Health Litigation Support

Effects of cyber-attacks on population health.
This project was a simple simulation model that identified the ways in which cyber-attacks could potentially impact health, and calculated the potential dollar amount of healthcare-related costs under a variety of scenarios and assumptions.

Trademark infringement.
Avalon has conducted a number of cases involving trademark infringement, using health economic theory and analytic methods. Our cases have involved hospital and health system trademark infringement, where a variety of data are used to determine the extent of competition between the two parties and the likelihood and amount of possible economic damages.

Reputation harm.
We have conducted a number of cases in the of reputation harm. These cases involve using economic analysis to determine the linkages, if any, between specific events and changes in consumer demand and facility revenue. Cases have involved events that may or may not have affected health system demand and revenue.

Role of Affordable Care Act (ACA) in calculating reasonable value of future care in personal injury cases.
We have been retained in a number of personal injury cases where we have been asked to calculate the reasonable value of future medical care under a variety of assumptions, including enrollment in an ACA plan. In the case of the latter, it is necessary to calculate the lifetime costs of ACA premiums plus the reasonable value of the medical care (if any) excluded from ACA coverage plans. These calculations can be tailored to specific geographic areas and with varying assumptions regarding life expectancy (i.e., certain injuries are associated with decrements in life expectancy).

Critical infrastructure.
Can a medical group avoid paying a large malpractice judgement based on the argument that, if the judgement is paid, the medical group will go out business? In some jurisdictions this is an argument that can be made. Avalon conducted analyses to determine the extent to which a medical group could be viewed as critical infrastructure to a community. The analysis relied on a complete understanding of the market, including existing levels of demand and how those levels might be met in the absence of the medical group in question.

Payer-provider disputes regarding air ambulance.
Avalon calculated the reasonable value of helicopter air ambulance services, and described how air ambulance rates are determined based on stand-by capacity and unusually high levels of bad debt. We conducted simulation models to determine service viability at varying levels of payment.

Valuation of past and future medical care claims and bills in traumatic injury liability
What is the “reasonable value” of medical care bills and claims in personal injury cases? Charges and billed amounts from medical care providers are often substantially more than what a typical buyer would be willing to pay. Willingness to pay, however, is the gold standard in economics for assessing reasonable value. With applications across all three of the Avalon practice areas, we have calculated reasonable value based on the economic theory of “willingness to pay,” relying on analyses of health economic databases. Our methodology has been applied to technology assessment models, health policy studies, payer-provider contracting disputes, and a variety of legal cases involving personal injury and medical losses.

Antitrust analysis of market exclusion case involving medical groups
As medical care becomes more integrated across the continuum of care and health insurers tighten their “in-network” panel of providers, the consequences of network exclusion are higher than in the past. Network exclusion can be the result of an “out of network” determination by a dominant payer (e.g., a commercial carrier with high market share), or through the actions of dominant providers operating in the same product and geographic market. This antitrust project stemmed from our earlier work in the area of health plan and medical group concentration in California. Read more on The Effect of Physician and Health Plan Market Concentration on Prices in Commercial Health Insurance Markets.

Analysis of market dynamics in network inclusion disputes between insurers and providers
Part of our business analytics, data analysis, and data mining practice, this project used a combination of claims analysis of a large commercial insurance database and a variety of other sources of “usual, customary and reasonable” (UCR) fees, we calculated ranges of reasonable value of fees for procedures and services.

Antitrust analysis of proposed acquisition of physician groups by a hospital system
A dominant health care provider system intended to acquire two large single-specialty medical groups operating in the same geographic market. We conducted an extensive analysis of claims data to simulate the potential impact of the acquisition on other providers in the market. We also constructed a literature-based conceptual framework to better understand the full range of potential impact on markets and the reasonableness of some of the community benefit assertions filed by the health system. Topics covered included input foreclosure, vertical restraints, network exclusion/inclusion, market structure, aftermarkets, hospital mergers, physician consolidation, risk spreading, economies of scope, and economies of scale.

Analysis of physician and health plan market structure in California
This project described the market structure of managed care organizations and physician organizations in California, a state with high levels of managed care penetration and selective contracting. We calculated Herfindahl-Hirschman (HHI) concentration indices for MCOs and POs in 42 California counties and estimated a multivariable regression model to examine the relationship between concentration measures and the prices paid by MCOs to POs. Multivariate price models suggest that PO concentration is associated with higher physician prices (p ≤ 0.05), whereas MCO concentration does not appear to be significantly associated with higher outpatient commercial payer prices. Topics covered included health plan mergers, health plan (insurer) concentration, physician (medical group) consolidation, vertical integration, horizontal integration risk spreading, economies of scope, and economies of scale. Read more on The Effect of Physician and Health Plan Market Concentration on Prices in Commercial Health Insurance Markets.

Analysis of competition between hospitals in trademark infringement case
Two large U.S. health systems claimed that the other was infringing on their trademarked name by operating facilities with similar names in some of the same alleged geographic markets throughout the region. We conducted an extensive analysis of hospital discharge data to determine whether the similarly named facilities were operating in the same geographic markets.

Nexus study of regulatory fees to offset negative externalities from tobacco use
As a result of our nexus study, the City of San Francisco currently enforces a regulatory fee imposed on tobacco sellers. The fee is distinct from existing statewide cigarette taxes in that it is designed to offset the negative externalities associated with tobacco product litter. City and municipal TPL costs are incurred through manual and mechanical clean-up of surfaces and catchment areas. According to some studies, public litter abatement costs to US cities range from $3 million to $16 million. TPL typically comprises between 22% and 36% of all visible litter, implying that total public TPL direct abatement costs range from about $0.5 million to $6 million for a city the size of San Francisco. The costs of mitigating the negative externalities of TPL in a city the size of San Francisco can be offset by implementing a fee of approximately $0.20 per pack. The project spanned several topics, including regulatory assessment, policy analysis, policy development, regulatory impact, city/state/federal policy, program evaluation, and predictive modeling. Read more on Tobacco Litter Costs and Public Policy, or read our blog on Tobacco Butts and Litter Costs.

Nexus study of regulatory fees to offset negative externalities of calorically sweetened beverages
The biological mechanism, experimental evidence, and consistent effects among diverse study designs provide a compelling case for a causal effect of CSB consumption on weight gain, obesity and/or overweight. This study used meta-analysis to calculate that, on average, about 9% of the cases of obesity in San Francisco are due to consumption of CSBs. The study then uses nationally representative medical expenditure data to estimate the percentage of annual medical payments that are attributable to obesity for those receiving health care services reimbursed by the City of San Francisco. The project spanned several topics, including regulatory assessment, policy analysis, policy development, regulatory impact, city/state/federal policy, program evaluation, and predictive modeling.

Analysis of health insurer data to determine source of irregular claims pattern
How can researchers use advanced data and statistical analysis to detect irregular claims patterns and fraud? We analyzed data from a regional commercial insurance carrier to determine the source of an irregular and unexpected trend in utilization for a specific medical care service. Working closely with the insurer we were able to identify the source of the irregularity. The overall aim of statistical analysis in the identification of fraudulent activity is to separate the “expected” from the “unexpected.” Statistical analysis can also identify “outliers”—those cases that look substantially different from other cases. Using complex regression analyses which make use of all of the data available (e.g., medical claims data merged with patient characteristics), statistical models can be used to predict medical care utilization. Statistical models can make use of data on market demand (e.g., patient characteristics, such as age, gender, income, etc.) and supply characteristics (e.g., number of physicians per capita, number of specialists per capita, number of hospital beds per capita, etc.). Read our blog How Big a Problem is Health Care Fraud, and How Can We Detect It?