More visible at Health Privacy Summit than Health Datapalooza.
On the first morning of the biggest conference on data in health care–the Health Datapalooza in Washington, DC–newspapers reported a bill allowing the Department of Veterans Affairs to outsource more of its care, sending veterans to private health care providers to relieve its burdensome shortage of doctors.
There has been extensive talk about the scandals at the VA and remedies for them, including the political and financial ramifications of partial privatization. Republicans have suggested it for some time, but for the solution to be picked up by socialist Independent Senator Bernie Sanders clinches the matter. What no one has pointed out yet, however–and what makes this development relevant to the Datapalooza–is that such a reform will make the free flow of patient information between providers more crucial than ever.
Bio-IT World shows what is possible and what is being accomplished
If your data consists of one million samples, but only 100 have the characteristics you’re looking for, and if each of the million samples contains 250,000 attributes, each of which is built of thousands of basic elements, you have a big data problem. This is kind of challenge faced by the 2,700 Bio-IT World attendees, who discover genetic interactions and create drugs for the rest of us.
Often they are looking for rare (orphan) diseases, or for cohorts who share a rare combination of genetic factors that require a unique treatment. The data sets get huge, particularly when the researchers start studying proteomics (the proteins active in the patients’ bodies).
So last week I took the subway downtown and crossed the two wind- and rain-whipped bridges that the city of Boston built to connect to the World Trade Center. I mingled for a day with attendees and exhibitors to find what data-related challenges they’re facing and what the latest solutions are. Here are some of the major themes I turned up.
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.
The bid for widespread home use may drive technical improvements.
For some people, it’s too early to plan mass consumerization of the Internet of Things. Developers are contentedly tinkering with Arduinos and clip cables, demonstrating cool one-off applications. We know that home automation can save energy, keep the elderly and disabled independent, and make life better for a lot of people. But no one seems sure how to realize this goal, outside of security systems and a few high-end items for luxury markets (like the Nest devices, now being integrated into Google’s grand plan).
But what if the willful creation of a mass consumer market could make the technology even better? Perhaps the Internet of Things needs a consumer focus to achieve its potential. This view was illuminated for me through a couple recent talks with Mike Harris, CEO of the home automation software platform Zonoff.
New report ties together devices, data, records, and aspects of care.
Reformers in health care claim gigantic disruption on the horizon: devices that track our movements, new treatments through massive data crunching, fluid electronic records that reflect the patient’s status wherever she goes, and even the end of the doctor’s role. But predictions in the area of health IT are singularly detached from the realities of the technical environment that are supposed to make them happen.
To help technologists, clinicians, and the rest of us judge the state of health IT, I’ve released a report titled “The Information Technology Fix for Health: Barriers and Pathways to the Use of Information Technology for Better Health Care.” It offers an overview of each area of innovation to see what’s really happening and what we need to make it progress further and faster.
O'Reilly report covers major trends and tries to connect the neurons
If visualization is key to comprehending data, the field of health IT calls for better visualization. I am not talking here of pretty charts and animations. I am talking, rather, of a holistic, unified understanding of the bustle taking place in different corners of health: the collection and analysis of genetic data, the design of slim medical devices that replace refrigerator-sized pieces of equipment, the data crunching at hospitals delving into demographic data to identify at-risk patients.
There is no dearth of health reformers offering their visions for patient engagement, information exchange, better public health, and disruptive change to health industries. But they often accept too freely the promise of technology, without grasping how difficult the technical implementations of their reforms would be. Furthermore, no document I have found pulls together the various trends in technology and explores their interrelationships.
I have tried to fill this gap with a recently released report: The Information Technology Fix for Health: Barriers and Pathways to the Use of Information Technology for Better Health Care. This posting describes some of the issues it covers.
LibrePlanet explores hopes and hurdles.
Free and open source software creates a natural — and even necessary — fit with government. I joined a panel this past weekend at the Free Software Foundation conference LibrePlanet on this topic and have covered it previously in a journal article and talk. Our panel focused on barriers to its adoption and steps that free software advocates could take to reach out to government agencies.
LibrePlanet itself is a unique conference: a techfest with mission — an entirely serious, feasible exploration of a world that could be different. Participants constantly ask: how can we replace the current computing environment of locked-down systems, opaque interfaces, intrusive advertising-dominated services, and expensive communications systems with those that are open and free? I’ll report a bit on this unusual gathering after talking about government.
An exploration of themes in Joel Gurin's book Open Data Now.
As governments and businesses — and increasingly, all of us who are Internet-connected — release data out in the open, we come closer to resolving the tiresomely famous and perplexing quote from Stewart Brand: “Information wants to be free. Information also wants to be expensive.” Open data brings home to us how much free information is available and how productive it is in its free state, but one subterranean thread I found in Joel Gurin’s book Open Data Now highlights an important point: information is very expensive.
In this article, I’ll explore a few themes that piqued my interest in Gurin’s book: the value of open data, the expense it entails, the questions of how much we can use and trust it, and the role the general public and the private sector play in bringing us data’s benefits. This is not meant to be a summary or a review of Gurin’s book; it is an exploration of themes that interest me, inspired by my reading of Gurin.
Open, trustworthy, and useful
“Open data” occupies hierarchies of usefulness. One way of describing its usefulness is the structure of its presentation, as Gurin and others such as Tim Berners-Lee have pointed out. Much data is still fairly unstructured, like the reviews and social media status postings that people generate by the millions and that are funneled into eager consumption by marketing analysts. Some data is more structured, existing as tables. And finally, a tiny fragment can be reached through the RESTful APIs supported by libraries in every modern programming language. Read more…
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.