A Glimpse Into HHS' Digital App Enforcement Priorities

July 25, 2023 | By Mary Kohler in Law360

You need to see a cardiologist. Your primary care doctor recommends two. But one's not taking new patients and the other's not covered by your health plan. So you consult an app that offers a prioritized list of available cardiologists who accept your insurance.

The first is Dr. Hack. He's in the next town but has a busy practice, but after driving an hour and spending two more in an overcrowded waiting room you discover he's horrible. What if you later realize Dr. Wright, a first-rate cardiologist, is five miles from your home but didn't pay to be listed? Worse, Dr. Hack only topped the list because he paid the platform a premium to come first.

The U.S. Department of Health and Human Services' Office of Inspector General evaluates whether financial arrangements might interfere with how federal program patients choose health care providers or receive care. And Dr. Hack's payment would raise an eyebrow.

In 2021, the OIG opined favorably on a web-based provider search tool.[1] The site delivered unfiltered results in exchange for providers paying monthly subscriptions and user click fees. It also specified that participating providers would not advertise on the site. Patients booked their own appointments.

This month, the OIG issued Advisory Opinion 23-04, a favorable opinion assessing another unnamed patient scheduling app.[2] The platform boasts an algorithm that employs user engagement and machine learning. It also books appointments and transfers patient data to the provider's electronic medical records, advancing the OIG's analysis beyond the website it considered just 18 months earlier.

While options proliferate in the app store, the OIG's guidance has been sparse. This can leave advisers and teams struggling to navigate issues where the methodical, regulated health care world meets tech's tendency to move fast and break things.

The Inducement Laws and the App

OIG opinions analyze two federal inducement laws: (1) the Anti-Kickback Statute, which prohibits paying for referrals under federal health programs; and (2) the beneficiary inducement prohibition of the civil monetary penalty law, which forbids steering patients to specific providers.

The platform's virtual marketplace helps patients find providers and book appointments. Patients enter search criteria, including insurance details, needed services, geographic preferences and appointment timing.

Providers can likewise establish boundaries. For example, a provider can set an upper threshold on the fees it pays the platform and limits the number of new patients it accepts each month. But providers cannot limit certain things — such as type of visits, e.g., refusing general consultations, or patient populations, e.g., excluding Medicaid patients.

The algorithm considers more than 180 patient and provider constraints, plus other data including user engagement, to offer a list of providers that best match a patient's criteria and have available appointments.

The Remuneration

The OIG considers the platform to be marketing, which it evaluates on facts and circumstances. It assessed the following factors: (1) compensation; (2) the marketer and its relationship to the patient audience; (3) nature of the marketing activity; (4) marketed item or service; (5) target population; and (6) safeguards to prevent fraud and abuse.

But facts can become untethered as Advisory Opinion 23-04 weaves them through its description of the platform and subsequent analysis. So it's helpful to focus on the remuneration streams that might trigger the inducement laws. The OIG identified three.

First, providers pay a fee for each new patient booked. Second, a provider can purchase optional advertisements to have their name appear more prominently for specific searches. Third, the platform is free for patients.

As expected, the developer certified all provider payments to reflect fair market value, and to avoid considering the patient's insurance status or the volume or value of any resulting referrals.

Per-Booking Fees

A fundamental concern for the OIG is how the money flows and how the algorithm will affect the provider names served up by the platform. So the OIG leans in on the prioritization and filtering criteria. It emphasizes that the algorithm should focus on matching preferences with provider characteristics, and not individual providers.

The OIG reasons that the provider per-booking fees, which accrue for each new patient booked, are acceptable because they are set in advance and are uniform across all providers. Also, they don't affect how often a provider's name appears in search results, or its order in the list. But the platform must transparently notify users that its results include only participating providers.

Still, the provider-specified spend caps, which temporarily pause a provider's participation, are tricky. The platform hides spend-capped providers from search results, but the OIG wants federal program patients to see a complete list.

Thus, it required platform changes to ensure that spend-capped providers remain visible to federal program patients, even if they can't book an appointment. The platform must also supply information about why the provider is unavailable and notify patients when their availability changes.

In addition, the developer certified that the spend caps would not interfere with a provider's existing patients' ability to book.

Importantly, the algorithm's use of machine learning means that its future development is not known and cannot be predicted. In a leap of faith, the OIG permits the functionality, but warns that if the algorithm begins to deprioritize providers with spend caps, its operation could become inconsistent with the developer's certifications, so ongoing monitoring will be important.

Value to Patients

According to the OIG, the platform's real appeal to patients is its free access to the marketplace. While many providers have their own online booking tools, the platform gives patients a one-stop shop to cut through the clutter, find a provider and secure an appointment.

In addition, providing the patient's intake information to the provider's electronic medical records promises to reduce the patient's paperwork hassle and the potential for data input errors by provider staff.

In the world of free apps, these features are table stakes. But providing free things to federal program patients can trigger both inducement laws.

The OIG has summarily concluded that the facts and circumstances do not point toward inappropriate patient benefits or steering, but its choice not to enforce the beneficiary inducement prohibition is an exercise of discretion.

Other Noteworthy OIG Factors

The OIG specifies that the developer is not a provider. This favorably affects the AKS analysis by addressing the OIG's concerns about the influential power of so-called whitecoat marketing. If, for example, a large health system developed a similar platform, the OIG's analysis would likely be different.

The platform does not involve delivery of health care services, but apps offering a wide variety of services abound, and the OIG's concerns about telehealth are well documented. Expect ongoing tensions and debate.

Advisory Opinion 23-14 does not say third-party advertisements will appear on the platform, but the OIG's 2021 opinion did. Consistent with its earlier thoughts, the OIG emphasizes that the platform should not recommend providers or advertise items or services that could be purchased during appointments. It also says that the platform should not target patients with other communications, and that sponsored results should be clearly marked as paid ads.

Still, it's not hard to envision an app like the platform linking to care options, e.g., telemedicine, diagnostic testing and pharmacy, or perhaps relevant ads, e.g., beta blockers when users identify as cardiac patients. Developments like these could test the limits of the OIG's thinking.

What's Next

The platform promises to be a useful patient tool, but it also takes another step toward commoditizing medical care. Currently, providers rely on peer referrals. This system rewards those who impress skilled colleagues with their results and demeanor. If a provider messes up or patients complain, primaries stop referring.

True, this system has its downsides. Some referrals happen for the wrong reasons. That's why inducement laws exist. The system is hard for newcomers. And bias is real. Apps like the platform can mitigate these issues. And they address an important patient need — empowerment.

But user preferences, engagement statistics and star ratings are only part of the equation. The OIG doesn't discuss whether providers might start trying to game the algorithm. Advisory Opinion 23-04's emphasis on prioritization and filtering suggests that the OIG might see the issue. But ensuring federal program patients get a complete provider list won't address the problem, and too much information can overwhelm.

If the platform's model measurably alters the flow of new patients, will providers feel pressure to redirect some focus from demonstrating excellence in the medical community to buffing up their online profiles?

It's not hard to imagine the next tool looking more like a professional social media app. In such a scenario, might Dr. Hack still top the list in the absence of a payment if he's the most effective influencer?

Patients benefit in unseen ways from the skilled recommendations of their providers. Judgment about who is best for a patient's specific needs can be more art than science.

For a routine colonoscopy, bedside manner might factor in because any competent gastroenterologist can do the job, but someone with a rare tumor needs the smartest specialist, even if they're gruff, or hard to schedule.

Whether machines can learn this degree of nuanced judgment remains to be seen. But we are on a path. And only time will tell where it is leading. But for today, teams wrestling with these issues have a fresh window into the OIG's evolving thinking, and that's helpful.

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Mary Kohler is founder and principal of Kohler Health Law P.C.

The opinions expressed are those of the author(s) and do not necessarily reflect the views of their employer, its clients, or Portfolio Media Inc., or any of its or their respective affiliates. This article is for general information purposes and is not intended to be and should not be taken as legal advice.

[1] U.S. Dept. of Health and Human Services, Office of Inspector General, OIG Advisory Opinion No. 21-20, Issued Dec. 13, 2021. https://oig.hhs.gov/documents/advisory-opinions/1013/AO-21-20.pdf.

[2] U.S. Dept. of Health and Human Services, Office of Inspector General, OIG Advisory Opinion No. 23-04, Issued Jul. 6, 2023. https://oig.hhs.gov/documents/advisory-opinions/1127/AO-23-04.pdf.