Researchers in Britain recently published a paper in Pediatrics showing a dramatic swing in admissions for childhood asthma after indoor smoking was banned by the British in 2007. A hospitalization trend that had been steadily around 2% fell to minus 9%. The trend was sustained. 10.1542/peds.2012-2592
I'm wondering, "What should Managed Care do with this information? What is the appropriate level of response? What haven't we tried already?"
We're already covering smoking cessation products and counseling. We've been trying to get our smokers to stop for a long time, but maybe this would be effective for those smokers with children. Is it a matter of further education? I'd like to think so, but I doubt it. I don't believe that any smokers out there remain ignorant of the dangers to themselves or their families.
Is it a matter of incentives? Do we need to consider paying people to stop smoking? What about making smoking cessation free such as no cost-share on nicotine aids? I hear a lot about the robust public health systems in Europe, but smoking remains a serious issue there as well.
I hate to be on the ban-smoking bandwagon, but I'm at a loss at what to do. PLEASE let me know in the comments if you've got a smoking cessation program in managed care that really works.
Neil Minkoff, MD, is medical director of MediMedia Managed Markets and also an independent health care consultant.
With apologies to James Taylor, I was recently introduced to a UNC-Chapel Hill professor of psychology, Dr. Edwin Fisher, from my alma mater and the university where the famous singer/ songwriter's father was dean of the School of Medicine. The work that Dr. Fisher is doing under the aegis of the American Academy of Family Physicians Foundation is on target for the Triple Aim.
Peers for Progress, designs, implements, and evaluates peer coach or peer educator programs worldwide. There are ample examples of successful and established programs led or facilitated by peer coaches, motivators, educators, or others, including Alcoholics Anonymous, Mended Hearts, and Weight Watchers. Peers for Progress is building a global network of peer-support organizations that are making a difference in the health of and lives of people affected by a range of health problems and their associated impact on the individual and on communities.
Peer support / peer coaching is truly a winning proposition with benefits to the coached, to the coaches, to better health and health care, and the price is right!
Peers for Progress:
Steven R. Peskin, MD, MBA, FACP, is associate clinical professor of medicine at the University of Medicine and Dentistry of New Jersey — Robert Wood Johnson Medical School.
When Kaiser Permanente Northern California rolled out a new electronic health record (EHR) system for outpatients a few years back, a team of researchers considered it a golden opportunity to evaluate how such systems affect care and outcomes.
The staggered implementation of the EHR system at 17 KP-owned medical centers from 2004 to 2009 allowed researchers to “examine the association between use of a commercially available certified EHR and clinical care processes and disease control in patients with diabetes,” says the study “Outpatient Electronic Health Records and the Clinical Care and Outcomes of Patients With Diabetes Mellitus” in the Oct. 12 issue of Annals of Internal Medicine.
Mary Reed, DrPH, the lead author, tells Managed Care that “patients’ diabetes and cholesterol control were actually significantly better when their physicians used an EHR compared to when they didn’t. We found that the patients who needed the most care, meaning their lab values were furthest out of control, were most helped by using the EHR.”
The study included nearly 170,000 patients and found that use of EHRs led to better control of diabetes and dyslipidemia.
The report says that when an EHR was used, patients with the greatest needs got increased testing, treatment, and physiologic improvement, and people already meeting glycemic and lipid targets had “decreased testing and treatment intensification.”
There’s a lot at stake, the study points out. Federal incentives for “meaningful use” of certified EHRs total about $29 billion, as much as $44,000 per doctor, and financial penalties for lack of certified EHR begin in 2015.
“The outpatient EHR completely replaced the paper-based medical record and a limited patchwork of pre-existing nonintegrated health IT tools” at the 17 medical centers,” says the study. “Use of those early health IT tools was limited because paper-based alternatives were still in use.”
It’s good to know that the investment will be worth it because “even with federal incentive payments … implementing a complete EHR system requires a large up-front investment in money and time, with careful coordination....”
Population management claims: The Seven Rules of Plausibility provide means to test the claims of population management vendors.
With case studies and commentary.
Everything in life has an “80–20” rule. Example: 20 percent of the population accounts for more than 80 percent of income; 80 percent of a ball club’s salary goes to 20 percent of its players, and so forth. The 80–20 rule is everywhere.
In population health spending, the 80–20 rule is that 80 percent of the time, there is no 80–20 rule. For instance, the Centers for Disease Control claims that the 50 percent of adults who have chronic disease account for 75 percent of health care spending. A 75–50 rule is about as far from an 80–20 rule as you can get, and means that costs are diffused throughout the system, rather than concentrated. (It is also not the slightest bit clear how they can define “chronic disease” so broadly that fully 50 percent of us have it. Are they including insomnia? Tooth decay? Dandruff? Ring around the collar? And how do they even know I suffer from these afflictions, let alone how much I spend on white noise machines, toothpaste, or Head and Shoulders? But we shall leave both these questions and Those Dirty Rings for another day.)
Consistent with that observation about unconcentrated costs, it turns out that large chunks of potential savings are not sitting in one place just waiting to be harvested by a vendor imploring people to smoke fewer Marlboros and eat more broccoli. Yes, the lesson will be: A simple, usually voluntary, program isn’t going to make a noticeable dent in health spending.
But try explaining this to the population health improvement industry (PHI), which prides itself on saying they do exactly that. Fortunately a modicum of math and critical thinking, using one or more of seven informal, common-sense rules can help determine whether this pride is justified. These rules are not footnoted or otherwise sourced, because there is no precedent and no governing body for validating PHI outcomes.
Or, to quote the immortal words of the great philosopher Groucho Marx: “Who are you gonna believe, me or your own eyes?”
The lesson from this chapter will be: A simple, usually voluntary, program isn’t going to make a noticeable dent in health spending.
The goal of these common-sense rules is not to validate every study that is truly valid, which would be a Herculean task, but rather to invalidate those claims that are obviously invalid, a first level of intellectual triage to avoid making misguided resource allocation decisions. The plausibility rules are as follows, with their shorthand in boldface:
The 100 Percent Rule: Outcomes explicitly or implicitly cannot require any element of cost to decline by more than 100 percent.
The Every Metric Can’t Improve Rule: Every element of resource use or group of people cannot decline in cost, through programs aimed generally at improving prevention. In particular, the actual costs associated with prevention, such as primary care visits, drug use, and health screening, must rise.
The 50 Percent Savings Rule: In a voluntary program with no incentives, declines in excess of 50 percent in any given resource category are the result of invalidity, not effectiveness.
The Nexus Rule: There must be a logical link between the goal of the program and the source of savings.
The Quality Dose–Cost Response Rule: Just as in pharmacology, cost cannot decline significantly faster or more than the related quality variables improve.
The Control Group Equivalency Rule: Control groups, if not prospectively sorted into two similar or equivalent groups, based on objective data, before members are even contacted to determine willingness to enroll, are likely to mislead. This is especially true of historic controls (meaning pre-post), matched controls, and using the non-disease group as a control for the disease group.
The Multiple Violations Rule: When one of these rules is violated, others are likely to be violated, as well.
There is a concept in testing called “face validity,” meaning what you’d expect it to mean: A study has face validity if it looks like it’s fairly measuring what it’s supposed to measure. These plausibility tests introduce a companion measure: “face impossibility.” An example has face impossibility if rather than challenge the data or the study design to question an outcome, you can simply tell from the numbers themselves as presented by the vendor that the outcome is impossible.
Every example here has face impossibility.
The textbook example of face impossibility is violating this plausibility rule: You cannot reduce a number by more than 100 percent, period. This is true no matter how hard you try. And just to avoid any potential misunderstandings by our readers Down Under, this also isn’t one of those things that’s the opposite in the Southern hemisphere.
Give it a shot yourself if you don’t believe me. A guy with two PhDs tried and even he couldn’t do it. He posted online — for the world to see if the world didn’t have better things to do with its time — the following: “Suppose you buy a stock at $10. It goes up to $50 and then down to $5. You’ve lost 450 percent.” Nope. Your stock has gone from $10 to $5, a fifth-grade textbook case of a 50 percent decline.
The 100 percent rule is a rule of math, and as mentioned earlier, rules of math are strictly enforced. That means the web page is wrong.
It’s lucky math isn’t a popularity contest because these guys aren’t the only ones who think you can reduce a number by more than 100 percent, as the conclusion of a case study from Vendor D suggests: “Wellness program participants are 225% less likely [boldface is actually theirs, believe it or not] to utilize Extended Illness Benefit than nonparticipants.” Note that for copyright reasons (even though this brochure wasn’t copyrighted) both the hospital’s name and the percentage reduction were changed. We did them a favor not just on the former but also on the latter, because the actual number they claimed was even higher.
Maybe “225 percent” wasn’t enough to excite their customers, because the Vendor D website now proclaims “390 percent’.’
It’s hard to tell which makes less sense: the numbers or the words. “390 percent” compared to what? There is also a misplaced modifier issue, as in “crossing the street, the bus hit me.” Or, perhaps they intended it to read this way. Perhaps the “400 percent losses” apply only to “employers associated with chronic disease,” such as Merck, Pfizer, and maybe Healthways or Alere. Presumably, being in the chronic disease business, they can make up their mathematically impossible losses in volume. The good news is that NASA employees don’t need to worry about their job security, because these people are obviously not rocket scientists.
However, even highly respected organizations can trip over the 100 percent rule. Here is a press release citing the Institute for Healthcare Improvement (IHI). The consensus would be that IHI is one of the most capable and influential organizations in the field. And yet…
More common violations of the 100 percent rule are not as flagrant. As a reader of these reports you can’t assume that your vendors will simply announce that they are violating basic rules of fifth-grade arithmetic. You will have to infer it.
The Center for Health Value Innovation
The Center for Health Value Innovation (CHVI) has a vision statement that says, “[CHVI] will be the trusted resource to demonstrate how engagement in health can improve accountability and economic performance.” In one of their presentations they showed a savings of $5,000 per person per year (net savings, meaning after fees are subtracted) generated by a care management program for commercially insured members, where this number was said to be for the “average” person. However, the average commercially insured person doesn’t even incur $5,000/year in paid claims — and certainly not in claims that could be considered even theoretically avoidable — making it impossible to reduce claims by this amount, especially net of fees — a clear violation of the 100 percent rule.
An example like this demonstrates the need for more instruction in the health outcomes numeracy field, both in general and also specifically because the CHVI, which itself provides instruction in outcomes-based contracting, was unable to recognize it is not possible to save $5,000/year/person in a commercial population.
The Why Nobody Believes mantra: If you insulate your house, you should save money overall, but you won’t save money on insulation.
Likewise, in health care you need to spend more money in some areas to save money overall. So, for instance, unless you believe it’s possible to talk people out of taking their drugs and have their inpatient utilization decline nonetheless, this Health Plan C slide has face impossibility (not to mention that the quantities in the first two columns don’t sum to the quantity in the last column.
The result could also have been caused by comparing people who volunteered to participate in the program to those who didn’t participate — a classic fallacy. We will bring it up a bunch more times before the book is done.
Years of doing valid outcomes measurement have confirmed the obvious: You can’t “move the needle” a lot without strong financial incentives. Want people to stay out of the ER? Sure, you can entice doctors to keep longer hours by paying them more, and that should reduce ER usage a little bit. Double the ER co-pay, though, and watch ER visits decline.
You especially can’t move the needle on chronic disease events, because most adverse events simply aren’t preventable with a few outbound phone calls.
And in reality, the needle-moving impossibility threshold, using programs without strong economic incentives/disincentives, is more like 20 percent. I chose 50 percent because there are so many outcomes studies showing greater improvements than that.
State agencies routinely accept outcomes that violate one or more of the plausibility rules, as we will see in-depth in the next chapter, and again in Chapter 8. Here is an example from Georgia Medicaid, prepared by Benefits Consulting Firm A. A word-for-word reconstruction of the summary page of their report is shown below:
The 50 percent savings rule would guide readers to look at Region 1’s 19 percent overall decrease. True, the 50 percent savings rule focuses on declines of 50 percent or more, but that’s 50 percent in any single category. A 19 percent overall decrease can — and will — easily be shown to require a >50 percent decline in hospitalizations, since disease management generates savings almost exclusively in hospital costs and ER costs, the latter being quite trivial, though, as compared to the former. Because the idea of disease management is to provide enough preventive services and self-care to avoid hospitalizations, typically the cost of non-hospital services increases in successful programs.
Let us, however, generously assume away any likely increase in non-hospital costs and say that the hospitalization reduction was achieved without increasing prevention-oriented costs. Next, let us add back in the actual fees, approximately $9 per member per month or roughly 2 percent, making the gross savings before fees 21 percent (19 percent + 2 percent).
Let us also make some assumptions for program outreach and intervention effectiveness that are generous to the program in that they exceed, in most cases by a lot, what most programs achieve:
- Hospital costs account for 50 percent of total costs in the Medicaid disabled population.
- 50 percent of hospitalizations are avoidable through phone calls.
- 50 percent of people are engaged by the program.
- 50 percent of those engaged are (without financial incentives, which were not provided) successful enough in losing weight, giving up cigarettes, and taking other steps so that they do indeed avoid hospitalizations.
We can build these assumptions into a table to determine how many hospitalizations would need to be avoided in the last bullet-pointed group — the sub-category in which the program was effective — in order to save 21 percent gross, meaning 19 percent net plus the 2 percent fees.
|Category||% of Total||Reduction in costs needed to get 21% overall gross savings|
|Hospital costs||50% of costs are hospital costs||42% of total hospital costs must be avoided|
|Avoidable hospital hosts||50% of hospital costs are avoidable through telephone disease management||84% of avoidable costs must be avoided|
|Engagement rate||50% of members are engaged||168% of total hospital costs must be avoided in the engaged population|
|Success rate||50% of engaged members are successful in avoiding avoidable hospitalizations||336% of avoidable hospitalizations must be avoided in the engaged population|
Obviously, despite generous assumptions for contact and success rates in disease management, the 19 percent net savings result that the state of Georgia accepted is so obviously a violation of the 50 percent savings rule that some might question whether the state’s administrators at the time accepted the findings not because they believed them but rather because the results justified further federally matched spending on the program.
Postscript: Vendor E, having grossly underbid the project, was later found to have made almost no outgoing phone calls to beneficiaries, and consequently agreed to return money to the state. So, Benefits Consulting Firm A was able to find mathematically impossible savings for the state despite the fact that the vendor allegedly generating those savings acknowledged not doing anything.
What list of states lying about finances would be complete without Illinois? Here is their press release, which claims more savings through disease management than the state actually spent on chronic disease events, a 100 percent rule and a poster child for face impossibility. (Oh, yes, and in actuality their chronic disease events did not even decline enough to pay for the program, a minor detail.) But some other bigger news at the time about that state’s governor relegated this news to the inside pages, sort of like Michael Jackson’s death did to Farrah Fawcett’s.
Listen carefully once again: You can only achieve savings in the categories in which you are trying to achieve savings. If costs decline in any other category, it had nothing to do with you. We will see two examples in our detailed case studies of this, but for now, consider this slide. We can’t say the name of — or even assign a code name to and then charge you for revealing the name of — the vendor shown in Figure 2 because this slide wasn’t published, but that doesn’t make it any less amusing.
It’s not just that everything declines. It’s that the biggest declines are in the two largely preventive categories (MD visits and drugs) where one would expect an increase — exactly contradicting the tenets of care management. Yes, once again that goes back to the observation that even if insulating your house saves money, the cost of the insulation itself doesn’t decline.
Perhaps Ned Flanders would be okay with this type of internal inconsistency because he believes everything in the Bible, including the “stuff that contradicts the other stuff” but obviously no one else would, right? Wrong. For three years these guys presented this material without anyone other than me noticing. To their credit, they did change their methodologies after I suggested doing so for the third time.
There is no way that events can decline if you don’t improve quality. If they do, that’s face impossibility. Usually the changes in quality variables are a smoking gun that invalidates the entire cost savings claim, as in Table 2.
|TABLE 2 How trivial quality improvements generate massive reductions in hospitalizations|
|% Cardiac Members||Base||Contract year 1||Improvement|
|With an LDL screen||75.0%||77.0%||2.0%|
|With at least one claim for a statin||69.0%||70.5%||1.5%|
|Receiving an ACE inhibitor or alternative||43.5%||44.7%||1.2%|
|Post-MI with at least one claim for a beta-blocker||0.89||0.89||0.0%|
|Hospitalizations per 1,000 cardiac members for a primary diagnosis of myocardial infarction||47.60||24.38||−48.8%|
Along with a lack of understanding of significant digits, percentages versus decimals, and changes in percentage versus changes in percentage points, this example clearly shows what is sometimes informally referred to in population health improvement as the “wishful thinking multiplier”:
% Event or cost reduction / % Improvement in quality indicators
Or, in wellness:
% Cost reduction / % Risk factor reduction
In this example, the wishful thinking multiplier is about 40, meaning that events fell about 40 times faster than the average of those four quality variables improved. The real wishful thinking multiplier, as we will see when we review the valid literature and review “mediation analysis” that connects the two, is only slightly greater than 0 for the first two to three years of a program.
Even the denominator itself can be gamed. Let’s start with quality indicators. Several vendors are partial to bragging that “10 of the 15 quality indicators either improved or stayed the same.” That means five deteriorated. If, of the 10 that improved or stayed the same, half stayed the same, as is often the case, no quality improvement took place. Five indicators got better and five got worse.
One of the vendor community’s favorite tools involving quality indicators is a “gaps in care” report, like this one in Figure 3.
The vendor reports that 43 percent of open care gaps were closed, while only 19 percent of closed care gaps opened up. Big success, right? Look again. This time, focus on the absolute number of gaps that changed over the course of the year. Turns out, there was virtually no change in open care gaps.
The wellness equivalent of quality indicator improvement, risk factor reduction, is equally if not more suspect and will be addressed in the wellness vignettes in the next chapter. It turns out that alleged risk factor reduction is often the result of fallacious measurement rather than actual impact. For instance, many wellness vendors measure only the engaged (participating) members, meaning the ones most likely to comply. We see this particular fallacy about once every 10 pages. And often vendors measure only the people who showed up in the baseline, against themselves a year later. That “historic control” fallacy is described in the next section.
One reason that there is a rule covering multiple violations is that you tend not to get impossible results without making myriad mistakes along the way. Some footage from the highlights reel:
Historic controls — meaning the same population before and after — creates a fallacy where people who were high-cost enough to make it into your “before” population will as a group regress to the mean, but formerly low-cost people not in the “before” population who regress upwards during the “after” period will be excluded from this analysis.
Matched controls — volunteers are compared to non-volunteers with similar claims and demographics — fail to control for motivation, which is the key to successful self-management of a disease.
Using the non-disease group as a control will overstate savings because people who don’t generate disease-specific claims because they are mildly chronically ill will often slip into the control group, and then explode in costs as their disease progresses, thus inflating the trend line.
One article traced what happened when you simply tracked the costs of people who were identified in the baseline year absent a program — a historic control. (Note that the study used a very tight algorithm to identify cardiac patients — a cardiac event in the baseline year.
Consider perhaps the best pure example of failure to control for motivation by using matched controls, taken verbatim from the white paper downloadable from the website of Vendor G:
- “We utilize an opt-in enrollment model to target those individuals who have high health confidence and the highest motivation to change their health situation,” and so as not to leave anything to chance, Vendor G then…
- “ ... provides incentives to participants in our Condition Management Programs.”
Farther down on their website, they note that they produce “valuable disease management reports” that “provide you with ROI.”
How do these “valuable disease management reports” determine an ROI? To what control group do they compare motivated, incentivized volunteers? They “match members in the measurement year with non-participating members with similar clinical, utilization, and cost characteristics.” They do precisely what a biostatistician or health services researcher would never do: They find (1) motivated volunteers, (2) bribe them to participate, and then (3) compare the results to people who lacked enough motivation to participate and were not given incentives.
“This approach is used because there is a need to compare the program participants to something [emphasis theirs] in order to judge whether there have been improvements.” In other words, they prefer to offer an obviously invalid ROI analysis than none at all. This is presumably because their customers, egged on by their consultants, demand to know: “What’s my ROI?” Yep, they want a number, notwithstanding that it is meaningless. Vendor G, to its credit, basically acknowledges online that this measurement is meaningless, and provides it only because their customers are insisting on it.
But we have run out of space for this excerpt.
The rest of the chapter, and all the other chapters, of course, are in the book.
Editor's note: The article that the author refers to appears below this one.
There have been unsavory rumors flying around the internet that disease management as practiced today may not be all that effective. I’m not going to reveal who started these rumors but her name rhymes with Archelle Georgiou. This person says disease management is “dead.” Since there are still many disease management departments operating around the country apparently oblivious to their demise (and disease management departments are people too, you know), I suspect this commentator was using the word "dead" figuratively, as in: “The second he forgot the third cabinet department, Rick Perry was dead." (Another example of presumably figurative speech in the death category would be: "After he denounced gays while wearing the Brokeback Mountain jacket, you could stick a fork in him.")
And if the rhymes-with-Archelle commentator intends “dead” as a synonym for “not in very good shape,” she certainly has a point. Not only does she have a point, but I would add more items to her list of reasons for the field's current troubles:
(1) The interval between diagnosis (the point where readiness to change is usually greatest) and successful patient contact can exceed three months;
(2) Predictive modeling “risk scores” that tell you only how sick someone was, dressed up as a “risk score,” not how sick they will be, even though they aren’t already high utilizers;
(3) Some interventions are so expensive that they exceed the cost of the disease;
(4) The physicians are still not involved;
(5) Rather than using actual mathematically sound methodologies to calculate results, many vendors and consultants damage the credibility of the entire endeavor by believing in the Outcomes Fairy.
Fortunately, there are improvements afoot to address all of these issues:
(1) Electronic medical records presage faster claims adjudication, and ICD-10s will mean much more detailed patient information than is possible today. And disease management departments are already coordinating with UM/precertification/discharge planning better than even two years ago. Together, these innovations will match people with programs much faster;
(2) Predictive modeling is increasingly including the lab scores. “Increasingly” meaning that instead of 1% of models having lab data, maybe 3% do. Still, it’s a start. Lab values allow actual prediction, instead of simply drawing a line connecting last year’s high claims to this year’s high risk scores;
(3) The cost of interventions is declining quite rapidly, largely with the advent of mHealth (use of mobile communications devices in health care), which is hugely overrated by venture capitalists as a vehicle for getting rich from, but quite appropriately rated as a way to facilitate contact with members if indeed privacy regulations get rewritten to assume that the only person who answers a cellphone is the owner of that phone, and hence no “opt-in” app is needed;
(4) Some physicians are getting involved because their contractual arrangements and accreditation, such as patient-centered medical homes, are requiring it;
(5) And finally, my own forthcoming book, Why Nobody Believes the Numbers: Separating Fact from Fiction in Population Health Management, will take care of the last item. Imagine the Outcomes Fairy-meets-The Hurt Locker. Credibility will be restored for those vendors whose outcomes are modest but valid. The introduction may be downloaded gratis from www.dismgmt.com .
Is disease management dead? No. It is going through a transition period in which older models are being replaced via “creative destruction” and plain old innovation with newer models. This isn’t too much fun now but ultimately this trial-and-error process should create health-improving interventions that are truly effective in preventing, forestalling and addressing some small but significant portion of the 75% of cost attributable to people with chronic disease.
So I think perhaps these two seemingly conflicting posts are in broad agreement, the only difference being that what I believe is well-founded, evidence-based optimism that the industry can innovate its way out of the current stagnation. On this point, only time will tell. In a few years we should know, to quote the immortal words of that aforementioned great philosopher Rick Perry, whether or not who is right.
Al Lewis is Executive Director of the Disease Management Purchasing Consortium
In 1995, Dr. Michael Rich published an article in the New England Journal of Medicine (NEJM) that fueled the start of an industry. In a randomized, controlled trial, he showed that an investing in proactive disease management (DM) activities could decrease the cost and improve the quality of life for patients with congestive heart failure.
The premise of disease management seemed intuitive:
- Systematically assure that evidence-based medicine is applied.
- Educate and empower patients to practice self-care.
- Intensely manage the sickest 5—10% of the patients driving 80% of the costs.
Healthplans, employers and other payers (and I) jumped on the bandwagon hoping that these programs would be a consumer-friendly silver bullet to escalating health care costs.
Cardiac Solutions, Matria, LifeMasters and American Healthways, among others, became household names. In addition, business opportunities abounded:
- Disease Management Association of America was founded in 1999.
- NCQA developed an DM accreditation program in 2002.
- Data analytics companies developed predictive modeling tools to better identify the highest risk patients.
- Employee benefits consultants promoted the “new new thing” for cost control.
But, behind the scenes, there was a lot of hand-wringing. On the eve of a major Disease Management conference, circa 2004, I remember sitting in the bar of an Orlando hotel having cocktails with DM gurus who who’d nabbed the coveted keynote speaker spots at this major forum. The Medicare Modernization Act of 2003 had just passed, and CMS had a mandate to test the disease management model in Medicare fee-for-service beneficiaries. I was shocked when my industry colleagues admitted that this $20 billion industry would only last as long as it would take for the pilots programs to be completed and CMS to analyze the results.
In the meantime…double-digit healthcare cost inflation fueled employers' demand for a wide array of condition-specific programs as a cost reduction strategy. According to Mercer Consulting, in 2010, 73% of employers offered disease management programs even though consistent, reproducible evidence of a positive ROI is still lacking.
It’s been seven long years since that Orlando meeting…and the time has come when disease management may finally—finally—fizzle and die.
The CMS demonstration programs started between August 2005 and January 2006 and preliminary results reported in 2008 concluded that "Results to date indicate limited success in achieving Medicare cost savings or reducing acute care utilization." The individual programs, all using nurse-based call centers, ended between December 2006 and August 2008, and the definitive results were published in NEJM in November 2011. In summary:
- Only 2 of 15 programs resulted in reduced hospital admissions. None generated net savings.
- There were only 14 significant improvements in process-of-care measures out of 40 comparisons.
- These modest improvements came at substantial cost to the Medicare program in fees paid to the disease management companies ($400 million), with “no demonstrable savings in Medicare expenditures.”
So, why did this intuitive approach to managing disease fail?
In my opinion, there have been 3 critical missteps in the evolution of this industry:
- NCQA Accreditation Standards: NCQA’s health plan accreditation standards require that DM programs be population-based—in other words, that ALL individuals with a condition be eligible for participation—not just those at highest risk who are most likely to benefit clinically and financially. Healthplans and vendors complied to achieve the marketing value of the NCQA gold seal of approval. Employers bought into this approach in the spirit of “prevention.” However, this peanut butter approach allocates time and money to individuals who are so low risk that there is little opportunity for clinically meaningful improvement that translates into financial savings.
- Toys and Trinkets: From glossy brochures to felt puppets to refrigerator magnets, DM companies have differentiated themselves with collateral materials that have sales appeal but have little impact on improving care or decreasing utilization. At the same time, all these all of these items inflate program costs and erode ROI. At the end of the day, DM that does not achieve a net savings is not successful.
- Over-reliance on Evidence-Based Medicine: Yes, EBM is the holy grail. However, the sole reliance on these standards in disease management interventions does not actually "manage disease” since avoidable costs are frequently due to subtle opportunities and gaps in care that exist as a result of multiple co-morbidities. For example: A patient in a diabetes DM program may also have rheumatoid arthritis. If the patient’s hand/joint pain is not well managed, it is unlikely that she will be able to comply with manually operating a glucometer to check her blood sugars. Unfortunately, the critical thinking required to truly coordinate care is difficult to systematically build into program design. Therefore, too often, it is absent.
So, if disease management doesn’t work, what does?
This month, hundreds of health care entities will be submitting proposals to CMS to get a piece of the $1 billion funding available through the Health Care Innovation Challenge. As CMS grants between $1 M and $30 M to various projects, let’s hope that they fund initiatives that reflect fresh, not "same old, same old," thinking for improving health and decreasing cost.
Technologies and platforms that are relatively low cost, scalable and seem the most promising are those that leverage:
- Social networking: Condition-specific communities of patients continue to proliferate and the user-generated content from “people just like me” is having a positive impact on compliance, self-care, and quality of life.
- Gaming: Health gaming is extending far beyond Wii Fit. Game developers are designing increasing numbers of consumer-oriented applications that address prevention, healthy lifestyles and disease self-management.
- Remote monitoring: Biometric and ambient activity sensors offer clinicians and caregivers 24/7 insight to a person’s clinical status so that care can be delivered when it’s needed rather than when it’s scheduled.
- Mobile/wireless health management applications: In addition to consumer-focused health management apps, mobile and wireless access to patient medical information accelerates how physicians make critical diagnostic decisions and can prevent delays in care.
- Environmental solutions: Innovative companies are poised to transform health care with disruptive products and systems that rely on design thinking — solutions that make it easy to be healthy, passively and continuously support better health, and don't rely on individual behavior change.
Proposals are due January 27, 2012. Awards will be announced in March. Results won’t be in for 3 years.
In the meantime, employers looking for immediate health care cost savings can save $2-5 per member per month in fees by terminating their disease management programs.
Disease Management: RIP
Archelle Georgiou MD is Chief Clinical Officer of EmpowHER and Senior Adviser to Triple-Free in Minneapolis, Minn.
Reprinted by permission from Archelle on Health
MANAGED CARE October 2011. ©MediMedia USA
Medco’s ‘gaps in care’ approach saves $900 million by targeting 15 chronic conditions
Its costs exceed $177 billion annually. It results in 125,000 deaths, nationwide. Nonadherence to medication is so prevalent that about half of the 3.2 billion prescriptions issued in the United States are not taken as directed.
To encourage compliance, Medco Health Solutions tried an innovative approach. The result? “For people nonadherent at any point in time, we can get 75 percent of them back on therapy in about 12 weeks,” reports Glen Stettin, MD, chief medical officer. Read more »
When an employer group shifts from one health plan to another, why not allow them to take their claims data to the next health plan? That way, the new plan would gain immediate knowledge of the specific disease burden faced by its new members and be able to act accordingly vis-à-vis care management programs and other interventions. As it stands now, the new plan would have to wait many months and even then would lack the history that the earlier plan no longer needs. And when the new plan “finds out" about a member's condition, it might be due to a claim for an event that could have been prevented had the carrier had access to the earlier data.
Here’s how the system would work. When a group signs up with a carrier, it could reserve the right to have its data transferred if it changes carriers. Obviously, it wouldn’t be able to “see” its own patient-identified data any more than it does now, but the data would accompany the change of carriers.
This can certainly be done without an Act of Congress. It might need to be safe-harbored by a brief regulation or administrative ruling under the Health Insurance Portability and Accountability Act, but I think one could argue that there is no prohibition against doing this now.
My question to the group is, what am I missing? Why aren’t we already doing this? Why wouldn’t a health plan offer this to an employer, for a fee, once it is clear that the employer is leaving for another carrier anyway?
Al Lewis is Executive Director of the Disease Management Purchasing Consortium
MANAGED CARE September 2011. ©MediMedia USA
Health insurance plans need only bolster the following accepted guidelines to see a return on a very little bit of investment
Heart failure results in substantial morbidity, mortality, and health care expenditures. There are 5.8 million people in the United States with heart failure, and this condition has one of the highest rates of hospitalization and rehospitalization. There are, fortunately, several therapies that are based on evidence and recommended by professional societies and that can reduce morbidity, mortality, and costs.
For example, a 2009 study in the Journal of the American Medical Association of 12,565 patients with HF investigated whether guidelines were being adhered to. It found that fewer than one third of patients who were eligible for aldosterone antagonist therapy were actually given these guideline-recommended drugs. At discharge, some hospitals prescribed them to no patients at all. Meanwhile, the rate of documented contraindication in the medical record was only 0.5 percent.
Yet for heart failure patients these drugs are proven to lower mortality, hospitalization, and rehospitalization rates. Read more »
MANAGED CARE June 2011. ©MediMedia USA
The disease has risen quickly to become the third-leading cause of death in the world — getting there much faster than anyone expected
The 2003 National Health Interview Survey found that chronic obstructive pulmonary disease would ascend from the fourth- to the third-leading cause of death in the United States by 2020. It was off by a mile.
COPD has already taken third place on the list, according to the U.S. Centers for Disease Control and Prevention (CDC).
It affects as many as 24 million Americans. There was an 18-percent increase in patients hospitalized for acute COPD exacerbations between 1998 and 2008, says Richard A. Mularski, MD, chairman of the American Thoracic Society’s quality improvement committee. He is also an investigator at the Kaiser Permanente Center for Health Research.
More than 822,000 patients are hospitalized annually for COPD. Among patients 64 years of age and younger, there were more than 230,000 hospitalizations in which COPD was the first-listed diagnosis, and many more in which COPD was involved.
COPD has an average length of stay of 4.8 days, Mularski points out, with direct costs for caring for patients coming to about $40 billion per year.
Meanwhile, our understanding of the disease has been changing. What was once thought of as a pulmonary problem is now considered a larger systemic disease that may involve many comorbidities. In this article, we use the definition of comorbid from Dorland’s Illustrated Medical Dictionary, 2007, “pertaining to a disease or other pathologic process that occurs simultaneously with another.”
COPD is now understood to involve inflammation, and inflammation is unwilling to remain neatly parked in the lungs. Rather, COPD-associated inflammation affects many organ systems, and recent studies have demonstrated a set of nonpulmonary morbidities associated with COPD.
“These are all intertwined,” says Brian Carlin, MD, chairman of the COPD Alliance — a multisociety organization that includes the American College of Chest Physicians. Carlin is an assistant professor of medicine at Drexel University College of Medicine, among other appointments.
As these are patients with complex conditions, “they require complex interventions” to keep them healthy and reduce hospitalization expenses, he says.
“We now understand that COPD is a systemic disease with synergistic interactions with other systemic diseases,” says Mularski. “You cannot succeed with these patients with just single interventions.”
According to a recent study by Bartolome Celli and colleagues in the American Journal of Respiratory and Critical Care Medicine, COPD “is a complex disease at the clinical, cellular, and molecular levels.” Currently “diagnosis, assessment, and therapeutic management are based almost exclusively on the severity of airflow limitation,” even while this measure “fails to adequately express this complexity.”
As each patient’s comorbidities differ, “a holistic approach makes sense,” says William M. Vollmer, PhD, a biostatistician and senior investigator at the Kaiser Permanente Center for Health Research.
“It is almost impossible to disentangle the comorbidities,” he says. “Look at what is going on overall with each patient.”
Costs far from trivial
As is so often the case, complexity does not bring easy answers. But answers are needed, as the costs are far from trivial.
No one truly knows the full extent of these costs, even though this subject has been studied numerous times. Several of the experts interviewed for this article noted that all existing economic analyses of COPD underestimate the actual costs greatly, in that these analyses focus on COPD as a pulmonary condition and do not count expenses associated with the comorbidities.
Hospitalizations and rehospitalizations are costly and, in COPD, the second is usually longer than the first, Carlin says.
Moreover, hospitals would be prudent to improve their treatment of COPD patients now, rather than be vulnerable once new Medicare rules on readmissions go into effect, he says. “Lack of payment for readmissions will be a strong motivator,” he says.
At the present time, approximately 1 in 4 COPD patients is re-hospitalized within 30 days of discharge, Mularski says, summarizing the findings of several studies.
It is in the interest of managed care “to invest up front to keep patients healthy and keep them out of the hospital,” says Carlin.
“Health plan medical directors should educate providers to have a checklist of some type, both for the initial diagnosis and for follow-up visits,” Carlin says. They should be considering such comorbidities as heart failure, arterial stiffness, right ventricular dysfunction, left ventricular diastolic dysfunction, metabolic syndrome, osteoporosis, peripheral skeletal muscle dysfunction, nutritional abnormalities, or cancer. Furthermore, diabetes and metabolic abnormalities should be considered, particularly for patients receiving steroids.
Also, there is some evidence that COPD is associated with increased risk of long-term mortality in patients with peripheral arterial disease and those with chronic kidney disease.
Further, “between 40 percent and 50 percent of patients with COPD suffer from depression,” Carlin points out. It is well-known that depression is associated with noncompliance with physician recommendations.
Informational e-mails from medical directors alerting providers to these issues might be beneficial by improving patient health and reducing hospitalizations and rehospitalizations. “The provider should be thinking of these conditions and rule them out in the outpatient arena,” Carlin says. Thus, in terms of an established comorbidity such as osteoporosis, the provider should be pursuing the following question: “Does this patient have osteoporosis and, if so, how should that be managed to avoid a hospitalization for fracture?” At that point, he says, it is not necessary to decide precisely how much of the patient’s osteoporosis or heart failure relates to inflammation generated by COPD and how much all these conditions relate to a history of smoking or other factors.
“It is essential with these patients to search out comorbidities,” Carlin says. “Too frequently, these patients are siloed into one diagnosis” which neither protects their overall health nor effectively keeps them out of the hospital.
Vollmer points out that inadequacies in the literature are a problem. The National Institutes of Health itself is focused primarily on individual diseases, he explains, and, thus, research is lacking on diseases that manifest as multiple comorbidities. “Research tends not to cut across outcomes,” he says.
Meanwhile, a great deal is being spent on ineffective treatments. A study of 69,820 patients hospitalized for acute exacerbations of COPD, reported by Peter Lindenauer and colleagues in the Annals of Internal Medicine, found that 45 percent of these patients received at least one nonrecommended test or treatment.
What is managed care doing now?
Using claims data and computerized programs to search out possible comorbidities and then alerting physicians to them “is the essence of the care considerations,” says Haydee Muse, MD, MBA, senior medical director at Aetna.
Aetna’s Care Engine constantly scans Aetna’s system to look for potential gaps. This is a proprietary technology platform, continuously analyzing claims and other data with reference to evidenced-based best practices and alerting the members and their physicians about possible care gaps and such inconsistencies as drug-drug interactions, missing preventive exams, or needed screening tests. If a member’s records, for example, indicate he has COPD but there is no evidence of spirometry testing, the member’s physician would then be informed.
For patients with COPD, Aetna supplements physician visits with “personalized outreach interventions for members,” with nurse case managers more likely to intervene during times of hospitalization or medical crisis and the disease management team to engage them at other times to help close gaps in care, says Muse.
Both case managers and the disease management nurses work one-on-one with members, educating them on their COPD action plans, she says. “For instance, they review the warning signs that symptoms might be getting worse and require treatment — such as recognizing when [patients are] getting shorter on breath, or paying attention to increased mucous, swollen ankles, fevers, and chills.”
Aetna’s National Clinical Improvement Work Group, which develops interventional quality programs for specific patient subpopulations, develops programs targeting patients with acute COPD exacerbations leading to hospitalization. The workgroup sends information to both providers and patients — such as the importance of avoiding metformin in COPD patients who have acidosis, or the need to address sleep apnea in patients with COPD.
Aetna’s Health Media furnishes highly personalized, self-paced online coaching sessions, she says, permitting members to chose between live and online smoking cessation programs.
Cigna is using claims data to attempt to tease out information on comorbidities. “We calculate a risk score that helps prioritize customers for outreach. The presence of comorbidities will often raise the risk score,” says Scott Josephs, MD, vice president and national medical officer for total health management.
“We also identify gaps in care through our Well Informed program,” Josephs says. “This program uses algorithms to combine medical, pharmacy, and laboratory findings to determine, for example, whether the patient is overdue for a blood test to monitor a particular medication.”
In approaching each patient, Cigna takes into account such factors as the patient’s ability to read and to comprehend medical terms, socioeconomic position, and “any financial or health barriers that might affect their ability to understand and manage their condition,” Josephs says. “We try to meet them where they are.”
The goals are preventing exacerbations and maintaining level of function, restoring the level of health whenever the patient becomes ill, and preventing complications of illness and of treatment.
Personal contact by health advocates is the key, he says. These advocates, mostly nurses, are provided with more than 40 hours of behavior-change theory training at Cigna and enrolled in a form of continuing education thereafter. They focus on educational, social, and functional barriers.
Smoking cessation is, of course, a prime concern with many COPD patients, and nicotine replacement therapy is provided free to appropriate patients. An example of another focus would be whether arthritis is interfering with inhaler use.
Cigna’s Well Aware Chronic Obstructive Pulmonary Disease program was developed using nationally recognized guidelines and the recommendations of such organizations as the American Thoracic Society and the Veterans Health Administration.
“Only by improving outcomes will you reduce costs,” Josephs says. “COPD is a problem that is increasing faster than was predicted and that needs attention.”
Several experts say that the cost of COPD treatment has been underestimated in many studies.