surgery operations, assessing improvements in pain, mobility, and well being; and (2) test the validity of these models using data from a tertiary hospital. Students will gain expertise in using data to calculate performance benchmarks, a skill that can be widely applied to issues within and outside of healthcare.