Category Archives: economics

How my PCP alerted me to the potential for abuse in telehealth

by Tom Liu

I recently called my primary care physician (PCP) for the first time in years to get my immunization records, and encountered a strange message saying he was not currently seeing patients. My mom had apparently encountered the same message weeks ago. “Maybe he retired,” she suggested.

I did a quick google search of my PCP’s name to find an alternate contact number, and instead found a shocking article from the local newspaper. Apparently my PCP has been indicted for falsifying tax returns and participating in an online pharmacy organization that provided prescription drugs without an in-person physician examination.

Remote Prescribing: Lucrative, Pervasive, and Very Illegal

I did a quick search online and confirmed that the practice of offering prescription drugs through a “cyber doctor” prescription, relying only on a questionnaire is indeed very illegal.

It is also very pervasive. The National Association of Boards of Pharmacy (NABP) reviewed 10,700 websites selling prescription drugs and found that 97% of them were “Not Recommended”. Of these, 88% do not require a valid prescription and 60% issue prescriptions per online consultation or questionnaire only.

What struck me was how this appeared to be a case where the market came together to produce a “triple win” for profit-seeking internet pharmacies, shady physicians (such as my own), and a subset of patients willing to pay a premium to access drugs (most commonly weight loss drugs, erectile dysfunction drugs, and commonly-abused antidepressants and painkillers).

According to one analysis, one such website offering prescriptions from its own doctors listed prices for fluoxetine (brand name Prozac) and alprazolam (brand name Xanax) that were roughly 400% to 1800% higher than prices from a more traditional Internet pharmacy not offering prescriptions. The fact that such “remote prescription” websites remain in business despite the huge price differential suggests that they are attracting patients willing to pay that premium to avoid seeing their regular doctor. And as for where that money is going—well, my doctor was alleged to have received roughly $2.5 million over six years.

Similar Incentives Could Exist for Telehealth Writ Large

Given the clear business case driving abuse in this model of “remote prescribing”, I wondered about the risks of overuse and abuse in the rapidly burgeoning field of telehealth more broadly. After all, one of the promises of telehealth is its ability to make the delivery of services more convenient for both patients and providers. A physician could vastly expand the number of patients he/she sees without leaving the office—which has been identified as a potent way to alleviate the physician shortage problem.

But that would only hold true if the proliferation of telehealth does not generate additional, potentially unnecessary demand. And substantial evidence points to the presence of physician-induced demand under a fee-for-service system. Currently, Medicare pays for a limited set of telehealth services under the same fee-for-service payment model used for in-person visits. Within Medicaid, while select states are experimenting with bundled or capitated payments that include telehealth, others are retaining their fee-for-service model.

In a testimony before the House Energy and Commerce Committee last month, Dr. Ateev Mehrotra, an expert on telehealth, noted, “To reduce health care costs, telehealth options must replace in-person visits.” I’m not convinced this is the case—especially when there is a clear financial incentive to provide more care.

“The very advantage of telehealth, its ability to make care convenient, is also potentially its Achilles’ heel. Telehealth may be ‘too convenient.’” — Ateev Mehrotra

In some cases, fee-for-service payments for telehealth may result in outright fraud, as my physician may have done. In others, it may simply encourage providers to err on the side of providing more care given uncertainties in a practice environment. In fact, a study led by Dr. Mehrotra found that PCPs were more likely to prescribe antibiotics during e-visits than in-person visits.

As various constituencies continue to debate the best approach for paying for telehealth, it is imperative for us to better understand how the incentives and convenience of telehealth interact to affect overall utilization. Blindly carrying our existing fee-for-service system into the new world of telehealth options may produce some unintended consequences.


Tom is a healthcare researcher with experience in public health and blindness prevention. Follow him on Twitter at  @tliu14 or check out his blog.

Saving money in healthcare, PCMH edition

by Allan Joseph

I wanted to quickly follow up on Tom’s excellent post from yesterday on patient-centered medical homes (PCMHs), which nicely outlined some conflicting results from recent research on the model. (Edited to complete the sentence.)

It really shouldn’t surprise us that PCMHs only saved money in the 10% of patients with the highest risk. Why? Take a look at this chart from the NIHCM, which is one of my favorites in all of health policy:

Distribution of Healthcare Spending

Notice that the top 10% of spenders (not the same as the top 10% of risk scores, but pretty close) account for just about two-thirds of healthcare spending. The vast majority of patients account for very little spending — there’s no savings to be had there. Healthcare spending is highly concentrated at the top.

Now let’s look at spending from another angle. According to the Robert Wood Johnson Foundationtwo-thirds of healthcare spending is on patients with multiple chronic conditions. That means at minimum, roughly one-third of healthcare spending in America is spent on those patients in the top 10% with multiple chronic conditions. (To get even deeper, at least 16% of spending is on patients in the top 10% with three or more chronic conditions.) Of course, that’s only a lower bound — I’d be surprised if the number wasn’t much higher.

So what does this have to do with PCMHs? Well, the core idea behind a PCMH is greatly increased care coordination. That’s precisely the type of intervention that will help sick patients who have multiple chronic diseases — or the very group that accounts for a huge portion of our healthcare spending. No wonder the investment in PCMHs paid off for the sickest patients. They’re the ones where all the money to be saved is.

Given that the vast majority of patients who might use PCMH services account for little health spending, we should expect spending money to build a broad PCMH structure to save money on net. Nor should we be surprised that there’s money to be saved by better coordinating the care of the sickest patients. That’s the whole idea of the “hotspotting” movement. That’s also why Tom was spot-on to focus on the idea of “risk-adjusted population health,” such as focusing care managers on the sickest patients or designing separate clinics that focus exclusively on high-risk patients.

Sometimes it’s worth stepping back and taking stock of our intuitions about what might reduce healthcare costs. If an intervention isn’t aimed at the sickest patients, it’s probably not going to save a lot of money. Don’t be surprised when it doesn’t.


Allan Joseph is a first year medical student at the Warren Alpert Medical School of Brown University, where he is pursuing an MD/MPP. You can follow him on Twitter @allanmjoseph.

PCMHs Don’t Work—Or Do They? Insights from Two Recent Studies (Of the Same Program)

by Tom Liu

A month ago, a JAMA study rocked the health wonk world by showing provocative evidence that Patient-Centered Medical Homes do not work. Evaluating 32 practices in the PA Chronic Care Initiative over a three-year period (2008-2011), the authors found that achieving NCQA PCMH recognition did not statistically reduce utilization or costs, and only improved one of 11 quality measures (nephropathy screening for diabetes). Aaron Carroll summarized the study and accompanying editorial over at The Incidental Economist. Mainstream media and health wonk blogs alike declared the death of the “touted medical homes model”.

That’s why I was surprised to read this headline last week:

Study: Medical homes cut costs for chronically ill members

The punch line: these two studies evaluated the same PA pilot project over the same time period (albeit with different practices and patient populations).


Medical Homes Work—But Only for High-Risk Patients

A close read of the studies reveals that their conclusions are not incongruous. Indeed, the more recent AJMC study found no significant decrease in utilization or costs across all patients, just as the JAMA study did. However, when the authors limited their analysis to the top 10% highest risk patients (defined by DxCG risk scores), they found significant decreases in inpatient utilization in all three program years, and significant decreases in costs in the first two.

We can’t discern if the JAMA study would’ve found the same significant effects if they did a sub-analysis of the highest risk patients. (Interestingly, they state in the Methods section, “we repeated our utilization and cost models among only patients with diabetes,” but the results of that analysis are nowhere to be found.)

These results underscore an insight that’s becoming increasingly clear: cost savings from care management are concentrated in the highest risk individuals.

But we can go one step further.


Cost Savings Came ONLY From High-Risk Patients

Among the 654 high-risk patients, the PCMH produced adjusted savings of $107 PMPM in the first year. That roughly comes out to an estimated $69,978 in overall savings. Almost all of this (and then some) came from an estimated 40 avoided hospitalizations (654 patients x 61 adjusted avoided hospitalizations / 1000 patients).

Among 6940 patients overall, the PCMH produced (statistically insignificant) adjusted savings of $10 PMPM in the first year—an estimated $69,400 in overall savings. Across this entire patient group there were an estimated 41-42 avoided hospitalizations.

In other words, this study didn’t just find that savings are concentrated among high-risk patients. Essentially all of the cost savings and avoided hospitalizations came from the top 10% high-risk patient cohort.

This doesn’t mean that other PCMH models couldn’t squeeze savings out of lower risk patients. It just means that this and many existing models haven’t found out how to.


How to Achieve “Risk-Targeted Population Health”?

That finding raises a broader question that these studies can’t answer: What prevented the hospitalizations among the high-risk patients, and more importantly, were those key interventions limited to only the high-risk patients?

For example, were the crucial interventions ones that were only used for high-risk patients, such as a dedicated care manager, targeted outreach messages, and special appointments for high-risk patients?

Or were they interventions that were indiscriminately used on all patients, such as standard patient education or practice-level infrastructure that all patients enjoyed (even if only high-risk patients “benefited” in terms of reduced hospitalizations)?

This question is important because all interventions (and the infrastructure to support them) have a cost. Developing patient registries, expanding EHR capabilities, maintaining after-hours access, and investing in new training all represent substantial financial investments. Less than $70,000 in savings among high-risk patients—while extremely meaningful and significant—would be wiped out by the $20,000 “practice support” and average $92,000 bonuses paid out to each PCP by the medical home program.

If all of the benefits and savings are coming from the high-risk patients, we need to devise ways to concentrate our costs as well. Implementing such “risk-targeted population health” may be the only way to make the financials work.

Some practices are trying to do this by using dedicated care managers for high-risk patients within their existing patient panels. Others are trying to create separate clinics entirely dedicated to high-risk patients—which would allow them to limit fixed costs to high-risk patients as well. In fact, the NHS in England announced this January they are piloting this latter approach, creating “complex care practices” of 400-500 high-risk patients drawn from surrounding practices.

Whichever approach proves most effective, one thing is clear from these two studies: we need to rethink our current PCMH model.


Tom is a health care researcher with experience in public health and blindness prevention. Follow him on Twitter at @tliu14 or check out his blog.

New Year’s Resolution? Let’s Make it about Cost

by Tom Liu

2013 has been the year of the (botched) insurance expansion. But if the experience of other countries is any lesson, we should hope for political attention in 2014 to be devoted to another looming issue.

Over dinner with a few panelists at the Lown Conference, I learned about their involvement with the World Bank’s Universal Health Coverage (UNICO) Study Series, a comparative analysis of efforts to achieve universal coverage in 22 countries and Massachusetts (“the People’s Republic of Massachusetts”, as one panelist fondly called it).


And Then Came the Cost Issue

Befittingly, this People’s Republic was recently profiled in Health Affairs for lessons learned from its experience with cost containment, an issue it has been grappling with since achieving near-universal coverage in 2006.

Lesson number one?

“The first lesson is that the implementation of near-universal coverage triggered a new political resolve to address the difficult challenges of cost containment.”

In other words, achieving near-universal coverage subsequently made the cost issue too dire to ignore. While there is debate over whether insurance expansion accelerated cost growth (some say yes, others no), the facts are that Massachusetts’ per capita health spending is 15% higher than the national average, and that it has the highest individual market premiums in the country.

Interestingly, the same sequence of events is playing out halfway across the world.


Taiwan’s Looming Health Budget Challenge

After returning from Boston, I had the opportunity to grab lunch with an individual who was involved with Taiwan’s health sector for a number of years. Through that, I learned that Taiwan is facing a remarkably similar cost challenge.

Taiwan’s National Health Insurance (NHI) system has been lauded as a model for developed nations. Established in 1995, it expanded coverage from 57% to 97% within a year. But as might be expected, this coverage expansion unleashed a surge in utilization, nearly doubling outpatient visits, hospital admissions, and use of ED services among the previously uninsured. Since then, growth in outpatient visits, ED visits, and surgeries has vastly outpaced overall population growth.

To date, Taiwan has addressed the cost containment problem partly through aggressive price setting—sometimes below the cost of providing those services. Yet ironically, this has pushed providers to rely on increasing utilization as their only survival lever. This supply side-induced demand, along with low co-pays, no gatekeepers, and the political difficulty of raising premiums has created a financial situation where NHI expenditures have outpaced revenues almost every year since 1998.

Price controls will likely only work in the short term. In the long term, the NHI will need to alter its incentives to reign in over-utilization while encouraging greater provider efficiency, much as BCBS has done in Massachusetts. Shifting to a DRG-based payment system by 2015 is a good first step.

We concluded our conversation with a pronouncement that struck me: “China’s health system is about 20 years behind Taiwan’s in its evolution, so I think it can learn a lot from Taiwan’s experience.”


Marching Toward Cost Escalation in China

There’s a lot of wisdom in that statement. To date, most reform efforts in China have focused on expanding access, particularly in rural areas. As described in this UNICO report, insurance coverage in rural China had plummeted from 90% of the population to less than 10% with the collapse of the commune system in the 1980s. In the last decade, through a series of programs and reforms, China achieved 93% insurance coverage nationally, and just announced this past August that it had achieved 99% rural insurance coverage. While the accuracy of the numbers can be disputed, there’s no doubt that a lot more people now have access to health care services.

Which raises the specter of cost escalation in China’s not too distant future. Alarmingly, insurance expansion in urban China has been found to lead to such supply-side demand inducement (e.g. unnecessary treatments, expensive technology) that getting insured can actually increase one’s financial risk. Furthermore, China’s current insurance schemes have been criticized for being too narrow in scope (not enough procedures covered) and depth (not enough reimbursement). If China deepens the value of its existing benefits (as is much needed), we could expect demand to surge even higher.


Seek Truth from Facts

I’ve breezed through a lot of details to keep this post manageable, and for those who are interested, there’s a wealth of information in these papers on the reforms in Massachusetts, Taiwan, and China.

But the experience of all three shows that upon achieving near-universal coverage, cost containment issues are sure to follow. It would therefore seem like a prime opportunity to seek truth from the facts of the trial-and-error already happening in other countries. As our nation’s health care marches toward a costly ruin, perhaps the time is ripe for a UNICO-like study series on cost containment.


Tom is a health care researcher with experience in public health and blindness prevention. Follow him on Twitter at @tliu14 or check out his blog.

Not-so-breaking: Incentives matter

by Allan Joseph

Normally, I don’t pay too much attention to surveys, especially when it comes to the nitty-gritty of how policy changes might affect a complex healthcare system. The average poll respondent tends to have better things to do than understand the minutiae of health policy, and rightfully so. But last week, Health Affairs released a survey of hospital executives that I found particularly telling about the American healthcare system.

There’s a lot going on here — as Ezra Klein noted, hospital executives are much more sanguine about the ACA’s effect on the healthcare system than anyone else, and they see their hospitals shedding costs over the next few years.

But I wanted to highlight this chart, which you’ve seen if you follow me on Twitter:


Look at the top three items on that list. Those are things you want a healthcare system to minimize! You want people to be healthy, and not come to the hospital. When they do come to the hospital, you want them to leave healthy enough to not come back. You hope to minimize the amount of emergency care, which is expensive and often high-acuity. Those are uncontroversially good goals, and they’re the top three ways hospital executives would respond to incentives to reduce costs.

The list goes on, of course — there are a lot of worthwhile goals, even if some of them might hurt the pharmaceutical or medical device companies, but I keep coming back to those top three. Those are the things we want. If we incentivize hospitals to do them, they will. That’s utterly unsurprising, but it also prompts the question of why they haven’t been done already.

Well, hospitals make money from having people in beds. So we incentivize them to hospitalize people. If we change the incentives, we can change the results.

Now, of course there are caveats. It’s easy for a hospital executive to say that these are things they would do in a hypothetical scenario. We don’t know if they’d actually do them — maybe they’d prefer to spend their time and money lobbying against the change in payment structures instead of implementing these changes. These are also fairly varied responses. Only two of the options have about 50% support as “top three” ways to reduce expenses. That suggests we can’t exactly predict what incentivizing cost reduction would result in.

But this is the promise of ACOs. This is why economists never shut up about incentives. And this is why the phrase “every system is perfectly designed to produce the results it gets” results in 13.3 million Google hits, attributed variously to Dr. Don Berwick and Dr. Paul Batanden.

There aren’t that many hard-and-fast truths about health policy. But there is one: incentives matter.


Allan Joseph is a first year medical student at the Warren Alpert Medical School of Brown University, where he is pursuing an MD/MPP. You can follow him on Twitter @allanmjoseph.