The Top Ten Advances in the Reorganization of Health Care

Let the breakthroughs begin!

Ask a typical physician to describe some of the most important advances in the history of modern medicine, and her list will likely include vaccines, anesthesia, antisepsis, antibiotics, imaging, obstetrical safety, contraception, early intervention trauma care, the fight against tobacco, heart-lung bypass, transplantation, and endoscopic surgery.  The public health-minded may also include clean water, food safety, motor vehicle safety and public water fluoridation.

Many of these successes caught the imagination of patients and doctors alike, which partially accounts for our collective societal hunger for more “big bang” medical advances. It's therefore no wonder that there was early credit for the "blank shots" of genomics, the electronic record and evidence-based medicine.
  
The Disease Management Care Blog doesn’t doubt that some breakthroughs (novel anti-cancer chemotherapies, the extension of human life-spans or a cure for spousal-induced DMCB deafness) are waiting just around the corner. These surprises are probably lurking outside the academic-media spotlight involving underfunded and contrarian scientists.  They will come from unexpected directions.

Yet, that doesn’t mean that there haven’t been momentous changes in health care. There have been and, what's more, they've escaped the attention of the medical-industrial complex.  That's because they have more to do with how health care delivery, thanks to the twin forces of the internet and consumerism, is being radically reorganized. 

Think of it as the Decade of Healthcare Redesign.  Ten elements of this Redesign include:

1. Downjobbing: many tasks that were restricted to highly trained specialists are increasingly being performed by non-physicians, patients and technology.

2. Social Media: patients can not only access the internet for information, they can use the internet to pool input and solicit personalized advice from like-minded individuals

3. Democratized Artificial Intelligence: In addition to social media, we’re on the verge of being able to remotely access AI to generate a reasonably accurate list of diagnoses, suggested tests and recommended do-it-yourself treatments that include the option of doing nothing.

4. The Decline of the Credential: while the academic-industrial complex will continue to churn out superbly trained physicians, massive on-line education will enable persons to gain a surprising level of lay-expertise.

5. Predictive Data Mining with Risk Stratification: we can’t afford to treat every person the same way. Analytics are already enabling individuals to understand which of their individual risk factors have the best ratio of actionability and pay-off.

6. Big Data and Concurrent Research: Going from analog/unique to digital/tabulated means that we can pool data and find correlations that lead to medical insight as fast as the fastest server and processor can crank the numbers.

7. Team-Based Care: Its impact at the bedside, in the operating room and in the primary care clinic is greater than the sum of its parts.

8. Remote Robotic Surgery: think of the World’s Best Surgeon being able to use ultra-fast satellite communication technology to wield surgical instruments from the other side of the country. Just so long as there’s in-person back-up near-by, wouldn’t you want that?

9. Medical Tourism: As the rest of the globe imports the best that western medicine has to offer minus the United States’ overhead costs, the cost of overseas air travel is no longer be an impediment to patients or insurers.

10 Big Government: For better or worse, Washington DC has its nose under the health care tent. In the unlikely event that Obamacare gets repealed, top-down diktats will forever be a part of the landscape.  Get used to it.

A Thursday Three-fer: Diabetes Predictive Modeling, The Threat of Ambulatory Care Write Offs and It's the National Debt, Stupid!

At Risk?

Diabetes Predictive Modeling: Evidence Based, Peer Reviewed and Open Domain:

As Accountable Care Organizations, Patient Centered Medical Homes, care management vendors and managed care organizations continue to grapple with health care costs, they want to know who is at greatest risk in the coming months.  When it comes to diabetes mellitus, John McAna and colleagues (one of whom is the Disease Management Care Blog) is riding to the rescue with their American Journal of Managed Care paper "A Predictive Model of Hospitalization Risk Among Disabled Medicaid Enrollees." 

While the data were based on two states' Medicaid claims data sets, the research may be generalizable to other populations.  Factors that most strongly predicted a future hospitalization were increasing age (especially more than 65 years), a prior pattern of repeated hospitalizations (especially 3 or more) and the Charlson Comorbidity Index. The good news is that all the independent variables and their odds ratios are not-only evidence based, they're available for use by your actuaries and statisticians as quick as you can download the paper (after signing in) at the bottom of page 4.

Rumored Ambulatory Care Write-Offs: An Achilles Heel of Integrated Delivery Systems and ACOs?

In its recent travels, the DMCB was informed by two credible and astute physician-leaders that hospitals that have recently acquired outpatient physician practices are typically "writing off" ambulatory care bills because a) contesting small fee disputes are relatively costly and b) the threat of Medicare "overcharge" or RAC audits is existential.  That's significant because those small charges add up into millions and can mean the difference between a profitable outpatient clinic system and a loss leader.

It's Not the Economy, It's Not the GDP, It's the National Debt, Stupid:

The DMCB also recalls repeatedly hearing that it was President Nixon who first called attention to the growing fraction of the nation's gross domestic product going toward health care. The problem was that no one knew what was the "right" percent of GDP.  Mr. Nixon thought 7% was too high. If 7% isn't, in retrospect, bad, why is the current level of about 18% so bad?  What's so different?

The answer: it really is different this time.  What's bad is that health care is responsible for the lion's share of the separate problem of the growing national debt, which has been directly linked to national security.  Yikes.

Big Data and the Coming New Value Proposition for Disease, Care and Wellness Management Providers

Disease Management Care Blog readers know that the its latest interest is "Big Data." While the researcher-DMCB has played in the sandbox of some insurance claims data sets, the idea of combining and combing through multiple terrabytes of clinical and public data remains a topic of endless fascination. It knows it's not alone.

So, it was only a matter of time until one of the major clinical journals published an article on the topic. JAMA has stepped forward, and not a moment too soon.

It's "must reading" for the disease and care management provider community.

Drs. Murdoch and Detsky point out that Big Data offers four value propositions:

1. Observational correlations may generate insights that cannot be found using standard research approaches. Scanning text for key words in electronic record systems involving hundreds of thousands of patients may find associations or trigger early warnings faster, quicker and cheaper than any formal scientific protocol or clinical trial.

2. Those insights, especially since they can be tailored to fit the circumstances of an otherwise unique patient, can be used to guide diagnosis or treatment. Physician judgement cannot be replaced, but if Big Data points out that there were other patients with a similar pattern of illness who responded best to one treatment versus another, patient outcomes could improve.

3. A Big Data approach to genomics can correlate genetic information with outcomes and further guide therapy. While the DMCB still wonders if "genomics," outside some narrow anecdotes, will always remain the science of the future, Big Data may turn out to be the key to finally unlocking its potential.

4. Since Big Data, by its very nature, can combine clinical information to other personal data (the foods you've bought or your driving history), Big Data will necessarily tilt toward the patient-consumer and away from the health care system. Not only does permission for access lie with the patient, but the insights will be less about sickness and more about wellness.

The authors do a good job of pointing out that there are plenty of challenges. Most doctors don't get it, privacy laws could be over-interpreted or enforced, it remains to be seen who will pay for it and Big Data is still in its infancy.  The DMCB also points out that while Medicare has just discovered that alternative research innovations are possible, Big Data promises to eclipse those approaches (like traditional time series analysis, propensity matching), again making CMS a day late and another dollar over budget.

The implications for the care management and population health community are considerable. The industry has amassed years of intellectual capital in the science of predictive modeling and Big Data is it's next step. Many care management vendors have multiple clinical partners and already have access to terrabytes of data involving millions of persons. Not only is the math and the informatics well within reach, they also "get" the tilt toward wellness and consumer empowerment. Last but not least, if anyone can monetize a value proposition like this and turn insights into revenue (or "shared savings"), these nimble vendors can.

A DMCB prediction: while academics will write about Big Data in scientific journals, the care management industry will be doing it.  In fact, they probably already are.

Two particularly good quotes to use to impress your CEO and stymie your competitors:

"Data has gone from refuse to riches."

and

Economic theory describes the quantitative conversion of 3 kinds of inputs (capital, labor, and raw materials) into outputs (goods and services)...The current revolution in data management makes it clear that a fourth kind of input, information, will become just as important as these other inputs in the future of many industries.

A Definition of "Big Data" for Health Care Providers and Five Useful Caveats

And you thought its only
function was to be an EHR?

Regular readers of the Wall Street Journal probably saw the Monday March 11 "big data" section that was filled with articles like this.

Written from a "business intelligence" perspective, there were precious few insights for the population and care management community. We're aware of the concept, but how, asks the Disease Management Care Blog, does it apply to our corner of the health care delivery system?

Unable to resist, the DMCB donned its snorkle and flippers and took a deep dive at the topic.

First off, when the DMCB performed a classic medical literature search using the key words "big data," it found that that the term has not entered the health care lexicon in a big way. Academics instead prefer to write about "registries," "data warehousing" and "predictive modeling." The DMCB also looked for a standard health care definition of "big data" and could find none in the published medical literature.

So, the DMCB offers up its own definition, culled from papers like this and this:

Health care "big data" is a branch of health care informatics that pools large and disparate data sets and applies a suite of mathematical approaches that derives associations, facilitates comparisons and generates insights that are otherwise not possible using standard mono-source analytics. It includes, but is not limited to, reporting, dashboards, ad-hoc queries, graphical displays, scorecards, predictive modeling, data mining and business intelligence. The data sets can be comprised of EHR data, insurance claims, pharmacy utilization, care management systems, consumer as well as government information, public health, surveys, point-of-contact information and web-usage.

The DMCB's simplistic off-the-cuff examples of big data queries include examining 1) the association between "hits" from a cluster of ISPs on an emergency room's web page and ER utilization, 2) complaints about a hospital's food service from family and the likelihood of being named in a malpractice suit, 3) looking for rare side effects among persons with a cluster of medical diagnoses who are using a just-released drug and 4) whether the number of household flat screens is a useful predictor of obesity.

Five DMCB caveats:

1. One data integrity trumps five Ph.Ds: The chief challenge is not the mathematics but combining and aligning the various databases.  Once the information is teed up, it's amazing how much can be done by a masters-level statistician and a desktop PC.

2. Associations, not causality. Whether a web page leads to ER visits or whether bad food fuels dissatisfaction is a different question.  It's possible that ER visits prompt web usage or that already dissatisfied patients find overcooked string beans icky. All the possibilities are still useful insights.

3. Not a panacea: It's "a" tool, not "the" tool.  Users will still need to also invest in faster, better and cheaper mundane data tasks (like admissions per thousand) while they simultaneously understand how big data's associations, comparisons and insights generate additional patient value.

4. Journey, not destination: There's a potent mix of art, science and wizardry in the evolving science of "big data."  There are no standard methodologies or best practices.  Get used to it.

5. Skepticism abounds: Data stakeholders who are used to standard analytics will refer, as the DMCB found out, to "big data" as "voodoo," and resist buy-in.  If a critical mass of an organization's leadership comes to believe it's useful, the rest will follow... eventually.

Image from Wikipedia

The Risk of (Improper) Risk Adjustment: Why The Doctor May See You Now

The stats guys go to work

The Disease Management Care Blog continues to welcome blog posts from outside authors. This is another one, courtesy of Erik Tollefson, who works in the health policy field. He can be reached at erikDOTmDOTtollefsonATgmailDOTcom

As payers increasingly pursue reimbursement strategies that share risk with health care providers, risk adjustment is emerging as an important topic in health policy research.  Indeed, for providers, the ability to properly identify the underlying health risks of a specific population could mean the difference between earning savings rather than paying penalties.  As the importance of risk adjustment ascends grows, increasing attention is being given to the robustness sophistication of adjustment methodologies. That includes including the data used to estimate the risk of a covered population risk. 

In a recent study published in the British Medical Journal (BMJ), John Wennberg and co-authors explore how under-recognized “patient observation bias” can contaminate risk adjustment results.

Previous studies have identified many pitfalls of using observational data as a proxy of a population’s true disease burden. One common example is the phenomenon of “upcoding.” This practice not only adjusts diagnostic codes to maximize reimbursement, it is a well-documented source of bias that spuriously increases variation between geographical regions and complicates risk adjustment.

Wennberg and colleagues show that patient observation bias is another potential pitfall in the science of risk adjustment that has not received such attention. The phenomenon can be quantified by using numerous proxies such as frequency of visits by physicians and the intensity of diagnostic tests (both laboratory and imaging) ordered. Previous research has shown that geographical areas with higher patient observation bias independently correlate with higher rates of comorbidity after being adjusted for all the other relevant patient factors that underlie true risk.

Using a sample of Medicare claims data, the authors computed two different risk adjustment measures: 1) using standard methods; 2) adjusting for potential observation bias.  Overall, the adjusted method (using visit intensity) emerged as a more accurate predictor of the population’s underlying burden of illness. Thanks to their ability to identify a source of bias that explained a greater portion of variation in important categories such as sex, age, and race, but was the authors were able, compared to the usual risk adjustment methods, to reduce the variation levels between different geographic regions.

The importance of accurate risk adjustment is not just limited to its potential impact on increasingly high financial stakes in the health care sector. The ability of policy makers to understand why variation occurs in health care costs and utilization across geographical areas has emerged as one of the key questions that may ultimately improve delivery and cut costs in a bloated health care system. This study offers important evidence that part of the answer may indeed lay in the supply-inducing doctors self-referring patients for unnecessary diagnostic procedures that littered Atul Gawande’s account of rising health care costs in McAllen, Texas.

Another important take home lesson, however, also applies: the quality of underlying data. With continued histrionics surrounding access to ever greater amounts of health care data, it helps to remember that qualitatively understanding what one is actually measuring is still as important as the accuracy of the underlying statistical methods.

Predictive Modeling: The Second Most Important Ingredient for Provider Accountability

When accountability fails

"Accountability."  Everyone wants it, right?

While it's one thing for health care providers to be "accountable" for costs, it's another for them to actually make money at it.  The Disease Management Care Blog is continuously amazed at how many physicians and administrators believe that dollops of "primary care," "prevention" and "wellness" will empty hospital beds and cause insurance money to appear like the morning dew on a windshield of a physician specialist's BMW. 

Believe that and the DMCB has an ORD-SFO United Airlines upgrade "departure management card" it would like to sell you.

If the DMCB could do only one thing to reduce costs within one fiscal year for a health care system , it wouldn't be PCPs, wellness or prevention. Instead, it would place one vastly overpaid case manager in every emergency room in the network.  His or her job would be to find alternative levels of care for persons that don't really need to be in the hospital and begin discharge planning for those who do.

The second thing the DMCB would do is implement predictive modeling.  That's why it's immodestly tooting its horn and suggesting research like this is so important.  In this web first publication, Chris Hollenbeak, Mark Chirumbole, Benjamin Novinger, Frank Din and your humble DMCB examined the baseline demographic data and medical conditions that can predict the likelihood of a future high cost hospitalization within a Medicaid population.  Armed with knowing which of its patients are at risk ahead of time is a critical level of intelligence for any newly "accountable" hospital, clinic, IPA, PHO or IDN.  By reaching out to high risk individuals prior to any crisis, physicians and administrators can engage these patients in outpatient settings and keep them away from the emergency room.

The good news is that the art and science of predictive modeling is within reach of most data bases, desk-top computers, statistical software packages and trained analysts.  The bad news is that interpreting and executing on that information remains a daunting challenge. 

Health systems like HealthCare Partners is a good example of how it can work well.  As this "accountability" movement in health care evolves, the DMCB will undoubtedly learn about other good examples. 

The systems that rely on vague notions about primary care, fail to understand the role of case management and ignore the business case for predictive modeling will form the bad examples.

Doctors "Versus" Predictive Modeling To Identify Patents At Risk: It's Really Docs AND Predictive Modeling.

Who can find patients for a DM program

One of the major advances in disease and population-based programs for chronic illness has been the discovery that not all persons with a chronic condition will benefit from care management.  Instead of blanketing everyone with education programs, the "secret sauce" is targeting just those patients who have the twin attributes of:

1) being at high future risk of a bad outcome (such as an avoidable hospitalizations or multiple emergency room visits) and

2) having modifiable risk, which is ultimately a function of having the "right" disease severity, receptivity to engagement in self care and social supports which, in turn, respond to outreach.

In their American Journal of Managed Care article, "Identification of Patients Likely to Benefit from Care Management Programs," Tobias Freund et al use the terms "cost intensive" and "care sensitive" to describe the twin attributes above. They set out to examine how they compared in two candidate populations for disease management programs: 1) one that was chosen by a "predictive modeling" program that uses mathematical algorithms to weigh demographic and insurance claims inputs to compute future risk and 2) one that was made up of patients chosen by their physicians.

If this sounds like a TV Jeopardy contest between IBM's "Watson" versus human brainiacs, it shouldn't.  In this study, there was no "gold standard" that could be used to gauge whether computers or docs were better at picking the patients at risk.  Instead, the authors merely compared the two populations.

This study was conducted in Germany, using one of the public and private funded "sickness funds'" claims and demographic data that provided insurance to a population of 6026 patients that were being cared for in 10 southwestern primary care practices.  The predictive model was "Case Smart Suite Germany," which selected the top 90th percentile of patients at risk using data from 2007 and 2008,  while the primary care physicians were simply asked to screen "all" of the sickness fund patients from 2009 and "select up to 30" patients that may benefit from a program aimed at helping them stay out of the hospital.

What the DMCB found interesting about this study was that the predictive model identified 301 patients, while the docs identified 203.  Only 32 patients were selected by both.  The PCP patients were generally younger (mean age of 66 years vs. 75 years), had lower rates of prior hospitalizations in 2007 and 2008 (.53 vs. 2.74), had a lower degree of co-morbid illness (a Charlson Index of 2.1 vs. 4.3) and generally lower rates of chronic conditions like diabetes, COPD and depression (mean number of conditions was 3.0 vs. 4.5).  Last but not least, the physicians were better at picking the "care sensitive" patients that had been previously entered into a previously existing disease management.

The main lesson here?  It's not predictive modeling "versus" physician identification, or trying to decide whether to target a care program based on what your IT Department is saying "or" what the docs are saying, it's both.  The physicians, thanks to their "proximity" to their patients are using yet-to-be-identified heuristics to spot patients that predictive modeling may miss.  

While this study is limited by being outside the U.S. (making it less generalizable) and restricted to a relatively small population, the DMCB thinks the implications are intriguing enough to possibly warrant a repeat study here in the States.  In addition, the disease and population care management community can use this approach and eventually compare how each selected population responds on a prospective and outcomes basis.