- 16 Interviewing Tips for User Studies — these apply to many situations beyond user interviews, too.
- The Backlash Against Big Data contd. (Mike Loukides) — Learn to be a data skeptic. That doesn’t mean becoming skeptical about the value of data; it means asking the hard questions that anyone claiming to be a data scientist should ask. Think carefully about the questions you’re asking, the data you have to work with, and the results that you’re getting. And learn that data is about enabling intelligent discussions, not about turning a crank and having the right answer pop out.
- The Science of Science Writing (American Scientist) — also applicable beyond the specific field for which it was written.
ENTRIES TAGGED "data"
New report covers areas of innovation and their difficulties
O’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.
Insights from a business executive and law professor
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.
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.
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.
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.
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.
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.”