healthcare technology

Personalized medicine is hoping to reach new heights thanks to the Cancer Moonshot, but won't get off the ground without a community-wide commitment to sharing big data.

The precision medicine community has long since recognized that sharing big data, including clinical records, genomic sequencing data, community-level health indicators, and research results, will be critical to making progress against cancer, neurodegenerative diseases, inherited conditions, and expensive chronic diseases like diabetes.

“Why is data sharing important? Because cancer is complex,” said Kenneth C. Anderson, MD, President-elect of the American Society of Hematology (ASH).  Anderson specializes in multiple myeloma, a blood cancer with treatment options that hinge on the genetic variances of each and every patient.

“We’re learning so much about cancer, and applying these insights to drug development has been incredibly fruitful,” he continued. “Now we have treatments that are specifically targeted to patients’ genetic mutations. Not only are these treatments more effective — because they correct a specific mutation — they also minimize harmful side effects that we see with traditional total-body anticancer medicines.”

However, the continued development of these treatments cannot be sustained without a commitment to data sharing, he added.

more at http://healthitanalytics.com/news/why-sharing-cancer-big-data-is-key-to-personalized-medicine

The big hairy audacious goal of most every data scientist I know in healthcare is what you might call the Integrated Medical Record, or IMR, a dataset that combines detailed genetic data and rich phenotypic information, including both clinical and “real-world” (or, perhaps, “dynamic”) phenotypic data (the sort you might get from wearables).

The gold standard for clinical phenotyping are academic clinical studies (like ALLHAT and the Dallas Heart Study).  These studies are typically focused on a disease category (e.g. cardiovascular), and the clinical phenotyping on these subjects – at least around the areas of scientific interest — is generally superb.  The studies themselves can be enormous, are often multi-institutional, and typically create a database that’s independent of the hospital’s medical record.

Duke University Medicine is using geographical information to turn electronic health records (EHRs) into population health predictors. By integrating its EHR data with its geographic information system, Duke can enable clinicians to predict patients' diagnoses.

According to Health Data Management, Sohayla Pruitt was hired by Duke to run this project; “I thought, wow, if we could automate some of this, pre select some of the data, preprocess a lot and then sort of wait for an event to happen, we could pass it through our models, let them plow through thousands of geospatial variables and [let the system] tell us the actual statistical significance,” Pruitt says. “Then, once you know how geography is influencing events and what they have in common, you can project that to other places where you should be paying attention because they have similar probability.”

iHealth Beat explains that the system works by using an automated geocoding system to verify addresses with a U.S. Postal Service database. These addresses are then passed through a commercial mapping database to geocode them. Finally, the system imports all U.S. Census Bureau data with a block group ID. This results in an assessment of socioeconomic indicators for each group of patients.

“When we visually map a population and a health issue, we want to give an understanding about why something is happening in a neighborhood,” says Pruitt. “Are there certain socioeconomic factors that are contributing? Do they not have access to certain things? Do they have too much access to certain things like fast food restaurants?”

Duke is working to develop a proof of concept and algorithms that would map locations and patients. They are also working on a system to track food-borne illnesses.

After decades as a technological laggard, medicine has entered its data age. Mobile technologies, sensors, genome sequencing, and advances in analytic software now make it possible to capture vast amounts of information about our individual makeup and the environment around us. The sum of this information could transform medicine, turning a field aimed at treating the average patient into one that’s customized to each person while shifting more control and responsibility from doctors to patients.

The question is: can big data make health care better?

“There is a lot of data being gathered. That’s not enough,” says Ed Martin, interim director of the Information Services Unit at the University of California San Francisco School of Medicine. “It’s really about coming up with applications that make data actionable.”

The business opportunity in making sense of that data—potentially $300 billion to $450 billion a year, according to consultants McKinsey & Company—is driving well-established companies like Apple, Qualcomm, and IBM to invest in technologies from data-capturing smartphone apps to billion-dollar analytical systems. It’s feeding the rising enthusiasm for startups as well.

Venture capital firms like Greylock Partners and Kleiner Perkins Caufield & Byers, as well as the corporate venture funds of Google, Samsung, Merck, and others, have invested more than $3 billion in health-care information technology since the beginning of 2013—a rapid acceleration from previous years, according to data from Mercom Capital Group. 

A life-sciences-as-a-service startup called Transcriptic has opened its APIs to the general public, allowing researchers around the world offload tedious lab work to robots so researchers can spend more of their time analyzing the results.

Using a set of APIs, researchers can now command Transcriptic’s purpose-built robots to process, analyze, and store their genetic or biological samples, and receive results in days.

The high concept idea, says Founder and CEO Max Hodak, is cloud computing for life sciences — only with “robotic work cells” instead of servers on the other end. “We see the lab in terms of the devices that make it up,” he said, meaning stuff like incubators, freezers, liquid handlers and robotic arms to replace human arms.

And although Transcriptic’s technology is complex, the process for getting work done is actually pretty simple. Researchers write code to tell the robots exactly what to do with the samples (right now, the company focuses on molecular cloning, genotyping, bacteria-growing and bio-banking), and then they send their samples to the Transcriptic lab.

Alternatively, Transcriptic’s robotic infrastructure can also synthesize samples for users.

And although Transcriptic’s technology is complex, the process for getting work done is actually pretty simple.

Researchers write code to tell the robots exactly what to do with the samples (right now, the company focuses on molecular cloning, genotyping, bacteria-growing and bio-banking), and then they send their samples to the Transcriptic lab. Alternatively, Transcriptic’s robotic infrastructure can also synthesize samples for users.

When the job is done, researchers get their results. That process can take anywhere from a day to weeks, Hodak explained, in part because the company’s operation is still pretty small and in part because “cells only grow and divide so quickly.”

more at http://gigaom.com/2014/07/15/genetic-researchers-have-a-new-tool-in-api-controlled-lab-robots/

The face of medical care is rapidly changing thanks to major advancements in the capture, proliferation, and analysis of medical data. Technologies like the electronic health records (EHRs) and personal health records (PHRs) are drastically improving the way data is aggregated and shared.

Now the hope is that big data analytics will help to make sense of seemingly endless streams of medical information.

As many doctors are painfully aware, outcome-oriented care is no longer a buzzword but a reality. The Center for Medicare and Medicaid Services has started to implement a program where payments are based on the ability of providers to meet key National Quality Strategy Domains (e.g. care criteria). Public payers are testing this new methodology, and private payers are expected to soon follow.

These big data analytics applications can also be relevant for the FDA, which may want to see how drugs perform in a non-test environment to ensure the appropriate patient populations are receiving the drug. I also expect pharmaceutical companies to actively scour this data to track drug efficacy post-release or identify markets that could “benefit” from increased penetration.

I am eager to see how the data evolution improves outcomes for doctors and patients.

When an imaging run generates 1 terabyte of data, analysis becomes the problem

Today's neuroscientists have some magnificent tools at their disposal. They can, for example, examine the entire brain of a live zebrafish larva and record the activation patterns of nearly all of its 100,000 neurons in a process that takes only 1.5 seconds.

The only problem: One such imaging run yields about 1 terabyte of data, making analysis the real bottleneck as researchers seek to understand the brain.

To address this issue, scientists at Janelia Farm Research Campus have come up with a set of analytical tools designed for neuroscience and built on a distributed computing platform called Apache Spark. In their paper in Nature Methods, they demonstrate their system's capabilities by making sense of several enormous data sets. (The image above shows the whole-brain neural activity of a zebrafish larva when it was exposed to a moving visual stimulus; the different colors indicate which neurons activated in response to a movement to the left or right.)

The researchers argue that the Apache Spark platform offers an improvement over a more popular distributed computing model known as Hadoop MapReduce, which was originally based on Google's search engine technology. 

more at http://spectrum.ieee.org/tech-talk/biomedical/imaging/can-computing-keep-up-with-the-neuroscience-data-deluge/

It's been tough to identify the problems that only turn up after medicines are on the market. An experimental project is now combing through data to get earlier, more accurate warnings.

No one likes it when a new drug in people's medicine cabinets turns out to have problems — just remember the Vioxx debacle a decade ago, when the painkiller was removed from the market over concerns that it increased the risk of heart attack and stroke.

To do a better job of spotting unforeseen risks and side effects, the Food and Drug Administration is trying something new — and there's a decent chance that it involves your medical records.

It's called Mini-Sentinel, and it's a $116 million government project to actively go out and look for adverse events linked to marketed drugs. This pilot program is able to mine huge databases of medical records for signs that drugs may be linked to problems.

The usual system for monitoring the safety of marketed drugs has real shortcomings. It largely relies on voluntary reports from doctors, pharmacists, and just plain folks who took a drug and got a bad outcome.

"We get about a million reports a year that way," says Janet Woodcock, the director of the FDA's Center for Drug Evaluation and Research. "But those are random. They are whatever people choose to send us."

Big data offers breakthrough possibilities for new research and discoveries, better patient care, and greater efficiency in health and health care, as detailed in the July issue of Health Affairs. As with any new tool or technique, there is a learning curve.