Andy Oram
Andy Oram is an editor at O'Reilly Media. An employee of the company since 1992, Andy currently specializes in open source technologies and software engineering. His work for O'Reilly includes the first books ever released by a U.S. publisher on Linux, the 2001 title Peer-to-Peer, and the 2007 best-seller Beautiful Code.
Geographic data is the glue for public health and treatment
Esri conference highlights uses of GIS data
We’ve all seen cool maps of health data, such as these representations of diabetes prevalence by US county. But few people think about how thoroughly geospacial data is transforming public health and changing the allocation of resources at individual hospitals. I got a peek into this world at the Esri Health GIS Conference this week in Cambridge, Mass.
OpenStack release offers more flexibility and aids to performance
The Havana release features metering and orchestration
I talked this week to Jonathan Bryce and Mark Collier of OpenStack to look at the motivations behind the enhancements in the Havana release announced today. We focused on the main event–official support for the Ceilometer metering/monitoring project and the Heat orchestration project–but covered a few small bullet items as well.
Where are the chances for change in health care – top-down or bottom-up?
Impressions from Strata Rx bolster different philosophies
Everyone seems to agree that health care is the next big industry waiting to be disrupted. But who will force that change on a massive system full of conservative players? Three possibilities present themselves:
Ticking all the boxes for a health care upgrade at Strata Rx
What is needed for successful reform of the health care system?
Here’s what we all know: that a data-rich health care future is coming our way. And what it will look like, in large outlines. Health care reformers have learned that no single practice will improve the system. All of the following, which were discussed at O’Reilly’s recent Strata Rx conference, must fall in place.
Building the Brain of Dr. Algorithm
Archimedes advances evidence-based medicine to foster model-based medicine
This posting is by guest author Tuan Dinh, who will speak about this topic at the Strata Rx conference.
Legendary Silicon Valley investor Vinod Khosla caused quite a stir last year when he predicted at Strata Rx that “Dr. Algorithm”–artificial intelligence driven by large data sets and computational power–would replace doctors in the not-too-distant future. At that point, he said, technology will be cheaper, more accurate and objective, and will ultimately do a better job than the average human doctor at delivering routine diagnoses with standard treatments.
I not only support Khosla’s provocative prophecy, I’ll add one of my own: that Dr. Algorithm (aka Dr. A) will “come to life” in three to five years, by the time today’s first-year med school students are pulling 30-hour shifts as new interns. But what will it take to build the brain of Dr. A? And how can we teach Dr. A to account for increasingly complex medical inputs, such as laboratory tests results, genomic/genetic information, family and personal history, co-morbidities and patient preferences, so he can make optimal clinical decisions for living, breathing patients?
OneHealth: Combining Patient-Generated Data With Communities and Feedback Loops
Evolution from a research tool to a platform for patient engagement
Bruce Springer of OneHealth will speak about this topic at the Strata Rx conference. This article was written by Patrick Bane of OneHealth in coordination with Bruce Springer.
According to a recent study performed by the Jesse Brown VA Medical Center and University of Illinois at Chicago, patient-centered care has demonstrated positive outcomes on patients’ health, patients’ self-report of health, and reduced healthcare utilization. The study’s results are consistent with previous research that the patient-centered care model improves the quality of care while simultaneously lowering the cost of care.
OneHealth’s behavior change platform extends the patient-centered model by connecting members anytime, anywhere through mobile and web applications. Member generate data in their daily lives, outside of a clinical setting, which creates a much richer dataset of behaviors that are required to understand the patients’ condition(s), and their readiness to change. Members freely choose what to do and their choices actively generate data in five classes of information:
How to bend fate in the health care field
Data that matters to patients
This article is by guest author Amik Ahmad. He is speaking on this topic at Strata Rx.
Distractions didn’t have a chance. My phone was devoid of reception. The New York Times mobile application searched impossibly for a Wi-Fi connection. Conditions perfect for focus: away from a world always on and connected, noisy, and belligerent with information overload. I could have found joy in a single byte. But instead, I was pushed to the limit of sensory deprivation, and I teetered on the edge of insanity. I spent nine hours of my life in a hospital waiting room.
Combining personal data with ratings for targeted medical information
HealthTap refines the answers returned to specific health queries
HealthTap is a community of doctors and clients seeking answers to health questions. Its central service provides immediate access to doctors and their knowledge either by doctors answering client questions in real time, or through a large database of previously answered questions and answers from doctors that are peer reviewed and tagged with recommendations by other doctors. By combining the doctors’ recommendations with data provided by each client on himself or herself, HealthTap provides customized results to queries. In this video, HealthTap CEO Ron Gutman explains unexpected lessons they’ve learned from offering the intelligent search service.
Ginger.io: Putting the Patient-Provider connection at the center
A tool for outreach to patients produces unexpected benefits
This posting was written by guest author Julia Bernstein of Ginger.io. Ginger.io’s CEO Anmol Madan will be speaking on this topic at Strata Rx.
The traditional, office-based model for health care is episodic. The provider-patient relationship exists almost completely within the walls of the exam room, with little or no follow-up between visits. Data is primarily episodic as well, based on blood pressure reading done at a specific time or surveys administered there and then, with little collected out of the office. And even the existing data collection tools—paper diaries or clunky meters—are focused more on storing data that on connecting the patient and provider through that data in real time.
There is no way to get in touch when, for instance, a patient’s blood sugar starts varying wildly or pain levels change. The provider often depends on the patient reaching out to them. And even when a provider does put into place an outreach protocol, it is usually very crude, based on a general approach to managing a population as opposed to an understanding of a patient. The end result is a system that, while doing its best within a difficult setting, is by default reactive instead of proactive.
Clinical discovery in the age of “Big Data”
Modern data processing tools, many of them open source, allow more clinical studies at lower costs
This guest posting was written by Yadid Ayzenberg (@YadidAyzenberg on Twitter). Yadid is a PhD student in the Affective Computing Group at the MIT Media Lab. He has designed and implemented cloud platforms for the aggregation, processing and visualization of bio-physiological sensor data. Yadid will speak on this topic at the Strata Rx conference.
A few weeks ago, I learned that the Framingham Heart Study would lose $4 million (a full 40 percent of its funding) from the federal government due to automatic spending cuts. This seminal study, begun in 1948, set out to identify the contributing factors to Cardiovascular Disease (CVD) by following a group of 5,209 men and woman and tracking their life style habits, performing regular physical examinations and lab tests. This study was responsible for finding the major risk factors for CVD, such as high blood pressure and lack of exercise. The costs associated with such large-scale clinical studies are prohibitive, making them accessible only to organizations with sufficient financial resources or through government funding.
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