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Tag: Data

#Dataviz + #Design + #Diabetes: The Beginning

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By Joyce Lee, MD

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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The IRS Scandal: Implications for HIPAA and the Affordable Care Act

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By Dr. Wes

As my head reels at the implications of the IRS scandal mushrooming in Washington, the IRS’s recently disclosed ability to access e-mails without warrant, the intricacy of the NSA PRISM wiretap techniques that includes their ability to acquire tech firms’ digital data, and even the Justice Department’s ability to secretly acquire telephone toll records from the Associated Press, I wonder (as a doctor) what all this means for the privacy protections afforded by the Health Insurance Portability and Accountability Act of 1996 (HIPAA) in our new era of mandated electronic medical records.  Are such privacy protections credible at all?

It doesn’t seem so.

Now it seems everyone’s health data is just as vulnerable to federal review as their Google search data.  This is not a small issue.  We have already seen that discovering “leaks” of personal health information has produced some very handsome rewards for the feds, so it is not beyond reason to think that HIPAA might also be a funding tool for our government health care administration disguised as a beneficent effort to protect the health care data of our populace.

But even more concerning is the role the IRS scandal has for America’s health care system.  After all, the Affordable Care Act is ultimately funded by the IRS by administering some 47 tax provisions.  These include the right to levy a penalty against businesses and individuals who don’t provide or acquire insurance and determining how to distribute annual subsidies to 18 million people who make less than $45,000 a year and thus qualify for subsidies in buying health coverage. In addition, the agency will collect taxes on medical devices and a surtax on people making more than $200,000 a year, as well as conducting compliance audits of tax-exempt hospitals.

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What Do Patients Really Think? A Report From the Third Annual Health Privacy Summit

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By Andy Oram

Health reform activists and privacy mavens have been at loggerheads for years. Those touting health reform complain that an oversensitivity to privacy risks would hold back progress in treatments. Running in parallel but in the opposite direction, the privacy side argues that current policies are endangering patients and that the current rush to electronic records and health information exchange can make things worse.

It’s time to get past these arguments and find a common ground on which to institute policies that benefit patients. Luckily, the moment is here where we can do so. The common concern these two camps have for giving patients power and control can drive technological and policy solutions.

Deborah Peel, a psychiatrist who founded Patient Privacy Rights, has been excoriated by data use advocates for ill-considered claims and statements in the past. But her engagement with technology experts has grown over the years, and given the appointment of a Chief Technology Officer, Adrian Gropper, who is a leading blogger on this site, PPR is making real contributions to the discussion of appropriate technologies.

PPR has also held three Health Privacy Summits in Washington, DC, at the Georgetown Law Center, just a few blocks from the Capitol building. Although Congressional aides haven’t found their way to these conferences as we hoped (I am on the conference’s planning committee), they do draw a wide range of state and federal administrators along with technologists, lawyers, academics, patient advocates, and health care industry analysts. The most recent summit, held on June 5 and 6, found some ways to move forward on the data sharing vs. privacy stand-off in such areas as patient repositories, consent, anonymization, and data segmentation. It also highlighted how difficult these tasks are.

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Knocking the Palooza Out of the Data

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By Jonathan Bush

Just back from the Health Datapalooza confab that took place last week – an event now in its 4th year hosted by the federal government. I had a few lingering thoughts to share. First, on the event name: I’m guessing it came out of my old business partner and current national CTO Todd Park’s experience in Washington, where trying to get any single distinct thought through “the interests” could knock the “palooza” out of a grown stallion.

You’d think the federal government would be the last ones to host a Datapalooza, but the fact is NO ONE ELSE has stepped up!

So they did.

And complain as I might about the G-men and G-women being industry conference conveners (makes me want to bathe with a wire brush) they pulled it off pretty darn well. The Department of Health & Human Services (HHS) attracted hundreds of serious entrepreneurs… and hundreds more wannabes (who real entrepreneurs desperately need in order to feel cool).

And boy were there some great bloopers…

Kaiser came blistering onto the scene with an open API—to the location and hours of operation of its facilities! Kinda sounds like Yelp to me…I’d be surprised if developers will come a runnin’ to that one. Kaiser’s CIO (a very cool guy whom they or anyone in health care would be lucky to get) broke this news in a two-minute keynote speech. Imagine President Obama announcing, in a State of the Union address, that the green vegetables in the White House cafeteria were now much crunchier!

HHS Secretary Kathleen Sebelius applied similarly excessive fanfare announcing the release of cost data for 30 ambulatory procedures. The whole idea that Toddy (Park) was trying to get going with this Palooza was not to release REPORTS on things but to release the SOURCE data so that anyone with proper security and privacy clearance could INVENT a million reports that no one had ever conceived before!

So here are my thoughts on all of this, some of which I shared at the conference in my way-longer-than-two-minute keynote:

1. Release the data!! Secretary Sebelius announcing the release of cost data for 30 ambulatory procedures during her keynote felt like the Secretary of Energy serving up a can of 10W30 to oil companies to drill into.

Her words were great. To wit: “The fact that this [unlocking the data] is growing by leaps and bounds is a good indication that we can leapfrog over years and decades of inaction into an exciting new future.” YES! GO GIRRRL!  OK, so…where’s the data?

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Datapalooza Report on Data Economics and a Call for Reciprocity

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By Adrian Gropper, MD

Uwe Reinhardt said it perfectly in a Tuesday plenary but I can only paraphrase his point: “health information is a public good that brings more wealth the more people use it.” Or, as Doc Searls puts it: personal data is worth more the more it is used. Datapalooza is certainly the largest meeting of the year focused on health data, and our Health and Human Services data liberation army was in full regalia. My assessment is: so far, so good but, as always, each data liberation maneuver also reveals the next fortified position just ahead. This post will highlight reciprocity as a new challenge to the data economy.

The economic value of health data is immense. Without our data it’s simply impossible to independently measure quality, get independent second opinions or control family health expenses. The US is wasting $750 Billion per year on health care which boils down to $3,000 per year that each man, woman and child is flushing down the drain.

Data liberation is a battle in the cloud and on the ground. In the cloud, we have waves of data releases from massive federal data arsenals. These are the essential roadmap or graph to guide our health policy decisions. I will say no more about this because I expect Fred Trotter (who is doing an amazing job of leading in this space) will cover the anonymous and statistical aspects of the data economy. Data in the cloud provides the basis for clinical decision support.
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Health Datapalooza Day One: How Will We Grow Data for Improving Health?

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By Andy Oram

An unfathomably complex entity such as a health system grows over time like a city. Right now, communications and data usage in the US healthcare system is a bit like a medieval town, with new streets and squares popping up in unpredictable places and no clear paths between them. Growth in health information has accelerated tremendously over the past few years with the popularity of big data generally, and we are still erecting structures wherever seems convenient, without building codes.

In some cities, as growth reaches the breaking point, commissioners step in. Neighborhoods are razed, conduits are laid in the ground for electricity and plumbing, and magnificent new palaces take the place of the old slums. But our health infomation system lacks its Baron Haussmann. The only force that could seize that role–the Office ofthe National Coordinator–has been slow to impose order, even as it funds the creation of open standards. Today, however, we celebrate growth and imagine a future of ordered data.

The health data forum that started today (Health Datapalooza IV) celebrated all the achievements across government and industry in creating, using, and sharing health data.

Useful data, but not always usable

I came here asking two essential questions of people I met: “What data sources do you find most useful now?” and “What data is missing that you wish you had?” The answer to first can be found at a wonderful Health Data All-Stars site maintained by the Health Data Consortium,which is running the palooza.

The choices on this site include a lot of data from the Department of Health and Human Services, also available on their ground-breaking HealthData.gov site, but also a number of data sets from other places. The advantage of the All-Stars site is that it features just a few (fifty) sites that got high marks from a survey conducted among a wide range of data users, including government agencies, research facilities, and health care advocates. Continue reading…

5. Use measurement to promote the concept of the rapid-learning health care system.

By Berenson, Pronovost & Krumholz

Initiatives to promote performance measurement need to be accompanied by support to improve care. Quality measure data should not only be technically correct, but should be organized such that their dissemination is a resource to aid in quality improvement activities. As such, quality measurement should be viewed as just one component of a learning health care system that also includes advancing the science of quality improvement, building providers’ capacity to improve care, transparently reporting performance, and creating formal accountability systems.

There are several strategies to make quality measure data more actionable for quality improvement purposes. For example, for publicly reported outcome measures, CMS provides hospitals with lists of the patients who are included in the calculation. Since the outcomes may occur outside the hospital for mortality and for readmissions that are at other hospitals, this information is often beyond what the hospitals already have available to them. These data give providers the ability to investigate care provided to individual patients, which in turn can support a variety of quality improvement efforts.

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Hacking Healthcare

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By Fred Trotter

There are two definitions of the word “Hacker”. One is an original and authentic term that the geekdom uses with respect. This is a cherished label in the technical community, which might read something like:

“A person adept at solving technical problems in clever and delightful ways”

While the one portrayed by popular culture is what real hackers call “crackers”

“Someone who breaks into other people computers and causes havok on the Internet”

People who aspire to be hackers, like me, resent it when other people use the term in a demeaning and co-opted manner.  Or at least, that is what I used to think. For years, I have had a growing unease about the “split” between these two definitions. The original Hackers at the MIT AI lab did spend time breaking into computer resources… it is not an accident that the word has come to mean two things.. It is from observing e-patients, who I consider to be the hackers of the healthcare world, that I have come to understand a higher level definition that encompasses both of these terms.

Hacking is the act of using clever and delightful technical workarounds to reject the morality embedded default settings embedded in a given system.

This puts “Hacking” more on the footing with “Protesting”. This is why crackers give real Hackers a bad name. While crackers might technically be engaged in Hacking, they are doing so in a base and ethically bankrupt manner. Martin Luther King Jr. certainly deserves the moniker of “protester” and this is not made any less noble because Westboro Baptist Church members are labeled protesters too.

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A Duty to Share Patient Information

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By Vince Kuraitis and Leslie Kelly Hall

The sharing of patient information in the US is out of whack — we lean far too much toward hoarding information vs. sharing it. While care providers have an explicit duty to protect patient confidentiality and privacy, two things are missing:

  • the explicit recognition of a corollary duty to share patient information with other providers when doing so is the patient’s interests, and
  • a recognition that there is potential tension between the duty to protect patient confidentiality/privacy and the duty to share — with minimal guidance on how to resolve the tension.

In this essay we’ll discuss:

1. A recent recognition in the UK

2. The need for an explicit duty to share patient information in the US

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Open Access: The Next Steps

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By Andy Oram


A useful and well-written summary of open access to publications in the medical field triggered some thoughts I’d like to share. The thrust of the article was that doctors need more access to a wide range of journal publications in order to make better decisions. The article also praises NIH’s open access policy, which has inspired the NSF and many journals.

My additional points are:

  • Open publication adds to the flood of information already available to most doctors, placing a burden on them to search and filter it. IBM’s Watson is one famous attempt to approach the ideal where the doctor would be presented right at the point of care with exactly the information he or she needs to make a better decision. Elsewhere, I have reported on a proposal to help experts doctors filter and select the important information and provide it to their peers upon demand–a social networking approach to evidence-based medicine.
  • Not only published papers, but the data that led to those research results should be published online, to help researchers reproduce the results and build on them to make new discoveries. I report in an earlier article on this site about the work of Sage Bionetworks to get researchers to open their data. Of course, putting up raw data leaves many challenges: one has to be careful to deidentify it according to accepted standards. One has to explain the provenance of the data carefully: how it was collected and massaged (because data sets always require some culling and error-correction) so it can be understood and properly reused. Finally, combining different data sets is always difficult because they are collected under different conditions and with different assumptions.