Susannah Fox on Teens & Digital Health Study

By JESSICA DAMASSA, WTF Health

How are teens and young adults engaging with digital health? Results of a national survey asking just that were released today by Susannah Fox (Former CTO at US Dept of HHS) and her research partner, Victoria Rideout.

You can check out the full report of the findings here, but I spoke with Susannah in April, just as she and Victoria were starting to draw some insights from their work.

Hearing her talk about the survey at this stage of synthesis is not only unique (most researchers won’t talk until the findings are published) but more so because it adds a layer of understanding to the final results now that they’re here.

We get her candor about how teens and young adults are a wildly viable – yet very overlooked – market for digital health…

We see how she’s trying to formulate a much larger hypothesis about what healthcare can learn about social media from a generation that has never lived without it and, more importantly, view it as having a positive impact on their well-being…

And, probably most inspiring to me, we see an approach to health data that stands out for its warmth. For it’s love, really. In a world of big data and clinical trials, it’s endearing to hear from someone who is taking a more anthropological approach and who has fallen absolutely, head-over-heels in LOVE with the personal side of her dataset.

As we all clamor for a patient-centered end, we’d be remiss to underestimate the value of a human-centered starting point. Watch Susannah Fox for a strong model of how this can be done in health research.

Filmed at Health DataPalooza, Washington DC, April 2018. Find more interviews with the people pushing healthcare to better tomorrow at www.wtf.health. 

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Who Cares About the Doctor-Patient Relationship? A Review of “Next In Line: Lowered Care Expectations in the Age of Retail- and Value-Based Health”

By KIP SULLIVAN, JD

A mere two decades ago, the headlines were filled with stories about the “HMO backlash.” HMOs (which in the popular media meant most insurance companies) were the subject of cartoons, the butt of jokes by comedians, and the target of numerous critical stories in the media. They were even the bad guys in some movies and novels. Some defenders of the insurance industry claimed the cause of the backlash was the negative publicity and doctors whispering falsehoods about managed care into the ears of their patients. That was nonsense. The industry had itself to blame.

The primary cause of the backlash was the heavy-handed use of utilization review in all its forms –prior, concurrent, and retrospective. There were other irritants, including limitations on choice of doctor and hospital, the occasional killing http://articles.latimes.com/1999/jan/24/news/mn-1260 or injuring https://www.cbsnews.com/news/supreme-court-wary-of-hmo-suit/ of patients by forcing them to seek treatment from in-network hospitals, and attempts by insurance companies to get doctors not to tell patients about all available treatments. But utilization review was far and away the most visible irritant.

The insurance industry understood this and, in the early 2000s, with the encouragement of the health policy establishment, rolled out an ostensibly kinder and gentler version of managed care, a version I and a few others call Managed Care 2.0. What distinguished Managed Care 2.0 from Managed Care 1.0 was less reliance on utilization review and greater reliance on methods of controlling doctors and hospitals that patients and reporters couldn’t see. “Pay for performance” was the first of these methods out of the chute. By 2004 the phrase had become so ubiquitous in the health policy literature it had its own acronym – P4P. By the late 2000s, the invisible “accountable care organization” and “medical home” had replaced the HMO as the entities that were expected to achieve what HMOs had failed to achieve, and “value-based payment” had supplanted “managed care” as the managed care movement’s favorite label for MC 2.0.

Today, few managed care advocates, and certainly no politician, would hold up HMOs as the goal of health care reform. Today, the managed care movement and politicians across the political spectrum, from Trump’s HHS Secretary Alex Azar to Bernie Sanders, promote ACOs and other “value-based payment” vehicles that Americans don’t understand and can’t see. [1]

So far, the strategy is working. With the possible exception of the increased use of narrow networks, the media is paying little attention to MC 2.0. The media is not reporting on the spread of “value-based payment” nostrums, and it is not warning the public that these nostrums are affecting the doctor-patient relationship even while they fail to contain inflation. [2] Not surprisingly, there are at this date no signs of an impending “value-based payment” backlash.

Unlike the media, the health policy literature does pay attention – lavish attention – to the “value-based payment” bandwagon. But like the media, the health policy literature pays virtually no attention to the impact “value-based payment” is having on the doctor-patient relationship. Health services researchers have yet to produce even a small body of research on doctors’ and patients’ views of how a half-century of managed care experiments – HMOs, PPOs, utilization review, limited choice, “coordination,” drug formularies, report cards, P4P, ACOs, medical homes, EHRs, bundled payments – has affected the doctor-patient relationship.

Voices from the trenches

Timothy Hoff’s latest book, Next in Line, seeks to fill that hole. It is a rare attempt by a bona fide member of the health services research community to understand the impact of managed care on the quality of the physician-patient dialogue. This requires actually talking to doctors and patients as opposed to collecting crude data on the “value” (the cost and quality) of doctors, hospitals, insurance companies, or ACOs. We have reams of studies that tell us, for example, what percent of the diabetics assigned to Tendercare ACO received an annual eye exam or were advised not to smoke https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/sharedsavingsprogram/Downloads/2018-and-2019-quality-benchmarks-guidance.pdf. We have virtually no research on how the spread of ACOs is affecting the quality of doctors’ interactions with their patients. “[F]ew seem to care … about promoting strong doctor-patient relationships…,” Hoff declares early in his book. (p 11)

Next in Line is based on interviews with 44 primary care doctors and 36 patients. The interviews were designed to find out what primary care doctors and patients think the doctor-patient relationship should look like and what it actually looks like under the onslaught of what the author variously calls “corporatized care,” “retail thinking,” and “value-based health care.” Hoff reports that doctors and patients share a nearly identical definition of the ideal relationship, and they share similar views on the damage “value-based payment” and the corporate takeover of medicine have inflicted on that relationship. Both doctors and patients define a relationship built on trust as the ideal relationship, and both parties perceive multiple forces around them destroying trust or preventing it from forming in the first place.

Hoff’s conclusion that patients want a trusting relationship with their doctor will surprise no one. But his report that doctors share that view, enthusiastically and universally, may surprise those who bought into the campaign, initiated nearly a half-century ago by Paul Ellwood (“the father of the health maintenance organization”) and other founders of the managed care movement, that doctors are driven by money and are not “patient-centered.” Ellwood and his intellectual heirs developed this stereotype of doctors to reinforce their evidence-free diagnosis that excessive volume of medical services sold (as opposed to the price at which those services were sold) was the primary cause of health care inflation. The fact that doctors value the trust of their patients is inconsistent with this stereotype. “[F]ew physicians flinched when asked to describe what a good doctor-patient relationship looked like,” Hoff writes. “Striking to me was the consistent manner in which doctors specifically used the words trust, respect, friendship, partnership and communication to help describe an effective, satisfying doctor-patient relationship. They used these words unprompted….” (p. 69)

Hoff describes in abstract terms the destructive forces set loose by the stereotype of the money-driven-doctor – “metric fever,” “corporate medicine,” and “retail thinking.” And he accurately describes problems caused by these forces, including “checklist medicine,” the “dumbing down” of medicine, and making doctor-patient communication “as ritualized as possible.” But, oddly, he never identifies the origin of those forces, namely, the managed-care movement’s grossly oversimplified diagnosis (overuse due to FFS payment and money-hungry doctors) and the movement’s evidence-free solutions (shifting risk to doctors and micromanaging them). His failure to do so is the main reason why the last chapter in the book, a chapter in which he recommends solutions, is so disappointing.

Spitballs versus rhinos

In the final chapter, Hoff offers a half-dozen ideas for “saving the doctor-patient relationship.” With the exception of his suggestion that doctors form unions, these suggestions are grossly inadequate and, in one case, ludicrous.

Hoff’s first suggestion is that doctors “start caring about building strong relationships with [their] patients.” (p. 173) This makes no sense. In previous chapters, Hoff has carefully documented how much doctors care about good relationships with their patients and what little control they have over the forces corrupting those relationships, and now he calls on them to “start caring” about their relationships with patients. I quote at length from the same paragraph to give you some idea of how muddled Hoff’s thinking is here: “Advance preparation for strong relationship-building matters more now than ever…. Knowing ahead of time how and why such relationships matter …, and being able to engage in requisite features such as empathy, compassion and listening – in ways that are efficient and do not require highly favorable conditions – raises the chance that tomorrow’s doctors can achieve some success in maintaining bonds with their patients.” (pp. 173-174) I have no idea what all those words mean.

He goes on to recommend these actions:

* teaching hospitals should expose young doctors to the opportunity to “work with the same patients over time” so they can learn the benefits of long-term relations with patients (as if that will somehow arm tomorrow’s doctors to go to war with the forces that are interfering with long-term relationships);

* doctors should join unions;

* insurance companies and other entities that bedevil doctors with their P4P schemes should include measures of “trust” in their ever-growing lists of “quality” measures (p. 181) and, to develop such measures, “entire exam room conversations can be recorded and then analyzed … for the presence of various relational features in the doctor-patient interaction” such as trust and empathy (p. 187);

* doctors could hire “concierge staff” to serve as “liaisons between specific doctors and patients” that would serve as “listening relay stations” between patients and doctors;

* smartphone apps could be used to create “real-time outlets for patients to ask question and be heard”; and, perhaps worst of all,

* “some consumers [could be] asked to pay extra … for the right to see their doctors more in person….”(p. 184).

With the exception of unionization, these suggestions are at worst technologically or financially infeasible, and at best the equivalent of shooting spitballs at a charging rhino. Hoff expressed his own disbelief in one of these suggestions – the notion of adding measures of “trust” to P4P schemes – in earlier chapters where he blasted “metric fever” and the emergence of “an entire hidden industry … devoted to making primary care physicians … look good to insurers and government agencies….” (p. 34) The notion that a credible, accurately risk-adjusted score for “empathy” or “trust” can be produced for even a few doctors, never mind all US physicians (with or without bugging the nation’s examining rooms) is absurd.

I surmise that Hoff’s inability to make more realistic recommendations stems from his ambivalence about MC 2.0. In certain parts of the book, he is very critical of “value-based payment” schemes – he calls them “half baked” and “magic bullets.” But in other parts he claims, without evidence, that these schemes have created some benefit and, apparently for that reason, are “here to stay.” When he wrote the last chapter, he must have resolved his ambivalence, at least temporarily, in favor of the conclusion that MC 2.0 is doing some good and, in part for that reason, will never go away even if it is damaging the doctor-patient relationship.

The problem must be named

I am under no illusion that exterminating the forces that are weakening the doctor-patient relationship will be easy. The managed care juggernaut has acquired enormous financial and political power over the last half-century. The managed care diagnosis (overuse caused by FFS payment) and solution (exposing providers to financial risk and micromanaging them) is now a well-established religion. But if we are ever going to defang the forces that are diminishing doctor and patient autonomy and weakening the doctor-patient relationship, we must name them and clearly describe their origins. Hoff’s favorite labels for the corrupting forces – “value-based payment,” “retail thinking,” and “corporate care” – are informative but, by themselves, are not informative enough. They do not tell us who unleashed those forces, upon what rationale, and with what evidence.

All of us who care about the future of the doctor-patient relationship must be more specific in our diagnosis of the crisis: The forces that threaten that relationship was unleashed by managed care theology – its evidence-free diagnosis and its evidence-free solutions. Those solutions are the cure that is worse than the disease. There are solutions to the modest amount of overuse that so excites managed care proponents. But managed care, be it the pre- or post-backlash version, is not one of them. Applying managed care to the overuse problem is like using a chainsaw is to cut butter – it is vast overkill.

We must also clearly describe the toxic side effects caused by managed care. Tim Hoff has described one of them – the degradation of trust between doctors and patients. I thank him very much for doing that.

References:

Book: Hoff, Timothy J, Next In Line: Lowered Care Expectations in the Age of Retail- and Value-Based Health Oxford University Press, 2018

[1] S 1804, the so-called single-payer bill Senator Sanders introduced in 2017, contains a section that authorizes the Department of Health and Human Services to extend every “reform activity” authorized by the Affordable Care Act and MACRA to the non-elderly. These “reform activities” include, of course, all the major elements of the iteration of managed care that emerged after the HMO backlash, including ACOs, medical homes, bundled payments, penalties for hospitals with “excess readmissions,” and the Merit-based Incentive Payment System, none of which are visible to patients. The Trump administration has taken no steps to repeal any of these “activities,” and has explicitly and enthusiastically endorsed the concept of “value-based medicine” and ACOs in particular.

[2] The evidence that the latest iteration of managed care is failing to cut US health care costs is overwhelming. There is, first of all, the fact that health care spending as a percent of GDP continues to grow at its historic rate. There is, furthermore, a growing body of literature demonstrating that none of the most important elements of Managed Care 2.0 save money. The research on Medicare ACOs, medical homes and bundled payments, which is the only reliable research, demonstrates these “reforms” are breaking even, and that’s only if we don’t count the costs ACOs, “homes,” and hospitals with bundled payment contracts incur in their efforts to cut their Medicare costs. (The exception to the statement that Medicare’s bundled payment program is not saving money is the joint replacement program, but the main reason that program saves money is that hospitals use their market power to lower the price of implants. Like HMOs, ACOs and “homes,” bundled payments were supposed to save Medicare money by reducing the volume of services, not their price.) Two other elements of MC 2.0, pay-for-performance and electronic medical records, are saving no money either.

Kip Sullivan, J.D., is a member of the Policy Advisory Committee of Health Care for All Minnesota

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Digital Health and the Two-Canoe Problem

By DAN O’NEILL

Digital Health and the Two-Canoe Problem

As healthcare gradually tilts from volume to value, physicians and hospitals fear the instability of straddling “two canoes.” Value-based contracts demand very different business and practices and clinical habits from those which maximize fee-for-service revenue, but with most income still anchored on volume, providers often cannot afford a wholesale pivot towards cost-conscious care.  That financial pressure shapes investment and procurement budgets, creating a downstream version of the two-canoe problem for digital health products geared toward outcomes or efficiency. Value-based care is still the much smaller canoe, so buyers de-prioritize these tools, or expect slim returns on such investment.  That, in turn, creates an odd disconnect.  Frustrated clinicians struggle to implement new care models while wrestling with outdated technology and processes built to capture codes and boost fee-for-service revenue. Meanwhile, products focused on cost-effectiveness and quality face unexpectedly weak demand and protracted sales cycles.  That can short-circuit further investment and ultimately slow the transition to value.

To skirt these shoals, most successful innovators have clustered around three primary strategies.  Each aims to establish a foothold in a predominantly fee-for-service ecosystem, while building technology and services suited for value-based care, as the latter expands.  A better understanding of these models – and how they address different payment incentives – could help clinicians shape implementation priorities within their organizations, and guide new ventures trying to craft a viable commercial strategy.

The first is to focus on Medicare Advantage (MA) and Managed Medicaid (MM), where risk-adjusted capitation and quality ratings approximate many features of value-based reimbursement, even when individual providers are still paid on a fee-for-service basis.  This limits the market (see figure below), but allows developers and designers to emphasize workflows and insights which can facilitate other pay-for-performance scenarios, as these proliferate.  Such an approach is particularly common among vendors like Evolent Health (EVH) and Lumeris, who offer technology and services geared toward MA and MM patients, which can extend to handle Medicare’s shared savings program and other accountable care arrangements.  Natural language processing engines and case management software often take a similar tack, targeting near-term revenue gains from risk adjustment or HEDIS scores, while securing a toehold for a longer-term push into medical management.  Finally, clinical data services also tend to focus on risk adjustment as an immediate business case, while building data-sharing infrastructure which could improve care coordination and curb costly duplication, once payment contracts broaden the incentives to do so.

The second strategy targets a different patient segment entirely – those with employer-sponsored, fee-for-service plans.  This coverage is plagued by high and unpredictable prices for hospital and physician services, which creates an arbitrage opportunity; technology-driven services can deliver financial returns by nudging patients toward better value or even replacing traditional providers altogether. Grand Rounds, for example, acts as a concierge, shepherding patients toward centers of excellence, which pursue fewer unnecessary procedures and have better outcomes.  Others, such as Livongo and Hinge Health, try to supplant traditional providers with remote coaches and self-management programs, delivered through mobile applications and other connected devices.  These services may improve patient outcomes, but the business case relies heavily on steering patients away from expensive interventions or facilities.  Today, firms pursuing this strategy generally sell to self-insured employers, but have used that niche to test models of lower-cost care and to build patient navigation and coordination tools.  Both should eventually be valuable for any provider network (or insurer) which is financially accountable for care quality and efficiency.

Finally, some digital health firms operate across all patient segments but target specific use cases which promise financial returns under both fee-for-service and value-based contracts.  Typically, they emphasize the fee-for-service case today and plan to pivot as outcomes-oriented care models expand. Referral networks may be the clearest example.  Fee-for-service revenue from referral traffic creates a business case for diagnostics firms and hospitals to fund health information exchange links with ordering providers.  As a result, hospitals, clinical labs and radiology groups pay electronic health record vendors (e.g. Athena Health or Practice Fusion) and intermediary hubs like ReferralMD to connect referring physicians to their service centers, mirroring pharmacies’ funding of electronic prescribing infrastructure.  Similarly, CoverMyMeds and Clarity Health (both since acquired) relied on the financial case for pharmaceutical manufacturers and specialists to streamline prior authorization hurdles for clinicians considering a specialty drug or referral.  Over time, all of these narrowly-targeted networks chip away at healthcare’s interoperability deficit and establish a foundation for the longitudinal care coordination required under value-based care.

The two-canoe challenge for digital health highlights a system-wide dilemma.  Physicians and hospitals cannot deliver consistently efficient, high-quality care without the necessary tools, but there is little incentive to invest in that toolkit until payment contracts ensure a financial return.  This disconnect can retard adoption of new technology, and hence slow overall progress toward value-based care. These three strategies, however, offer pathways for innovation which is financially viable in today’s hybrid reimbursement landscape and helps assemble the infrastructure for cost-effective, outcomes-driven healthcare.

Dan O’Neill is currently an RWJF Fellow at the National Academy of Medicine. He previously served as a Senior Vice President with Change Healthcare, and in leadership roles with several other digital health firms.

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Will Computers Really Replace Radiologists?

By SAURABH JHA

There is hope, hype and hysteria about artificial intelligence (AI). How will AI change how radiology is practiced?  I discuss this with Stephen Borstelmann, a radiologist in Florida and a scholar in machine learning.

Listen to our discussion on the Radiology Firing Line Series, hosted by the Journal of the American College of Radiology and sponsored by Healthcare Administrative Partners.

About the author:

Saurabh Jha is a radiologist and contributing editor to THCB. He hosts the Radiology Firing Line Podcasts

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Giving Consumers the Tools and Support They Need to Navigate Our Complex Healthcare System

By CINDI SLATER, MD, FACR

As physicians and healthcare leaders, we are already well aware that the majority of patients do not have the information they need to make a medical decision or access to appropriate resources, so we didn’t need to hear more bad news. But that is precisely what new research once again told us this spring when a new study showed that almost half of the time, patients have no idea why they are referred to a GI specialist.

While the study probably speaks to many of the communications shortcomings we providers have, across the board our patients often don’t know what care they need, or how to find high-value care.  Last year, my organization commissioned some original research that found that while a growing number of patients are turning to social websites such as YELP, Vitals, and Healthgrades to help them find a “high quality” specialist, the top-ranked physicians on these sites – including GI docs – are seldom the best when we look at real performance data.  Only 2 percent of physicians who showed up as top 10 ranked on the favorite websites also showed up as top performers when examining actual quality metrics. (The results shouldn’t surprise you as bedside manner has little to no correlation with performance metrics such as readmission rates).

As providers and health care leaders, we lament that our patients are not better informed or more engaged and yet across the board, we have not given them the tools or resources they need to navigate our complex system. But now for some good news: all hope is not lost, and patients can become better consumers, albeit slowly, if we all do our part.

First, as health care leaders, we must continue efforts to educate all patients that quality of care matters and that quality varies. Part of this is helping patients understand that not all medical care is of equal value.  Unfortunately, as reported in Modern Healthcare, the Choosing Wisely campaign hasn’t made much of a dent moving the needle, but the work that has been born out of this initiative continues, and some medical systems are looking to take it to the next level. Cultural change is hard and it is slow, so we must “keep on keeping on” in these efforts, despite glacial rates of progress.

Second, those of us who wear a provider hat must compel patients to participate in their care. Initiatives like OpenNotes are a great addition, offering patients and families a full transparent electronic medical record they can even contribute content to themselves. Research has shown such initiatives improve doctor-patient communication and trust and spur shared decision-making.

Of course, even the best of resources can often be overwhelming, especially in the face of a patient dealing with something like a breast cancer diagnosis. That is why employers, the largest providers of health care coverage in this country, have an obligation to help their employees. (And in helping their employees they can usually help themselves by lowering their healthcare spend when employees make higher value choices). Several years ago, we saw high deductible health plans soar in popularity; they were all the rage, and supposed to be the panacea to get patients more engaged. Today we know better, and according to Willis Towers Watson in their 2015 annual survey, the top performing employers also rely heavily on value-based insurance designs and network strategies, like Centers of Excellence, to steer their employees to higher value care, as part of their overall benefit design. With these in place, employees aren’t shopping for care in the dark; they have some guide rails which push them toward higher quality providers and higher value treatment options.

New data shows savvy employers are even taking these “guardrails” to a new level. The latest NBGH annual survey of employee benefits revealed that 36 percent of employers plan to offer their employees high touch concierge services this year. This is about more than steering employees toward higher-value choices; it is about actually talking them through the decision-making process.

Finally, a suggestion and one note of caution for all of us – we cannot rely on technology alone to solve this problem. The technology industry has made a fortune creating “apps” and other tools that promise to get patients more educated and engaged in their care and to help them make better healthcare decisions. But at the end of the day, we have to be careful we cannot rely on technology alone to save us. First, doing so ignores the “digital divide,” which in health care also means specific populations have less access to tech or are just less likely to use tech (e.g., the worker in a manufacturing plant not tied to his smartphone, or the older patient who can’t read the screen). But second, and more importantly, tech can never take the place of a real conversation between provider and patient or provider and healthcare navigator. These conversations often reveal additional concerns the patient might have or other real-world limitations. For example, Martha can’t refill her prescription because she doesn’t have a reliable source of transportation to the pharmacy. It takes a person-to-person conversation to reveal these issues and a thinking human-being to help problem solve.

If we continue work to support these conversations, educate patients, and give them benefit designs that help guide them, we can make a dent in this enormous problem. After all, if understanding your health care choices is giving you an ulcer, shouldn’t you know that is why your primary care doc referred you to a GI specialist?

Dr. Cindi Slater is the SVP for Medical Affairs for ConsumerMedical. She received her M.D. from the University of Pennsylvania and is board certified in internal medicine. Dr. Slater has been a Clinical Instructor of Medicine at Harvard Medical School and practiced internal medicine and urgent care at the Brigham & Women’s Hospital in Boston for over 20 years.

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Health in 2 Point 00, Episode 41

After the longwinded-ness of last episode, Jessica DaMassa runs a tight ship today. Lantern’s demise, GSK & 23andme’s huge deal, yet another big chunk of change for American Well, and what was going on at #GoogleNext18 with Google Cloud in health –all asked by Jessica and answered by me, in under the 2 minute wire–Matthew Holt

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AI to the Rescue: 5 Semi-Finalists Advancing Through the RWJF AI and the Healthcare Consumer Challenge!

By DIANA CHEN

Decision making is a daunting task. Combined with navigating health insurance jargon, scattered health information, and feeling crummy as you rush to find care during the onset of a cold, making decisions can be an absolute nightmare. However, artificial intelligence (AI) enabled tools have the potential to change the way we interact with and consume healthcare for the better. AI’s ability to comprehend, learn, optimize and act are keys to organizing the varying nuisances of the healthcare experience.

In a 2018 survey by Accenture, healthcare consumers indicated they would likely use AI for after hours care, support in navigating healthcare services, lifestyle advice, post-diagnosis management, etc. While AI in health is not limited to these functions, the report highlights consumers’ trouble in making informed healthcare decisions, hence this may be an area where AI can truly help.

With more consumers demanding assistance in healthcare decision making, the Robert Wood Johnson Foundation and Catalyst @ Health 2.0 have come together to host the RWJF AI and the Healthcare Consumer Challenge. The challenge was created to find unique innovators creating AI-enabled tools to aid healthcare consumer decision making. As demonstrated by the submissions, AI is currently being investigated to help patients find appropriate providers, estimate the cost of care, have a more personalized health companion, and better understand personal health options. Furthermore, AI may improve health literacy and responsible health decision making by providing curated information that pertains to each individual’s needs. In a landscape where the shift to value-based care is gaining importance, AI has the opportunity to support personalized clinical strategies and even create systems that measure long-term outcomes for patients and providers.

As the challenge enters Phase 2, we are thrilled to congratulate 5 semi-finalists who have demonstrated promising solutions to help consumers make informed healthcare decisions!

Congratulations to:

• Buoy – Buoy is a platform that uses a probabilistic expert system to analyze systems, risk factors, and diagnoses in real time to direct patients to the right care at the right time.
• INF Robotics (RUDY) – INF Robotics created RUDY, the premier intuitive mobile social robot that is designed to promote an active and independent lifestyle while keeping older adults connected to their care community.  
• Patient Price – Patient Price is a mobile application that offers a simplified search experience for patients. It is designed to help patients compare costs, answer most common questions, and ultimately connect individuals with the best provider for their needs.
• Sensentia – Sensentia is a solution that simplifies and enhances benefit inquiries in payer call centers. Their AI solution understands natural language from any audience and provides accurate answers reasoned from structured and unstructured data.
• Zatient – Zatient is an artificially intelligent knowledge network to bridging the human and analytical aspects of decision making by intelligently synthesizing subjective, heuristic, and factual information into understandable and actionable outcomes.

The five semi-finalists were selected based on the following criteria:  impact of the solution, UI/UX design and functionality, leverage of AI, and patient engagement. Each of our semi-finalists will be awarded $5,000 to continue developing their solution or create a functioning prototype/working application. The semi-finalists will move onto a final judging round by an expert panel during our premiere live pitch event at the Health 2.0 Fall Conference.

Not only will our semi-finalists be able to demo their solutions in front of digital health industry leaders, but they will also receive cash prizes to invest in the advancement of their companies. The first place solution will win a grand prize of $50,000, second place will win $15,000, and the third prize will win $10,000.

For updates on the semi-finalists of the RWJF AI Challenge and to learn about additional digital health innovation programs, subscribe to the Catalyst @ Health 2.0 Newsletter, and follow @catalyst_h20 on Twitter.

Diana Chen is a Program Associate at Catalyst @ Health 2.0.

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Health in 2 Point 00, Episode 40

In this episode Jessica DaMassa asks about the thunder from Down Under, well the noise about the My Health Record program in Australia, the latest on women in health IT from Rock Health, and nearly gets to Click Therapeutics $17m round…all under the watchful gaze of Buddha–Matthew Holt

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Making Sense of the Health Care Merger Scene

By JEFF GOLDSMITH

In the past 12 months, there has been a raft of multi-billion-dollar mergers in health care. What do these deals tell us about the emerging health care landscape, and what will they mean for patients/consumers and the incumbent actors in the health system?

Health Systems

There have been a few large health system mergers in the past year, notably the $11 billion multi-market combinations of Aurora Health Care and Advocate Health Care Network in Milwaukee and suburban Chicago, as well as the proposed (but not yet consummated) $28 billion merger of Catholic Health Initiatives and Dignity Health. However, the bigger news may be the several mega-mergers that failed to happen, notably Atrium (Carolinas) and UNC Health Care and Providence St. Joseph Health and Ascension. In the latter case, which would have created a $45 billion colossus the size of HCA, both parties (and Ascension publicly) seemed to disavow their intention to grow further in hospital operations. Ascension has been quietly pruning back their operations in markets where their hospital is isolated, or the market is too small. Providence St. Joseph has been gradually working its way back from a $500 million drop in its net operating income from 2015 to 2016.

Another notable instance of caution flags flying was the combination of University of Pittsburgh Medical Center (UPMC) and PinnacleHealth, in central PA, which was completed in 2017.   Moody’s downgraded UPMC’s debt on the grounds of UPMC’s deteriorating core market performance and integration risks with PinnacleHealth. As Moody’s action indicates, investor skepticism about hospital mega-mergers is escalating. Federal regulators remain vigilant about anti-competitive effects, having scotched an earlier Advocate combination with NorthShore University HealthSystem in suburban Chicago. The seemingly inevitable post-Obamacare march to hospital consolidation seems to have slowed markedly.

However, the most noteworthy hospital deal of the last five years was a much smaller one: this spring’s acquisition of $1.7 billion non-profit Mission Health of Asheville, NC, by HCA. This was remarkable in several respects. First, it was the first significant non-profit acquisition by HCA in 15 years (since Kansas City’s Health Midwest in 2003) and HCA’s first holdings in North Carolina.  While Mission’s search for partnerships may have been catalyzed by a fear of being isolated in North Carolina by the Atrium/UNC combination, Mission Health certainly controlled its own destiny in its core market, with a 50% share of western North Carolina. Mission was not only well managed, clinically strong and solidly profitable, but its profits rose from 2016 to 2017, both from operations and in total.

Precisely because it was not a distress sale, and because Mission was in an unassailable market position, this deal should have sent shockwaves through the non-profit hospital industry. Yet, there was remarkably little public discussion of its significance. There was no burning platform here. Rather, the ability of HCA to lower Mission’s operating expenses with its austere management culture and break even on Medicare may have been viewed as a key to long-term sustainability by Mission’s board, as well as access to HCA’s more-or–less bottomless capital pool.

HCA’s willingness to be patient and wait for the right deals, and crucially, its ability to break even at Medicare rates, are the real sources of its long-term strength. It may well be that HCA’s ability NOT to follow the herd, and to decide which assets, markets, and relationships make sense long term is more valuable than mass and scale. The Rick Scott Columbia HCA had 360 hospitals at “peak roll-up.” The present, better focused HCA is a much stronger company at half the number of hospitals.

Implications

So many large non-profit and investor-owned health systems formed as roll-ups of smaller enterprises are struggling to generate operating earnings just now, including many prominent market leading systems. For this reason, many other potential roll-ups in the vein of Ascension-Providence and Atrium-UNC might not survive the courtship stage. Those roll-ups might actually weaken the combined enterprise by burdening them with hospitals that could not have survived on their own and which probably should close.  Bigger may no longer equal stronger in hospital management.

It has never been clear how actual patients would benefit from vastly greater scale of hospital operations. The burden of proof is on the industry that patients will notice a difference in service quality or lower prices from further consolidation of hospital systems. It is not clear that benefits to patients or their physicians has played any meaningful role in the flurry of post-Obamacare deals.

Physicians – Is Vertical Integration Inevitable?

In December 2017, United HealthGroup’s $100 billion subsidiary Optum purchased the troubled DaVita Medical Group for $4.9 billion. This deal set off a frenzy of speculation that United was positioning itself to become the next Kaiser. Industry pundits opined that Optum and United will transform itself into a closed panel vertically integrated care system that would enable United to sell a comprehensive exclusive care system product. I believe this is not a strong likelihood.

Optum’s first entry into the physician group business was opportunistic, obtaining a captive physician delivery system in Nevada as part of United’s 2008 acquisition of Sierra Health Plan.  The physician group asset did not belong in the health plan part of United and was therefore lodged in Optum as a one-off. Subsequent Optum acquisitions in California, Texas and Florida consisted of successful risk contracting Independent Practice Associations with significant and diverse (e.g. non-United) contracts. Some of those IPAs had a core multi-specialty employed medical group at its core. Optum’s early strategy was not a “physician employment” strategy, but rather not dissimilar to that of MedPartners or Phycor in the 1990’s: buying risk-bearing contracts through the acquisition of physician enterprises that had negotiated them.

Obamacare was expected to catalyze a wave of capitation. Owning risk-bearing physician groups was an asset-light way of playing this presumed shift to capitation. However, the expected post-ACA surge in delegated risk contracting did not materialize. Optum ceased buying care system assets in 2012 because the bidding for physician groups, particularly from health systems, had gotten out of hand. They resumed buying in 2016, adding urgent care centers and ambulatory surgical centers to the portfolio, in addition to the DaVita deal.

While some have claimed that Optum now employs 47,000 physicians, this number seems more likely to be the sum of its IPA networks.  The actual employed physician cadre is probably more like 15 thousand, a number smaller than the combined Permanente Medical Groups inside Kaiser.  There are roughly a million licensed physicians in the United States.

Presently, Optum has care system assets in markets which contain 70% of the US population, but there is limited “integration” among the care system assets, or between Optum and United’s Health Insurance operations. Obviously, United’s health insurance subscribers can use Optum’s group physicians. But Optum patients are not required to or even encouraged to use United’s health insurance products. It would damage the Optum care system asset value to exclude other insurers from paying Optum for a physician or ambulatory care.

Despite its large footprint, I believe that Optum’s strategy in the physician space is disciplined but opportunistic “conglomerate” style diversification. In only two markets, greater Los Angeles and San Antonio, does Optum have a significant local market share in the risk-bearing care system market? Optum has not shown any interest in canceling the substantial number of non-United network contracts and going “closed panel.” Nor is there yet evidence of a backlash from non-United insurers in anticipation of a closed panel strategy that would cause United’s health insurance competitors to shun contracting with Optum care system assets. United/Optum has more to lose than to gain in contracting advantage by closing their panels.

Optum is also unlikely to diversify into the slow growing hospital business. Despite a “buyers’ market” for hospital-based physician enterprises like Envision, Team Health, and MedNax, Optum has thus far studiously avoided acquiring hospital-linked assets. Rather, it is capable of surrounding hospitals with low-cost alternatives and stepping in front of them where possible as risk bearing physician-based care systems, leaving hospitals in those markets, as one analyst put it, as “stranded assets.” We will be watching the “integration” of these diverse Optum assets closely but are skeptical that “integration” will yield significant earnings or growth potential.

Implications

Regardless of who owns their physicians, a significant fraction of Americans will need to use the hospital as they age, and an increasing percentage will be publicly funded. Though successfully organized physicians can rigorously minimize the use of the hospital by substituting lower cost non-hospital alternatives (e.g. in surgery and imaging), the residual demand for hospital care related to complex conditions and for the fragile elderly seems likely to grow, not shrink, in years ahead.

The challenge hospitals face is making money at publicly funded rates and driving out the unnecessary or inappropriate use of its services. Hospitals can learn from Optum’s long time horizons, its market-by-market pragmatism about organizational models and insistence on deals being “accretive” rather than “mission driven.” Strategic discipline is the best response to the threat posed by Optum and other organizers of physician care.

Consumers may or may not be willing to “bond” with a corporate giant like Optum. They are likely to make their decisions about where they get their physician care based on responsiveness to their needs and the strength of the physician relationships that develop.

Optum seems unlikely to noticeably lower the cost of physician care to patients, as there are yet no demonstrable economies of scale in physician services.

Pharma Distribution: The “Amazon is Coming” Freak-out

In December 2017, CVS, the nation’s largest drugstore chain, and Aetna, the nation’s fifth largest health insurer, announced a $69 billion merger. Aetna had been blocked from its planned acquisition of rival Humana over anti-trust concerns. But CVS, the acquirer, had a much larger and more urgent concern – the mooted entry of Amazon into the pharmaceutical supply chain, either through wholesale distribution, direct-to-consumer strategy or both.

As its retail sales have slowed, CVS has become increasingly dependent on their CVS-Caremark

pharmaceutical benefits management (PBM) business both for revenue and earnings growth. The entire complex and costly US pharmaceutical supply chain is buckling under the financial pressure created by rising drug spending. The PBM business model has come under increasing regulatory scrutiny over concerns over lack of transparency and that PBM rebates negotiated with pharmaceutical firms do not seem to be reaching consumers. In the Aetna transaction, CVS looked to diversify out of its two main businesses to re-establish growth and establish closer and more comprehensive relationships with corporate customers.

Of course, retail in all its forms is being disrupted by Amazon. That Amazon might disrupt the pharmaceutical market by selling directly to consumers became a good deal less speculative with Amazon’s recent $1 billion acquisition of PillPack. BOTH of CVS’s current businesses may be in Jeff Bezos’ crosshairs.

Having said this, the CVS Aetna combination is an “out of the frying pan-into the fire” merger. CVS will discover that the health plan business is actually an extremely fragile web of short-term contracts between the insurer and employers, as well as between the insurer and its care networks. Many of these latter contracts may not be renewed under their present terms, which have been highly favorable to and profitable for insurers. This is because many care systems have been bitterly disappointed by the lack of return to them from the deep front-end discounts made in those contracts in anticipation of rapid growth in “narrow network” lives which have not materialized.

Health insurance is nearing the end of an exceptional profit cycle begun during the roll-out of Obamacare. The creation of health exchanges and the new narrow network contracts designed for them catalyzed a 2010-2014 hospital pricing panic similar to that which ensued on the rollout of PPOs in the mid-1990s. This pricing panic has damaged hospital system earnings and prevented them from recouping escalating losses from Obamacare Medicare rate concessions and the 2012 federal budget “sequester,” which cut Medicare rates by 2% annually going forward.

As with the Optum-DaVita combination, much has been made of the “vertical integration” aspect of Aetna having access to CVS’ network of instore clinics. CVS’s clinical assets – its 1,100 Minute Clinics – are more “nurse in a broom closet” than “doc in the box.” Fully loaded with CVS’s hefty corporate overhead, the Minute Clinics probably lose $20 a visit, with the fond hope of making some of it up on shampoo sales. Despite outgoing Aetna CEO Mark Bertolini’s vision of the CVS clinics as a health care equivalent of Apple’s Genius Bar, CVS/Aetna won’t be a credible player in disease management or anything else complicated by relying on a spindly network of nurse-driven instore clinics. As they did before the deal, consumers will find in CVS’s clinics a great place to get flu shots and back to school physicals, though.

It is also not clear how either business will grow as a result of the combination. CVS Aetna’s consolidation won’t lower the cost of health care for Aetna’s members or corporate clients, nor bring Aetna new health benefits customers.  Though Aetna has a number of large national accounts, it remains a marginal player in most large geographical markets, the venue where bargaining clout really matters. Having a bunch of drugstores and a PBM will not increase Aetna’s leverage with its care networks, hospital or physician. It will also not materially lower Aetna’s drug spend.

On the CVS side, merging with Aetna won’t drive more customers into CVS’s stores, or bring them any additional PBM business, because CVS/Caremark already managed Aetna’s pharmacy claims. And it might lose CVS the recently announced pharmacy benefits management deal with Aetna’s competitor, Anthem, that looked for a new PBM after dumping Express Scripts. There are no good reasons why Anthem would want to contract with a PBM owned by a competitor to their core business.

Implications

Hospitals ought not to be threatened by the CVS-Aetna combination, nor the copycat CIGNA-Express Scripts deal that followed it. Neither is likely to affect the prices paid for the specialty IV drugs that have pressured hospitals in the past several years.

Amazon’s core strengths – merchandising clout, logistics and cloud computing – are minimally relevant to health care provision. Amazon has no significant presence in any service business at the present time, other than cloud computing. But as suggested earlier, the pharmaceutical supply chain is ripe for disruption. Anything that lowers the cost of drugs to patients or care givers will help both cope with tightening cash flows and be welcomed by all.

While Amazon’s future incursion into health care remains “notional” at this point, the spate of deals that have been spawned by the mere potential of its entry into the pharmaceutical business resembles nothing so much as one of those chain reaction freeway collisions, where the first driver was distracted by the sight of a large moose walking out of the woods and up to the roadway. It is worth noting that other tech company invasions of the so-called “health care vertical”- Apple, IBM, Microsoft, Google – have not gone very well.

The Future of Mega-Medicine

In his 2012 book Anti-Fragile: Things that Gain from Disorder, finance guru Nassim Taleb makes a convincing argument that scale and the search for security in the corporate and financial world actually increased those institutions’ fragility and exposure to franchise risk. The reciprocal drive of health systems and health insurers, in particular, to become larger and more “unavoidable” may, ironically, have made them more, rather than less, vulnerable to economic shocks. This includes the effects of the inevitable economic downturn that awaits the American economy in the next year or two.  Larger health care organizations are inevitably more bureaucratic and take far longer to make decisions.

On the narrower issue of “integration,” the economic literature on the effectiveness or economic benefits of vertical integration in health care is remarkably devoid of evidence of consumer or societal benefits, or even benefits to the organizations themselves  https://www.nasi.org/research/2015/integrated-delivery-networks-search-benefits-market-effects.

Health care remains the most intimate personal service in the US economy. Health care organizations that wish to consolidate are increasingly constrained by the legal and political consequences of their actions. They are also increasingly tempting targets for the hostile populist sentiments accumulating on both the left and right sides of the political spectrum.

The lack of evidence of measurable consumer benefits and the rising risks haven’t yet stopped the wave of consolidation in health care. Despite the pro-merger puffery of prominent strategy consulting firms and bankers, it remains to be seen if $50 billion-plus mega-corporations can connect with real people on a consistent basis and deliver measurable benefits that meaningfully affect their health.

Jeff Goldsmith is the national adviser to Navigant Consulting and President of Health Futures, Inc. He is a veteran health care industry analyst and forecaster.

 

 

 

 

 

 

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Are doctors bribed by pharma? An analysis of data

By Rafael Fonseca MD & John A Tucker MBA, PhD

A Critical Analysis of a Recent Study by Hadland and colleagues

Association studies that draw correlations between drug company-provided meals and physician prescribing behavior have become a favorite genre among advocates of greater separation between drug manufacturers and physicians. Recent studies have demonstrated correlations between acceptance of drug manufacturer payments and undesirable physician behaviors, such as increased prescription of promoted drugs. The authors of such articles are usually careful to avoid making direct claims of a cause-effect relationship since their observations are based on correlation alone. Nonetheless, such a relationship is often implied by conjecture. Further, the large number of publications in high profile journals on this subject can only be justified by concerns that such a cause-and-effect relationship exists and is widespread and nefarious. In this article, we will examine a recent paper by Hadland et al. which explores correlational data relating opioid prescribing to opioid manufacturer payments and in which the authors imply the existence of a cause-and-effect relationship.¹

We propose the relationship between transactions between the private sector (e.g., meals provided, consulting payments) and prescribing habits can fall into one of three categories:

Type	Effect	Comments
0	There is no cause-effect relationship between these transactions and prescribing habits. Correlative observations may merely be reflections of practice patterns, and likelihood to use a drug category.	No harm exists.
Ia	There is a demonstrable cause-effect for transactions and prescribing patterns. However, this relationship is associated with increased use of drugs that have been proven to be an improvement over the current standard.	The effect is beneficial for patients. “Beneficial marketing.”
Ib	An adverse causative effect is documented with establishment of causation. There is a possibility of patient harm.	Patient harm occurs because the wrong medication is administered and contravenes medical standards. A minor damage is done but arguably exists, if a physician prescribes a more expensive medication when a cheaper alternative exists. “Nefarious marketing.”

 

Hadland et al.: Opioid Prescriptions and Manufacturer Payments to Physicians

The authors of this paper linked physician-level data from the 2014 CMS Open Payments database to 2015 opioid prescribing behavior described in the Medicare Opioid Prescribing Database. They explored the hypothesis that meals and other payments increase physician opioid prescribing by examining the association between receipt of meals and other financial benefits with the number of opioid prescriptions written[1]. Specifically, they found the following:

1. A nearly linear relationship between the number of opioid manufacturer-provided meals accepted by a prescriber and the number of opioid prescriptions written. The relevant data is provided in Figure 1 below. Prescribers who received nine meals from opioid manufacturers in 2014 prescribed opioid analgesics at slightly more than 3x the rate of those who accepted only one meal.
2. When broken down by physician specialty, those who accepted any payment from opioid manufacturers wrote between 1.2% more and 11% more opioid prescriptions as those who did not accept any such payments (Table 1).

Figure 1.

Figure reproduced from JAMA Internal Medicine 2018, volume 178, 861-3 under the Fair Use provisions of Section 107 of the U.S. Copyright Act.

Table 1.

Table reproduced from JAMA Internal Medicine 2018, volume 178, 861-3 under the Fair Use provisions of Section 107 of the U.S. Act.

Hadland et al. conclude that

Amidst national efforts to curb the overprescribing of opioids, our findings suggest that manufacturers should consider a voluntary decrease or complete cessation of marketing to physicians. Federal and state governments should also consider legal limits on the number and amount of payments.

While no cause-and-effect relationship between payments and prescribing habits has been demonstrated by this correlative study, the implication that one exists is made clear in the authors’ recommendations. In our analysis below, we attempt a deeper dive to determine whether such a cause-and-effect relationship exists.

Our View: It is More Complicated than That….

To better understand the issues presented by the Hadland’s correlative study, we undertook an independent analysis of the same data. We repeated the Hadland data extraction from the CMS sources cited in the paper. We associated payments with prescribing behavior using physician name and geographical information as described by Hadland. Despite the lack of detail provided in the publication, we closely reproduced the number of opioid prescribers, the number of opioid prescribers accepting payments, and the total number of payments described in the Hadland paper. The only discrepancy we found between our data and that reported by Hadland is that we found a more substantial total payment amount of $13.1M vs. the $9.1M reported by Hadland et al. We found no simple explanation for this discrepancy, as the total payment amount was consistently about 50% higher than that described by Hadland when stratified by source or by payment type. While we are not able to firmly assess the source of this difference given the lack of a detailed protocol in the paper, we believe that part of the difference may have arisen by including a more comprehensive range of opiate products in our analysis relative to that used by Hadland.

 How Large is the Association Between Manufacturer Payments and Prescribing Volume?

Our first criticism of the Hadland analysis is directed at the non-standard presentation of the data in Figure 1. The most widely accepted way to present the relationship between two continuous variables such as payments and the prescription count is a correlation diagram. We present the data in this manner in Figure 2 (Note the logarithmic Y axis). Doctors who accepted no free meals from opioid manufacturers wrote between 0 and 1000 opioid prescriptions in 2015. As did those who accepted 50 or more.

Figure 2. Correlation Diagram Relating Number of Opioid Prescriptions Written to Number of Drug Maker Meals Accepted

 This graph gives a very different impression than the presentation of the same data in Figure 1. Why is that? Here we have shown every data point, though some are hard to see because there are so many of them (345K to be exact). In Hadland’s presentation of the data, they grouped the prescribers into categories based on the number of meals that they accepted. They calculated the mean for each group, which hides the tremendous variation in prescribing behavior within each group. The error bars are shown in Hadland’s figure are not standard deviations (a measure of within-group variation) but standard errors (A measure of how precisely the mean has been estimated). The latter value is derived from the former by dividing by the square root of the number of data points, which ranges as high as 8468 for some of the categories in Hadland’s figure. So a clear representation of within-group variation would show error bars as much as 92-fold larger than those shown.

A similar criticism can be directed at the presentation of the data in Table 1. Comparing mean prescribing rates between those who accepted any payment and those who accepted none gives a non-representative picture because the distributions are highly skewed. Imagine a cancer trial in which 5 patients live 2, 3, 3, 4, or 20 months. Reporting that the average survival was 7.5 months and the standard deviation was 8.3 months really doesn’t give a very meaningful picture of what happened in the trial. Similarly, Hadland et al. report that physicians who accepted payments in 2014 wrote 539 +/- 945 prescriptions in 2015, while those who did not wrote 134 +/- 281. Who are the physicians who wrote less than zero prescriptions in 2015, and what does a negative prescription look like? This type of bizarre result arises from applying statistical methods appropriate to a normal distribution of values to a data set that is decidedly non-normal.

The problems become even more apparent when we compare these numbers to the authors’ statement in the text that those who accepted payments in 2014 increased their prescription count in 2015 by 1.6, while those who did not accept payments in 2014 reduced their prescription count by 0.8. How is the difference (2.4 prescriptions) equal to 9.3% of 134 prescriptions (Table 1)? And doe a relative increase of 2.4 prescriptions per year from a base of 539 prescriptions merit publication in JAMA Internal Medicine and a call for legislation?

Are Drug Companies Paying Doctors to Write Prescriptions?

While the correlation between meals and opioid prescriptions is much weaker than implied by the figures presented in Hadland et al., a reasonable person might still object that ANY exchange in which prescriptions result from a conscious or unconscious quid pro quo for free lunches is morally unacceptable (Type Ib). We would certainly take that position. So let’s analyze whether the relationship is causative or merely correlative. Hadland’s implicit hypothesis is that doctors are writing opioid prescriptions in “exchange for pizza.” An alternative explanation might be that attending manufacturer informational sessions at which meals are served and prescribing opioids might both be driven by having a practice that involves treating many pain patients. Let’s look at the data and see if we can distinguish between these possibilities.

• If Doctors are writing prescriptions in exchange for payments, one would expect that the number of prescriptions would rise predictably with the payment amount.

In practice, we find this is not the case.

Regressing the number of opioid prescriptions written on total payments received, we find r² for the correlation is 0.01. Thus only 1% of the total variation amongst prescribers is associated with variation in the amount of payment received. (The gap in the graph between $0 and $10 arises because CMS does not require reporting of payments below $10).

Figure 3. Relationship Between the Number of Opioid Prescriptions Written and Total Payments Received

 

• If doctors are writing prescriptions as quid pro quo for industry payments, one would expect that non-meal payments would show a correlation with prescribing similar to the correlation with meals shown in Figure 1.

Alternatively, if both attendance at educational sessions at which meals are served and opioid prescribing are driven by having a practice that involves treating many pain patients, one might expect a very modest or no correlation of prescribing with non-meal payments.

In practice, we see the latter (Figure 4).

Figure 4 was drawn using Hadland’s categorical style of presentation to allow direct comparison to Figure 1. While Hadland found that opioid prescribing tripled as the number of industry-sponsored meals increased from one to nine, we find no trend in toward increased prescribing among those who received between $0.01 and $65,536 in non-meal payments from opioid manfacturers. In fact, the geometric mean rate was nearly identical for those receiving less than $1 in non-meal payments (711 prescriptions) and for those receiving $32,000 to $64,000 (718 prescriptions). For the 58 physicians who received more than $65,536, the rate of prescribing was increased by nearly twofold relative to those receiving less than a dollar, but due to large within group differences, this difference was not statistically significant.

The fact that opioid prescribing correlates with the number of meals accepted but not with the total amount of non-meal payments received suggests that attendance at educational events at which meals are served and opioid prescribing are both driven by practice characteristics. In contrast, these data are difficult to accommodate within the theory that the association of prescribing rates with meals accepted is due to quid pro quo, or that companies are bribing doctors to prescribe their products.

Figure 4. Geometric Mean Prescribing Rates by Total Non-Meal Payments Received

• If doctors are writing prescriptions in exchange for free meals, one would not expect meals provided by the manufacturer of non-opioid pain treatment to be associated with increased opioid prescribing. If doctors with large pain practices are more likely to attend informational lunches about pain products, such an association is expected and natural.

In practice, we find that the association of increased opioid prescribing with attendance at informational lunches offered by the manufacturers of pain therapeutics is independent of whether the pain product is an opioid!

St. Jude Medical is a medical device company that sells neuromodulation devices for the treatment of chronic pain. Those who attended St. Jude lunches prescribed opioids at the same rate as doctors who attended an equal number of lunches sponsored by opioid manufacturers. This observation holds up equally well when looking only at those who attended St. Jude lunches but did not attend any opioid lunches. We found similar associations with lunches provided by manufacturers of other non-opioids products (data not shown).

Figure 5. Relationship Between Attendance at Industry-Sponsored Lunches and Opioid Prescribing: St. Jude vs. Opioid Manufacturers

 

 

Conclusion

Correlation is not causation. While many advocates of reduced interactions between “commercial” interests and physicians have implied or directly suggested a quid pro quo between industry meals and other financial interactions and prescribing habits, correlation alone does not prove a quid pro quo relationship. In the case of opioid prescribing, we believe that we have presented a strong case that 1) the relationship between industry payments and prescribing is much weaker than has been presented in the literature, and 2) that prescribing and attendance at manufacturer-sponsored informational lunches are both driven by practice characteristics, rather than the meals themselves driving prescriptions (Type 0 relationship).

We believe that much of what has been published regarding the correlation of prescribing with industry payments and sponsored meals suffers from the shortcomings described in this short note. In particular, many of these papers conflate causation with correlation. In cases where fairly simple and obvious analyses would serve to differentiate between the authors’ preconceptions and alternative interpretations of the data, these analyses have not been performed. We urge all with an interest in this area to approach these data with the highest possible level of objectivity, as is our responsibility as scientists. We have done our best to do so here, and commit to doing so in our planned analyses of other papers in this area.

We look forward to a stimulating debate with those who have other data bearing on this issue, or other interpretations of the data presented herein.

References

Hadland SE, Cerdá M, Li Y, Krieger MS, Marshall BL. Association of pharmaceutical industry marketing of opioid products to physicians with subsequent opioid prescribing. JAMA internal medicine. 2018

[1]. This analysis, as well as alternative analyses performed by the present authors, was limited to the prescribing behavior of those who wrote at least ten opioid prescriptions in 2015 due to redaction of counts between 1 and ten by CMS.

About the authors

Rafael Fonseca is a hematologist at the Mayo Clinic in Arizona and John is a medicinal chemist residing in Northern California.

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