Amazon has transformed the way we read books, shop online, host websites, do cloud computing, and watch TV. Can they apply their successes in all these other areas to healthcare?
Just last week, Amazon announcedComprehend Medical, machine learning software that digitizes and processes medical records. “The process of developing clinical trials and connecting them with the right patients requires research teams to sift through and label mountains of unstructured clinical record data,” Fred Hutchinson CIO Matthew Trunnell is quoted saying in a MedCity News article. “Amazon Comprehend Medical will reduce this time burden from hours to seconds. This is a vital step toward getting researchers rapid access to the information they need when they need it so they can find actionable insights to advance life-saving therapies for patients.”
Deriving insights from data and making those available in a user-friendly way to patients and clinicians is just what we need from technology innovators. But these tools are useless without data. If an oncology patient is hospitalized, her provider may not be informed of her hospitalization for days or even weeks (or ever). And the situation is repeated for that same patient receiving care from cardiologists, endocrinologists, and other providers outside of her oncology clinic. When it comes to personalized health and medicine, both the quantity and quality of data matter. Providers need access to comprehensive patient health data so they can accurately and efficiently diagnose and treat patients and make use of technology that helps them identify “actionable insights.”
FDA Commissioner Scott Gottlieb has said biosimilars are “key to promoting access and reducing health care costs. And it’s a key to advancing public health.” While the Administration works to reduce barriers to bringing biosimilars to market, payers and providers can help increase adoption of biosimilars in clinical practice and ensure cost savings.
Biosimilars are developed in a similar way as existing biologics and have the same safety, efficacy, and quality profiles, but are more competitively priced to ensure more patients have access to these important medicines and that the system can afford them. A ten-year growing body of real-world use in the EU shows biosimilar medicines increase usage of biologic medicines, while matching their reference biologics in terms of safety, efficacy and quality.
On Episode 62 of Health in 2 Point 00, Jess and I are reporting from Nashville—while enjoying some delicious barbecue. We’re in town for AHIP’s Consumer Experience & Digital Health Forum, where Jess did an amazing job as a moderator and I was on a panel. In this episode, Jess asks me about my key takeaways from the forum, what the deal is with Tivity Health acquiring Nutrisystem, and how I managed to get into a fight on Twitter while at AHIP. —Matthew Holt
There was a very sobering piece in NEJM by the FDA last month in which the authors try to explore what went wrong with the Keynote-183, Keynote-185 and checkmate 602 trials testing PD-1 inhibitors combinations with pomalidomide or lenalidomide and dexamethasone in multiple myeloma. Interim analysis of Keynote 183 and 185 revealed detrimental effects on overall survival (OS) with hazard ratios of 1.61 and 2.06, not explained by differences in toxicities alone. The checkmate 602 trial was also halted in light of these findings and also showed higher mortality in the nivolumab combination arm.
In the thoughtful NEJM piece, the authors make at least three important points. First, they question why these PD-1 inhibitors were tested in combination despite their having limited single-agent activity. In fact, a couple of years ago, Vinay Prasad and I asked the same question: why are novel cancer drugs being tested in combination despite having limited activity as a single agent? We found that these drugs, even when ultimately approved, provide relatively low value and recommended that drugs with poor single agent activity not be tested in combinations unless there are specific reasons to expect synergy.
The second important point in the article is that many cancer drug approvals are lately based on durable response rates in single arm trials without a control group, a situation in which it is difficult to evaluate the safety and efficacy of drug combinations. Indeed, without an RCT, the oncology community would never have known these signals of detrimental effect. If the FDA had approved these PD-1 inhibitors in multiple myeloma on the basis of non-randomized trials, which it often does in other oncology contexts, who knows how long it would have taken to recognize the increased mortality in patients—and at what cost. This is another reason why we need RCTs more now than ever. Finally, the authors point out that these PD-1 inhibitors in multiple myeloma were directly advanced to phase 3 trials after phase 1 trials were completed, without phase 2 information. Indeed, in a recent paper, Alfredo Addeo and I showed that a substantial percentage of drugs that fail in phase 3 trials do not have supporting phase 2 data. Continue reading…
The 2019 ACA plan year is notable for the increase in insurer participation in the marketplace. Expansion and entry have been substantial, and the percent of counties with one insurer has declined from more than 50 percent to approximately 35 percent. While urban areas in rural states have received much of the new participation, entire rural states have gained, along with more metropolitan urban areas.
Economic theory and common sense lead most to believe that increased competition is unquestionably good for consumers. Yet in the paradoxical world of the subsidized ACA marketplace, things are not so simple. In some markets, increased competition may result in a reduction in the purchasing power of subsidized consumers by narrowing the gap between the benchmark premium and plans that are cheaper than the benchmark. Even though the overall level of premiums may decline, potential losses to subsidized consumers in some markets will outweigh gains to the unsubsidized, suggesting that at the county level, the losers stand to lose more than the winners will win.
One way to illustrate this is to hypothetically subject 2018 marketplace enrollees to 2019 premiums in counties where new carriers have entered the market. Assuming that enrollees stay in the same metal plan in both 2018 and 2019, and that they continue to buy the cheapest plan in their metal, we can calculate how much their spending would change by income group.
Under these assumptions, in about one quarter of the counties with federally facilitated marketplaces (FFM) that received a new carrier in 2019, both subsidized and unsubsidized enrollees would be better off in 2019, meaning that they could spend less money and stay in the same metal level. In about thirty percent of these counties, all enrollees are worse off. In almost all of the rest, about forty percent, there are winners and losers, but in the aggregate, the subsidized lose more than the unsubsidized win. Overall, in about 70 percent of FFM counties with a new carrier, subsidized enrollees will lose purchasing power, while in about 66 percent of these counties, unsubsidized customers will see premium reductions. In population terms, about two-thirds of subsidized enrollees in counties with a new carrier will find plans to be less affordable, while a little more than half of unsubsidized enrollees will see lower premiums.
Consumers aren’t taking their healthcare providers’ words for it anymore. They’re taking charge and leading a digital revolution where individuals have the power to make their own educated decisions about care.
According to the Healthcare Consumer Insight & Digital Engagementreport by Binary Fountain, a leading online reputation management platform, 51 percent of people who have a physician share their personal healthcare experiences via online ratings, review sites and social media.
Once shared, this information is immediately available to the entire world with just the click of a button. And people are taking full advantage of this. In fact, 80 percent of respondents in the 2018 Customer Experience Trends in Healthcare report by Doctor.com have used the internet to make a healthcare-related search in the past year. Another 81 percent said they read reviews about a referred provider.
Consumers’ accessibility to detailed, personalized experiences could make or break medical sales companies. Unfortunately, if these trends aren’t addressed appropriately, medical sales teams around the country will feel the impact.
By further empowering the general public, medical sales leaders can give their teams the tools needed to excel in the field. Here’s how:Continue reading…
In a recent essay, VIVIO Health’s CEO Pramod John guides us through four sensible drug policy changes and supporting rationales that could make drug pricing much fairer. Reading through it, one is struck by the magnitude of the drug manufacturing industry’s influence over policy, profoundly benefiting that sector at the deep expense of American purchasers. As Mr. John points out, the U.S. has the world’s only unregulated market for drug pricing. We have created a safe harbor provision that allows and protects unnecessary intermediaries like pharmacy benefit managers. We have created mechanisms that use taxpayer dollars to fund drug discovery, but then funnel the financial benefit exclusively to commercial interests. And we have tolerated distorted definitions of value – defined in terms that most benefit the drug manufacturers – that now dominate our pricing discussions.
The power of this maneuvering is clear in statistics on health industry revenues and earnings. An Axios analysis of financial documents from 112 publicly traded health care companies during the 3rd quarter of 2018 showed global profits of $50 billion on revenues of $636 billion. Half of that profit was controlled by 10 companies, 9 of which were pharmaceutical firms. Drug companies collected 23% of the total revenues during that quarter, but retained an astounding 63% of the profits, meaning that the drug sector accounts for nearly two-thirds of the entire health care industry’s profitability. Said another way, the drug industry reaps twice the profits of the rest of the industry combined.
The United States ranks number one in the world for health care spending as a percentage of GDP. That sounds great… but, for instance, Texas ranks only 11th worldwide when it comes to performance. That’s because of access to care.
The country’s health care rankings are likely to get worse as 673 rural hospitals in the U.S. are at risk of closing. Here’s what has happened: the need for care greatly outpaces available funding, especially for public hospitals. Something must be done.
If public funding is no longer available, alternative funding can be secured in numerous ways. The simplest way to access alternative funding is through a public-private partnership (P3) engagement. However, alternative funding for public hospitals, health care clinics and university medical centers can be found from other sources as well. Finding funding is not a problem when private-sector investors, large equity funds, pension programs, asset recycling and EB5 programs all stand ready to invest in public-sector projects.
Moving to a P3 health care model would allow hospitals to secure immediate funding and utilize private-sector expertise and best practices while transferring all risks. The launch of health care P3s would also ensure new construction, new jobs and hundreds of additional health care options for people. Continue reading…
But ACOs could pave the way for more significant cost-cutting based on competition.
By KEN TERRY
The Medicare Shared Savings Program (MSSP), it was revealed recently, achieved a net savings of $314 million in 2017. Although laudable, this victory represents a rounding error on what Medicare spent in 2017 and is far less than the growth in Medicare spending for that year. It also follows two years of net losses for the MSSP, so it’s clearly way too soon for anyone to claim that the program is a success.
The same is true of accountable care organizations (ACOs). About a third of the 472 ACOs in the MSSP received a total of $780 million in shared savings from the Centers for Medicare and Medicaid Services (CMS) in 2017 out of the program’s gross savings of nearly $1.1 billion. The other MSSP ACOs received nothing, either because they didn’t save money or because their savings were insufficient to qualify them for bonuses. It is not known how many of the 838 ACOs that contracted with CMS and/or commercial insurers in 2016 cut health spending or by how much. What is known is that organizations that take financial risk have a greater incentive to cut costs than those that don’t. Less than one in five MSSP participants are doing so today, but half of all ACOs have at least one contract that includes downside risk.
As ACOS gain more experience and expand into financial risk, it is possible they will have a bigger impact. In fact, the ACOs that received MSSP bonuses in 2017 tended to be those that had participated in the program longer—an indication that experience does make a difference.
However, ACOs on their own will never be the silver bullet that finally kills out-of-control health spending. To begin with, 58 percent of ACOs are led by or include hospitals, which have no real incentive to cut payers’ costs. Even if some hospitals receive a share of savings from the MSSP and/or private insurers, that’s still a drop in the bucket compared to the amount of revenue they can generate by filling beds instead of emptying them. So it’s not surprising that physician-led ACOs are usually more profitable than those helmed by hospitals.
At long last, we seem to be on the threshold of departing the earliest phases of AI, defined by the always tedious “will AI replace doctors/drug developers/occupation X?” discussion, and are poised to enter the more considered conversation of “Where will AI be useful?” and “What are the key barriers to implementation?”
As I’ve watched this evolution in both drug discovery and medicine, I’ve come to appreciate that in addition to the many technical barriers often considered, there’s a critical conceptual barrier as well – the threat some AI-based approaches can pose to our “explanatory models” (a construct developed by physician-anthropologist Arthur Kleinman, and nicely explained by Dr. Namratha Kandulahere): our need to ground so much of our thinking in models that mechanistically connect tangible observation and outcome. In contrast, AI relates often imperceptible observations to outcome in a fashion that’s unapologetically oblivious to mechanism, which challenges physicians and drug developers by explicitly severing utility from foundational scientific understanding.