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Body + biology + behavior: Intel exec explains how technology is making N=1 care possible

6:23 am by | 3 Comments

Eric Dishman, general manager of Intel's Health & Life Sciences Group, speaks at HIMSSS 2014.

Eric Dishman, general manager of Intel’s Health & Life Sciences Group, speaks at HIMSSS 2014.

“Your [genomic] sequence changed everything we know about your diseases,” a clinician told Eric Dishman, general manager of Intel’s Health & Life Sciences Group, about two years ago. “We mistreated you for more than two decades.”
His doctors changed his regimen based on this new information. Within three months, Dishman was cancer-free and, as a result, a candidate for a life-saving kidney transplant.
“It was miraculous,” Dishman said Tuesday during a “view from the top” session at the Healthcare Information and Management Systems Society (HIMSS) conference in Orlando, Fla.
Dishman explained that he had his genome sequenced after a chance encounter with a co-worker who had access to testing. Another Intel employee donated a kidney to him. He also had the financial means and emotional support to see the whole process through to the end.
“This is not exactly a scalable model of N=1 care just yet,” Dishman said.

N=1 care is Dishman’s term for personalized medicine, with healthcare decisions made based not on probabilities and what a doctor is most familiar with, but rather on facts specific to each individual patient. According to Dishman, N=1 care is customized for individual complexity, taking into account what he calls “3 Bs”: body, biology and behavior, and often a fourth one, beliefs.
Technology—genomic and information—enables this customization and often leads to better care, and sometimes simple ideas make a difference.

Dishman noted that he was diagnosed with a rare form of kidney cancer at age 19 while an undergrad at the University of North Carolina. Not long after, he was waiting in a clinic for an endoscopy as a prelude to an experimental treatment, chatting with another patient. That woman convinced him to leave.

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“Eric, they don’t know anything about you,” she told him. People in clinical trials for the experimental treatment he was getting were much older, and the results weren’t relevant to him, Dishman explained.

The sentiment has stuck with him for nearly 25 years, and informed development of Intel’s Health Guide, the home health gateway that now is sold by the Intel-GE Care Innovations joint venture. One of the first telehealth units tested at Intel was for a woman whose diabetes was worsening as she often missed doctor’s appointments. The clinic was unaware that the woman was caring for her mother who had Alzheimer’s disease, and it was too expensive for her to hire a caregiver and pay for her own treatment.

That type of personal knowledge led to the addition of remote monitoring to the woman’s care plan, Dishman said, giving an example of what he called “care anywhere.”

Dishman then touted the Sotera Wireless ViSi Mobile wireless patient monitor, which he called an “ICU on a wrist,” as another beacon of the age of personalized medicine. “For better or for worse, we’re not going to have a single data point anymore,” he said. And that’s where advanced analytics tools come in.

Dishman brought Dr. Andrew Litt, chief medical officer of Dell, up on stage to discussed treatment of neuroblastoma in children, centering on a girl named Brooke who got the deadly cancer at age 3.

“Frankly, the chances for children like Brooke are pretty bad,” Litt said.

But scientists at the Phoenix-based Translational Genomics Research Institute (TGen) posited that drugs not previously tested on that specific form of cancer might work on Brooke, based on the pathway the drugs take to attack particular mutations. By analyzing the girl’s genome, TGen was able to determine that Brooke’s physicians should try levodopa, a medication more suited for Parkinson’s disease than brain cancer.

Today, Brooke is healthy, Litt said. TGen since has done similar work for about 15 children, and hopes to conduct a clinical trial with at least 100 young patients.

One issue that came up was that it often took a full week to analyze the massive amounts of data in a gene sequence — Dishman’s own sequence filled a 5-terabyte hard drive — and three weeks to determine potential treatments.

“If you have a kid with cancer, you don’t want to wait three weeks for an answer,” Litt said. For the TGen experiment, Dell brought in high-powered analytics hardware and software, which cut the computing time to just six hours and the whole process down to five days.

And although the cost of sequencing a genome is falling rapidly, the cost of pulling in clinical data and “care anywhere” data and making sense of the whole picture is not, according to Dishman.

“That’s why analytics is where the hockey puck is going,” he said.

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Neil Versel

By Neil Versel

Neil has been covering healthcare IT since 2000, across a wide range of publications. He specializes in health information technology, mobile healthcare, healthcare quality, physician practice and hospital management and healthcare finance.
Visit website | More posts by Author

3 comments
Curtis A Bagne
Curtis A Bagne

Personalized medicine needs a breakthrough in phenomics much greater and more disruptive for medicine and healthcare than what the $1,000 genome from Illumina is doing for genomics. Here are two examples, one for diagnosis and one for treatment evaluation.

1) DSM-10 for diagnosis still classifies patients as by signs and symptoms of disease as distinct from measures of how molecular parts and other aspects of living systems interact over time in living systems. This convention makes it hard to identify genetic predictors of disease.

2) Current gold standard clinical trial designs as for drug development and regulation still use group averages in a way that washes out the effects of genetic differences. This convention makes it hard to identify genetic predictors of differential response.

Too much of genotype/phenotype mapping is like trying to nail phenomic jelly to a wall with genetic nails.

DataSpeaks offers an algorithm for Computed iBiomarker Phenotypes from time-ordered data. This algorithm is designed to help solve this mapping problem to advance personalized medicine.