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Health Care Price Variation and Transparency

Paper Session

Friday, Jan. 3, 2025 8:00 AM - 10:00 AM (PST)

Clift Royal Sonesta, Calder Room
Hosted By: Health Economics Research Organization & American Economic Association
  • Chair: Leemore Dafny, Harvard University

Ruling Out Sources of the Gender Gap in Physician Pay: Lessons from Negotiated Prices

Benjamin Chartock
,
Bentley College
Nandita Radhakrishnan
,
Brown University
Kosali Simon
,
Indiana University
Christopher Whaley
,
Brown University

Abstract

Previous research documents a gender-based pay gap for physicians, with male physicians out earning their female counterparts by approximately 25%, or $2 million over the course of a 40 year career (Whaley, Jena, et al. 2021). However, the literature has not yet explored possible mechanisms. Here, we rule out many of the common explanations, by using a rich database that contains all negotiated reimbursement rates for physician services by private insurance companies (the Transparency in Coverage database). We next hypothesize and test an alternative potential mechanism for the gender pay gap related to how practices divvy up revenue.

Through a 2020 federal rule, most group health plans and health insurance issuers in the group and individual market are required to disclose prices negotiated with healthcare providers. These data, which we use through an agreement with Clarify Health, contains physician identifiers, allowing us to add publicly available demographic information on physicians. In total, we obtain prices negotiated in 2022 for 533,119 physicians, for Evaluation and Management codes (CPTs 99202-99205 and 99211-99215) across four major national payers (Aetna, Humana, Cigna and United). Clarify Health combines these negotiated reimbursement rates with physician-level volumes of services for private health insurance as well as for Medicare insurance, obtained from commercial insurance claims data and a 100 percent sample of Medicare fee-for-services (FFS) enrollees.

With these prices and volume data, we document several descriptive facts that rule out common explanations for gender pay differentials. First, we rule out that male physicians are paid by insurers at higher rates than females are on a code-by-code basis. Instead, we find females have a higher median payment rate, holding for non-surgical, primary, and surgical specialty care. Second, we reject the hypothesis that male physicians bill a larger share of their volume for more costly privately insured patients and instead

Better to be Big or Blue?: An Analysis of Medical Price Variation among National and Regionally Dominant Health Insurers in 2024

Jean Abraham
,
University of Minnesota
Connor Ryan
,
Pennsylvania State University
Steven Parente
,
University of Minnesota

Abstract

Rising U.S. healthcare prices are a primary driver of eroding financial access to medical care. Evidence increasingly suggests both significant price variation across hospitals, physicians, and payers within geographic markets as well as higher prices over time in markets that have become more consolidated. Two federal regulations implemented in 2021-2022 include the Hospital Price Transparency and Transparency in Coverage (TIC) rules require most U.S. hospitals and health insurers to publicly disclose detailed medical service pricing information previously considered proprietary in consumer-friendly and machine-readable formats (CMS, 2024).
This study is one of the first to utilize the TIC data to analyze medical service price variation and to examine its association with local provider market concentration. We use February 2024 releases for two national, for-profit insurers (UnitedHealth Care and Aetna-CVS Health) and two non-profit Blue Cross and Blue Shield plans in Florida and Illinois, states selected due to strong market presence of one or both the national insurers as well as a dominant Blue Cross and Blue Shield plan.
We extracted prices across all these insurers’ plans to construct an insurer-plan-provider-service level file. We focus on 70 ‘shoppable’ services, including primary care, behavioral health, and radiology. We augment with provider- and geographic-specific information from National Plan and Provider Enumeration System (NPPES) and the American Hospital Association Annual Survey.
We used core-based statistical areas (CBSAs) to define our geographic markets. For hospitals, we define a Herfindahl-Hirschman Index using the AHA survey data. To create a measure of physician services concentration, we use billing relationships captured by National Provider Identifiers (NPIs) and Taxpayer Identification Numbers (TINs). Specifically, we measure concentration or density as the number of unique NPIs associated with a corporate entity with a TIN.
We first summarize levels and variability in prices for the 70 services, examining

The Opacity of Price Transparency

David Wehrly
,
University of Michigan
Max Pany
,
Harvard University
Will Fox
,
Milliman
Michael Chernew
,
Harvard University

Abstract

Purchasers’ inability observe the prices of, and thus shop for, health care services has been blamed for high prices and spending. Discussions to address this issue with greater price transparency have generally assumed that commercial carriers define and pay for services using a common payment methodology. We show that this is often not the case.
Specifically, we investigate the distribution of payment methods using the HHS mandated Transparency in Coverage (TiC) data. For inpatient care, we identify 4 payment methods: (1) Fixed-dollar diagnosis-related groups (DRGs), (2) Per diem revenue codes, (3) Percent of charges, or (4) mixed approaches that may vary by service and/or use homegrown codes. For outpatient facility care, we analogously define 4 payment approaches: (1) Fixed-dollar CPT codes, (2) Fixed-dollar ambulatory payment classifications (APCs), (3) Percent of charges, or (4) mixed approaches that may vary by service and/or use homegrown codes.
We downloaded January 2024 TiC data from Turquoise Health for every group market health plan offered by nine of the 10 largest commercial carriers, excluding Kaiser Permanente. We examined 5 markets, chosen because the BCBS plan in their state was among the nine insurers included. We compute, separately for inpatient and outpatient facility care, the percent of plan-hospital observations using each methodology. Additionally, for each plan-market cell, we compute inpatient and outpatient Herfindahl indices (HHIs) based on the share of payment methods used by that plan in that market. An HHI of 10,000 for a plan indicates that all hospitals in the market are paid by the plan using the same payment methodology. Finally, for each hospital, we compute inpatient and outpatient HHIs based on the share of plans using each payment methodology at the hospital. An HHI of 10,000 for a hospital indicates that all plans pay that hospital in the same way.

Discussant(s)
Michael Chernew
,
Harvard University
Christopher Whaley
,
Brown University
Kelly Yang
,
Indiana University
JEL Classifications
  • I0 - General
  • H0 - General