"data" entries

Health IT is a growth area for programmers

New report covers areas of innovation and their difficulties

infofixO’Reilly recently released a report I wrote called The Information Technology Fix for Health: Barriers and Pathways to the Use of Information Technology for Better Health Care. Along with our book Hacking Healthcare, I hope this report helps programmers who are curious about Health IT see what they need to learn and what they in turn can contribute to the field.

Computers in health are a potentially lucrative domain, to be sure, given a health care system through which $2.8 trillion, or $8.915 per person, passes through each year in the US alone. Interest by venture capitalists ebbs and flows, but the impetus to creative technological hacking is strong, as shown by the large number of challenges run by governments, pharmaceutical companies, insurers, and others.

Some things you should consider doing include:

Join open source projects 

Numerous projects to collect and process health data are being conducted as free software; find one that raises your heartbeat and contribute. For instance, the most respected health care system in the country, VistA from the Department of Veterans Affairs, has new leadership in OSEHRA, which is trying to create a community of vendors and volunteers. You don’t need to understand the oddities of the MUMPS language on which VistA is based to contribute, although I believe some knowledge of the underlying database would be useful. But there are plenty of other projects too, such as the OpenMRS electronic record system and the projects that cooperate under the aegis of Open Health Tools

Read more…

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Four short links: 9 April 2014

Four short links: 9 April 2014

Internet of Listeners, Mobile Deep Belief, Crowdsourced Spectrum Data, and Quantum Minecraft

  1. Jasper Projectan open source platform for developing always-on, voice-controlled applications. Shouting is the new swiping—I eagerly await Gartner touting the Internet-of-things-that-misunderstand-you.
  2. DeepBeliefSDK — deep neural network library for iOS. (via Pete Warden)
  3. Microsoft Spectrum Observatory — crowdsourcing spectrum utilisation information. Just open sourced their code.
  4. qcraft — beginner’s guide to quantum physics in Minecraft. (via Nelson Minar)
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Four short links: 27 March 2014

Four short links: 27 March 2014

Understanding Image Processing, Sharing Data, Fixing Bad Science, and Delightful Dashboard

  1. 2D Image Post-Processing Techniques and Algorithms (DIY Drones) — understanding how automated image matching and processing tools work means you can also get a better understanding how to shoot your images and what to prevent to get good matches.
  2. Scientists Need to Learn to Sharedespite science’s reputation for rigor, sloppiness is a substantial problem in some fields. You’re much more likely to check your work and follow best data-handling practices when you know someone is going to run your code and parse your data.
  3. METRICSMeta-Research Innovation Center at Stanford. John Ioannidis has a posse: connecting researchers into weak science, running conferences, creating a “journal watch”, and engaging policy makers. (says The Economist)
  4. Grafana — elegant dashboard for graphite (the realtime data graphing engine).
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What’s Up With Big Data Ethics?

Insights from a business executive and law professor

by Jonathan H. King & Neil M. Richards

Photo provided courtesy of Jonathan H. King.

Photo provided courtesy of Jonathan King

If you develop software or manage databases, you’re probably at the point now where the phrase “Big Data” makes you roll your eyes. Yes, it’s hyped quite a lot these days. But, overexposed or not, the Big Data revolution raises a bunch of ethical issues related to privacy, confidentiality, transparency and identity. Who owns all that data that you’re analyzing? Are there limits to what kinds of inferences you can make, or what decisions can be made about people based on those inferences? Perhaps you’ve wondered about this yourself.

We’re obsessed by these questions. We’re a business executive and a law professor who’ve written about this question a lot, but our audience is usually lawyers. But because engineers are the ones who confront these questions on a daily basis, we think it’s essential to talk about these issues in the context of software development.

Photo provided courtesy of Neil M. Richards.

Photo provided courtesy of Neil M. Richards.

While there’s nothing particularly new about the analytics conducted in big data, the scale and ease with which it can all be done today changes the ethical framework of data analysis. Developers today can tap into remarkably varied and far-flung data sources. Just a few years ago, this kind of access would have been hard to imagine. The problem is that our ability to reveal patterns and new knowledge from previously unexamined troves of data is moving faster than our current legal and ethical guidelines can manage. We can now do things that were impossible a few years ago, and we’ve driven off the existing ethical and legal maps. If we fail to preserve the values we care about in our new digital society, then our big data capabilities risk abandoning these values for the sake of innovation and expediency.

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Can data provide the trust we need in health care?

Collecting actionable data is a challenge for today's data tools

One of the problems dragging down the US health care system is that nobody trusts one another. Most of us, as individuals, place faith in our personal health care providers, which may or may not be warranted. But on a larger scale we’re all suspicious of each other:

  • Doctors don’t trust patients, who aren’t forthcoming with all the bad habits they indulge in and often fail to follow the most basic instructions, such as to take their medications.
  • The payers–which include insurers, many government agencies, and increasingly the whole patient population as our deductibles and other out-of-pocket expenses ascend–don’t trust the doctors, who waste an estimated 20% or more of all health expenditures, including some thirty or more billion dollars of fraud each year.
  • The public distrusts the pharmaceutical companies (although we still follow their advice on advertisements and ask our doctors for the latest pill) and is starting to distrust clinical researchers as we hear about conflicts of interest and difficulties replicating results.
  • Nobody trusts the federal government, which pursues two (contradictory) goals of lowering health care costs and stimulating employment.

Yet everyone has beneficent goals and good ideas for improving health care. Doctors want to feel effective, patients want to stay well (even if that desire doesn’t always translate into action), the Department of Health and Human Services champions very lofty goals for data exchange and quality improvement, clinical researchers put their work above family and comfort, and even private insurance companies are trying moving to “fee for value” programs that ensure coordinated patient care.

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Podcast: thinking with data

Data tools are less important than the way you frame your questions.

Max Shron and Jake Porway spoke with me at Strata a few weeks ago about frameworks for making reasoned arguments with data. Max’s recent O’Reilly book, Thinking with Data, outlines the crucial process of developing good questions and creating a plan to answer them. Jake’s nonprofit, DataKind, connects data scientists with worthy causes where they can apply their skills.

A few of the things we talked about:

  • The importance of publishing negative scientific results
  • Give Directly, an organization that facilitates donations directly to households in Kenya and Uganda. Give Directly was able to model income using satellite data to distinguish thatched roofs from metal roofs.
  • Moritz Stefaner calling for a “macroscope”
  • Project Cybersyn, Salvador Allende’s plan for encompassing the entire Chilean economy in a single real-time computer system
  • Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed by James C. Scott

After we recorded this podcast episode at Strata Santa Clara, Max presided over a webcast on his book that’s archived here.

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An Invitation to Practical Machine Learning

PracticalMachineLearning_covDoes it make sense for me to have a car? If so, which one is the best choice for my needs: a gasoline, hybrid, or electric?  And should I buy or lease?

In order to make an effective decision, I need to understand key issues about the design, performance, and cost of cars, regardless of whether or not I actually know how to build one myself. The same is true for people deciding if machine learning is a good choice for their business goals or project.  Will the payoff be worth the effort?  What machine learning approach is most likely to produce valuable results for your particular situation? What size team with what expertise is necessary to be able to develop, deploy, and maintain your machine learning system?

Given the complex and previously esoteric nature of machine learning as a field – the sometimes daunting array of learning algorithms and the math needed to understand and employ them – many people feel the topic is one best left only to the few.

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Four short links: 3 March 2014

Four short links: 3 March 2014

Vanishing Money, Car Hackery, Data Literacy Course, and Cheaper CI

  1. The Programming Error That Cost Mt Gox 2609 Bitcoins — in the unforgiving world of crypto-currency, it’s easy to miscode and vanish your money.
  2. Ford Invites Open-Source Community to Tinker AwayOne example: Nelson has re-tasked the motor from a Microsoft Xbox 360 game controller to create an OpenXC shift knob that vibrates to signal gear shifts in a standard-transmission Mustang. The 3D-printed prototype shift knob uses Ford’s OpenXC research platform to link devices to the car via Bluetooth, and shares vehicle data from the on-board diagnostics port. Nelson has tested his prototype in a Ford Mustang Shelby GT500 that vibrates at the optimal time to shift.
  3. Making Sense of Data — Google online course on data literacy.
  4. Cost-Efficient Continuous Integration at Mozilla — CI on a big project can imply hundreds if not thousands of VMs on Amazon spinning up to handle compiles and tests. This blog post talks about Mozilla’s efforts to reduce its CI-induced spend without reducing the effectiveness of its CI practices.
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Healthcare Lessons from the Data Sages at Strata

Other industries can show health care the way

This article was written with Ellen M. Martin.

Most healthcare clinicians don’t often think about donating or sharing data. Yet, after hearing Stephen Friend of Sage Bionetworks talk about involving citizens and patients in the field of genetic research at StrataRx 2012, I was curious to learn more.

McKinsey points out the 300 billion dollars in potential savings from using open data in healthcare, while a recent IBM Institute of Business Value study showed the need for corporate data collaboration.

Also, during my own research for Big Data in Healthcare: Hype and Hope, the resounding request from all the participants I interviewed was to “find more data streams to analyze.”

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Four short links: 10 February 2014

Four short links: 10 February 2014

Sterling Zings, Android Swings, Data Blings, and Visualized Things.

  1. Bruce Sterling at transmediale 2014 (YouTube) — “if it works, it’s already obsolete.” Sterling does a great job of capturing the current time: spies in your Internet, lost trust with the BigCos, the impermanence of status quo, the need to create. (via BoingBoing)
  2. No-one Should Fork Android (Ars Technica) — this article is bang on. Google Mobile Services (the Play functionality) is closed-source, what makes Android more than a bare-metal OS, and is where G is focusing its development. Google’s Android team treats openness like a bug and routes around it.
  3. Data Pipelines (Hakkalabs) — interesting overview of the data pipelines of Stripe, Tapad, Etsy, and Square.
  4. Visualising Salesforce Data in Minecraft — would almost make me look forward to using Salesforce. Almost.
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