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Health in 2 Point 00, Episode 83 | Health 2.0 HIMSS Europe

Today on Health in 2 Point 00, Jess and I are in Helsinki for Health 2.0 HIMSS Europe. In Episode 83, Jess asks me about Roche cheating on mySugr—Roche announced a new partnership with digital diabetes provider GlucoMe, about the new $100 million hospital venture fund in Iowa coming from UnityPoint Health, and about Infermedica’s recent $3.65 million raise for their cool symptom checker complete with an AI chatbot. Stay tuned for more updates from the conference. —Matthew Holt

Young People Need To Turn Out For Their Health

By MERCEDES CARNETHON PhD

This month, we saw historic turnout at the polls for midterm elections with over 114 million ballots cast.  One noteworthy observation regarding voter turnout is record rates of participation by younger voters aged between 18 to 29 years old.  Around 31 percent of people aged 18 to 29 voted in the midterms this year, an increase from 21 percent in 2014, according to a day-after exit poll by Tufts University.

Surely their political engagement counters the criticism that millennials are disengaged and disconnected with society and demonstrates that millennials are fully engaged when issues are relevant to them, their friends, and their families. Why, then, do we not see the same level of passion, engagement and commitment when young adults are asked to consider their health and well-being?

I have had the privilege of being a member of the National Heart, Lung and Blood Institute-funded Coronary Artery Risk Development in Young Adults (CARDIA) study research team. In over 5,000 black and white adults who were initially enrolled when they were 18 to 30 years old and have now been followed for nearly 35 years, we have described the decades-long process by which heart disease develops. We were able to do this because, in the 1980s when these studies began, young adults could be reached at their home telephone numbers. When a university researcher called claiming to be funded by the government, there was a greater degree of trust.

Unfortunately, that openness and that trust has eroded, particularly in younger adults and those who may feel marginalized from our society for any number of valid reasons. However, the results—unanswered phone calls from researchers, no-shows at the research clinic and the absence of an entire group of adults today from research studies, looks like disengagement. Disengagement is a very real public health crisis with consequences that are as dire as any political crisis.
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Livongo Health adds $20m, Tullman interview

Livongo Health is creating a tech-based service that aims to supersede the glucometer. Headed by former Allscripts CEO (and THCB interview regular) Glen Tullman, it raised another $20m from Kleiner Perkins, DFG & General Catalyst today. I grabbed 10 minutes to talk to Glen Tullman this morning. he had very interesting things to say not only about his business but Cerner, Epic & open systems too.
[youtube]https://youtu.be/4w-pHj91PKM[/youtube]

A Deeper Dive into the Rio Grande Valley

Screen Shot 2014-10-04 at 8.22.11 AMLast week, Dr. Bob Kocher and I took to the pages of the New York Times to detail a health care success story in Southern Texas.  In a region once featured for its extreme health care costs and poor health outcomes, a group of physicians motivated by new incentives in the Affordable Care Act has started to change the equation. The Rio Grande Valley ACO Health Providers achieved eye-popping savings in their first year – coming in $20 million below its Medicare baseline and receiving reimbursements totaling over $11 million while also achieving better health outcomes for its patient population.

The savings number made for an impressive headline.

But as is often the case, other information had to be left on the cutting room floor. We dive a little deeper into the RGV ACO below:

The Central Role of Information Technology

Dr. Jose Pena, Chief Medical Director of the Rio Grande Valley ACO, emphasizes that one of the first and most difficult tasks for the newly-formed organization was developing an IT infrastructure that would serve their needs.  “Using what was there wasn’t really an option,” says Dr. Pena, “so we built our own infrastructure.”

Forgoing a single EHR solution, the Rio Grande Valley now operates on a mix of cloud and office-based systems. The ACO developed software to identify metrics from various EHR systems, migrate that information to the cloud, and view real-time performance of providers. “IT accounted for 40% of our costs,” says Dr. Pena, “but the importance of proper reporting – to our leadership team, and to CMS – was at the top of our list.” The ACO identifies its customized IT system as foundational to its success.

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International Classification of Diseases Hampers the Use of Analytics to Improve Health Care

By ANDY ORAM

andy oramThe health care field is in the grip of a standard that drains resources while infusing little back in return. Stuck in a paradigm that was defined in 1893 and never revised with regard for the promise offered by modern information processing, ICD symbolizes many of the fetters that keep the health industries from acting more intelligently and efficiently.

We are not going to escape the morass of ICD any time soon. As the “I” indicates in the title, the standard is an international one and the pace of change moves too slowly to be clocked.

In a period when hospitals are gasping to keep their heads above the surface of the water and need to invest in such improvements as analytics and standardized data exchange, the government has weighed them down with costs reaching hundreds of thousands of dollars, even millions just to upgrade from version 9 to 10 of ICD. An absurd appeal to Congress pushed the deadline back another year, penalizing the many institutions that had faithfully made the investment. But the problems of ICD will not be fixed by version 10, nor by version 11–they are fundamental to the committee’s disregard for the information needs of health institutions.

Disease is a multi-faceted and somewhat subjective topic. Among the aspects the health care providers must consider are these:

  • Disease may take years to pin down. At each visit, a person may be entering the doctor’s office with multiple competing diagnoses. Furthermore, each encounter may shift the balance of probability toward some diagnoses and away from others.
  • Disease evolves, sometimes in predictable ways. For instance, Parkinson’s and multiple sclerosis lead to various motor and speech problems that change over the decades.
  • Diseases are interrelated. For instance, obesity may be a factor in such different complaints as Type 2 diabetes and knee pain.

All these things have subtle impacts on treatment and–in the pay-for-value systems we are trying to institute in health care–should affect reimbursements. For instance, if we could run a program that tracked the shifting and coalescing interpretations that eventually lead to a patient’s definitive diagnosis, we might make the process take place much faster for future patients. But all a doctor can do currently is list conditions in a form such as:

E66.0 – Obesity due to excess calories

E11 – Type 2 diabetes mellitus

M25.562 – Pain in left knee

The tragedy is that today’s data analytics allow so much more sophistication in representing the ins and outs of disease.Take the issues of interrelations, for instance.

These are easy to visualize as graphs, a subject I covered recently.

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Cheeseburger Please, and Make It a Double

cheeseburger

Consider that for the last year or so, we have been treated a deluge of entreaties to reduce our salt intake, with the American Heart Association going so far as to claim that daily sodium intake should not exceed 1,500 mg. This puts it at odds with the Institute of Medicine, and now European researchers whose data indicates that the healthy range for sodium intake appears to be much higher.

Our conversation about  sodium, much like advice about purportedly evil saturated fats and supposedly beneficial polyunsaturated fats, exemplifies a national obsession with believing eating more or less of a one or a small number of nutrients is the path to nutritional nirvana.

A few weeks back, an international team of scientists did their level best to feed this sensationalistic beast by producing what’s become known since then as the meat-and-cheese study, because it damned consumption of animal proteins.

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Will the Uninsured Become Healthier Once They Receive Health Care Coverage?

David OrentlicherThe Affordable Care Act might not bend the cost curve or improve the quality of health care, but it will save thousands of lives, as millions of uninsured persons receive the health care they need.

At least that’s the conventional wisdom.

But while observers assume that ACA will improve the health of the uninsured, the link between health insurance and health is not as clear as one may think. Partly because other factors have a bigger impact on health than does health care and partly because the uninsured can rely on the health care safety net, ACA’s impact on the health of the previously uninsured may be less than expected.

To be sure, the insured are healthier than the uninsured. According to one study, the uninsured have a mortality rate 40% higher than that of the insured. However, there are other differences between the insured and the uninsured besides their insurance status, including education, wealth, and other measures of socioeconomic status.

How much does health insurance improve the health of the uninsured? The empirical literature sends a mixed message. On one hand is an important Medicaid study. Researchers compared three states that had expanded their Medicaid programs to include childless adults with neighboring states that were similar demographically but had not undertaken similar expansions of their Medicaid programs.

In the aggregate, the states with the expansions saw significant reductions in mortality rates compared to the neighboring states.

On the other hand is another important Medicaid study. After Oregon added a limited number of slots to its Medicaid program and assigned the new slots by lottery, it effectively created a randomized controlled study of the benefits of Medicaid coverage. When researchers analyzed data from the first two years of the expansion, they found that the coverage resulted in greater utilization of the health care system.

However, coverage did not lead to a reduction in levels of hypertension, high cholesterol or diabetes.

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#Dataviz + #Design + #Diabetes: The Beginning

I love interactive data visualization (#dataviz).  It is one of the things that I definitely wanted to explore when I came out to the Bay Area on sabbatical, because I believe that it has great potential for helping both patients and clinicians with diabetes management.  The sheer volume of numbers available for this disease is overwhelming; we need #dataviz tools that can help us achieve greater understanding and make actionable clinical decisions to improve health.

This is what we usually see in clinic: numbers written down on a piece of paper.

Yes there are computer systems that link to blood glucose meters, but there are a number of complexities with the downloading of blood sugar numbers in clinic (which deserves an entire blog post sometime in the future).

You can see there is some visual analysis and annotation that we do perform, albeit primitive.  The circles represent high blood sugars (>150 mg/dl)and the triangles represent low blood sugars (<70 mg/dl). This is almost better than the cave painters don’t you think?

But even the minority of patients who download their BS to the computer, are viewing dashboards like this.

Pie charts, need I say more? I can extract some useful insights from these charts, which improve over the previous one I showed, but a few things strike me: (1) some of the scatter plots overlay weeks of data, which I don’t find helpful because you can’t tell how BS on a given day are responding and relate them to life events; (2) some visualizations show a lot of numbers in many of the sections, and it just becomes onerous to go through them and find trends; (3) many provide statistics (area under the curve, MAD%) which I think only a minority of families and children really understand; (4) although some of the software programs do provide interactivity and let you see the data at different time scales (day, week, month), if you change to a different view, you are stuck trying to remember in your head what you saw on a previous screen because you can’t see the multiple levels at once; (4) finally, I find that the user interface and design could use major improvement.

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Is Technology Making Us Fat?

As we look back over the past year and some of the amazing medical breakthroughs like wearable robotic devices, genomic sequencing and treatments like renal denervation that are improving people’s lives, it bears reflection on what else we could be doing better. Our world has changed more in the past century than in thousands of years of human history. We not only know more about our biology than ever before, but science and technology are unlocking the secrets of the very building blocks of our health. Somehow, in the midst of this incredible innovation, we’ve gotten fat, and not just a little. The result? Alarming rates of obesity and related chronic disease that threaten to crush us physically and financially.

But is it technology’s fault that we’ve become fat? A recent study by the Milken Institute that tied the amount an industrialized country spends on information and communication technologies directly to the obesity rates of its populations thinks so.

Most of us are guilty of a little overindulgence around the holidays but for many, overindulgence is a normal way of life. As economies transition to more sedentary, the physical movement that burned calories and kept us fit simply does not occur. Our lifestyles compound the issue — dual-income homes rely on the convenience of packaged meals, and our leisure activities have shifted to heavy “screen time” with movies, games and social media.

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What Do mHealth Leaders Need Most From Our Government? Clarity.

My job and my life intersected in a profound way when my daughter was diagnosed with Type I diabetes. Years working in mobile innovation didn’t prepare me for how personally relevant mHealth so quickly became. Her clinical trial at Stanford University, supported by the National Institutes of Health through Congress’ Special Diabetes Program, featured a world-class endocrinologist working alongside software coders, applications developers, algorithm writers, network engineers and other mobile innovators. They were all pushing together for what could be a revolution in diabetes management—the artificial pancreas.

Recently I had the opportunity to talk about my daughter’s experience and share my thoughts on how government can help encourage the next wave of mHealth innovation, when I was invited to testify before Congress on mobile innovation and health care.

America’s leadership in the mobile economy — 40,000 apps and counting in the broad mHealth category — matches America’s leadership at the cutting edge of medical technology.

Mobile devices, wireless networks and targeted applications are enabling better, more seamless and cost-effective care that empowers and informs stakeholders on both sides of the stethoscope.

The virtuous cycle of investment in the mobile ecosystem — from networks, to handsets and tablets, to applications — provides an unparalleled foundation for dramatic advances in the nation’s health and wellness. My message to Congress was to lean in and strike a reasonable and circumspect balance that both protects patient safety and privacy and propels the dramatic, mobile-fueled advances we are seeing through American medicine today.

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