My father, sister and I sat in the near-empty Chinese restaurant, picking at our plates, unable to avoid the question that we’d gathered to discuss: When was it time to let Mom die?
It had been a grueling day at the hospital, watching — praying — for any sign that my mother would emerge from her coma. Three days earlier she’d been admitted for nausea; she had a nasty cough and was having trouble keeping food down. But while a nurse tried to insert a nasogastric tube, her heart stopped. She required CPR for nine minutes. Even before I flew into town, a ventilator was breathing for her, and intravenous medication was keeping her blood pressure steady. Hour after hour, my father, my sister and I tried talking to her, playing her favorite songs, encouraging her to squeeze our hands or open her eyes.
Doctors couldn’t tell us exactly what had gone wrong, but the prognosis was grim, and they suggested that we consider removing her from the breathing machine. And so, that January evening, we drove to a nearby restaurant in suburban Detroit for an inevitable family meeting.
Comparative Effectiveness Research (CER) is suddenly a hot topic at all the health care conferences. How come? Everybody agrees that we have to decrease per-capita cost and increase quality. Why? Government programs like Medicare and Medicaid foot more than 50% of our nation’s health bill, and if everything stays the same these programs will go belly up (bankrupt) in 8 years. Big problem.
Health and Human Services (HHS) has defined comparative effectiveness research as conducting and synthesizing research comparing the benefits and harms of different interventions and strategies to prevent, diagnose, treat, and monitor health conditions in “real world” settings. In other words, CER is figuring out what treatments, tests, and drugs work and which ones don’t work.
John E. Wennberg spent a whole career at Dartmouth studying American medicine, and he comes to the startling conclusion that 60% of Medicare is spent on supply sensitive care (physician visits, consultations, imaging exams, and hospital and ICU admissions) and 25% on preference sensitive care (PSA tests, mammography, and elective surgery). Although we assume that this care is based on solid scientific evidence, Wennberg states that “medical science is virtually silent on such matters” as how often to see a patient, what test to order, and whether to admit a patient to the hospital or ICU. Some evidence based medicine experts state that only about 20% of what physicians do is based on sound science.
I was absolutely delighted that after several polite “maybe later” responses I was able to recently interview Victor Fuchs, the Henry J. Kaiser Professor Emeritus at Stanford University. Vic is best known as the “Father of Health Economics” and perhaps less well known (but more importantly to me!) the professor who taught the first health economics class that I ever took.
Matthew Holt: Victor, thanks so much for agreeing to come on THCB. I must admit Vic when I joined your class I had no idea about your background and reputation in health economics. So, I was just delighted to figure out that I blundered in right at the top. It’s a real pleasure to have you on the line
Victor Fuchs: I think you’re doing a great job and therefore I’m glad to spend some time with you.
Matthew Holt: Fantastic! You’ve, obviously, been observing and commenting on– and more recently sort of promoting ideas around –health reform for quite a while now, so let’s jump into a couple of things that you’ve published very recently, in fact just these past few weeks.
The first is a paper in The New England Journal of Medicine about a conceptual future for new biomedical innovation and I’d be grateful if you could just explain just a little bit what your general concepts are here. You’ve been working on this for quite a while. In fact, back when I was in your class, you were publishing some stuff with Alan Garber about technology assessment and this is sort of a continuation to that in some ways. So, I’d love to hear your thoughts.
Victor Fuchs: Well, I think there were two key elements here, one of them better understood by a larger audience and one of them I think rather new. Let me do the new one first. The new one is that we are going through what I call the second demographic transition.
The first transition was when every country had high mortality and high fertility and then the mortality especially of young people started to drop, but fertility did not drop right away, so you had a divergence there and in some cases it lasted for a couple of decades and during that time the population soared because there was this discrepancy between mortality and fertility.
You see the high fertility made sense when mortality was high because you wanted to have at least a couple of children survive to adulthood, but when mortality dropped it didn’t sink into people’s consciousness right away, so it took quite a bit of period which the historians and the demographers referred to as the demographic transition, okay. I don’t know if you’re familiar with it or not, but —
Matthew Holt: Yeah, I get the concept and there’s been some stuff written about that in terms of the impact on social security and healthcare.
Victor Fuchs: And some of the third world countries are going through it now, but now the second demographic transition is the one that I talk about in the NEJM piece a little bit. It has the following elements.
First is that a very large and increasingly large percentage of the population cohort lives until age 65, whereas at the beginning of the 20th century only a small percentage lived until 65. Now we’re going to 80% and we’ll eventually approach close to a 100% living till 65.
The second element is that life expectancy at 65 is increasing and it’s increasing at a quite brisk pace in recent decades. You put those two things together and you find out a very large and growing percentage of all the additional years that are lived if you have increasing life expectancy will be lived after 65.
Reed Abelson and Gardiner Harris, the authors of the June 4th New York Times article critical of the Dartmouth Atlas and research, have acknowledged Elliott Fisher and my concerns and clarified the record in their posting on the New York Times webpage. They originally claimed that we failed to price adjust any of the Atlas measures. They now acknowledge that we do, but they’re hard to find on the Atlas website, a point we concede. They originally claimed that quality measures were not available on the Atlas website. They now acknowledge that quality measures are on the website, but they don’t like them. We agree quality measures can be better – the type of research we do is always open to improvement — and Dr. Fisher has recently co-chaired an NQF committee with precisely this goal. (See our more detailed response.)
But the primary purpose of this posting is to respond to the attack by Mr. Harris on the professional ethics of the Dartmouth researchers. The key issue seems to be whether the research in two landmark 2003 Annals of Internal Medicine articles (here and here) were misrepresented by the Dartmouth researchers. In his posting Mr. Harris asserts:
In an aside, when was the last time you saw researchers so profoundly mischaracterize their own work? How is it possible that they could claim their annals pieces concluded something when they didn’t? I can’t remember ever seeing that happen.
We are disappointed by this accusation. We can understand Mr. Harris’s frustrations in understanding the research, as it is often nuanced and tricky to follow. This lack of understanding is illustrated by their recent New York Times posting, where they state:
In statistical terms, [the Dartmouth researchers’] claim is referred to as a negative correlation between spending and health outcomes, which means that when spending goes up, the health of patients goes down.
They have confused the idea of a correlation (high spending hospitals on average do slightly worse on quality and outcomes) with causation (if a hospital spends more money, outcomes for those patients will get worse).
The more fundamental point, however, is their claim that we misrepresenting the two 2003 Annals of Internal Medicine studies written by Dr. Fisher and others. Ms. Abelson and Mr. Harris state that
The Dartmouth work has long been cited as proving that regions and hospitals that spend less on health care provide better care than regions and hospitals that spend more…. As the article noted, [Dr. Fisher] asked in Congressional testimony last year, “Why are access and quality worse in high-spending regions?”
The main point of Reed’s and my pieces about the Dartmouth work is that the data are simply not good enough to guide spending decisions in the government’s $484 billion Medicare program. If the Dartmouth researchers had acknowledged this point, our story would not have been all that interesting. But they cannot bring themselves to do this, and in fact they have repeatedly exaggerated and mischaracterized their own work in public settings to suggest it can be prescriptive.
An ancillary point was to warn those on capitol hill, the administration and journalists to be wary of those highly popular maps from the Atlas. You have scoffed that it’s a small thing that the Dartmouth researchers fail to adjust their online data for price and illness. But misunderstandings about this are widespread. That landmark piece by Dr. Gawande that you cited used the Atlas’s unadjusted data. Dozens of stories in newspapers and magazines around the country have used the unadjusted data to criticize health institutions. Even David Cutler, among the top health economists in the country, was unaware that the atlas offered largely unadjusted data.
Accuracy may seem a small point to you. It is not to us.
Our Friday piece also pointed out that Dr. Elliott Fisher and Mr. Jon Skinner claimed that their 2003 Annals pieces had found a negative correlation between spending and outcomes. In fact, the pieces found no correlation between spending and outcomes. This is not a small distinction. If there’s a negative correlation, cuts in spending will actually improve health. If no correlation has been found, then cuts become far harder and perhaps more painful. We cannot go into reforms of our healthcare system believing that the work will be easy. But that is what the Dartmouth researchers have suggested, and this siren song has had an enormous impact on Capitol Hill.
In an aside, when was the last time you saw researchers so profoundly mischaracterize their own work? How is it possible that they could claim their annals pieces concluded something when they didn’t? I can’t remember ever seeing that happen.
Over the weekend, the two New York Times reporters who challenged the core findings of the Dartmouth Atlas of Health stuck to their guns in a detailed response to the rejoinder to their critique. The Dartmouth Atlas, which documents regional variation in Medicare spending, provides the intellectual underpinning for assertions by health care reformers (including those in the White House) that 30 percent of all health care spending is wasted and does not improve either the quality or outcome of care.
The Times‘ original critique (see this GoozNews post) contained three main ideas:
The Dartmouth researchers fail to adjust their maps for regional variations in prices;
The Dartmouth researchers fail to adjust their maps for illness burden; and
The assertion that more spending leads to worse outcomes is not borne out by the data. In some cases more spending leads to better outcomes.
Some of this back-and-forth may sound like a quibble over language. Is it “30 percent” of health care is wasted or “up to 30 percent,” as the Dartmouth researcher now state in public? Reed Abelson and Gardiner Harris provide a link to the original 21-page response to their queries. “We think the 30 percent estimate could be too low,” the Dartmouth researchers wrote in a highlighted section.
On the other hand, the Times reporters appear to be taking a step back on the price issue. They went back to David Cutler, the Harvard health care economist whom they originally quoted. Cutler told them that the original 2003 articles by John Wennburg and Elliott Fisher of Dartmouth that appeared in the medical literature did, in fact, adjust for price. “But he said he agreed with the Times assertion that most of the atlas’s maps and rankings, as distinct from the group’s academic work, are not fully adjusted for prices,” Abelson and Harris wrote.
Notably, Cutler is now hedging his bets on the “30 percent is waste” argument. “Some believe that number is higher, and others think that it’s lower,” he wrote in the latest Health Affairs. “But there is little disagreement that health care is characterized by enormous waste.”
In my view, it is the dispute over quality that really needs to be explored by other researchers and policymakers. Eliminating waste ought to improve quality. It has always been true in manufacturing that reducing steps and reducing waste not only reduces costs, but it improves the quality of the finished product.
There’s no reason to think it won’t be true in delivering a complicated service like health care, which some have compared to building and flying jet airplanes. Doing more than necessary to get the job done will only increase the possibility that errors will occur in the process, which in health care translates into more complications, further costs and, in some cases, lost lives.
Yet the Times reporters continue to assert through their dissection of the Dartmouth data that more spending on more services may actually result in higher quality. They go back to the original research — the two studies published in 2003 — to make their point:
The researchers are incorrect in saying that the results of those 2003 studies were “all in the same direction.” In fact, two of the various measures of quality and mortality cited in the articles actually seemed to show that more spending could correlate to better care. [footnotes 2 and 3] Heart attack patients in the most expensive regions, for example, were more likely to receive necessary beta blockers – a positive correlation between spending and quality. Similarly, hip fracture patients experienced “a small decrease in mortality rates” in more expensive places – another positive correlation.
We have very poor metrics for measuring quality of care, and one of the examples they cite shows why. Giving beta blockers is a “process” measure. We know from clinical trials that giving beta blockers after a heart attack improves outcomes. But does it improve outcomes the same in regions where the ratio of obesity-related heart attacks to stress related heart attacks differ? Does it have the same effect in regions with higher proportions of mild heart attacks (because they are more likely to use a sophisticated blood test to categorize chest pains as a heart attack) than it does in a region with a higher proportion of serious heart attacks?
These are the confounding variables that no data set can capture adequately until it fully reflects both the diagnoses of the incoming patients as well as the care delivered. The Dartmouth Atlas data, which relies on Medicare claims, falls far short of that goal. And the Times reporters, by trying to re sift the data to make a counterpoint, only add another blind man’s hands on the elephant in the room — the absence of electronic data about the actual medical conditions of the patients behind those Medicare claims.
Merrill Goozner has been writing about economics and health care for many years. The former chief economics correspondent for the Chicago Tribune, Merrill has written for a long list of publications including the New York Times, The American Prospect and The Washington Post. His most recent book, “The $800 Million Dollar Pill – The Truth Behind the Cost of New Drugs ” (University of California Press, 2004) has won acclaim from critics for its treatment of the issues facing the health care system and the pharmaceutical industry in particular. You can read more pieces by Merrill at GoozNews, where this post first appeared.
Reed Abelson and Gardiner Harris in the New York Times are questioning some of the key assumptions behind the Dartmouth Atlas of Health, which for twenty years has documented wide variations in Medicare utilization rates across the country and used that to claim huge savings could be obtained by rooting out waste in high-spending regions. In February, Harris reported on a commentary by Sloan-Kettering’s Peter Bach in the New England Journal of Medicine that argued the Dartmouth analysis failed to adjust for illness severity. I reported on the Medicare Payments Advisory Commission’s similar analysis here.
This time, the Times’ two most thoughtful health care reporters bring quality into the discussion. After describing a map in Office of Management and Budget director Peter Oszag’s office that divided the nation into low-spending beige regions and high-spending brown regions, they write:
For all anyone knows, patients could be dying in far greater numbers in hospitals in the beige regions than hospitals in the brown ones, and Dartmouth’s maps would not pick up that difference. As any shopper knows, cheaper does not always mean better. . . The debate about the Dartmouth work is important because a growing number of health policy researchers are finding that overhauling the nation’s health care system will be far harder and more painful than the Dartmouth work has long suggested. Cuts, if not made carefully, could cost lives.
For documentation, the reporters used quality data generated by the Wisconsin Collaborative on Healthcare Quality, which I wrote about a month ago forThe Fiscal Times.
This is an important debate. But as is often the case in journalism, the attempt to reduce complex realities into a single-factor analysis that can be summarized in a headline or a single “why this story is important” paragraph can leave a mistaken impression. Regional variation in Medicare spending is one indicator of gross overutilization. Something is happening when a hospital in McAllen, Texas does twice as many knee implants per Medicare beneficiary as a hospital in Baton Rouge, Louisiana. (An earlier version of this post compared McAllen to Rochester, MN, which actually has a slightly higher rate of knee implants per 1,000 Medicare enrollees.)
There’s no doubt that despite my thoughts that Obama wouldn’t (and shouldn’t) have pushed health reform in 2009, it was a very big year for health care. Death panels, public options et al—one hundred thousand visits to THCB in August don’t lie.
So what should you look for next?
The finish is the start: It looks like some version of the Senate bill will be a done deal by sometime late January. That means that there’s about two years of health care industry players figuring out what it all means. The biggest two questions are; what will the types of plan sold in the exchanges look like? (high deductible with some preventive care thrown in is most likely), and what will the cuts and changes in Medicare payment actually look like in practice? (More of the same or real re-alignment around some kind of bundling). All these changes need reactions from the incumbents to reorganize around the new revenue streams.
The challenge of constraining costs while maintaining or enhancing the quality of medical care is vividly brought to life by Atul Gawande in his recent widely-read New Yorker essay. The anecdotal evidence presented in the article is compelling as a description of how physician practices can relate to excessive costs. The assertion that the observations in McAllen also explain McAllen’s costliness is an inductive exercise that may go too far. Physician ownership of imaging facilities, ownership interest in hospitals and more subtle forms of self-referral are all substantially present in large healthcare market areas across the country. Is McAllen an extreme example of a bad physician culture or is there another explanation? Our analysis of Medicare data from McAllen Texas demonstrates that exceptionally high rates of chronic disease and poverty explain much of the variation in cost.
According to Gawande, McAllen Texas has a physician culture that promotes high cost, low quality care. By comparison El Paso is portrayed as having a similar patient population to McAllen with lower costs of care. Grand Junction, Colorado, however, the antithesis of McAllen according to the article, is credited with having a physician culture that promotes low costs and high quality. Ultimately Gawande warns that by failing to change the physician culture nationally, “McAllen won’t be an outlier. It will be our future.” But is McAllen really an outlier, a harbinger of physician income-enhancing practices run amok?
A fair comparison between McAllen and Grand Junction would include a more precise analytic methodology than could be offered in Dr. Gawande’s article. Such an analysis is important: the correct diagnosis of the health care cost crisis is an essential step in selecting an effective prescription. If McAllen is not an outlier and Grand Junction is not a paragon, then the solution is not to simply tamp down variation by exporting Grand Junction values to McAllen. If the physician practices reported by Dr. Gawande in McAllen lead to explainable patterns of costs according to current norms, then those practices are part of a national phenomenon right now, not in a nightmare future.
An analysis of the Medicare population in the three counties can place Dr. Gawande’s observations in a more complete context. Medicare beneficiaries enjoy a standardized benefit package, and detailed data are available on the services they receive. We can use cost data for Medicare enrollees in the three counties to test Dr. Gawande’s assertions regarding McAllen’s and Grand Junction’s comparative health care costs. Medicare fee-for-service claims provide us with service level payment and patient health status information; Medicare monthly enrollment data details HMO affiliation, Part B premium assistance and beneficiary demographics. The Centers for Medicare and Medicaid Service supplies researchers (including the Dartmouth Health Atlas team) with data of this type for Medicare policy analysis.
Accurate Medicare Cost Comparisons
The city of McAllen lies at the center of Hidalgo County, one of the costliest areas for Medicare. The population is racially diverse, low income and exhibits high levels of chronic disease. El Paso is similar to McAllen but with less poverty. Grand Junction is the county seat of Mesa County, a largely white and relatively wealthy region. In Exhibit 1 annualized Medicare fee-for-service payments for the counties of McAllen, El Paso and Grand Junction show wide divergence in the total Medicare spends per beneficiary.2
Exhibit 1: Annualized Payments per Medicare Beneficiary by County of Residence, 2006
El Paso, Texas
Grand Junction, Colorado
The payments in McAllen’s county are twice as high as in El Paso’s and nearly three times as high as in Grand Junction’s but adjustments are required to the statistics to make the comparison fair. These adjustments should include normalization for Medicare coverage type and population health. Relatively few McAllen area Medicare beneficiaries are enrolled in HMOs (2%) in comparison to Grand Junction (42%) and El Paso (16%). Medicare publishes costs only for services paid on a fee-for-service basis; some services supplied by cost-based HMOs (more common in Grand Junction than in either McAllen or El Paso) are included and some are not. As a result the cost of care for counties with high numbers of Medicare HMO enrollees is under reported. In addition, while all eligible individuals receive hospital insurance under Part A of Medicare, beneficiaries must pay a monthly premium to receive outpatient coverage under Medicare Part B, or are enrolled by Medicaid if they are poor enough to meet the state’s income requirement. The percent of the population in the different counties without full Part A and Part B benefits varies. In Grand Junction almost twice as many beneficiaries do not have Parts A & B coverage compared to McAllen (4% versus 8%). The outpatient expenses of this population are not included in Medicare expenditure reports. In Exhibit 2 HMO enrollees and Medicare beneficiaries without full Medicare benefits are removed from the comparison.
Exhibit 2: Comparative Annualized Payments per Medicare Beneficiary by County after Managed Care and Medicare Part A&B Adjustments, 2006
El Paso, Texas
Grand Junction, Colorado
When the analysis is restricted to cost and enrollment data only for Medicare fee-for-service beneficiaries covered by both Part A and Part B, Grand Junction’s beneficiary annual costs rise by almost 25%. The difference between McAllen and Grand Junction beneficiary costs falls, but McAllen Medicare costs, now for populations with the same coverage, are still well over twice those for Grand Junction.
County Socio-Demographic Characteristics
The dissimilarities between the McAllen and Grand Junction county populations are extensive. The socio-demographic characteristics of a population affect its access to care, ability to pay out of pocket for uncovered care and rates of disease associated with diet and life history. The costs of Medicare co-pays and deductibles can be substantial barriers to access, and history of health care coverage and access to preventive care vary substantially based on socio-demographic variables. Low-income individuals often reach Medicare enrollment age with a lifetime history of access and cost barriers, a potent mixture. Barriers to access to care can lead to expensive hospital care for conditions normally treated on an outpatient basis.
Grand Junction Medicare enrollees are 98% white and only 11% require assistance in paying for their Medicare Part B premium (a proxy for low income status). In contrast McAllen and El Paso are both 26% Hispanic, and a higher proportion of Medicare beneficiaries rely on Medicaid to pay for Part B — 36% in El Paso and 48% in McAllen. To assess how socio-demographic factors affect Medicare costs, Exhibit 3 compares costs for beneficiaries with and without Part B premium assistance.
Exhibit 3: Comparative Annualized Payments by County and Need for Premium Assistance, 2006
Premium Assistance-No(not low income)
Premium Assistance-Yes(low income)
El Paso, Texas
Grand Junction, Colorado
Expenditures are consistently higher for low income beneficiaries, but McAllen is still more expensive than Grand Junction for both income groups — more than 45% more expensive for low-income beneficiaries and more than twice as expensive for those not receiving premium assistance. However, the Grand Junction advantage is not as great for the low-income population as for higher income beneficiaries. Could it be that a good part of the McAllen “excess” is simply due to its larger proportion of low-income Medicare beneficiaries compared to Grand Junction?
But socio-economic differences in themselves cannot explain cost differences. What makes the low income population so much more expensive to care for? And why is El Paso, which also has a large low-income Medicare population, so much less costly to Medicare than McAllen?
Exhibit 4 uses estimates of population rates of disease derived from Medicare hospital and physician claims to reveal that the prevalence of chronic disease is substantially higher in the McAllen Medicare beneficiary population than in Grand Junction or El Paso; and that the proportion of the McAllen Medicare population with more than two of these conditions is a whopping 52%, in comparison to 23% in the Grand Junction area and 34% in El Paso.
Exhibit 4: Disease Prevalence by County, 2006
Single Selected Condition Rates per 1,000
Ischemic Heart Disease
Chronic Respiratory Disease
Multiple Conditions Population Percentage
None of the Selected Conditions
One Condition Only
Many of the disease rates for the McAllen population are more than double those for Grand Junction. If the Medicare population in McAllen is truly that much sicker wouldn’t we expect the payments to be greater? A comparison of expenditures for Medicare enrollees without a diagnosis of diabetes or heart disease in the last year shows that costs for these standard populations are statistically very close (Exhibit 5).
Exhibit 5: Medicare Monthly Payments per Patient without a Diagnosis in the Year for Diabetes or Heart Disease, 2006
Monthly Per Person Payments
El Paso, Texas
Grand Junction, Colorado
By eliminating diabetes, ischemic heart disease or heart failure from the population payment measures the Grand Junction advantage is completely removed. Grand Junction is just as costly as McAllen for populations without one of these conditions.
Even though diabetes and heart disease are both costly and highly prevalent in McAllen, beneficiaries experience a wide range of costly illnesses, and patients with multiple conditions are difficult to treat. We used a more sophisticated risk adjustment method to take into account an array of concurrent conditions.3 Beneficiaries in the top risk scores, the sickest patients, make up 27% of the McAllen, 16% of the El Paso and only 12% of the Grand Junction populations. Average Medicare payments were then computed for each risk group in each county (Exhibit 6). The effect on costs of accounting for this measure of illness burden is dramatic.
Exhibit 6: Annual Medicare Payments by Risk Level
Taking into account the disease combinations eliminates the apparent low cost difference across the full range of disease risk scores. If the disease levels in the McAllen population were magically made to match the Grand Junction disease distribution, but experienced McAllen-level costs, annualized Medicare payments would fall from $13,150 to $6,145. The morbidity-adjusted per beneficiary payment rate for McAllen is 10% higher than the $5,579 Medicare per beneficiary annualized payments observed in Grand Junction, substantially less than the observed 300% payment differential seen in the unadjusted data.
Discussion and Implications
McAllen is different from many areas of the United States: it is sicker and poorer. The observed differences in the rates of chronic disease are highest for those conditions rampant in low income American populations: diabetes and heart disease. Further, Medicare beneficiaries in McAllen have significantly higher rates of co-occurring chronic conditions. As a result the costs of caring for McAllen Medicare population appears high in comparison to other areas but not abnormally so. McAllen suffers from a tremendous burden, but it not caused by its physicians: the care they provide leads to costs that are substantially comparable to the other counties in the article once adjustments are made for the magnitude of the health problems they face. The disturbing pattern of physician practices uncovered by Dr. Gawande sounds a warning not because it foretells a McAllen-like future but because it portrays the on-going crisis that affects both McAllen and Grand Junction and it is national in scope. Physician culture is only part of the McAllen story.
Patients with chronic disease, especially those with multiple conditions, are extremely costly to treat. Cost savings will not be realized by denouncing and penalizing medical systems because they treat patient populations with high rates of disease. Instead health care reform must develop policies that support streamlining and coordinating care for beneficiaries with multiple chronic conditions, wherever they reside. Policies that support lifetime continuity of coverage, disease prevention and early treatment, could reduce healthcare costs for populations who now reach Medicare eligibility with a history of under-service. Physician culture has a role to play: Accountable Care Entities are intended to reduce barriers to access by facilitating care coordination. The high costs of care in places like McAllen will not be dramatically reduced by transforming physician ethics and organization if the roots of the crisis are in the interaction between class, demographics and chronic disease.
1) The payment amounts and beneficiary counts are from CMS claims and enrollment data that includes a 5% sample of the Medicare population. The data is hosted by JEN Associates Incorporated of Cambridge Massachusetts, a CMS MRAD contractor.
2) A risk score ranging from 1 to 13 was computed for each beneficiary using diagnoses found on Medicare physician and hospital claims. Beneficiaries with scores greater than 9 are not observed in the Grand Junction 5% data in numbers sufficient for analysis. The grouping and scoring system was developed by JEN Associates Inc. of Cambridge Massachusetts for Medicare and Medicaid program planning and evaluation applications. Diagnoses are selected based on correlation with future hospitalization, nursing home entry and death and grouped according to a disease’s functional impact.
Daniel Gilden is a health services researcher with 20 years of hard core quant experience.He’s the President of JEN Associates which provides highly specialized analysis of Medicare and Medicaid data. He contacted THCB regarding the fuss about the McAllen, TX “overuse” story. In his calculations the data suggests something very different from the “practice variation” theory–the patients really are sicker. As this goes counter to decades worth of work by Wennberg et al, we invited Daniel to share his data and methodology with THCB. And we invite those of you who like this kind of research but may disagree with Daniel’s analysis to respond. Finally it’s worth noting that if his conclusions are true this has huge implications for overall health care policy…Matthew Holt
Dr. Atul Gawande has provided a chilling description of the problems facing true health reform in his recent New Yorker article. In The Cost Conundrum he describes how medical care is provided in McAllen, Texas, which is second only to Miami as the most expensive healthcare market in the country. McAllen’s per capita expenditures are twice those in El Paso, Texas, a city with similar demographics.
There are no good reasons for the differences. McAllen’s population isn’t demonstrably sicker and the care isn’t measurably better. There is also little understanding among the participants about what causes the higher spending. What is chilling is how easy the medical care environment in El Paso could become like McAllen’s.
Gawande refers to the accountable care organization (ACO) concept proposed by Elliott Fisher and colleagues at Dartmouth University. They propose that physicians whose practices are focused around a specific hospital be given incentives to lower the overall costs of patient care.
Payer Costs are Provider Revenues
The ACO has merit as a goal, but the challenge is in forming them. Getting very intelligent people such as physicians and hospital administrators to change their behaviors, especially if such changes may reduce their income, will be difficult. We need ways to encourage voluntary participation of both physicians providing care in the hospital and those who decide who should be hospitalized.
The Dartmouth data show that in areas like McAllen, there is much more interventional work, such as tests, procedures and admissions, than in areas like El Paso. With more access to, and time with, primary care physicians there is less need for interventional work. This means redistributing resources from the interventionists to primary care clinicians.
It is hard to imagine a new ACO with interventionists and primary care physicians achieving this redistribution. The interventionists often wield scalpels and have a ready ally in the hospital that depends on them to keep beds filled.The Answer Lies in Separation, Not Amalgamation
Interventionists should partner with the facility in which they do most of their work. Elsewhere, I describe these new care delivery teams (CDTs) that are effectively the inpatient side of Fisher’s ACOs. CDTs would be voluntary associations of a facility (usually a hospital) and those physicians whose work depends on the facility.
Unlike Fisher’s ACO, the CDT specifically excludes office-based physicians responsible for the ongoing treatment of patients. The CDT also need not include all eligible physicians at the hospital, just the voluntary paticpants.
The CDT may be a single entity with physician employees or a loose collaboration of independent physicians and a facility, collectively deciding its own governance rules. The key is that the CDT takes responsibility for an episode of care at a fixed price. Physicians might be compensated by salary, fee-for-time, or fee-for-service and may share in the gains or losses of the CDT.
CDTs will focus on how to provide inpatient care more efficiently and at higher quality. (Quality measurement is critical in any reform; see my overall proposal. Savings will be achieved not through lower net provider income, but through better management and clinical decisions. For example, instead of routinely repeating imaging, radiologists may review well-done MRI and CAT scans done elsewhere. Orthopedists can agree on the necessary implants, allowing the hospital to strike better deals with suppliers. Nurses may be empowered to implement routine procedures reducing infection rates. Lowering Interventional Costs and Rewarding High Quality Care
CDTs by themselves will not solve the key problem identified by Gawande — the overuse of interventional services. To address that problem, we need to redirect patients toward those physicians who provide high quality care at lower overall cost. This can be achieved by combining (1) a mechanism shifting resources from interventional care to effective outpatient management with (2) a way to identify those physicians who provide such effective care.
A comprehensive realignment of the payment system can accomplish this, but in the interim, a voluntary major risk pool (MRP) can move us in the right direction. The MRP covers hospitalizations and chronic illness. This coverage for insurers eliminates costly underwriting. The MRP, however, is not simply reimbursing plans for expenses incurred; it directly offers attractive bundled payments to CDTs. These episode-based payments allow CDTs to do what they do best—high intensity acute care—and reap increased income. Higher provider incomes within CDTs are not inconsistent with lower costs to the MRP as the CDT reduces the resources needed from suppliers outside the CDT.
The MRP obtains electronic copies of claims from the insurers who are its clients and from Medicare, more information than the Dartmouth group has. After linking all the data and substituting coded identifiers, the MRP will make available the data under arrangements ensuring patient confidentiality.
The Power of the Electronic Matchmaker
Insurers and others accessing the MRP data will see there are local providers with efficient practice patterns, but not their names. An intermediary trusted by physicians will serve as an electronic “matchmaker,” transmitting messages from insurers seeking efficient physicians. By remaining anonymous until a “deal is struck,” efficient physicians will negotiate better remuneration—probably not just higher fees, but payment for ongoing patient management, telephone and e-mail consultations, and other innovations. Some physicians may band together, perhaps by sharing electronic medical records, forming real or virtual group practices—the outpatient component of the ACO.
The major risk pool is the mechanism reallocating dollars. More effective chronic illness management will lower admission rates and the MRP will transfer more dollars to those health plans directing more patients to efficient ambulatory care providers. To find those providers, health plans will negotiate better payment arrangements. To steer patients towards those providers, plans will provide new incentives and sources of information. We can create what Fisher and Gawande have in mind, as long as we think about how to manage the transition.
McAllen and El Paso are almost 800 miles apart—a long day’s drive. To move away from the expensive McAllen model of care, we need not just a destination but a plan how to get there. The self-interest of the players is currently driving us in the wrong direction. By harnessing that self-interest with realigned incentives we can reform the system. Without taking account of the incentives, we will never get to where we need to go.
Harold S. Luft is Professor Emeritus in health policy at University of California, San Francisco, and author of Total Cure: The Antidote to the Health Care Crisis (Harvard University Press, 2008). More information is available at www.haroldluft.com.