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Written By: Jason Shafrin - Jul• 27•17

Amazon to dive into health care?

Written By: Jason Shafrin - Jul• 27•17

The answer is yes according to CNBC.

Amazon has started a secret skunkworks lab dedicated to opportunities in health care, including new areas such as electronic medical records and telemedicine. Amazon has dubbed this stealth team 1492…

Amazon has become increasingly interested in exploring new business in healthcare. For example, Amazon has another unit exploring selling pharmaceuticals, CNBC reported in May.The new team is currently looking at opportunities that involve pushing and pulling data from legacy electronic medical record systems. If successful, Amazon could make that information available to consumers and their doctors. It is also hoping to build a platform for telemedicine, which in turn could make it easier for people to have virtual consultations with doctors, one of the people said.

The group is also exploring health applications for existing Amazon hardware, including Echo and Dash Wand. Hospitals and doctor’s offices have already dabbled in developing skills for Amazon’s voice assistant Alexa, which presents a big opportunity for the e-commerce company.

Unsurprisingly, Amazon is also considering healthcare applications for their Echo product as well as their Alex voice assistant. Many may not know, but Amazon is already in the healthcare business, as a leading seller of medical supplies.

Appropriate Use Criteria, or how I learned to love CMS telling doctors what to do

Written By: Jason Shafrin - Jul• 25•17

As part of Section 218(b) of the Protecting Access to Medicare Act, CMS instituted the appropriate use criteria (AUC) for the use of advanced diagnostic imaging.  In order to be reimbursed for these diagnostic imaging services, physicians must consult with and document that they used AUC software before recommending advanced diagnostic imaging.  Failing to document use of clinical decision support (CDS) with AUC criteria would lead to the claim being rejected (i.e., not paid).

At first glance, this seems like a great idea. AUCs are evidence based guidelines that can ostensibly help physicians use diagnostic imaging appropriately.  However, are top-down mandates helpful for improving care?  Aren’t there already clinical guidelines in place to assist practitioners?

Although AUC are evidence-based, my guess is that they will be ineffective in practice and physicians will see them as an administrative burden.

Even Atul Gawande–one of the strongest advocates of checklists–recognized that purely top-down approaches typically do not work. He strongly advocates for the clinical team taking the lead in adopting checklists.  However, that does not always happen:

…the most extreme example, they turned [our 19-item checklist] into an 81-item checklist. It was impossible to use. We’d specifically designed it to be something you could run through in 60 seconds or less at each pause point, so you weren’t distracted from the main operation. And you could see that the administration in the hospital had got hold of it, and they were using it to try to impose their ideas. And essentially, the clinical team was not the team that were designing and controlling the checklist. Invariably, you look at that and you know that everybody is completely ignoring it, and it has become just a tick box effort instead of an enabler of greater capability.

This is a key concern with AUC.  Will it actually be a tool to improve care or will doctors not take it seriously and just consider it another paperwork task that takes them away from patient care?

Although initially the AUC provisions were going to go into effect on January 1, 2018, the implementation of AUC for diagnostic imaging is being delayed until January 1, 2019.  This will give providers more time to implement the necessary software, but I am skeptical that CMS dictates on practicing medicine will be taken to heart by real-world providers.

 

Innovation in small markets

Written By: Jason Shafrin - Jul• 24•17

The introduction of new treatment technologies typically occurs where there is a large market.  A lot of innovations are developed to treat disease that affect a large number of people in the developed world because the financial returns are large.  It is less likely to observe innovation in the treatment of rare diseases or diseases that affect individuals in developing countries.  This issue is particularly relevant as the technology to produce new precision medicines comes online.

One of the most widely known regulations to incentivize the development of treatments for rare diseases is the United States Orphan Drug Act (ODA) of 1983.  Other countries, however, have also enacted legislation to incentivize investment in rare diseases. For instance, a paper by Iizuka and Uchida (2017) looks at legislation enacted in Japan.  Japan provides research grants for rare and intractable diseases.  However, Japan also incentivizes innovation through patient demand incentives.

In 1973, the [Japanese] government started implementing a policy that reduces patient cost sharing for a subset of intractable diseases.  Japan implements universal health coverage, and patients below age 70 pay coinsurance of 30% for any medical treatment covered by public health insurance. The demand-side policy reduces patients’ out-of-pocket spending by setting a stop-loss, a maximum amount of monthly out-of-pocket expenditure, for the treatment of qualified intractable diseases, which ranges between 0 to 23,100 JPY per month based on their family income.

Japan also has an ODA-type policy that promotes R&D for conditions that affect <50,000 Japanese patients and are serious diseases with high medical needs.

The authors of the study use a difference-in-difference methodology because in 2009, Japan added 17 intractable diseases to the list of conditions eligible for reduced cost sharing.

Using clinical trials data taken from public registries, we identified the effect of the policy using the DID approach, exploiting the institutional detail that the diseases covered by the policy increased in an arbitrary fashion during our data period. We found that the demand-side policy increased firms’ incentive to innovate. Specifically, firm-sponsored new clinical trials increased as much as 181% when covered by the policy.

(more…)

HWR is up

Written By: Jason Shafrin - Jul• 24•17

Steve Anderson has posted  Health Wonk Review: Are We There Yet? Edition at medicareresources.org. Check it out.

It’s Okay to Be a Coward about Cancer

Written By: Jason Shafrin - Jul• 23•17

That is the title of an interesting Time article from cancer surviver Josh Friedman. Friedman is a well-known screenwriter whose work includes credits for such franchises as Terminator, Avatar and War of the Worlds. The article was prompted in part by John McCain’s recent brain cancer diagnosis (glioblastoma to be specific).

One excerpt is especially poignant.

As a storyteller I think hard about the tales we tell. Toughness and courage are staples of our cultural business. But these are not how we survive cancer. We survive cancer through luck, science, early detection and real health insurance. If we survived through courage, I probably wouldn’t have.

Universal Basic Income

Written By: Jason Shafrin - Jul• 21•17

Universal basic income is the idea that all individuals in a society should be guaranteed a minimum income.  The logic behind this approach is one of equity.  Many members of society feel that all individuals are entitled to some basic level of financial well-being regardless of their skills, ability or willingness to work.

Current government approaches often aim to subsidize certain groups based on their family situation (e.g., single mothers), health (e.g., disabled) or current income (e.g., earned income tax credit).  Administering these programs can be costly.  Universal basic income is simple to administer, provides the maximum fairness, and may have fewer work disincentives than current programs.  For instance, an individual may wish to take a job with higher pay but if doing so means they would lose their Medicaid benefit, they may decide not to do so.  In effect, their marginal income tax rate would be very high, even potentially more than 100% once one incorporates the value of non-cash benefits.

Universal basic income does suffer from at least two problems.  The first is that it may be more succeptible to corruption.  Whereas current government programs may require (onorous) paperwork to verify program elibility, part of universal basic income’s cost savings could come from a lower administrative need, which could result in additional corruption.  For instance, people may stop informing the government that their elderly loved ones have died because they may want to continue getting their loved ones basic income check.  Also, raising a community’s basic income could reduce the number of people interested in participating in the labor market.

One particular study by Calnitsky and Latner (2017) looks at exactly this question based on data from the Mincome initiative, where the province of Manitoba instituted a basic income program for a certain town. They authors found:

Would people work less if their basic needs were guaranteed outside the market? Never before or since the Dauphin experiment has a rich country tested a guaranteed annual income at the level of an entire town. A community-level experiment accounts for the fact that people make decisions in a social context, not in isolation. Using hitherto unanalyzed data we find an 11.3 percentage point reduction in labor market participation, and nearly 30 percent of that fall can be attributed to “community context” effects. Additionally, we show that withdrawals were driven disproportionately by young and single-headed households. Participants who provide qualitative explanations for work withdrawals typically cite care work, disability and illness, uneven employment opportunities, or educational investment.

 

Atul Gawande on AI

Written By: Jason Shafrin - Jul• 19•17

Tyler Cowen has one of his “Conversations” with Atul Gawande.  The interview is interesting throughout.  Below is an excerpt from their discussion on artificial intelligence.

TYLER COWEN: …How far are we from having an AI that is capable of actually doing diagnosis to people? That is, they might speak into a Skype connection, something like Watson would hear what they say, and they would then diagnose the person well enough that this would be a usable form of healthcare? Is that far, close?

ATUL GAWANDE: Massively far. I think it’s one of the hardest things. You want me to tell why?

COWEN: Tell us why, yes.

GAWANDE: OK, the diagnosis process—people imagine what it is, is that people come to you with a crisply defined problem. “I have symptom one, two, three. I have data to add to it, and now give me the answer.”

The reality is, first of all, people come to you often unable to explain what their problem is. “I have pain.” “Where?” “Hmmm. Well, it’s sort of here.” And they’ll point with a hand. “Well, do you mean there under your rib cage, or you mean in your chest, or . . .”

So you have this probing process that is part of it and how they tell the story. Then there’s also how their story had evolved over time, and they often have to put it in their words. It’s more of a narrative than it is a straight set of data. That’s problem one.

IBM Watson put their AI on this problem, and it would never be the problem I would have put them on. The second part of it is that it changes over time, and you’re adding data along the way. You’re integrating it with a little bit about your view of the understanding of the person and their likelihood to even say that something is a major symptom or not.

There is no question that you can augment the human capability. But the idea that you pull out your phone and it would give you the diagnosis—it is still one of the hardest problems in reducing error in medicine, is the fact that we still have a high rate of error, and the sources of the error have to do with the human being rather than the calculation.

COWEN: But say you only get 15 minutes with your doctor, which is pretty common, and as you know, those conversations don’t always run so well. People are intimidated, they forget the right question to ask. You could have three hours talking to something like Watson. Maybe 80 percent of the dialogue is nonsense, but at the end, you apply machine learning.

And keep in mind, the alternative now is that people use Google, which is in a sense the world’s number one doctor. So AI only needs to be better than Google, which is already a form of AI. In that sense, isn’t it just around the corner that it would be a marginal improvement on what we have today?

GAWANDE: Yeah, one is the replacement question. Can I simply have something that will make the diagnosis? And lots of reasons why that’s difficult. But to augment the human capability, absolutely. There already are programs. One example is called Isabel, where the clinician, having elicited all of this information, can simply put the observations into a list. It will allow them to recognize, “OK, fine. You think that what they have is diagnosis one, but here are eight others in rank order of consideration compared to the one that you think.”

There have been plenty of studies, and it’s been around for more than a decade without the need for AI. This is just crunching some basic data to begin with that can add real value. I think the puzzle of it is that you need that capability to integrate information coming from the person interpreted and be able to get it into these kinds of systems. And in many cases, people may be able to do some of that over time for themselves.

Economic Burden of ACPA+ patients with Rheumatoid Arthritis

Written By: Jason Shafrin - Jul• 17•17

That is the topic of my most recent article in the Journal of Managed Care & Specialty Pharmacy along with co-authors Mahlet Gizaw Tebeka, Kwanza Price, Chad Patel, and Kaleb Michaud.  The abstract of the article–titled “The Economic Burden of ACPA-Positive Status Among Patients with Rheumatoid Arthritis“–is below.

BACKGROUND: Anticitrullinated protein antibodies (ACPAs) are serological biomarkers associated with early, rapidly progressing rheumatoid arthritis (RA), including more severe disease and joint damage. ACPA testing has become a routine tool for RA diagnosis and prognosis. Furthermore, treatment efficacy has been shown to vary by ACPA-positive status. However, it is not clear if the economic burden of patients with RA varies by ACPA status.

OBJECTIVE: To determine if the economic burden of RA varies by patient ACPA status.

METHODS: IMS PharMetrics Plus health insurance claims and electronic medical record (EMR) data from 2010-2015 were used to identify patients with incident RA. Patients were aged ≥ 18 years, had ≥ 1 inpatient or ≥ 2 outpatient claims reporting an RA diagnosis code (ICD-9-CM code 714.0), and had an anticyclic citrullinated peptide (anti-CCP; a surrogate of ACPA) antibody test within 6 months of diagnosis. Incident patients were defined as those who had no claims with an RA diagnosis code in the 6 months before the first observed RA diagnosis. The primary outcome of interest was RA-related medical expenditures, defined as the sum of payer- and patient-paid amounts for all claims with an RA diagnosis code. Secondary outcomes included health care utilization metrics such as treatment with a disease-modifying antirheumatic drug (DMARD) and physician visits. Generalized linear regression models were used for each outcome, controlling for ACPA-positive status (defined as anti-CCP ≥ 20 AU/mL), age, sex, and Charlson Comorbidity Index score as explanatory variables.

RESULTS: Of 647,171 patients diagnosed with RA, 89,296 were incident cases, and 47% (n = 42,285) had an anti-CCP test. After restricting this sample to patients with a linked EMR and reported anti-CCP test result, 859 remained, with 24.7% (n = 212) being ACPA-positive. Compared with ACPA-negative patients, adjusted results showed that ACPA-positive patients were more likely to use either conventional (71.2% vs. 49.6%; P < 0.001) or biologic (20.3% vs. 11.8%; P < 0.001) DMARDs during the first year after diagnosis and had more physician visits (5.58 vs. 3.91 times per year; P < 0.001). Annual RA-associated total expenditures were $7,941 for ACPA-positive and $5,243 for ACPA-negative patients (Δ = $2,698; P = 0.002). RA-associated medical expenditures were $4,380 for ACPA-positive and $3,427 for ACPA-negative patients (Δ = $954; P = 0.168), whereas DMARD expenditures were $3,560 and $1,817, respectively (Δ = $1,743; P = 0.001).

CONCLUSIONS: RA-related economic burden is higher for patients who are ACPA-positive compared with those who are ACPA-negative. Providers may wish to inform patients diagnosed with ACPA-positive RA about the likely future disease and economic burden in hopes that both stakeholders can be more proactive in addressing them.

Why Medicaid patient access to physicians is limited.

Written By: Jason Shafrin - Jul• 16•17

In short, the reason is that Medicaid reimbursement rates for providers is too low.  Saurabh Jha, however, explains the point a bit more artistically in his Health Care Blog piece.

Medicaid pays a cardiologist, with years of training, $25-40 for a consultation to manage a complex patient with multiple comorbidities, on polypharmacy, where the cardiologist must indulge in shared decision making and also ensure the patient adheres to statins.  For comparison, my personal trainer charges me $80. There’s no shared decision making – he tells me to do “burpees” and I must abide or face his wrath.

Note that although Medicaid patients do face challenges to timely access to physicians, particularly in certain regions or for certain specialties, a majority of physicians do accept Medicaid.  The Kaiser Family Foundation found that:

About 70% of office-based physicians accept new Medicaid patients, compared to about 85% who accept new patients with private insurance or Medicare.

This finding does vary across specialty.  For instance, psychiatrists are especially likely to not accept Medicaid patients.