In September of this year, New York City Councilwoman Julie Menin announced her plan to introduce a series of bills that would create further price transparency requirements for hospitals, with noncompliance resulting in high financial penalties.
The bill package would create an office of hospital accountability that would inform the public as to how much hospitals are charging for various services via a price transparency information portal, where hospitals would be required to provide certain key pricing information to the public. Currently, such pricing data is not typically available for public access, and patients typically have little knowledge regarding how much they will be charged for services.
It has been four years since Congress enacted the Eliminating Kickbacks in Recovery Act (“EKRA”), codified at 18 U.S.C. § 220. EKRA initially targeted patient brokering and kickback schemes within the addiction treatment and recovery spaces. However, since EKRA was expansively drafted to also apply to clinical laboratories (it applies to improper referrals for any “service”, regardless of the payor), public as well as private insurance plans and even self-pay patients fall within the reach of the statute.
Building on attempts in recent years to strengthen the Department of Justice’s (DOJ’s) white collar criminal enforcement, on September 15, 2022, Deputy Attorney General Lisa Monaco announced revisions to DOJ’s corporate criminal enforcement policies. The new policies, and those that are in development, further attempt to put pressure on companies to implement effective compliance policies and to self-report if there are problems. Notably, the new DOJ policies set forth changes to existing DOJ policies through a “combination of carrots and sticks – with a mix of incentives and deterrence,” with the goal of “giving general counsels and chief compliance officers the tools they need to make a business case for responsible corporate behavior” through seven key areas:
Over the spring and summer, I did a series of posts on extracting quality information from FDA enforcement initiatives like warning letters, recalls, and inspections. But obviously FDA enforcement actions are not the only potential sources of quality data that FDA maintains. FDA has what is now a massive data set on Medical Device Reports (or “MDRs”) that can be mined for quality data. Medical device companies can, in effect, learn from the experiences of their competitors about what types of things can go wrong with medical devices.
The problem, of course, is that the interesting data in MDRs is in what a data scientist would call unstructured data, in this case English language text describing a product problem, where the information or insights cannot be easily extracted given the sheer volume of the reports. In calendar year 2021, for example, FDA received almost 2 million MDRs. It just isn’t feasible for a human to read all of them.
That’s where a form of machine learning, natural language processing, or more specifically topic modeling, comes in. I used topic modeling last November for a post about major trends over the course of a decade in MDRs. Now I want to show how the same topic modeling can be used to find more specific experiences with specific types of medical devices to inform quality improvement.
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Recent Updates
- HHS Extends the Antidiscrimination Provisions of the Affordable Care Act to Patient Care Decision Support Tools, Including Algorithms
- It’s Been a Long Time Coming – FDA’s Final Rule on Regulation of Laboratory Developed Tests (LDTs) as Medical Devices Has Arrived
- Medical Clinic’s Use of NDAs to Suppress Negative Online Reviews Violates Federal Consumer Review Fairness Act, Washington Federal Judge Finds
- Breaking Down the Legal Challenges Surrounding State Licensure Restrictions for Telehealth Providers
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