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    Dr. Dick Johannes

    “We’ve entered an unprecedented period of change in healthcare. New demands on all fronts—clinical, financial and political—are redefining the future of our industry.”

Dr. Dick Johannes

Vice President of Clinical Research, CareFusion

Growing up on a dairy farm in central Wisconsin, Dick Johannes never imagined he’d one day be named chief medical resident at Johns Hopkins Hospital or go on to become an advisor to former president Jimmy Carter and the Carter Foundation. It’s the diversity of healthcare, the changing nature of the industry and the ability to work with patients and practitioners of different backgrounds that continues to inspire him today.

Dr. Johannes is currently vice president of Clinical Research at CareFusion. In addition to his clinical practice, he previously served as a faculty member in gastroenterology and biomedical engineering at The Johns Hopkins University School of Medicine. He has served on several National Quality Forum (NQF) committees related to public reporting of health outcomes and is the current CareFusion representative to the NQF.

Dick earned a bachelor’s degree in chemistry from the University of Wisconsin, a doctor of medicine from The Johns Hopkins University and a master’s degree in computer science from The Johns Hopkins University.

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Dr. Dick Johannes - Vice President of Clinical Research, CareFusion

Today, CMS is creating incentives for hospitals to adopt enhanced healthcare information technology (HIT), introducing new concepts and standards like “meaningful use.” But what is the future for widespread interoperability in healthcare and what will that journey require?

As seen in Surgical Products magazine and TechNation.

Recently, on our way to a morning meeting, Dr. Carlos Nunez and I got to talking about model railroads – a mutual interest and hobby. We recounted the history of Digital Command Control [DCC] – a system that utilizes digital computer technology to operate model railroad trains. DCC was first introduced in the 1990s and dramatically changed model railroad technology: For the first time, when running multiple locomotives on the same track, you could move each train in different directions and at different speeds.

Similar to most technological breakthroughs, numerous manufacturers began engineering their own early and highly proprietary versions of Command Control for model railroads. While this brought the technology to the marketplace, it introduced a new problem for users as these early approaches were incompatible with one another. We could now run trains independently on a model railroad, but couldn’t take a locomotive to a friend’s house with a different system and expect it to run. 

Enter the National Model Railroad Association (NMRA) who assembled a working group to develop a common standard in model train technology. German firm Lenz Electronics gave – yes gave – their previously proprietary protocol to the NMRA to be adopted as a standard1. Suddenly, in order to obtain the NMRA imprimatur, all manufacturers had to conform to single technical standard. Not only did the technology succeed but it literally exploded. It now supports amazing lighting effects, an array of advanced automation capabilities and even digital sound. Best of all, the technology is fully interoperable. Some firms chose not to adopt the common standard and eventually disappeared. But most of those who embraced the NMRA DCC standard continue to thrive today. 

It was at this point in our discussion that Carlos asked, “Where is the NMRA for healthcare?” Great question! Today, CMS is creating incentives for hospitals to adopt enhanced healthcare information technology (HIT), introducing new concepts and standards like “meaningful use.” But what is the future for widespread interoperability in healthcare and what will that journey require?

To me, one of the benefits to interoperability is that it changes the criteria for success from simply having the data to what can be accomplished with the data. It’s about transforming data into actionable information. Consider the following patient data elements: Age, obesity, bed rest, use of hormone replacement therapy or oral contraceptives, planned major surgery, known diagnosis of cancer, history of venous thromboembolism [VTE] and hypercoagulability. This data comes from varied sources – the ambulatory electronic medical record, the laboratory system, the admission diagnoses, the order entry system and the patient’s medication list.

But when you bring them all together, a score can be calculated, and if that score reaches four, a computer algorithm could search the order system for evidence of mechanical or pharmacological prophylactic measures to prevent venous thromboembolism. The absence of such prophylactic orders creates the opportunity for an electronic intervention in the form of a recommendation for VTE prophylaxis.

Several years ago, investigators at the Brigham and Women’s Hospital in Boston2 set out to understand if such an intervention had quantifiable merit. They established a study, assigning patients to either the use of electronic alerts or to the conventional standard of care. The primary end point was deep-vein thrombosis [DVT] or pulmonary embolism [PE] in 90 days. The results were stunning. The algorithm reduced the risk of DVT or PE in 90 days by 41 percent. What’s even more impressive is that the prophylaxis compliance rate in the intervention group was only 33 percent. Of course, some of the cases had clear reasons not to anticoagulate, but that does not explain why mechanical measures were not employed. Better data always opens the door of opportunity, but that door will often have surprising new doors behind it. 

This year’s annual HIMSS conference continued to prioritize the importance of interoperability in the meaningful use era but at the same time balanced the magnitude of achieving interoperability as a challenge that faces the healthcare industry overall.  There’s no doubt that interoperability is a monumental goal for healthcare3,4,5.  But, how is it going to happen; who’s going to lead the way, and when?

 

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References:

1)      Polesgrove M,DCC Projects and Applications. Kalmbach Publishing, 2006.

2)      Kucher N, Koo S, Quiroz R, Cooper JM, Paterno MD, Soukonnikov B, Goldhaber SZ. Electronic alerts to prevent venous thromboembolism among hospitalized patients. N Engl J Med. 2005 Mar 10;352(10):969-77.

3)      James B. E-health: steps on the road to interoperability. Health Aff (Millwood). 2005 Jan-Jun;Suppl Web Exclusives:W5-26-W5-30.

4)      Jha AK, DesRoches CM, Campbell EG, Donelan K, Rao SR, Ferris TG, Shields A, Rosenbaum S, Blumenthal D. Use of electronic health records in U.S. hospitals. N Engl J Med. 2009 Apr 16;360(16):1628-38.

5)      Kuo MH, Kushniruk AW, Borycki EM, Hsu CY, Lai CL. National strategies for health data interoperability. Stud Health Technol Inform. 2011;164:238-42.

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Dr. Dick Johannes - Vice President of Clinical Research, CareFusion

I recently had an opportunity to hear a presentation by James Orlikoff who spoke on healthcare reform at one of our annual meetings. Mr. Orlikoff is a senior consultant to the Center for Healthcare Governance and the national advisor on governance and leadership to the American Hospital Association and Health Forum. During his talk, he shared a slide titled “What Fueled the Reform Fire,” on which he referenced the “Gawande/McAllen Effect.” For those who aren’t familiar with this concept, the name comes from an op-ed piece published in the June 1, 2009, issue of The New Yorker magazine, written by Atul Gawande, a general surgeon at the Brigham & Women’s Hospital.

I recently had an opportunity to hear a presentation by James Orlikoff who spoke on healthcare reform at one of our annual meetings. Mr. Orlikoff is a senior consultant to the Center for Healthcare Governance and the national advisor on governance and leadership to the American Hospital Association and Health Forum. During his talk, he shared a slide titled “What Fueled the Reform Fire,” on which he referenced the “Gawande/McAllen Effect.” For those who aren’t familiar with this concept, the name comes from an op-ed piece published in the June 1, 2009, issue of The New Yorker magazine, written by Atul Gawande, a general surgeon at the Brigham & Women’s Hospital.  Dr. Gawande is one of the primary movers behind the use of checklists to promote safety in operating rooms, which means this won’t be the last time you’ll see him referenced on this blog.

Dr. Gawande’s article, titled “The Cost Conundrum” encapsulates what I feel is one of the most complex subjects in healthcare today – changing reimbursement mechanisms for U.S. hospitals. His story centers on McAllen, Texas, a border town near the southernmost tip of the state and one of the biggest national spenders on healthcare. Gawande notes:

Only Miami—which has much higher labor and living costs—spends more per person on healthcare. In 2006, Medicare spent fifteen thousand dollars per enrollee [in McAllen], almost twice the national average. The income per capita is twelve thousand dollars. In other words, Medicare spends three thousand dollars more per person here than the average person earns.”    

Well, there’s an attention grabber!  Despite the inflated healthcare costs, McAllen is an economically depressed area. The area is plagued by high unemployment, high rates of alcohol abuse and high rates of obesity – all of which might inflate healthcare costs.  Gawande points out, however, that El Paso County has nearly the same patient demography as Hidalgo County, where McAllen is located, but Medicare payments are far less, roughly $7,500 per enrollee. Further, despite fewer specialists in Hidalgo County, data from 2001-2005 shows that patients were 66 percent more likely to see ten (yes, ten) subspecialists in a six-month period.  They were also more likely to undergo “big ticket” procedures, such as nerve conduction studies, urine-flow studies, gallbladder operations, knee replacements, breast biopsies, echocardiograms, implantable defibrillators, cardiac-bypass operations and coronary stents. 

No one suggests this data represents misuse, just dramatic overuse. Along these lines, Gawande in his study outlined some factors to consider:

  1. While increased access can and often does lead to increased costs, high tech medicine is not synonymous with high Medicare costs. Case in point: Olmsted County, Minn., is home to the Mayo Clinic – one of the most advanced healthcare systems in the country. Olmsted County’s Medicare spend, however, ranks among the lowest 15 percent in the nation.  
  2. Healthcare spending – and variability in spending – is not widely publicized or recognized by healthcare administrators. For example, hospital executives in McAllen were legitimately surprised by these findings in their own hospitals. This data is further complicated by widespread variation in spending and reimbursement models among private insurers.

Let me make a couple of my own observations, as well: 

  1. First, I think many people still have a problem accepting that higher cost does not necessarily imply higher quality when it comes to healthcare. It’s just not how we’re used to seeing the world. Consider your experience buying cars, real estate, clothing or food. The “you get what you pay for” philosophy seems so pervasive but it may not be true when it comes to healthcare services. For those in doubt, have a look at the report by Baicker and Chandra that shows the inverse relationship between Medicare spending and quality measures.
  2. While reading The New Yorker article, I couldn’t help but think that if the healthcare professionals in McAllen had instead been automotive manufactures, their story could have  landed on the  cover of Business Week, if not Time magazine.  Is there some kind of fundamental disconnect related to the economics of healthcare? 

The plans set forth to reform healthcare in the U.S. represent a monumental shift in how we purchase, pay for and consume healthcare – and aim to help right what has become a seriously misaligned system. With the emergence of Accountable Care Organizations (ACOs), bundled payments and performance-based reimbursement through Value Based Purchasing, we’re beginning to witness and test new models of care delivery.  While these initiatives have many desirable characteristics, there is trepidation among service providers. Who can get this right?  Will the new models really work?

As Gawande states, “We can turn to insurers (whether public or private), which have proved repeatedly that they can’t do it. Or we can turn to the local medical communities, which have proved that they can.”  

Systems like Mayo Clinic, Kaiser, Geisinger and Intermountain Health may be important models for the future, but can a culture of “patients first and economics will follow” truly become embraced across the vast plane of the U.S. healthcare system?  If spending rates continue at their current pace, we’ll need to find out and find out fast.

 

 

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Dr. Dick Johannes - Vice President of Clinical Research, CareFusion

Stepping back from healthcare for a moment, let’s conduct a chemistry experiment. Consider what would happen if I were to mix two same-sized beakers of equally concentrated sodium hydroxide and hydrochloric acid 99 times. Each time, I’d get a large beaker of salt water. There is not a sporting chance that when I did it the 100th time, I’d get a beaker of New England clam chowder, (as delicious as that surprise would be.) Because of our deep understanding of this aqueous chemical reaction, all I need do is reliably observe the process of mixing, and I can safely infer the outcome.

Stepping back from healthcare for a moment, let’s conduct a chemistry experiment. Consider what would happen if I were to mix two same-sized beakers of equally concentrated sodium hydroxide and hydrochloric acid 99 times. Each time, I’d get a large beaker of salt water.  There is not a sporting chance that when I did it the 100th time, I’d get a beaker of New England clam chowder, (as delicious as that surprise would be.) Because of our deep understanding of this aqueous chemical reaction, all I need do is reliably observe the process of mixing, and I can safely infer the outcome.

This idea that process predicts performance seems simple enough, but when you try to apply the lessons learned from this experiment to systems as complex as healthcare delivery, it doesn’t quite measure up.

Take, for example, the Surgical Care Improvement Project, a list of 20 measures developed collaboratively by more than 40 organizations and vetted through the National Quality Forum (NQF) with the admirable goal of reducing surgical mortality and morbidity by 25 percent. The Centers for Medicare and Medicaid Services (CMS) publicly report nine of the 20 measures, and three of those nine measures focus on prophylactic antibiotics and measure the process of healthcare delivery as a way of predicting outcomes.

At first glance, this sounds just like the salt water chemistry experiment. But what has actually happened?  Studies are emerging that suggest there is a poor relationship between adherence to the measures and the desired outcomes as it relates to the timing, choice and discontinuation of prophylactic antibiotics [Hawn 2008 2010, Ingraham 2010, Stulberg 2010]. Should we find this surprising?  Maybe not, given that similar stories have also emerged for other CMS-reported measures, namely heart failure [Fonarow 2007], pneumonia [Werner 2006] and Acute Myocardial Infarction [Bradley 2006].

Does this mean such quality efforts only add cost to care and are simply dismal failures that should be scrapped? I, for one, would emphatically answer “no.” First of all, it is hard to argue that adhering to the basic tenets underpinning these measures would not be beneficial.  Most of the principles are directionally correct, even if they didn’t attain accepted levels of significance in later research studies. But, perhaps they did something else in the process: Enabling the healthcare community to discover some very interesting and potentially important new insights, such as that antibiotic selection may be a more important measure than antibiotic timing.

We just don’t understand systems as complex as healthcare delivery with sufficient precision to expect anything as predictable as solution chemistry. The repeated story of heart failure measures, myocardial infarction measures, pneumonia measures and now most recently prophylactic antibiotic measures being only weakly linked to their desired outcomes does cast light onto the limitations of using process as a proxy for outcome in current healthcare measures. It’s an important lesson, especially as CMS places more emphasis on tying hospital reimbursements to quality and reporting.

 

References

  1. Hawn MT. Surgical care improvement: should performance measures have performance measures. JAMA. 2010 Jun 23;303(24):2527-8.
  2. Hawn MT, Itani KM, Gray SH, Vick CC, Henderson W, Houston TK. Association of timely administration of prophylactic antibiotics for major surgical procedures and surgical site infection. J Am Coll Surg. 2008 May;206(5):814-9; discussion 819-21.
  3. Ingraham AM, Cohen ME, Bilimoria KY, Dimick JB, Richards KE, Raval MV, Fleisher LA, Hall BL, Ko CY. Association of surgical care improvement project infection-related process measure compliance with risk-adjusted outcomes: implications for quality measurement. J Am Coll Surg. 2010 Dec;211(6):705-14.
  4. Stulberg JJ, Delaney CP, Neuhauser DV, Aron DC, Fu P, Koroukian SM. Adherence to surgical care improvement project measures and the association with postoperative infections. JAMA. 2010 Jun 23;303(24):2479-85.
  5. Fonarow GC, Abraham WT, Albert NM, Stough WG, Gheorghiade M, Greenberg BH, O’Connor CM, Pieper K, Sun JL, Yancy C, Young JB; OPTIMIZE-HF Investigators and Hospitals. Association between performance measures and clinical outcomes for patients hospitalized with heart failure. JAMA. 2007 Jan 3;297(1):61-70.
  6. Bradley EH, Herrin J, Elbel B, McNamara RL, Magid DJ, Nallamothu BK, Wang Y, Normand SL, Spertus JA, Krumholz HM. Hospital quality for acute myocardial infarction: correlation among process measures and relationship with short-term mortality. JAMA. 2006 Jul 5;296(1):72-8.
  7. Werner RM, Bradlow ET. Relationship between Medicare’s hospital compare performance measures and mortality rates. JAMA. 2006 Dec 13;296(22):2694-702.
  8. Pronovost PJ, Miller M, Wachter RM. The GAAP in quality measurement and reporting. JAMA. 2007 Oct 17;298(15):1800-2.
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