Understanding Variations in Hospital Costs in Support of Value-Based Care Decisions (2021-2022)
Healthcare expenditure in the United States accounts for nearly one-fifth of the United States gross domestic product, yet healthcare costs remain ambiguous and confusing to patients and clinicians alike. This is due to several features of healthcare markets that make them decidedly different from markets for other types of goods and services, namely asymmetry of information, moral hazard due to insurance mechanisms, barriers to entry and highly regulated products.
Dissecting the degree to which informational asymmetry (i.e., whether a particular healthcare service is comparable to others like it and in what ways) drives high prices and exhorts high payments from payers is difficult because hospitals and insurers rarely disclose the negotiated rates of reimbursement. This lack of transparency in healthcare pricing makes it difficult for patients to make informed decisions when seeking high-value/low-cost care.
To address this challenge, the Centers for Medicare and Medicaid Services (CMS) introduced a regulation that requires hospitals to publicly post both searchable and machine-readable files of payments by individual, identifiable payers on their websites, effective January 1, 2021. This regulation is intended to enable patients, providers and health plans to understand the average net prices for specific types of care in order to reduce some of the informational asymmetry that is ultimately believed to be a driver of high healthcare costs. This new regulation also paves the way for researchers to generate crucial value/cost comparisons to demystify the healthcare market and enable individuals to make informed decisions about their healthcare.
In partnership with Yale University, this project team will collect, collate and analyze newly available healthcare pricing data in order to characterize the variation in commercial and/or government-sponsored payments for common general surgical procedures and its association with common, publicly reported quality metrics; investigate the link between hospital and insurer market concentration and price variation; and examine the association between price variation and area-level measures of population health by geopolitical boundaries.
The project includes three components:
Component 1: Data Gathering
Team members will focus on surgical procedures performed in an inpatient setting that can either be classified as “non-referred” and “highly referred.” To construct the database, team members will partner with ongoing data collection efforts at Yale University to collate all machine-readable files for a subset of high-profile and publicly rated hospitals and convert this information into analyzable data.
Component 2: Data Analysis
The hospitals whose data are collated will be identifiable by their Medicare provider number and linkable to data already collected and processed in prior work. For analysis, the variable of interest will be payer-specific price, and price will be modeled as a function of hospital-level and region-level factors. Because aggregated outcomes may obscure crucial differences, team members will conduct sensitivity analyses that exploit particular variation in patient or payer-mix (e.g., examining effects in counties at state borders).
Component 3: Communication of Findings and Stakeholder Engagement
The project’s findings will likely be of interest to care delivery organizations, patients and policymakers whose roles include antitrust legislation and healthcare system oversight. The team will communicate findings to health and health policy journals, and commentary and narrative syntheses will be targeted at non-academic outlets.
Price data repository; manuscripts; conference presentations
Ideally, this project team will be comprised of 2-3 undergraduate students, 2-3 graduate students and 1 undergraduate student from Yale University. Interested undergraduate students should have experience in at least one common statistical programming language (preference for Stata or R) and markdown, as well as a demonstrated aptitude in introductory mathematical microeconomics and econometrics. While majors such as economics, statistics, or computer science may provide interested students with a formal background and experience, students from all majors are welcome to apply.
Interested MBA, MPP or doctoral studies who have not yet begun their dissertation writing are also encouraged to apply. Applicants should have knowledge in at least one statistical programming language, experience with academic writing in general medicine or economics journals and a background in either healthcare markets or healthcare quality measurement.
Students will have the opportunity to develop both academic and professional skills. Academically, students in disciplines with a quantitative focus will refine and practice skills necessary for future work in any topic area, including data management and econometrics. Students will also learn how to present to small intramural groups and communicate their findings in presentations to diverse regional and national audiences. Given the diverse backgrounds of the team leaders and faculty, team members will have the chance to direct their efforts towards a particular type of audience – from clinicians to policymakers. Finally, students will either be individually or jointly responsible for progress on a single analytical aspect of the project and individually responsible for the writing and advancement of a peer-reviewed manuscript.
The entire team will meet weekly over video conference for project updates and twice monthly for review of analyses and manuscript writing. The tentative meeting time for Fall 2021 is Mondays from 2:30-3:30 p.m.
Marcelo Cerullo and Yuqi Zhang will serve as project co-managers.
Summer 2021 (optional) – Spring 2022
- Summer 2021 (optional): Establish research agenda; identify working groups; complete research training requirements; begin orientation around related datasets; renew IRB; begin data gathering and collation
- Fall 2021: Divide into analytical teams; begin data analysis
- Spring 2022: Complete data analysis; identify target journals/conferences; present findings; submit abstracts
Academic credit available for fall and spring semesters; summer funding available
- Marcelo Cerullo, Duke National Clinician Scholar, School of Medicine-Surgery
- Ryan McDevitt, Fuqua School of Business
- James Roberts, Arts & Sciences-Economics
- Yuqi Zhang, Duke National Clinician Scholar, Yale New Haven Hospital-Surgery
/undergraduate Team Members
Paul Sabharwal, Computer Science (BS)
/zcommunity Team Members
Andrew Esposito, Yale New Haven Hospital-Surgery
William Laird, Undergraduate Student, Yale University
Haddon Pantel, Yale New Haven Hospital-Colorectal Surgery