Purging Healthcare of Unnatural Acts

BY UWE REINHARDT

Everyone knows (or should know) that forcing a commercial health insurer to write for an individual a health insurance policy at a premium that falls short of the insurer’s best ex ante estimate of the cost of health care that individual will require is to force that insurer into what economists might call an unnatural act.

Remarkably, countries that rely on competing private health insurers to operate their universal, national health insurance systems all do just that. They allow each insurer to set the premium for a government-mandated , comprehensive benefit package, but require that each insurer “community-rate” that premium by charging the company’s individual customers that same premium, regardless of their health status and even age (with the exception of children).

American economists wonder why these countries do that, given that in the economist’s eyes community-rated health insurance premiums are “inefficient,” as economists define that term in their intra-professional dictionary. 

The Affordable Care Act of 2010 (ACA, otherwise known as “ObamaCare”) also mandates private insurers to quote community-rated premiums on the electronic market places created by the ACA, allowing adjustments only for age and whether or not an applicant smokes. But within age bands and smoker-status, insurers must charge the same premium to individual applicants regardless of their health status.

As fellow economist Mark V. Pauly points out in an illuminating two-part interview with Saurabh Jha, M.D., published earlier on this blog, aside from the “inefficiency” of that policy, it has some untoward but eminently predictable consequences. It happens when healthier people disobey the mandate to purchase insurance, leaving the risk pools of those insured in the ACA market places with sicker and sicker individuals, thus driving up the community-rated premiums. As Pauly points out at length, a weakly enforced mandate on individuals to be insured can become the Achilles heel of community rating.   

Every actuary or economist who instructs students on this point probably therefore opines that, ideally, for the sake of economic “efficiency” and to overcome the untoward side effects of community-rated premiums, one would like to allow insurers to peg the premium charged an individual applicant on the best actuarial estimates of likely future outlays for that individual, that is, to charge individuals so-called “actuarially fair” premiums and publicly subsidize those applicants faced with very high premiums. The word “fair” here signifies that chronically healthy individuals are not asked to cross-subsidize chronically sick people through the premiums they pay, which many Americans consider fair. General taxpayers pay those subsidies.

Alas, thinking about the administrative steps needed to translate our profession’s normative dicta into workable operations in the trenches has never been our profession’s strong suit. Not surprisingly, then, not much is said about that crucial step in the Jha-Pauly interview. But we can muse about what life in those trenches might turn out to be. After all, we do have some idea of how a market for individually purchased health insurance that is based on risk rated (actuarially fair) premiums works, because that is how the pre-ACA market for individual and small-group policies worked in this country.

As Pauly acknowledges, the administrative costs of such a system are high. It is so because every applicant must submit to the insurer a very detailed description of his or her health status and those submissions must then be converted into customized, risk-rated premiums. That information, of course, could be conveniently harvested by hackers.

Pauly cites some estimates of the administrative costs of risk-rating in individual insurance markets; but they strike me as low.  A clearer picture can be had from the horse’s mouth, so to speak, namely the Council for Affordable Health Insurance which represents insurance carriers active in the individual and small group market.  On the third page of a letter dated May 7, 2010, addressed to the National Association of Insurance Commissioners (NAIC), the Council noted inter alia that pre-ACA

“The NAIC standards for individual market loss ratios vary between 55%-65%, depending on the type of plan. In fact, most guaranteed renewable plan loss ratios are set by the states at 60% or less.”

What the Council calls “market loss ratios” is otherwise known as the “medical loss ratio” (MLR). It represents the fraction of the premium that insurers lose on procuring actual health care for their insured. The remaining fraction (1- the MLR) goes for marketing, administration and the insurer’s profits. Thus an MLR of “60% or less” implies that 40% or more of the premium does not buy health care, but goes for marketing, administration and profits. It takes a certain fortitude on the part of private health insurers to acknowledge that huge spillage into overhead so openly.

It is hard to imagine that any other country would tolerate so large a leakage of the premium into overhead. While economists would nevertheless call such a market “efficient,” as the profession defines that term, there is no reason why non-economists should take the economists word for it. Indeed, it can be doubted that non-economists would apply that felicitous term to any health insurance system that burns up to 40% or even more of the premium the insured pay just on marketing, administration and profit efficient, if the insured were aware of it. 

We also know from the pre-ACA days that millions of Americans then were denied insurance coverage outright by private insurance over preexisting conditions. Eliminating that problem by mandating “guaranteed issue” was one of the major goals of the ACA. Estimates of the percentage of applicants denied coverage in pre-ACA days vary. Some estimates put the number at 1 out of 7 (i.e., 14%). Other sources quote much higher figures. In an Issue Brief, the Kaiser Family Foundation provides a table with numbers, by state, of “declinable conditions.” It is worth citing here at length from Jonathan Cohn’s “ObamaCare’s New Paperwork Is Simpler than Private Insurers”:

“If you want to know which conditions attract scrutiny, you can consult a Blue Cross Michigan underwriting guide. The list of “unacceptable medical conditions,” which you’ll find on page 23, starts out like this: “Abnormal pap (unless there have been 2 subsequent normal ones), Addison’s disease, Adrenal gland disorders, AIDS, ARC (AIDS related complex), HIV+, Alcohol abuse or alcoholism (unless 12+ years since recovery), Amyotrophic lateral sclerosis (ALS), Alzheimer’s disease, Aneurysm, Angina pectoris, Aplastic anemia, Arteriosclerotic heart disease, Atrial fibrillation or flutter, Ascites, Autism and Aspergers syndrome, Autoimmune diseases.” Highlights later in the alphabet include Cancer, Congenital Disorders, Heart Murmurs, Lupus, Parkinson’s Disease, and, of course, Pregnancy.”

What would happen to those denied coverage by insurance companies in the scheme envisaged by Pauly? Would they be assigned to a high risk pool?

Unfortunately, while we do know a lot about the modus operandi of a classic, medically-underwritten (risk rated) market for individually purchased health insurance, there is no experience in the U.S. on how to link such a market to the system of public subsidies that Pauly calls for in the Jha-Pauly interview. How exactly would this work?

Relevant variables here presumably would be, on the one hand, the insured’s “disposable income” and, on the other, the risk-rated health-insurance premiums quoted that individual by competing insurers. “Disposable income” would have to be carefully defined for this purpose. Is it just available money income after taxes and transfers? Or should it be the latter figure minus required outlays for other necessities, such as food, housing, clothing, fuel for transportation to and from work, etc.? Besides that question, a series of others come to mind, to wit:

1. For what benefit package would the premiums be quoted? Who would specify that package? What might be excluded from the package? Would there be lifetime limits on coverage or annual limits on specific services (e.g., prescription drugs)?
   
2. What would happen if the specified benefit package underlying the public subsidy were shallow and the insured got severely ill, facing unaffordable medical bills for clinically beneficial products (e.g., drugs) and services not covered by the policy? Who would pay those bills? Would the cost of such care be shifted to paying patients through higher prices? Or would the insured simply be denied those clinically beneficial products and services, at the risk of avoidable, premature death or chronic illness?

3. Would premiums be reset annually, in light of changes in the insured’s changing health status?

4. Could an insurer cancel a policy ex post if the applicant had inadvertently or deliberately concealed a particular medical condition from the insurer when applying for coverage?
   
5. What would be the risk-based premium on which any subsidy would be based? Would it be the lowest one quoted in the relevant market area? And what would that area be?

6. Would the insured’s out-of-pocket cost for the coverage be linked to his or her disposable income, as it is under ObamaCare?
   
7. Who would manage the market place in which individual policies are sold, or would the market return to pre-ACA operations, managed solely by an uncoordinated net of insurance brokers?

8. Who would pay the brokers’ commissions, the insurance companies, setting up an evident conflict of interest, or the insured, as it should be.

9. Would people denied coverage by insurers in this free market be enrolled in a high risk pool? If so, what would be its design parameters – the benefit package, the premium charged the individual, the magnitude of public subsidies, and so on?

Others undoubtedly can think of yet other questions; but let these suffice.

Probably because of the complexity of operating a risk-rated private health insurance system supported by public subsidies, few nations operating universal health insurance systems have adopted that approach. In fact, I cannot think of any.

Switzerland, Germany and the Netherlands, for example, all rely on a system of competing private health insurance carriers. Unlike the U.S., they do not have government-run health insurance programs at all. Competing private insurers can quote different premiums for the same, government-specified benefit package, which tends to be quite comprehensive. But unwilling to subject their citizens to the complexity and vagaries on risk-rated health insurance markets, these countries mandate that the premiums quoted by competing insurers be strictly community rated, even with respect to age. Somehow these countries have been able to make this work for decades, without the collapse of their health insurance markets. If we Americans were not as insular and proud as we are, we might explore how these countries manage to do that and learn from it.

We would discover that somehow these nations make the mandate to be insured stick to the point of garnishing the wages of individuals disobeying the mandate to be insured. Perhaps these nations also succeed in persuading young and healthy people that they, too, might fall very ill at some time in the future. One can then view community rating as the analog of a call option on a stock. In this case, it is a call option on a low premium in case one falls seriously ill, the price of the option being the overpayment relative to actuarial costs when healthy.

In the U.S., we consider it unacceptable for government to force individuals to purchase from a private vendor a product they do not wish to buy. To many Americans this makes sense – hence the strong opposition to the individual mandate to be insured – ironically much favored by Republicans during the 1990s but now decried by them. The mandate to be insured has been a major rallying point for those who oppose ObamaCare.

One way to overcome that problem might be to abandon the mandate altogether, but to offer individuals a deal they are less likely to refuse. In his “Averting a Health Care Backlash,” for example, Paul Starr as early as 2009 counseled the Obama Administration to let individuals opt out of the mandate to be insured, but to allow them back into ObamaCare only after 5 years. 

I would have been much rougher. In a blog post entitled “Rugged Individualism vs. Social Solidarity,” published in The New York Times, I proposed that individuals opting out of a system built on social solidarity, with community-rated premiums, should never be allowed to rejoin it later, aside from some very special circumstances. It would be a deal fewer people would refuse. Rugged individuals who turn their back on social solidarity can be asked tough it out on their own when misfortune strikes, rather than rediscovering in those calamitous moments the beneficence of the community. Unfortunately, too many of them follow the mantra “when the going gets tough, the tough run to the government,” as can be seen every time a hurricane wreaks havoc on some area or uninsured rugged individuals fall seriously ill or have a serious accidents, when they expect the best available health care, even if they cannot pay for it. A forgiving nation has always enabled this behavior.

Starr’s and my approach can be viewed as market approaches, albeit ones structured to contain a so-called “nudge” to obtain coverage.

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What Is This Strange & Confusing Healthcare Language We Have Invented?

By PAUL KECKLEY

Public understanding about how our health system operates is woefully low: surveys show only one in five adults has functional knowledge about how to choose a physician, hospital or insurance plan, or compare treatment options. The lexicon we use in our industry lends to this confusion: powerful words and phrases that convey something different depending on the user’s intent.

As we debate the replacement for the Affordable Care Act, it might be worthwhile to ask lawmakers to clarify what they mean when they use them and examine our own uses in tandem:

Quality: In U.S. healthcare, quality is not defined by a consistent set of metrics that address diagnostic accuracy and clinical outcomes.  Physicians associate it with access to a clinician; insurers associate it with necessary care; employers with provider network scale and premium costs and the public thinks it’s about scheduling and parking, not results. There are a dozen websites where information about the quality of care in hospitals and medical practices is available, but each has its own methodology and results vary widely. As a result, every hospital and every physician affirms they deliver “high quality care” and every insurer tells its enrollees, groups and regulators its plans are “high quality”. Little wonder quality is confusing.

Affordability: Healthcare affordability is an abstract concept: in the U.S., it’s theoretically a relationship between total out of pocket payments as a percent of household income for premiums, co-pays, deductibles, and over the counter therapies. But there’s no consensus about what constitutes an acceptable level of affordability. In the Affordable Care Act, a threshold above 9.5% was deemed appropriate for employer-sponsored insurance premium affordability, but insurance premiums vary widely based on what’s covered and much isn’t. And is affordability when applied to healthcare spending a different calculus when compared to housing and food expenditures which seem more straightforward?

Access: Does giving individuals the opportunity to purchase an insurance plan or see a specific physician that’s out of their reach financially constitute accessibility? In the Veteran’s Health Administration, a standard for access to primary care is 30 days or less and vets have no co-pays: it’s a straightforward standard for access. There are no standards for access to physicians, hospitals, therapies or other time-sensitive products and services in our system. Should there be?

Value: The most over-used word in our lexicon is value. Each stakeholder calculates the costs and outcomes of their output differently and conveniently each result is “high value”. In the Affordable Care Act, value was codified for hospitals around the Hospital Value Based Purchasing Program that specified metrics for determining hospital value. Each sector in healthcare opines about its value proposition offering metrics that are prone to self-preservation. 

Costs: Policy-makers calculate health spending in the U.S. based on what’s spent for providers, drugs, facilities, technologies, insurance and administration concluding the U.S. system is the world’s most costly. But our pluralistic payer system combined with the 10% whose care is paid for by others means providers, especially hospitals, end up providing social services that are calculated differently in other systems of the world. As a result, total spending on healthcare, as a percent of GDP, is actually higher in France, Sweden, Switzerland, Germany and the Netherlands than in the U.S. And in all of these, the total is well above 25% of their GDP.

And there are many other terms and phrases that play key roles in healthcare shorthand: accountable care, comparative effectiveness and evidence-based care, healthcare consumerism, physician-patient relationships and patient centered care, precision medicine, alternative health and many more. Each of these mean something different to the users, and rarely is there consistency in implied intent.

Regrettably, after seven years of debate about the transformation of the health system, the public’s still confused by our lingo. The era of social media and 24/7 news cycles lends to soundbites that obfuscates understanding. Just as “fake news” and “alternative facts” are now part of our political discourse, so is our dependence on terms and phrases that mislead or confuse.

Maybe as we engage in Health Reform 2.0, we should develop a glossary of key words and phrases so we aren’t lending to the public’s confusion about what we mean. It’s worth the effort.

 

P.S. Some common themes are emerging from the GOP’s efforts to repeal the affordable care act. First, the process will involve executive orders, legislation and new regulations all on the table by the end of this year. Suspension of the individual and employer mandates will be among the first wave of these orders. Second, implementation will be phased over a 2-3 year period, with much of the responsibility shifting to states for Medicaid, insurance coverage and more. Three, it’s unlikely Medicare reforms, in the form of a premium support replacement, will get thru Congress, but limits on supplemental coverage and expansion of Medicare Part C plans are likely to advance. Fourth, insurance coverage permitting purchases across state lines in tandem with expansion of high risk pools will be on the table. Lawmakers will make concessions to insurers including a requirement for continuous coverage for those with pre-existing conditions, and income-based tax credits so lower income individuals can purchase coverage will continue. Stay tuned. The question is this: how will the campaign promises by President Trump, the cost cutting appetite in the Freedom Caucus and the looming Campaign 2018 battle factor into the final product?

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WaPo Leaked Tape of GOP Repeal & Replace Talks is Troubling. But Also Reassuring …

By STEVEN FINDLAY

“We’re telling those people that we’re not going to pull the rug out from under them, and if we do this too fast, we are in fact going to pull the rug out from under them.” 

– Rep. Tom MacArthur (R-N.J)

“The fact is, we cannot repeal Obamacare through reconciliation.  We need to understand exactly: what does that reconciliation market look like.  And I haven’t heard the answer yet.” 

– Rep. Tom McClintock (R-Calif)

“It sounds like we are going to be raising taxes on the middle class in order to pay for these new tax credits.” 

– Sen. Bill Cassidy (R-La) 

These quotes, and many others, from a leaked recording of the Republican closed-door strategy session in Philadelphia last week are both jarring and reassuring.   

They reveal in harsh light what the media, pundits, and commentators have been saying for weeks: the Trump administration and congressional Republicans are in a deep quandary about the best path forward on repeal and replace, and are just beginning to weigh the pros and cons of the complex policy options involved. 

But the discussion also shows us that rank and file Republican lawmakers understand the difficulty of the task and know the political price they’ll pay if they screw it up.  Their remarks also imply frustration with the cavalier, ill-informed, and mixed-message statements coming out of the White House.

Trump himself addressed the group.  Paraphrasing:  Obamacare is a disaster.  It’s a disaster.  We could just let it die on it’s own because it’s a disaster and the Democrats will come begging us to fix it.  But we have to do something to help the people…by fixing the disaster. 

Dr. Tom Price (R-Ga)—Trump’s nominee to be HHS Secretary—did his best to stay above the fray at his confirmation hearing on Tuesday Jan. 24.  He shed almost no light on what repeal and replace should look like or on what timeframe it should be done.  He was poised and steady, and even charming at times, but also sly and deeply less than forthright.  Of course, he was quite busy fending off charges that he sought legislation numerous times that benefited companies he had invested in.   

His evasive answers were almost comical at times.  In case you missed it: 

Sen. Sherrod Brown (D-Ohio):  “President Trump said he’s working with you on a replacement plan for the ACA, which is nearly finished and will be revealed after your confirmation.  Is that true? 

Price:  “It’s true that he said that, yes.”  (The packed room breaks into laughter, according to The Washington Post.)   

Brown:  “Did the president lie about this, that he’s not working with you?”

Price:  “I’ve had conversations with the president about healthcare.” 

Make no mistake, the loose and evasive talk and the actions Trump has taken so far (the one-page executive order and the enrollment outreach directive), are already having an impact.   Consumers, including my 29-year old daughter, are wondering if they’ll be able to keep or have access to ACA exchange insurance, and if it won’t be crappier coverage for the same or more money.  Insurers are reevaluating exchange participation for 2018.  Employers see only disruption ahead, and worry about over-reliance on shifting costs to workers with a failure to address underlying costs.  Governors, Democrat and Republican, are worried their Medicaid expansions are in jeopardy. 

By the way, the administration did not brief Republican leaders in Congress, or governors, on the executive order until one day before it was issued.   

And in an inexcusable misstep, the White House last Thursday issued a directive to halt all exchange enrollment outreach and advertising before the Jan 31 enrollment deadline.  Less than 24 hours later, after a tsunami of criticism, the White House pulled back on some of the order but kept a limit on ads.  Said a spokesperson:  “We aren’t going to continue spending millions of taxpayer dollars promoting a failed government program.”   

Such brazenly politically motivated shortsightedness speaks for itself. 

I could get just as partisan here.  After all, the Republicans have been the gang that couldn’t shoot straight on healthcare for 50 years.  They have never supported universal coverage or a right to health care, and have fought most coverage expansions.  They didn’t support the creation of Medicare and Medicaid in the early 1960s until Lyndon Johnson twisted arms.  They fought Medicaid expansions for the next 40 years—a program that now provided a lifeline to 70 million low- and middle-income Americans.  They viciously killed Hillarycare and stonewalled passage of the ACA.  The sole exception is George W. Bush’s successful push for a prescription drug benefit for Medicare beneficiaries.

And, of course, they have spent the last six years spreading falsehoods—I guess we can call those lies or propaganda now—about Obamacare.  “Government-run healthcare.”   “Costs taxpayers billions.”  “Death panels.”  “Comes between you and your doctor.”  “Much more expensive coverage.” 

(In fairness, Obama himself and other ACA proponents have uttered their share of falsehoods and used propaganda to sell and advance the ACA.)   

The hope at this point—and it’s a long shot, I know—is that both sides lower the temperature of the rhetoric, agree on the complexity of the task ahead, and start getting down to brass tacks to design a replacement (because, the ACA is going to be repealed) which preserves what works in the ACA.  Call it whatever you want.  In the meantime, no repeal and delay.   It’s quite clear that is a dangerous path that will end up harming mullions of Americans.    

The policy preferences of both sides can be knit together.  It won’t be easy but it can be done.   As one example, both sides can almost certainly rally around more state flexibility, possibly in the short term.  There’s a partial ACA repeal bill already in the hopper on that as of last week, with Cassidy of Louisiana and Susan Collins of Maine as sponsors. 

As for the politics, the Republicans need 60 votes in the Senate to pass a meaningful replacement that avoids doing serious damage to the individual insurance market.  It would be better to get to 65 or 70.  That way, the replacement is truly bipartisan and modifications needed it the future can be debated in that spirit. 

It’s way past time to stop the nonsense and hyper-partisanship on healthcare. Lives hang in the balance.   

Steven Findlay is an independent journalist, policy analyst, researcher and consumer advocate.   

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Data For Improving Healthcare vs Data For Exasperating Healthcare Workers

BY TOM BURTON

The phrase “healthcare data” either strikes fear and loathing, or provides understanding and resolve in the minds of administration, clinicians, and nurses everywhere. Which emotion it brings out depends on how the data will be used. Data employed as a weapon for purposes of accountability generates fear. Data used as a teaching instrument for learning inspires trust and confidence.

Not all data for accountability is bad. Data used for prescriptive analytics within a security framework, for example, is necessary to reduce or eliminate fraud and abuse. And data for improvement isn’t without its own faults, such as the tendency to perfect it to the point of inefficiency. But the general culture of collecting data to hold people accountable is counterproductive, while collecting data for learning leads to continuous improvement.

This isn’t a matter of eliminating what some may consider to be bad metrics. It’s a matter of shifting the focus away from using metrics for accountability and toward using them for learning so your hospital can start to collect data for improving healthcare.

Data for Accountability

Data for accountability is a regulatory requirement. It’s time consuming and can be less than perfect. But it has made a difference in some areas. Readmission and cancer rates are decreasing and patient satisfaction scores are increasing.

But an accountability model of results improvement places the focus on people rather than processes. This watchdog approach tends to make many people defensive, resistant to learning, and impedes continuous improvement. It initiates the cycle of fear, as first described by Scherkenbach (Figure 1), where fear of repercussion leads to denial and blame shifting, followed by other negative behaviors that continue to produce a culture of fear. Yet most errors that jumpstart this cycle are the result of flawed processes rather than flawed individuals.

Figure 1: The Cycle of Fear (Scherkenbach, 1991).

Data for accountability can be not only punitive, but also detrimental toward improving outcomes. For example, a well-known CMS core measure is 30-day, all-cause, risk-standardized readmission rate following hospitalization for heart failure. In the process of complying with this metric, it’s possible that patients who should be back in the hospital are not being readmitted. In the process of hitting a metric for administering a beta-blocker therapy, it’s possible to obscure notes to ensure a favorable entry into a patient’s record.

Data collection for accountability is time consuming. According to a survey by Health Affairs, physician practices in four specialty areas spend more than 785 hours a year reporting on quality measures. Staff spends 12.5 hours and physicians spend 2.6 hours per week on this work. And when anticipating reward or punishment based on a metric, healthcare systems dwell excessively on data accuracy. A lot of effort goes into determining which patients should or should not be included in any given metric.

Deming and Over-Emphasis of Numbers

Deming said the right amount of effort focused on a process improves it. The right amount of focus means recognizing the many different factors that play into a number and learning what you can, but not over-emphasizing the importance of that number.

Too much focus sacrifices quality in other areas. For example, say there are five key factors that impact diabetes care, but only one is tied to an accountability report or a bonus metric. The other four factors may suffer because clinicians are focusing on the one key metric, rather than focusing on all five metrics that impact the patient’s health. This is sub-optimization, a negative attribute of collecting data for accountability.

Because of the number of regulatory metrics, many organizations sub-optimize them given their time limitations, even though other metrics have greater impact on cost of care and improving patient health. Organizations can’t focus on improving the more relevant metrics because they sub-optimize on the regulatory metrics that hold them accountable for compliance.

Extreme focus results in gaming the system by manipulating numbers. There are a variety of disingenuous ways to game the system because too much emphasis is placed on the metric versus the improvement: forging documents, stretching the truth, burying notes, refusing to see patients that could negatively impact a metric. Gaming the system goes to the point of cheating. Processes aren’t improved at all, only the reporting is.

If organizations put as much effort into improving the process as they do with documenting their results, they wouldn’t need the regulatory metric in the first place. To avoid penalties down the road, they spend thousands of hours improving a definition of accountability, when the time could be much better spent improving processes.

Figure 2: Ways to get a better number (“Healthcare: A Better Way,” 2014).

This is like the student who studies just to pass the test, but never gains any knowledge. He is memorizing answers to specific problems without realizing that the whole reason for the test is to understand physics or philosophy, not just how to get an A. When healthcare workers go back and alter data, it doesn’t improve patient care.

Data for Improving Healthcare

Data for improving healthcare, or data for learning, requires an internal strategy that addresses specific clinical process improvement areas, and quality and cost control areas. While accountability data is driven by external reporting requirements, clinical or operational data for outcomes improvement is driven by a strategy to better understand a process and the root cause of process failures. This data analysis increases understanding of patient populations, as well as clinical and cost outcomes, by understanding process variation that leads to different results.

Data that’s tracked for establishing a baseline, and then for comparing to the baseline to determine whether or not an improvement occurred, is data for learning.

If the focus is on learning versus reporting, then the data can be so much more revealing. Let’s look again at a cohort of heart failure patients. If we measure whether patients are taking a medication simply to track, learn, and increase medication compliance to benefit patients, then the data’s purpose changes. It becomes about learning and improvement.

Data for Learning Has its Drawbacks

The people and processes involved with collecting data for learning can still be subject to some of the pitfalls associated with collecting data for accountability. A lot of time is spent reporting on quality metrics, often at the expense of improving things. Too often, the focus is on simply reporting or achieving a ranking. It’s up to each healthcare organization to use data and metrics effectively to learn about its own processes.

Time is also a factor with data for learning. Too often, perfect data becomes the goal for learning and improvement work, when all that’s needed is sufficient data that can reveal when a new process is better than the old process. Clinicians may sometimes shoot for a level of data accuracy that’s only required of audited financial reports, or a double blinded study, when all they need to know is if a new process is helping a patient more than the old process. Should a new process be leveraged for discharge or is an existing process better? A quick solution might be to setup an A/B test of two quasi-experimental designs, try them in two units for a month, get rough numbers, and then figure out which process works better. A service line could start using those kinds of numbers without being 100 percent accurate. Eighty percent can still be very useful, and much more cost effective.

Developing New Measures for Learning

Eventually, the industry will adopt new measures that matter most to patients, such as how quickly someone can return to work following hip replacement surgery. The International Consortium for Health Outcomes Measurement (ICHOM) focuses on outcomes measures—metrics for learning—and has developed Standard Sets for 13 conditions. They plan to have additional sets that will cover 50 percent of all diseases by 2017. Health Catalyst’s care management applications will be able to capture this type of data, as well as patient-reported outcomes, both subjective and objective.

Accountability vs. Learning: Another Perspective

In January 2003, Brent James, MD, Executive Director of the Intermountain Institute for Healthcare Delivery Research, Don Berwick, MD, the former CMS Administrator, and Molly Coye, MD, the former Chief Innovation Officer for UCLA Health, wrote for the journal, Medical Care, about the connections between quality measurement and improvement. The article describes two pathways for “linking the processes of measurement to the processes of improvement.”

1. The Selection Pathway (accountability)

The selection pathway is based on ranking and reputation. Patients select clinicians, employers select health plans, rating agencies accredit hospitals, and doctors refer to other doctors all based on measured performance. The purpose behind this pathway is to not only hold people accountable, but to judge whether a physician or hospital is good or bad. Supposedly, when people self-select away from a bad physician to a good physician, or from a bad hospital to a good hospital, that motivates a change in behavior. This pathway doesn’t lead to improvement by underperformers, but the outcomes of care appear to improve because of this self-selection process.

2. The Change Pathway (learning)

The change pathway goes after understanding processes in order to improve them. People and organizations are not labeled as bad or good; processes are labeled as less or more effective. This way, the data’s purpose becomes how to help an organization learn about the breakdowns and failures in the process, not whether people or organizations are performing poorly.

The article makes this analogy: “A grocery shopper can select the best bananas without having the slightest idea about how bananas are grown or how to grow better bananas. Her job is to choose (Pathway I). Banana growers have quite a different job. If they want better bananas, they have to understand the processes of growing, harvesting, shipping, and so on, and they have to have a way to improve those processes. This is Pathway II.”

Figure 3: In this illustration of accountability (the Selection Pathway), outliers below a minimum standard are identified and reduced, but no real improvement occurs.

Figure 4: In the more effective approach to improvement, the focus is on better care processes to shift the overall curve.

The Synergy of Data for Accountability and Improvement

Regardless of the intent behind data collection, it must be used responsibly. Extravagant executive dashboards can be an overuse of data, especially if no improvement comes from the effort. The diverted effort has nothing to do with outcomes improvement and distracts from other metrics that deserve attention. A bottom-up approach is preferred, where improvement efforts are designed with actual processes behind them, rather than a top-down approach that creates fire drills and produces wasted work.

As James and others wrote: “‘Pathway I’ (Selection) can be a powerful tool for getting the best out of the current distribution of performance. ‘Pathway II’—improvement through changes in care—can shift the underlying distribution of performance itself.”

We are getting smarter about this as an industry. It’s fair to say that both regulatory and clinical metrics can play on the same team. What needs to change is the focus on accountability and judgment so everyone can achieve the desired goal of learning and improvement.

Tom Burton is co-founder and executive vice president of Healthcatalyst.

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Simulated ACOs vs Real-World ACOs

By KIP SULLIVAN

CMS began two Medicare ACO experiments in 2012 – the Pioneer program and the Medicare Shared Savings Program (MSSP). Data on these programs available at CMS’s website paints a discouraging picture of the programs’ ability to cut costs. But two papers published in the last two years in the Journal of the American Medical Association paint a much rosier picture. A paper written by David Nyweide et al. claimed to find the Pioneer ACO program generated gross savings two times more in 2012, and slightly more in 2013, than CMS reported. Similarly, a paper  by J. Michael McWilliams claimed to find the MSSP program saved money in 2014 while CMS’s data says it lost money.

What explains the discrepancy? Answer: The JAMA papers examined simulated ACO programs, not the actual Pioneer and MSSP programs. Moreover, Nyweide et al. neglected to report that shared savings payments would have greatly reduced the gross savings, and both Nyweide et al. and McWilliams ignored the start-up and maintenance costs the ACOs incurred. (JAMA’s editors redeemed themselves somewhat by publishing a comment  by former CMS administrator Mark McClellan which warned readers that Nyweide et al. failed to measure the “shared savings payments to the ACOs” and “the investments of time and money” made by the ACOs.)

In this essay I will describe how the simulations reported in the JAMA papers differed from the actual ACO programs, and I’ll question the ethics of conflating simulated with actual results.

Results from the real world

CMS does not make it easy to determine whether its ACO programs save money. In fact, it is fair to say CMS is routinely deceptive. When CMS releases ACO data, it announces only the total savings achieved by a minority of ACOs and ignores the costs CMS incurs. [1] But CMS does post spread sheets on its website that permit the more dogged among us to calculate net figures, that is, the savings CMS celebrates minus the losses CMS won’t talk about.

Here are the net savings for Medicare for each of the first four years of the Pioneer program. 

2012: 0.2 percent;

2013: 0.5 percent;

2014: 0.7 percent;

2015: 0.1 percent.

Here are the net losses for the MSSP program for its first four years, presented as three “performance years” (because of the uneven start dates for this program in 2012):

2012-2013: -0.2 percent;

2014: -0.1 percent;

2015: -0.3 percent. [2]

To sum up, after four years of trying, the Pioneer ACOs cut Medicare’s costs by somewhere between one- and seven-tenths of a percent annually, while the MSSP ACOs raised Medicare’s costs by somewhere between one- and three-tenths of a percent annually. Note that these underwhelming results do not include the start-up and maintenance costs incurred by the ACOs nor the costs CMS incurred to administer these complex programs. Note also that these results are entirely consistent with four decades of research on managed care experiments, including HMOs, “medical homes,” “coordinated care,” and the Physician Group Practice Demonstration. [3] As Robert Laszweski put it in 2012 commenting on a CBO report on managed-care tactics, “(Almost) nothing works.”

But according to Nyweide et al., the Pioneer ACOs achieved gross savings of 4.0 percent in 2011 and 1.5 percent in 2012, above the 1.2 and 1.3 percent gross savings figures for those years according to CMS data. [4] Similarly, McWilliams’ JAMA paper found both gross and net savings for MSSP ACOs in 2014 (McWilliams found net savings of 0.7 percent) while CMS’s data indicated a net loss of a tenth of a percent that year and net losses in all other years.

If at first you don’t succeed, simulate

The design of the simulated versions of the Pioneer and MSSP ACO programs that Nyweide et al. and McWilliams examined varied substantially from the design of the real programs. The single most fundamental difference is the comparison group used to determine ACO spending. The real-world programs determine the performance of the ACOs by comparing the Medicare expenditures on patients “attributed” to ACO doctors in “performance years” with expenditures on patients attributed prior to the performance year. Nyweide et al. and McWilliams chose a different set of providers and patients to serve as the comparison groups: They chose providers (and their attributed patients) who had not signed up with an ACO.

Other important differences between the simulated and real ACO programs include differences in methods used to attribute patients to doctors (for example, whether to count visits to specialists and primary care doctors or only primary care doctors, and whether to look back two years versus three years to attribute patients) and in calculating savings and losses generated by ACOs (how many years to look back to create the baseline expenditure, whether to trend the baseline forward using national or local inflation rates, and what risk-adjustment method to use to adjust the baseline and the performance year expenditures to reflect changes in patient health).

These design changes are significant. The change in attribution methods means, obviously, the experimental group of patients (those in ACOs) was not the same experimental group tested by the real-world ACO programs. The difference in the algorithms used to calculate savings and losses means the simulated ACOs experienced different rewards and penalties from the real-world ACOs, and that in turn should have caused doctors and hospitals in the simulated programs to behave differently from the providers in the real programs.

In short, the authors of the JAMA papers changed every important parameter. They changed the control group, the experimental group, the method for calculating how the ACOs affected costs and, with that, the size of and distribution of rewards and penalties.

Why simulate when you have real ACO programs right in front of you?

Simulations (as the word is used in science) can serve very useful purposes. Modeling or simulating an existing system under conditions that differ from real-world conditions can improve our knowledge of how the system works and suggest ways to improve it. As one expert  on simulations puts it, “Another broad class of purposes to which computer simulations can be put is in telling us about how we should expect some system in the real world to behave under a particular set of circumstances.”

But what justification is there for studying a simulated version of existing ACO programs – a version in which the programs are subjected to “a particular set of circumstances” that have never been applied in the real world – and declaring the results of the simulation to be the results of the real-world program? I can’t think of any.

The rationale offered by Nyweide et al. and McWilliams for applying imaginary conditions to the real-world ACO programs was that the design of the Pioneer and MSSP programs was too crude to measure accurately the performance of the ACOs. They argued that their simulated design would make their measurements more accurate than CMS’s. This hypothesis (decoupled from the Alice-in-Wonderland claim that simulated results equal real-world results) is plausible. But it needs to be demonstrated, not merely declared. If it were proven, it would suggest ways CMS could improve the accuracy of the carrots and sticks it applies to ACOs.

But Nyweide et al. and McWilliams made no attempt to prove their claim that their simulated model could measure ACO performance more accurately than CMS does now. They merely asserted that claim, and JAMA’s editors let them get away with it.

In their own words

Because some readers may be having a hard time believing smart people could behave so irrationally, I will quote the authors. In the quotes I present below the authors use the label “evaluation” to describe their allegedly more sophisticated design, and they characterize CMS’s method of calculating savings as a mere “payment formula” or “actuarial calculation,” as if CMS were obsessed with cost-cutting and cared not a whit whether the payments and penalties they administer to ACOs correspond to the impact ACOs are actually having on costs.

I begin with Nyweide et al. That paper was based on research reported by L&M Policy Research in their evaluation of the first two years of the Pioneer program. Here is how L&M justified examining a simulated version of the Pioneer program rather than the actual program:

Savings and losses under the payment formula [meaning the real ACO program] are calculated with the goal of establishing an incentive to reduce spending compared to a benchmark. The goal of the evaluation [i.e., L&M’s simulation] is to estimate what costs and other outcomes would have been in the absence of the Pioneer model, which necessitates employing different approaches than those used to calculate payment. (p. 2) [5]

Do you see how empty and manipulative that statement is? L&M asserts that their “goal” is different from and superior to CMS’s, that L&M’s goal is to “estimate what costs … would have been in the absence of the Pioneer model” whereas the cost-cutters at CMS have no interest in determining “what costs would have been in the absence of the Pioneer model.” CMS, we are to believe, adopted crude formulas for decreeing that savings and losses occurred with little or no regard for the actual impact ACOs had on expenditures. [6]

Nyweide et al. made the same argument in their JAMA paper: They portrayed themselves as sophisticated scientists interested in knowing the real truth, while the pencil-pushers who designed the actual programs didn’t really care whether they rewarded only those ACOs that really saved money. Here’s what Nyweide et al. wrote: “Between 2012 and 2013, Pioneer ACOs generated approximately $183 million in savings to the Medicare program relative to projected spending levels….  However, these results do not account for many factors that may confound the relationship between the model intervention and patient outcomes.” (p. 2153) There was no further discussion of what these “many factors” might have been nor any evidence introduced to support the claim that the bean counters at CMS never thought about confounders. The implication was just left hanging – the people at CMS who designed the original ACO programs didn’t care about confounders while Nyweide et al. do.

McWilliams made the same argument in his JAMA paper on MSSP ACOs and in the paper  in which he laid out his methodology for his JAMA paper. He claimed CMS’s “actuarial calculations” of savings “may” not be accurate, and offered a single, biased illustration:

Savings based on [CMS’s] actuarial calculations, however, may differ from actual spending reductions. For example, the substantial geographic variation in Medicare spending growth calls into question the validity of savings estimated by comparing spending in an ACO with a benchmark derived from a national rate of spending growth. If an ACO is located in an area with high spending growth, its savings could be underestimated. (p. 2)

That’s it. That’s all McWilliams can say to back up his claim that CMS’s method of measuring ACO expenditures is crude and the method he used for his simulation is better. Note also the bias in McWilliams’ example of CMS’s alleged lack of sophistication – savings could be underestimated in high-cost areas. Well, yes, but if that’s true, then what about ACOs in low-cost areas? Won’t their savings be overestimated? McWilliams was silent on that possibility.

The claim that CMS didn’t think enough or at all about accurate measurement contradicts common sense and CMS’s own statements. For example, in their 2011 final MSSP rule (76 FR 19528), devoted pages and pages to the question of how to measure savings. In that rule, CMS wrote, for example, “our proposed approach … will result in a more accurate benchmark.” (p. 67914)

Can we learn anything from the JAMA papers?

Let us, for the moment, forgive Nyweide et al. and McWilliams for announcing to the world that simulated results are real-world results, and let’s ask whether we can learn anything from their papers. These papers might be helpful if we could conclude that they demonstrate techniques for improving the accuracy with which CMS measure’s ACO savings and losses. Well-designed simulations should help us understand how to improve existing systems. Unfortunately the papers don’t do that. In fact, it’s quite possible that accuracy of measurement achieved by Nyweide et al.’s simulation was worse than it is in the real programs.

The most important confounder in any comparison of patients is differences in patient health and income. Adjusting for these differences is commonly called “risk adjustment.” The new comparison group that Nyweide et al. inserted into their simulated version made accurate risk adjustment even more essential and slightly more difficult. That’s because of the method Nyweide et al. used to create their control group guaranteed that the control and experimental (ACO) patient pools would vary on at least one crucial dimension – continuity of care or, if you prefer, patient loyalty. Nyweide et al.’s method of assigning patients to the control group was to first select out from all Medicare patients in a given region those who “belong” to doctors in ACOs. They determined “belongingness” by assessing where patients generated a plurality of their primary care visits. All patients who didn’t make the cut – who didn’t get assigned to an ACO doctor by the plurality method – got thrown back into the pool of “control” patients.

By definition, then, the comparison group in Nyweide et al.’s simulation consisted of less loyal patients – patients who have less continuity of care than ACO patients. We don’t need to know which way the causality runs – healthier patients lead to greater continuity, or continuity leads to healthier patients – to know that this method of creating a control group only makes accurate risk adjustment more important. Yet despite decades of trying, neither CMS nor anyone else has come up with a risk adjuster that is remotely accurate. The fact that Nyweide et al. reported declines in utilization in every single category of medical service, including primary care, for the ACOs is circumstantial evidence that the ACOs got healthier patients and that Nyweide et al. were unable to adjust their expenditure data accurately. [7]

We cannot conclude, therefore, that the simulations taught CMS or the rest of us anything useful about how to improve the measurement of ACO performance. All we can say with certainty is (a) Nyweide et al. and McWilliams presented no evidence for claiming their method of measuring ACO performance is superior to the method CMS has been using, and (b) they badly misled their readers by claiming the positive results their simulated ACOs achieved should be viewed as results of the real-world ACOs.

[1] Here is how Ben Umansky describes an example of CMS subterfuge :

CMS emphasizes [in its August 2015 press release] the $806 million in savings generated by the 92 MSSP ACOs that qualified for a shared savings payment [in 2014]. It also acknowledges that 89 other ACOs held expenditures below their benchmarks, but not by enough to qualify for a shared savings payment. It makes no mention of the 152 ACOs whose expenditures were above benchmark. Fortunately, CMS has released ACO-level data that make it possible to reconstruct the full picture. As a complete group, the 333 MSSP ACOs kept spending only $291 million below benchmark—a cost savings to Medicare, yes, but one smaller than the $341 million in shared savings payments made to the 92 top performers.

[2] In this footnote, I describe my sources for the savings and losses of the Pioneer and MSSP programs listed in the text. I have calculated the yearly results for both programs using CMS’s data, but rather than list myself as the source for those figures, I thought it would add to the credibility of my figures if I listed other sources.

Readers can find the 2012 and 2013 results for both programs at p. 4 of an April 2015 CMS document .

Readers can find the total savings and losses and total spending in dollar terms for 2014 for  the MSSP program in a blog comment  by David Introcaso and Gregory Berger, and total savings and losses for both programs for 2014 in the comment by Umanksy I referred to in footnote 1. I calculated total benchmark spending for 2014 for the Pioneer ACOs from CMS data and divided Umansky’s net dollar savings into total spending.

Readers may find the 2015 figures for both programs in table 2 p. 3 in this report  from MedPAC, and at pp. 7-8 of the transcript of the morning session of MedPAC ‘s October 6, 2016 meeting.

[3] The Physician Group Practice (PGP) Demonstration was the first test of the ACO concept conducted by CMS. Ten carefully selected PGPs tried to lower Medicare spending over a five-year period (2005 to 2010)  and , as a group, failed. As the final evaluation of the demo put it, “Seven of the ten participants had currently or previously owned a health maintenance organization (HMO).”(p. ES-3) And yet for all their experience wielding managed care tools, the PGPs succeeded only in raising Medicare’s costs by 1.2 percent over the five years. According to the “final report” on the demo, “[T]he demonstration saved Medicare .3 percent of the claims amounts, while performance payments were 1.5 percent of the claims amounts,” for a net loss of 1.2 percent. (p. 64)

For a review of the inconclusive evidence that HMOs save money, see my 2001 Health Affairs article.

[4] These 4.0 and 1.5 percent figures are taken from the editorial by Mark McClellan that accompanied the Nyweide et al. paper.

[5] After characterizing their simulation as an “evaluation” and CMS’s design as a mere “payment formula,” L&M went on to offer this summary of how their simulation differed from CMS’s actual Pioneer program: The primary variances between the payment and evaluation approaches include different (1) baseline populations…; (2) comparison populations….; (3) approaches in trending methods….; and (4) risk-adjustment methods. As such, findings between the financial payment calculations and the evaluation necessarily differ, both at an aggregate level and for individual Pioneer ACOs. (p. 2)

[6] It is possible CMS forced or induced L&M to print the empty statement I quote. A peculiar “disclaimer” on the “acknowledgements” page of the L&M report suggests CMS demanded statements or commitments to methods that L&M was not comfortable with. The acknowledgement seems to say that L&M should not be held responsible for defects in their report caused by “constraints” imposed upon them by CMS.

[7] I’m not the only one who has noticed the fact that Nyweide et al.’s method of creating a control group aggravates patient differences. L&M noted the problem in its second-year evaluation in a section discussing patient “satisfaction” surveys: “[I]t is possible that these CAHPS [Consumer Assessment of Healthcare Providers and Systems] results are confounded, given that beneficiaries are aligned or assigned to an ACO because they receive regular care from ACO providers.” (pp. 31-32)

The Nyweide et al. paper contains evidence suggesting that this problem is real and not fixable. Nyweide et al. reported that the ACOs reduced utilization in every category of medical service, including primary care. Not only that, they did it in the space of just two years! Utiliization had to go up somewhere, presumably in the primary care category, in order for utilization to fall elsewhere. The Medicare ACOs are literally built up from a base of primary care visits, for Pete’s sake! Why did primary care visits fall along with utilization of all other types of services? The most obvious explanation for the failure of utilization of at least some categories of medical care to rise, and for the immediate positive impact of ACOs, is that the ACOs were assigned healthier and wealthier patients on Day One and Nyweide et al. were unable to correct accurately for that fact.

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Should Doctors Think?

By HANS DUEVEFELT, MD

It has been suggested that to improve quality in healthcare we must reduce variability in how diseases are diagnosed and treated.

It has been inferred that clinical outcomes would improve exponentially if doctors would only follow established guidelines instead of their own whims.

I take that to mean if doctors didn’t think for themselves so much, the health of our nation would be better. I take that to mean that we may be overqualified for the simple work of delivering “evidence based care”.

That is the fantasy of the non-clinician creators of our new medical world order.

Doctors spend all these years learning biology, biochemistry and physics. We learn about anatomy, physiology and pathology. Eventually we study diseases. Then we learn how to practice what we were taught. Finally, more than a decade after we started, do we earn the right to practice independently, only to become the obedient instruments of a healthcare system that demands conformity and disciplines those who put their training to use by questioning politically motivated health policies and overly simplistic clinical guidelines.  

Let me count the ways…

Let us look at how simple it is to deliver evidence based care for, say, a healthy 65 year old woman, new to Medicare:

If I personally had met this woman at the beginning of my career, I would have been under pressure from many authorities to get her started on postmenopausal estrogen if she wasn’t already on it. It was only 15 short years ago that the Women’s Health Study stopped their estrogen trial because of finding out about breast cancer and cardiovascular risk increases. The experts and the guidelines had told us her risk for heart attack would decrease; a complete reversal of marching orders.

But now we know better.

The highly promoted “Welcome to Medicare” visit tells us to perform a baseline EKG. The US Public Health Service Taskforce on Prevention (USPSTF) tells us that’s a waste of time.

Medicare tells us to screen her for cognitive impairment. The USPSTF tells us not to.

Medicare requires us to do a simple vision screen. The USPSTF tells us not to.

All primary care practices now ask screening questions about illicit drug use. The USPSTF tells us not to.

The USPSTF and the American Cancer Society tell us not to suggest a clinical breast exam or Self Breast Examinations. The National Comprehensive Cancer Network tells us to recommend both. Our sample patient should have mammograms biannually according to USPSTF and the Cancer Society, annually according to the NCCN, the Mayo Clinic and many others.

Our sample patient has a blood pressure of 138/86. Our current guideline, JNC 8, says she’s okay, CMS declares her prehypertensive and the SPRINT study suggests starting antihypertensive medication for people with her blood pressure if they have additional risk factors, but they didn’t study people exactly like her, so we don’t quite know what the best course of action is.

If she happens to have had two fasting blood sugars between 115 and 125, she is only a prediabetic in this country, but a full blown diabetic across the border in Canada.

Such a precise science, medicine! Just follow the guidelines…

So what’s a primary care doctor to do for this particular female patient – who isn’t even sick? This ought to be a pretty straightforward scenario. It would be different if she came to us with multiple medical problems.

Just think.

(Really!)

Hans Duevefelt, MD is a Swedish-American physician. He practices in Maine.

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Headlines we expect to see in 2017

By BILL REID

Every year, around this time, we are inundated with healthcare industry predictions. Most of these seem to be more retrospective than forward thinking – taking what seem to be fairly obvious trends and simply saying “Finally, this year will be the year that [fill in the blank] happens!”  Well, here are my predicted headlines for 2017.

• Healthcare Organization Wakes Up In Strange Place, Reports Massive Headache

A Healthcare Organization reportedly just woke up this morning in a stranger’s apartment, with a massive hangover. Note on pillow says, “Thanks for a great night, big spender. I haven’t had so much fun in a long time. Had to go run a few API errands, but feel free to stay as long as you’d like. Oh, it looked like you may have overdone it – aspirin in the bathroom. Love, EMR xxoo. ”

• Healthcare Interoperability Finally A Reality

Today Epic announced that it had finally penetrated 100 percent of the healthcare market and therefore interoperability was no longer an issue. The final CIO holdout was quoted saying, “We decided that we could no longer resist the movement. We give up.”

• Foolproof Security Strategy Unveiled: Don’t Click on $h!t

After years of investment and study, one Chief Information Security Officer seems to have found the cure to all information security problems plaguing hospitals. “After careful observation, we noticed a common pattern among our users: they click on links sent to them in email. Once we told them to stop clicking on them. As a result, we noticed our ransomware problem begin to clear up. Sure, our employees don’t get to find out if they’ve won a vacation package, or if they can help out a Nigerian princess, but we are safer.”

• Affordable Care Act Files for Divorce, Claims Infidelity

Today, ACA (better known to her friends as Obamacare), filed for divorce from the U.S. healthcare system, claiming marital infidelity. ACA was overheard yelling, “I thought you wanted me. You told me that I was beautiful, that we were perfect for each other, that you were committed. But, then I find you in bed with a new Congress. What am I supposed to think? We had plans!” Healthcare system, when asked to comment, said, “We just moved too fast. It turns out I wasn’t ready. It turns out that I still like to mess around with Fee for Service. I am not ready to make a value based commitment.” What is unclear at this point is who will get custody of the covered lives and how they’ll divide up the physicians.

• Family Physician Found Dead in Pool of Alphabet Soup

Yesterday, Dr. Smith reportedly drowned in a large pool of alphabet soup. His practice manager stated, “There were just too many letters floating around. It was so confusing. Dr. Smith said he felt like he was wasn’t able to keep his head above it all. We tried to throw him new payment plans, but those only pulled him further under. We knew we were running out of time, and that the consultants were on their way, but he just couldn’t keep treading water.”

• Consumer-Driven Healthcare Recalled, Engagement Engine Too Weak

In breaking news, there has been a massive recall of consumer-driven healthcare. “We’d start it up and it would sound great, but as soon as we’d tried to put it in gear it’d stall,” said one hospital administrator. Said one patient, “I thought I would get to drive, but it turns out that there is no steering wheel. I only get to play with the radio. It looked good on the lot, but it really is a lemon.”

• Orthopedic Units Claim They Have No Legs To Stand On

Early this year, 800 hospitals seem to have come down with the rare CJR virus. This rapidly moving disease hits hips and knees hard and lasts for 90 days. Said one orthopedist, “We just have no way to control it. It seems too highly-variable. Some get to go home and stay home for the full recovery, but too many are getting discharged to skilled nursing facilities or finding themselves back in the hospital.” Sources report that there are new strains emerging that seem to affect the heart and other systems.

• Roto-Rooter Finds Big Data Clogging Local Hospital Systems

After months of mystery, it seems that that there is a massive Big Data clog affecting the local hospital. Common spreadsheets were not able to clear up the issue, and even a SQL snake was ineffective at cutting through. “We finally had to call the pros. We accumulated all this data and were not sure what to do with it. It kept building up. All our standard tools were not working and panic set in. We knew we needed to identify patient cohorts for risk management soon, but were really struggling to clear the clog,” said Roto-Rooter. “Yes, we have seen this a lot lately as hospitals slowly begin to gather data, thinking their systems can handle it. But before they know it, it’s coming out too fast and with too much volume. We get called, come in with MatLab and other tools and are soon able to clear out the mess. For the future, we recommend installing some new storage and getting some data science skills in house to prevent this from happening again.”

William Reid is the SVP and CISO of SCI Solutions. He’ll be back soon with a more serious take on the issues underlying these Fake News stories.

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Digital Health, Health Reform & the Underserved – Where Will 2017 Lead?

by LYGEIA RICCIARDI

In these first days of the Trump Administration, there is a great deal of uncertainty, but it’s clear that healthcare will remain in the spotlight. Repealing and replacing “Obamacare” is still at the top of the Republican party’s—and President Trump’s—agenda.

Congress and Trump have already taken steps to repeal the Affordable Care Act (ACA), though a replacement for it has yet to be articulated. Trump promises “insurance for everybody” in a form that is “much less expensive and much better,” but has yet to reveal details about how to meet his goals.

While changes in healthcare policy will have ramifications for all Americans, members of underserved populations are likely to be disproportionately impacted because they are statistically less healthy  and are also the least likely to have health insurance coverage. Parts of the ACA address Medicaid, which provides health insurance to 70 million people—by definition among the poorest Americans. Nine million whites make up the largest racial group of people who have gained coverage as a direct result of the ACA, but significant numbers of minorities, including 3 million African Americans and 4 million Hispanics, have also gained coverage. The ACA also helps LGBT Americans by forbidding discrimination due to gender or sexual orientation, and by enabling same-sex families to apply for joint healthcare coverage. According to a report issued by the nonpartisan Congressional Budget Office on January 17^th, if the ACA were to be rolled back without a replacement, 18 million people would lose health insurance in the first year. There would also be significant restrictions in reproductive health services for women.

Thousands of Americans have been participating in rallies across the country  urging Congress not to dismantle the law, and support for it has recently reached record levels according to a poll by NBC News:

Maintaining the ACA was also a consistent theme highlighted in the women’s marches in Washington DC and cities and countries around the world the day after the presidential inauguration.

By contrast, however, many Trump supporters—including some members of underserved and minority groups—believe that Obama and the Democratic party have left them and their needs behind in many domains, including healthcare. From their perspective, a lack of jobs, high crime rates, and other factors resulting from failures in leadership have contributed to economic hardship and growing epidemics of poor health. As Trump said on the campaign trail, “To those African-Americans and Latinos suffering in our country, I say very simply, ‘What in the hell do you have to lose? Vote for Donald Trump. I am going to fix it.’”

If Trump makes good on his promises, including repealing the ACA, some believe Americans would be much better off overall, particularly since the ACA itself is, by some measures, part of the problem. For example healthcare exchanges in Arizona which were set up in response to the ACA left people in most of the state with little or no choice among healthcare insurance providers and steeply rising healthcare premiums.

Digital Health and the Underserved 

Against this backdrop of political uncertainty, how should we think about the relevance of digital health for underserved communities? In my opinion, particularly if underserved populations are at risk of losing health insurance coverage and related benefits, whether in the short or long term, it’s more important than ever to leverage digital tools to maximize scarce healthcare resources.

In addition, some of the same digital infrastructure and tools that can improve health are also critical to healing and strengthening democracy—Internet access, mobile phones, data analytics that help us understand and avert or address problems, and participation in virtual communities.  It’s important to invite members of diverse (and particularly underrepresented) groups to a broader forum to share their perspectives on healthcare and other topics so we can begin to close the acrimonious rifts that have been tearing the country apart throughout the election process.

Though we don’t know the details of policies under the Trump Administration, Republicans have traditionally favored open market solutions that put greater economic responsibility in the hands of states (as opposed to the federal government) and individuals. In healthcare, this is likely to translate into block grants to states to pay for healthcare services, and greater consumer responsibility for choosing among insurance providers and direct payment for healthcare goods and services. Assuming this general trend, people will need more access to data and support in making sense of it to make informed decisions.  This is critically important and challenging given low literacy levels, especially among members of underserved populations—nearly half of Americans have trouble understanding and using health information, a fact which already results in an estimated $230 billion/year in healthcare costs.

Last year I wrote a blog series published by HIMSS on Leveraging Digital Strategies to Address Health Disparities, which covered the following topics based on research and conversations with front line experts:

• Measure What You Want to Manage – on defining disparities, and the role of data and population health
• Navigate the Digital Divide (it’s more complex than you may realize!) – on technology adoption and use by particular population groups
• Design for Your Population’s Needs – on the particular requirements of elderly and disabled populations
• Speak The Right Language on literacy and cultural sensitivity
• Engage the Community on trust and community collaboration

Regardless of the political climate, the topics and approaches explored in that series apply, I believe. I learned from people on the front lines of using digital technologies to address health disparities, and their insights are as valid as ever. Now, a year later, I hope we can constructively explore and learn more together in a more interactive format, even as the political stakes and related emotions run high. Please join the Twitter chat on digital health, health reform, and the underserved on January 31 at 8:30 PM Eastern at #hcldr. Following are the topics we’ll cover:

Chat Topics 

T1  To what extent do you think an ACA “repeal and replace” is likely to impact underserved populations specifically?

T2 What are the areas of greatest opportunity to use digital health to benefit underserved populations?

T3 How do we best avoid deepening the “digital divide” in healthcare?

T4  What are some real life examples of effective uses of digital health involving underserved populations?

Relevant Resources 

Throughout the year, John Sharp, Senior Manager, Consumer Health IT at the Personal Connected Health Alliance/HIMSS and I have kept our eyes out for news on multiple angles of the topic of digital health and the underserved. Here are a few we’d like to flag for you:

• Right Place, Right Time: Consumer Perspectives  (Jan 17, 2017) Report by the Altarum Institute and Oliver Wyman on consumers’ use of healthcare information, with an emphasis on underserved populations
• Fighting the Injustice of Health Disparities: Honoring the Legacies of Dr. Martin Luther King Jr. & Dr. John M. Eisenberg  (January 16, 2017) Blog post and citation of a speech posted on the Disruptive Women in Healthcare blog by Robin Strongin
• Using Genetic Technologies to Reduce, Rather Than Widen, Health Disparities ] (August, 2016) Article by Caren E. Smith et al in Health Affairs about genetics, research, and health disparities

• This Group Gives Unused Fitness Trackers Second Lives (August 16, 2016) Article in Boston Magazine by Jamie Ducharrme on Recycle Health, which collects wearables and donates them to research studies focused on underserved populations
• Can a Digital Health Startup Prevent Diabetes in Medicaid Patients?  (July 29, 2016) Stat News article by Andrew Joseph on Omada Health and its diabetes research
• Hispanics and Mobile Access to the Internet (July 20, 2016) Research by Anna Brown et al on mobile adoption by Hispanic Americans

• Primary Care Implementation of After-Visit Summaries for Patients with Limited Health Literacy (2016) Paper by Courtney Lyles, PhD et al commissioned by the National Academies of Sciences, Engineering, and Medicine Research on how to improve legibility of after visit summaries for low literacy populations

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And the Democrats Wonder Why They Lost the Election?

By ALLISON BOWLES

Now I have insurance. But I can’t use it. What am I supposed to do? I know this one is long but it’s worth a read if you want to understand issues pertinent to the Affordable Care Act. My personal story illustrates many of the problems with the ACA.

I started taking notes on the Health and Human Services Secretary hearing, and I will share more as I scrutinize the hearing in more detail but let’s start with the breakdowns below and my experience with Obamacare (aka The Affordable Care Act or the ACA). Here goes:

These are the breakdowns of who gets what coverage in the United States:

Medicare 18% – 52m

Employer 61% – 178m

Medicaid 22% – 62m

Individual 6% – 18m (exchanges cover 4% of the 6%–these are the people who have been forced onto the Obamacare plans)

Note: this writer is in the BOTTOM of the barrel here (Individual). Most of the individuals in the “Individual” category are either the upper contingent of the working poor, those who work for small businesses like restaurants or family owned grocery stores and the like that don’t provide health insurance benefits (more and more common these days), and/or sole proprietors like myself. Many health care providers are self employed hence we have been forced into the Obamacare exchanges if we are not high earners. High earners won’t buy on the marketplace and will purchase individual plans outside of the marketplace.

I find it interesting to note that the 22% who pay little into the system get better coverage than the 6% who actually work and pay taxes into the programs that support their health while much less is paid into the healthcare for those who work and earn between adjusted gross incomes of $23,540K (yes, exactly) and $45K per year. Adjusted gross income versus gross income are pertinent here but not necessary to go into detail about this at this point.

I have no objection to the poor being covered by Medicaid. However, I do find it OUTRAGEOUS that the United States is paying less for healthcare costs for those individuals who are actually working and paying into the system and making every attempt to rise above their circumstances. This seems to be shortsighted — at the least — as workers need to be kept healthy otherwise they wind up in the category of non-workers who will wind up on Medicaid anyway (or dead and then voila! They won’t cost the taxpayer a dime).

Before Obamacare came into existence, I had coverage through a New York State program (thankfully) that assisted sole proprietors. I paid for that program and at that time, I paid much more than anyone who had an employer-subsidized plan (a $400-$500 monthly premium was unheard of for an employer-subsidized plan but not for an individual plan; most of my clients on employer-subsidized plans did not pay anywhere near this amount for their plans monthly, at least not directly). I had Oxford Health Insurance and a decent network of doctors that I could visit a ten minute walk from my home through New York Presbyterian Hospital at 168th Street & Broadway (I live off of 181st Street). 

My benefits were more limited than employer-subsidized plans (for example, I did not get outpatient physical therapy, mental health, or post-surgical inpatient rehabilitation coverage). Without inpatient rehabilitation coverage, I was screwed when I had lumbar spinal fusion surgery (L4&L5 discs) in April of 2013. 

I was essentially partially crippled by an “equipment failure” and thus endured two surgeries within 48 hours. I was in bad shape and was recommended from two to four weeks of inpatient rehabilitation by the doctors. Because my insurance did not cover inpatient rehabilitation, I was sent home barely able to function. I managed for three years with little if no support from family (which is usually what socialized medicine counts on to plug in the gaps caused by nursing and other staff shortages). If one is alone and with no family support, one is essentially SOL.

I went to my first medical followup appointment alone. I could barely walk or stand. The doctor’s office found me on a bench across from the reception desk nearly passed out. That was my first visit to the doctor following major spine surgery — no help, no aid, no support. 

Then Obamacare came into being approximately 8 months later and my life became a shit-show. I lost every single one of my doctors whilst in the middle of this healthcare crisis. I still could not walk more than a quarter mile, I could not even drive my car, and I’d had to shut down my midtown office due to the fact that I couldn’t physically get there. I was unable to use buses and subways since I could not manage to go up and down stairs.

I had supported Obama’s vision of providing health insurance for all notwithstanding my doubts about government sponsored healthcare. However, had I known that I would be lied to (told I wouldn’t lose my doctors), been provided with the skinniest network of doctors imaginable, and that my local Democratic party representatives would not lift a finger to help me when I could not obtain care, I NEVER would have supported this plan.

I have known people who have compared my situation to their purchase of so-called COBRA plans (those are plans negotiated by employers with much larger networks than Obamacare plans). THERE IS NO COMPARISON BETWEEN AN OBAMACARE PLAN AND AN EMPLOYER-SPONSORED HEALTH PLAN. None whatsoever. 

First of all, the network of providers in Obamacare plans are — and I’m guestimating here but don’t think I’m far off — somewhere between 10-20% of the size of the network of employer-sponsored plans. Not only did I lose ALL of my doctors, the ten minute walk to my previous medical doctors’ offices became 45 minutes to 1-1/2 hours of travel to get to whatever new providers I was able to find.

I have yet to replace all of the doctors I lost. When you rip the rug out from under a sick individual by taking away all of her doctors, she is not physically capable of replacing those doctors (particularly when travel to those new doctors’ offices is near impossible and is time-consuming). Yesterday, I traveled 1-1/4 hours to get to my endocrinologist’s office from my home in Washington Heights to 59th and Park Avenue. Theoretically that trip should have taken less time however trains were slow yesterday and so a minimum of a 45 minute trip took half an hour longer. 

How would I have been able to make this trip when I couldn’t walk? I couldn’t get to the midtown area in New York City for at least ONE YEAR. How was I to manage with no support at all? 

Now, this is where left-wing Democrats start explaining to me that some people will “fall through the cracks.” The people who say this aren’t usually the people who do the falling so they are more than content to watch others fall. 

It was egregious enough that the President I voted for betrayed me by lying to the public about the costs of these plans (my premium costs were $600 per month with the government subsidy covering roughly a quarter of that cost; my copays and medication costs were anywhere between $100-$200 per month). I met my out-of-pocket limit of $2000 which means I spent an additional $167 per month for my medical costs. This adds up to $767 per month – roughly a $150 subsidy for a network that initially didn’t even exist because BCBS had not even completed its contracts with providers, and then when it did exist, it was so thin to be practically negligible.

The only providers in my neighborhood in Washington Heights that take Obamacare policies are doctors you would not send your children to much less yourself.

I was offered “assistance” by a local church whose prayer leader met with me. The assistance she provided was a piece of paper. It was a copy of the New York State of Health’s Official Healthplan Marketplace. She could not understand why, a few months before the Obamacare plans went into effect, I was worried about losing my doctors. She thought I should “wait” until it actually happened to be concerned. This was the “help” offered me by the local Liberal-oriented church. It was more than my local congressman or senator’s office would do.

When I called my local congressman’s office to complain that I could not access care in January of 2014 because I had no insurance identification and the insurance company did NOT ANSWER THEIR PHONES for three weeks (!!!), I was told that they had no power to do anything about a “private” insurance plan. I said, “Private? I bought this policy on a New York State government website! Private? Are you kidding me?” They maintained this position and essentially blew me off.

When I attempted to find information on the internet and essentially just said “I’ve lost my doctors, does anyone have any ideas as to what I should do,” I was viciously attacked by Democrats who assumed that this sick person without any medical care was a closet Conservative attempting to knock the Affordable Care Act. I was accused of presenting a false story in the guise of a Conservative-hater of the ACA, and it was also suggested that I had a mental health disorder. 

And the Democrats wonder why they lost this last election. This is what’s called willful ignorance and I direct my fury at the party that I was loyal to and that betrayed me. The Democrats have betrayed all of us and now we get this racist, misogynistic moron in place of probably the best president we have had in generations. Democrats, he couldn’t do it alone.

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What 32 Million Tweets Tell Us About Health & the Twitterverse

 By YEUNG, MAY and TRUJILLO

How can we gauge whether America is prioritizing health and well-being? Since public attitudes toward health-related topics are widely shared on social media, we gazed into the mirror that is Twitter and tried to answer that question by sifting through 32 million health-related tweets, one of the largest social media samples ever collected for health research.

Posts and conversations on Twitter have the potential to shed light on the public’s views about a seemingly endless array of health-related topics—obesity, exercise and fitness, safe sex, alcohol use, medication adherence and mental health. Accordingly, researchers have turned to social media to better understand these topics.

The set of health-related tweets was built from those originating in the U.S. over the course of one year, 2014. A tweet made its way into the sample if it contained a predetermined health-related keyword such as “well-being,” “fitness” or “medical.” Discussions about acute care and illness slightly edged out those about well-being. Tweets about well-being often seemed like an electronic bulletin board for good behaviors, such as eating right or exercising. Those about illness often mentioned health care services and medical occupations. 

RAND conducted this research in collaboration with the Robert Wood Johnson Foundation, which is promoting and developing a “Culture of Health,” a framework to encourage public engagement in well-being, and partnerships to support innovative health-related initiatives. The aim is to build healthier, more-equitable communities.

In a Culture of Health, public discourse would emphasize health-promotion activities (such as exercise) and well-being (for example, stress management), rather than illness and treatment. To assess public attitudes about these subjects—as reflected in Twitter conversations—we developed a measure to analyze discussion of these topics.

The more often people discussed well-being, the more important we assumed it was to them. For example, these two tweets illustrate that engaging in health-promoting activities seems to improve well-being:

Just completed a 5.16 mi walk – Enjoying this beautiful evening weather nice fitness walk! 🙂

im so proud of myself my doctor said ive been losing weight and that im all healthy and im so happy :)))))

Large-scale events can also shift online conversation. Some of the notable spikes and dips in the ratio of health discussion during this period appear to have been driven by recurring events (such as a New Year’s Day spike in well-being tweets) or major health events (like a spike in acute care tweets during the Ebola outbreak in the United States).

In addition, a fair number of health-related tweets were commercial (such as spam or advertising) and did not reflect the personal perspective of an individual tweeter. However, these commercial tweets still may shed light on other aspects of the social media environment (such as exposure to unhealthy advertising) that may influence health behaviors.

Mining the 2014 tweets for health-related posts and discussions has given us a baseline to understand the evolution of online health discourse going forward. Over time, this baseline information will help test the hypothesis that wellness-related discussion will increase relative to that of acute care, especially in communities or populations where Culture of Health-style interventions or programs are put in place.

Why do we expect this to happen? As well-being and healthy living are given increased emphasis in society, the public should increasingly prioritize wellness and health promotion over illness and treatment.

Social media is still in relative infancy, so developing ways to better understand what people choose to say and share with others online is likely to only grow in importance. Additional analysis of online discourse could seek further insight about community or civic engagement on health-related matters or other topics related to well-being. There also may be opportunities to test and extend well-established findings that people with healthier in-person social networks tend to be healthier themselves.

The ability to find links between social discourse and better health raises the possibility that interventions focusing on online social networks could be an avenue for improving health attitudes and behavior in the future. As a pervasive outlet for personal expression and networking, social media should continue to gain power as a tool for understanding and influencing health as a shared priority.

Douglas Yeung is a behavioral scientist and Linnea Warren May is a project associate at the nonprofit, nonpartisan RAND Corporation. Matthew Trujillo is a program officer at the Robert Wood Johnson Foundation.

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