Cray Cray and the PGA

Happy Friday.

For golfers everywhere, it’s an especially special week (and weekend).

Normally, the PGA Championship is the last major championship of the year, played in the heat of the August sun.

This year, the tournament has been moved up to mid-May, and is being played at what they call the “peoples’ country club,” Bethpage Black.

Bethpage Black is the hardest of a number of golf courses open to the general public in Bethpage State Park in Long Island, New York.

It has also been home to a couple of U.S. Opens, one in 2002 and again in 2009…it was, in fact, the first public golf course to host a U.S. Open.

So, that’s the backstory. And while everyone is excited to hear about Tiger Woods play after winning the Masters this year, it was Brooks Koepka, three-time major and one-time PGA Championship winner who sunk putts from every which direction and every which length yesterday who took the lead at 7 under par.

Koepka is due back on the Black this PM, and while he has a couple of great players making chase, including Jordan Spieth who’s in today at a cumulative 5 under and Dustin Johnson (-4), it appears Koepka is in charge of his destiny this PM.

As for destiny, let’s jump over to some Friday PM tech news.

First up, for those of you who remember the hey day of supercomputing (whenever that was), you’ll remember Cray Inc.

Hewlett Packard Enterprise is taking Cray off the board for $1.30 billion, roughly $35 per share and a premium of 17.4 percent to Cray’s last close, according to Reuters.

At last count, Cray’s supercomputing systems can handle big data sets, converged modeling, simulations, AI, and analytics workloads.

If this news makes you ill, you might want to check into Health at Scale. TechCrunch is reporting that the AI healthcare startup has raised $16M in a Series A round.

The startup has founders with both medical and engineering backgrounds, and writes that it “wants to bring machine learning to bear on healthcare treatment options to produce outcomes with better results and less aftercare.”

The idea is to make treatment decisions more data-driven. While they aren’t sharing their data sources, they say they have information, from patients with a given condition, to doctors who treat that condition, to facilities where the treatment happens. By looking at a patient’s individual treatment needs and medical history, they believe they can do a better job of matching that person to the best doctor and hospital for the job. They say this will result in the fewest post-operative treatment requirements, whether that involves trips to the emergency room or time in a skilled nursing facility, all of which would end up adding significant additional cost.

Anything to improve the condition of the American healthcare system.

CVS to Buy Aetna in $69 Billion Deal

CVS Health said on Sunday that it had agreed to buy Aetna for about $69 billion, in a deal that would combine the drugstore with one of the biggest health insurers in the United States, according to a report from The New York Times.

The merger comes at a time of turbulent transformation in health care. Insurers, hospitals and pharmacy companies are bracing for a possible disruption in government programs like Medicare as a result of the Republicans’ plan to cut taxes. Congress remains at an impasse over the future of the Affordable Care Act, while employers and consumers are struggling under the weight of rising medical costs, including the soaring price of prescription drugs. And rapid changes in technology have raised the specter of new competitors — most notably Amazon. A combined CVS-Aetna could position itself as a formidable figure in this changing landscape. Together, the companies touch most of the basic health services that people regularly use, providing an opportunity to benefit consumers. CVS operates a chain of pharmacies and retail clinics that could be used by Aetna to provide care directly to patients, while the merged company could be better able to offer employers one-stop shopping for health insurance for their workers.
– via www.nytimes.com

 

But as the Times goes on to observe, critics worry customers could find their healthcare choices sharply limited (i.e., less choice of where to fill a prescription or get care if so many roads lead through a combined CVS/Aetna.

But in the announcement, the companies pointed out clear synergies that would benefit patients:

the two companies emphasized their ability to transform CVS’s 10,000 pharmacy and clinic locations into community-based sites of care that would be far less expensive for patients. “We think of it as creating a new front door to health care in America,” CVS Health’s chief executive, Larry J. Merlo, said in an interview. The merger would establish a new way of delivering care, with nurses, pharmacists and others available to counsel people about their diabetes or do the lab work necessary to diagnose a condition, Mr. Merlo said. “We know we can make health care more affordable and less expensive.”
– via www.nytimes.com

Looming in the background, the Times observes, a lingering Amazon and Jeff Bezos, rumored to be preparing for an entry into the pharmacy business.

As to antitrust considerations, both companies played down the prospect of regulation, arguing that the takeover is a “vertical merger” combining companies in two different industries.

JDRF and IBM Collaborate to Research Risk Factors for Type 1 Diabetes in Children

IBM and JDRF, the leading global organization funding type 1 diabetes (T1D) research, today announced a new collaboration to develop and apply machine learning methods to analyze years of global T1D research data and identify factors leading to the onset of T1D in children.

T1D affects approximately 1.25 million Americans, and it currently does not have a cure. This research collaboration is expected to create an entry point for T1D in the field of precision medicine, by combining JDRF’s connections to research teams around the globe and its subject matter expertise in T1D research with the technical capability and computing power of IBM.

“At JDRF, we are absolutely committed to seeing a world without type 1 diabetes, and with this partnership, we’re adding some of the most advanced computing power in the world to our mission,” said Derek Rapp, JDRF President and CEO.

“JDRF supports researchers all over the world, but never before have we been able to analyze their data comprehensively, in a way that can tell us why some children who are at risk get T1D and others do not. IBM’s analysis of the existing data could open the door to understanding the risk factors of T1D in a whole new way, and to one day finding a way to prevent T1D altogether.”

IBM scientists will look across at least three different data sets and apply machine learning algorithms to help find patterns and factors at play, with the goal of identifying ways that could delay or prevent T1D in children. In order to match variables and data formats and compare the differing data sets, the scientists plan to leverage previously collected data from global research projects.

Data analysis will explore the inclusion of genetic, familial, autoantibody and other variables to create a foundational set of features that is common to all data sets. The models that will be produced will quantify the risk for T1D from the combined dataset using this foundational set of features. As a result, JDRF will be in a better position to identify top predictive risk factors for T1D, cluster patients based on top risk factors, and explore a number of data-driven models for predicting onset.

“Nearly 40,000 new cases of type 1 diabetes will be diagnosed in the U.S. this year. And each new patient creates new records and new data points that, if leveraged, could provide additional understanding of the disease,” says Jianying Hu, Senior Manager and Program Director, Center for Computational Health at IBM Research. “The deep expertise our team has in artificial intelligence applied to healthcare data makes us uniquely positioned to help JDRF unlock the insights hidden in this massive data set and advance the field of precision medicine towards the prevention and management of diabetes.”

Future phases of the collaboration may consist of furthering the analysis of big data toward the goal of better understanding causes of T1D. They may also consist of analyzing more complex datasets, such as microbiome and genomics or transcriptomics data. Finally, but no less importantly, the knowledge gained through these efforts could also help JDRF in its pursuit of a cure for people with T1D.

IBM and University of Alberta Publish New Data on Machine Learning Algorithms to Help Predict Schizophrenia

IBM scientists and the University of Alberta in Edmonton, Canada, have published new data in Nature’s partner journal, Schizophrenia1, demonstrating that AI and machine learning algorithms helped predict instances of schizophrenia with 74% accuracy.

This retrospective analysis also showed the technology predicted the severity of specific symptoms in schizophrenia patients with significant correlation, based on correlations between activity observed across different regions of the brain. This pioneering research could also help scientists identify more reliable objective neuroimaging biomarkers that could be used to predict schizophrenia and its severity.

Schizophrenia is a chronic and debilitating neurological disorder that affects 7 or 8 out of every 1,000 people. Those with schizophrenia can experience hallucinations, delusions or thought disorders, along with cognitive impairments, such as an inability to pay attention and physical impairments, such as movement disorders.

“This unique, innovative multidisciplinary approach opens new insights and advances our understanding of the neurobiology of schizophrenia, which may help to improve the treatment and management of the disease,” says Dr. Serdar Dursun, a Professor of Psychiatry & Neuroscience with the University of Alberta. “We’ve discovered a number of significant abnormal connections in the brain that can be explored in future studies, and AI-created models bring us one step closer to finding objective neuroimaging-based patterns that are diagnostic and prognostic markers of schizophrenia.”

In the paper, researchers analyzed de-identified brain functional Magnetic Resonance Imaging (fMRI) data from the open data set, Function Biomedical Informatics Research Network (fBIRN) for patients with schizophrenia and schizoaffective disorders, as well as a healthy control group. fMRI measures brain activity through blood flow changes in particular areas of the brain.

Specifically, the fBIRN data set reflects research done on brain networks at different levels of resolution, from data gathered while study participants conducted a common auditory test. Examining scans from 95 participants, researchers used machine learning techniques to develop a model of schizophrenia that identifies the connections in the brain most associated with the illness.

The results of the IBM and University of Alberta research demonstrated that, even on more challenging neuroimaging data collected from multiple sites (different machines, across different groups of subjects etc.) the machine learning algorithm was able to discriminate between patients with schizophrenia and the control group with 74% accuracy using the correlations in activity across different areas of the brain. 

Additionally, the research showed that functional network connectivity could also help determine the severity of several symptoms after they have manifested in the patient, including inattentiveness, bizarre behavior and formal thought disorder, as well as alogia, (poverty of speech) and lack of motivation.

The prediction of symptom severity could lead to a more quantitative, measurement-based characterization of schizophrenia; viewing the disease on a spectrum, as opposed to a binary label of diagnosis or non-diagnosis. This objective, data-driven approach to severity analysis could eventually help clinicians identify treatment plans that are customized to the individual. 

“The ultimate goal of this research effort is to identify and develop objective, data-driven measures for characterizing mental states, and apply them to psychiatric and neurological disorders” said Ajay Royyuru, Vice President of Healthcare & Life Sciences, IBM Research. “We also hope to offer new insights into how AI and machine learning can be used to analyze psychiatric and neurological disorders to aid psychiatrists in their assessment and treatment of patients.”

The Research Domain Criteria (RDoC) initiative of NIMH emphasizes the importance of objective measurements in psychiatry. This field, often referred to as “computational psychiatry”, aims to use modern technology and data driven approaches to improve evidence-based medical decision making in psychiatry, a field that often relies upon subjective evaluation approaches.

As part of the ongoing partnership, researchers will continue to investigate areas and connections in the brain that hold significant links to schizophrenia. Work will continue on improving the algorithms by conducting machine learning analysis on larger datasets, and by exploring ways to extend these techniques to other psychiatric disorders such as depression or post-traumatic stress disorder.

You can learn more about IBM Watson Health solutions here.

IBM Watson Training To Scan For Retina Abnormalities

IBM Research has announced new research developments in IBM Watson’s ability to detect abnormalities of the eye’s retina.

The Melbourne based IBM researchers have trained a research version of Watson to recognize abnormalities in retina images, which could in the future offer doctors greater insights and speed in their early identification of patients who may be at risk of eye diseases – such as glaucoma, a leading cause of blindness in the developed world.

The research began in 2015 and the latest work has focused on streamlining some of the manual processes experienced by doctors today. This includes distinguishing between left and right eye images, evaluating the quality of retina scans, as well as ranking possible indicators of glaucoma.

Glaucoma has been named “the silent thief of sight” as many patients remain undiagnosed until irreversible vision loss occurs. Glaucoma can be treated but early detection is critical, with doctors currently relying on regular eye examination screening programs.

The researchers applied deep learning techniques and image analytics technology to 88,000 de-identified retina images accessed through EyePACS®, to analyze key anomalies of the eye.

The research results demonstrate Watson’s ability to accurately measure the ratio of the optic cup to disc – which is a key sign of glaucoma – with statistical performance as high as 95 percent. The technology has also been trained to distinguish between left and right eye images (with up to 94 percent confidence), which are important for downstream analysis and for the development effective treatment programs.

The research is expected to continue to improve over time as the research technology expands to detect features of other eye diseases such as diabetic retinopathy and age-related macular degeneration.

You can learn more about IBM Research efforts here.

IBM Announces Joint Blockchain Collaboration Initiative With U.S. Federal Drug Administration

IBM Watson Health has signed a research initiative with the U.S. Food and Drug Administration (FDA) aimed at defining a secure, efficient and scalable exchange of health data using blockchain technology.

IBM and the FDA will explore the exchange of owner mediated data from several sources, such as Electronic Medical Records, clinical trials, genomic data, and health data from mobile devices, wearables and the “Internet of Things.” The initial focus will be on oncology-related data.

Transformative healthcare solutions are possible when healthcare researchers and providers have access to a 360-degree view of patient data. Today, patients have little access to their health data and cannot easily share with researchers or providers. 

Giving patients the opportunity to share their data securely, for research purposes or across their healthcare providers, creates opportunities for major advancements in healthcare. Blockchain technology, which enables organizations to work together with more trust, is designed to help make this a reality.

By keeping an audit trail of all transactions on an unalterable distributed ledger, blockchain technology establishes accountability and transparency in the data exchange process. In the past, large scale sharing of health data has been limited by concerns of data security and breaches of patient privacy during the data exchange process.

IBM and the FDA will explore how a blockchain framework can 
potentially provide benefits to public health by supporting important use cases for information exchange across a wide variety of data types, including clinical trials and “real world” evidence data. 

New insights combining data across the healthcare ecosystem can potentially lead to new biomedical discoveries. Patient data from wearables and connected devices for example, can help doctors and caregivers better manage population health.  

The collaboration will also address new ways to leverage the large volumes of diverse data in today’s biomedical and healthcare industries. A secure owner-mediated data sharing ecosystem could potentially hold the promise of new discoveries and improved public health.

The initiative with the FDA is a two-year agreement. IBM Watson Health and the FDA plan to share initial research findings in 2017.

You can learn more about IBM Blockchain capabilities here.

Dr. Watson Finds Bedside Manner

Back in September of 2011 I mentioned in this blog post that one of Watson’s first jobs outside of playing Jeopardy! was going to be in the healthcare industry.

Well, earlier today WellPoint, Inc. and Memorial Sloan-Kettering Cancer Center today unveiled the first commercially developed Watson-based cognitive computing breakthroughs.

These innovations stand alone to help transform the quality and speed of care delivered to patients through individualized, evidence based medicine.

Check out this short video to learn more about how physicians and other medical professionals are able to use IBM’s Watson technology to help them with their medical diagnostic tasks.

The American Cancer Society projects that 1.6 million new cancer cases will be diagnosed in the U.S. this year alone.  Studies suggest that the complexities associated with healthcare have caused one in five health care patients to receive a wrong or incomplete diagnosis.

These statistics, coupled with a data explosion of medical information that is doubling every five years, represents an unprecedented opportunity for the health care industry and next generation cognitive computing systems, to combine forces in new ways to improve how medicine is taught, practiced and paid for.

For more than a year now, IBM has partnered separately with WellPoint and Memorial Sloan-Kettering to train Watson in the areas of oncology and utilization management.

During this time, clinicians and technology experts spent thousands of hours “teaching” Watson how to process, analyze and interpret the meaning of complex clinical information using natural language processing, all with the goal of helping to improve health care quality and efficiency.

“IBM’s work with WellPoint and Memorial Sloan-Kettering Cancer Center represents a landmark collaboration in how technology and evidence based medicine can transform the way in which health care is practiced,” said Manoj Saxena, IBM General Manager, Watson Solutions (see my interview with Manoj at last fall’s InterConnect event in Singapore further down in the post).

“These breakthrough capabilities bring forward the first in a series of Watson-based technologies, which exemplifies the value of applying big data and analytics and cognitive computing to tackle the industries most pressing challenges.”

Evidence Based Medicine: Addressing Oncology Issues By Quickly Assimilating Massive Amounts Of Medical Information

To date, Watson has ingested more than 600,000 pieces of medical evidence, two million pages of text from 42 medical journals and clinical trials in the area of oncology research.

Watson has the power to sift through 1.5 million patient records representing decades of cancer treatment history, such as medical records and patient outcomes, and provide to physicians evidence based treatment options all in a matter of seconds.

In less than a year, Memorial Sloan-Kettering has immersed Watson in the complexities of cancer and the explosion of genetic research which has set the stage for changing care practices for many cancer patients with highly specialized treatments based on their personal genetic tumor type.

Starting with 1,500 lung cancer cases, Memorial Sloan-Kettering clinicians and analysts are training Watson to extract and interpret physician notes, lab results and clinical research, while sharing its profound expertise and experiences in treating hundreds of thousands of patients with cancer.

“It can take years for the latest developments in oncology to reach all practice settings. The combination of transformational technologies found in Watson with our cancer analytics and decision-making process has the potential to revolutionize the accessibility of information for the treatment of cancer in communities across the country and around the world,” said Craig B.Thompson, M.D., President of Memorial Sloan-Kettering Cancer Center. “Ultimately, we expect this comprehensive, evidence-based approach will profoundly enhance cancer care by accelerating the dissemination of practice-changing research at an unprecedented pace.”

The Maine Center for Cancer Medicine and WESTMED Medical Group are the first two early adopters of the capability. Their oncologists will begin testing the product and providing feedback to WellPoint, IBM and Memorial Sloan-Kettering to improve usability.

Speeding Patient Care Through WellPoint’s Utilization Management Pilot

Throughout WellPoint’s utilization management pilot, Watson absorbed more than 25,000 test case scenarios and 1,500 real-life cases, and gained the ability to interpret the meaning and analyze queries in the context of complex medical data and human and natural language, including doctors notes, patient records, medical annotations and clinical feedback.

In addition, more than 14,700 hours of hands-on training was spent by nurses who meticulously trained Watson. Watson continues to learn while on the job, much like a medical resident, while working with the WellPoint nurses who originally conducted its training.

Watson started processing common, medical procedure requests by providers for members in WellPoint affiliated health plans in December, and was expanded to include five provider offices in the Midwest. Watson will serve as a powerful tool to accelerate the review process between a patient’s physician and their health plan.

“The health care industry must drive transformation through innovation, including harnessing the latest technology that will ultimately benefit the health care consumer,” said Lori Beer, WellPoint’s executive vice president of Specialty Businesses and Information Technology. “We believe that WellPoint’s data, knowledge and extensive provider network, combined with the IBM Watson technology and Memorial Sloan-Kettering’s oncological expertise can drive this transformation.”

Watson-Powered Health Innovations

As a result, IBM, Memorial Sloan-Kettering and WellPoint are introducing the first commercially based products based on Watson. These innovations represent a breakthrough in how medical professionals can apply advances in analytics and natural language processing to “big data,” combined with the clinical knowledge base, including genomic data, in order to create evidence based decision support systems.

These Watson-based systems are designed to assist doctors, researchers, medical centers, and insurance carriers, and ultimately enhance the quality and speed of care.  The new products include the Interactive Care Insights for Oncology, powered by Watson, in collaboration with IBM, Memorial Sloan-Kettering and WellPoint.

The WellPoint Interactive Care Guide and Interactive Care Reviewer, powered by Watson, designed for utilization management in collaboration with WellPoint and IBM.

New Interactive Care Insights for Oncology  

• The cognitive systems use insights gleaned from the deep experience of Memorial Sloan-Kettering clinicians to provide individualized treatment options based on patient’s medical information and the synthesis of a vast array of updated and vetted treatment guidelines, and published research.
• A first of-its-kind Watson-based advisor, available through the cloud, that is expected to assist medical professionals and researchers by helping to identify individualized treatment options for patients with cancer, starting with lung cancer.
• Provides users with a detailed record of the data and information used to reach the treatment options. Oncologists located anywhere can remotely access detailed treatment options based on updated research that will help them decide how best to care for an individual patient.

New WellPoint Interactive Care Guide and Interactive Care Reviewer 

• Delivers the first Watson-based cognitive computing system anticipated to streamline the review processes between a patient’s physician and their health plan, potentially speeding approvals from utilization management professionals, reducing waste and helping ensure evidence-based care is provided.
• Expected to accelerate accepted testing and treatment by shortening pre-authorization approval time, which means that patients are moving forward with the first crucial step toward treatment more quickly.
• Analyzes treatment requests and matches them to WellPoint’s medical policies and clinical guidelines to present consistent, evidence-based responses for clinical staff to review, in the anticipation of providing faster, better informed decisions about a patient’s care.
• WellPoint has deployed Interactive Care Reviewer to a select number of providers in the Midwest, and believes more than 1,600 providers will be using the product by the end of the year.

Watson: Then and Now

The IBM Watson system gained fame by beating human contestants on the television quiz show Jeopardy! almost two years ago. Since that time, Watson has evolved from a first-of-a-kind status,  to a commercial cognitive computing system gaining a 240 percent improvement in system performance,  and a reduction in the system’s physical requirements by 75 percent and can now be run on a single Power 750 server.

The transformational technology, named after IBM founder Thomas J. Watson, was developed in IBM’s Research Labs. Using advances in natural language processing and analytics, the Watson technology can process information similar to the way people think, representing a significant shift in the ability for organizations to quickly analyze, understand and respond to vast amounts of Big Data.

The ability to use Watson to answer complex questions posed in natural language with speed, accuracy and confidence has enormous potential to improve decision making across a variety of industries from health care, to retail, telecommunications and financial services.

For more information on IBM Watson, please visit www.ibmwatson.com.

You can also follow Watson on Facebook here, and via Twitter at hashtag #IBMWatson.

And below, you can see the aforementioned video where I interviewed IBM Watson general manager Manoj Saxena about Watson’s future at last year’s IBM InterConnect event.

Ooh Ooh That Smell — IBM’s 2012 “5 in 5”: Innovations Of The Senses

IBM released its annual “5 in 5” list yesterday, the seventh year in a row whereby IBM scientists identify a list of innovations that have the potential to change the way people work, live and interact during the next five years.

The IBM 5 in 5 is based on market and societal trends, as well as emerging technologies from IBM’s R&D labs around the world. This year, the 5 explores innovations that will be underpinnings of the next era of computing, what IBM has described as “the era of cognitive systems.”

This next generation of machines will learn, adapt, sense, and begin to experience the world as it really is, and this year’s predictions focus on one element of the this new era: The ability of computers to mimic the human senses — in their own manner, to see, smell, touch, taste and hear.

But before you try and spoon-feed your iPad some vanilla yogurt, let’s get more practical.

These new sensing capabilities will help us become more aware, productive, and help us think — but not do our thinking for us.

Rather, cognitive systems will help us see through and navigate complexity, keep up with the speed of information, make more informed decisions, improve our health and standard of living, and break down all kinds of barriers — geographical, language, cost, even accessibility.

Now, on to our five senses.

1) Touch: You will be able to touch through your phone.  Imagine using your smartphone to shop for your wedding dress and being able to feel the satin or silk of the gown, or the lace on the veil, from the surface on the screen. Or to feel the beading and weave of a blanket made by a local artisan half way around the world. In five years, industries like retail will be transformed by the ability to “touch” a product through your mobile device.

IBM scientists are developing applications for the retail, healthcare and other sectors using haptic, infrared and pressure sensitive technologies to simulate touch, such as the texture and weave of a fabric — as a shopper brushes her finger over the image of the item on a device screen. Utilizing the vibration capabilities of the phone, every object will have a unique set of vibration patterns that represents the touch experience: short fast patterns, or longer and stronger strings of vibrations. The vibration pattern will differentiate silk from linen or cotton, helping simulate the physical sensation of actually touching the material.

2) Sight: A pixel will be worth a thousand words. We take some 500 billion photos a year, and 72 hours of video is uploaded to YouTube every minute. But computers today only understand pictures by the text we use to tag or title them; the majority of the information — the actual content of the image — is a mystery.

In the next five years, systems will not only be able to look at and recognize the contents of images and visual data, they will turn the pixels into meaning, making sense out of it similar to the way a human views and interprets a photographs. In the future, “brain-like” capabilities will let computers analyze features such as color, texture patterns or edge information and extract insights from visual media, having a potentially huge impact on industries ranging from healthcare to retail to agriculture.

But please, no Escher drawings, at least for now…that’s just plain mean.

3) Hearing: Computers will hear what matters.  Ever wish you could make sense of all the sounds around you and be able to understand what’s not being said? Within five years, distributed systems of clever sensors will detect elements of sound such as sound pressure, vibrations and sound waves at different frequencies.

It will interpret these inputs to predict when trees will fall in a forest or when a landslide is imminent. Such a system will “listen” to our surroundings and measure movements, or the stress in a material, to warn us if danger lies ahead.

I’m ever hopeful such systems will be able to “listen” to my golf swing and help me course correct so I can play more target golf!

4) Taste: Digital taste buds will help you to eat smarter. What if we could make healthy foods taste delicious using a different kind of computing system built for creativity? IBM researchers are developing a computing system that actually experiences flavor, to be used with chefs to create the most tasty and novel recipes. It will break down ingredients to their molecular level and blend the chemistry of food compounds with the psychology behind what flavors and smells humans prefer.

By comparing this with millions of recipes, the system will be able to create new flavor combinations that pair, for example, roasted chestnuts with other foods such as cooked beetroot, fresh caviar, and dry-cured ham.

“Top Tasting Computer Chefs,” anyone?

5) Smell: Computers will have a sense of smell. During the next five years, tiny sensors embedded in your computer or cell phone will detect if you’re coming down with a cold or other illness. By analyzing odors, biomarkers and thousands of molecules in someone’s breath, doctors will have help diagnosing and monitoring the onset of ailments such as liver and kidney disorders, asthma, diabetes, and epilepsy by detecting which odors are normal and which are not.

Already, IBM scientists are sensing environment conditions to preserve works of art, and this innovation is starting to be applied to tackle clinical hygiene, one of the biggest healthcare challenges today. In the next five years, IBM technology will “smell” surfaces for disinfectants to determine whether rooms have been sanitized. Using novel wireless mesh networks, data on various chemicals will be gathered and measured by sensors, and continuously learn and adapt to new smells over time.

Watch the video below to listen to IBM scientists describe some of these new innovations and their potential impact on our world.

Watson Goes Back To School

Im my capacity as a cheerleader for my virtual big brother, IBM’s Watson technology, I’ve received a lot of questions along the way about how does IBM plan to use the technology in industry, and how can we most effectively put Watson to work.

Great questions, and the answer is, it depends.

Yesterday, for example, we announced a new program in partnership with the Cleveland Clinic in Cleveland, Ohio, that will create a collaboration to advance Watson’s use in the medical training field.

The IBM researchers that built Watson are going to work with Cleveland Clinic clinicians, faculty, and medical students to enhance the capabilities of Watson’s Deep Question Answering technology for the area of medicine.

Calling Dr. Watson

Watson’s ability to analyze the meaning and context of human language and quickly process information to piece together evidence for answers can help healthcare decision makers, such as clinicians, nurses and medical students, unlock important knowledge and facts buried within huge volumes of information.

Watson has been gaining knowledge in the field of medicine, and Cleveland Clinic with IBM recognized the opportunity for Watson to interact with medical students to help explore a wide variety of learning challenges facing the medical industry today.

Rather than attempting to memorize everything in text books and medical journals (now acknowledged as an impossible task), students are learning through doing — taking patient case studies, analyzing them, coming up with hypotheses, and then finding and connecting evidence in reference materials and the latest journals to identify diagnoses and treatment options in the context of medical training.

This process of considering multiple medical factors and discovering and evidencing solution paths in large volumes of data reflects the core capabilities of the Watson technology.

Watson Providing Problem-Based Learning Curriculum

Medical students will interact with Watson on challenging cases as part of a problem-based learning curriculum and in hypothetical clinical simulations.

A collaborative learning and training tool utilizing the Watson technology will be available to medical students to assist in their education to learn the process of navigating the latest content, suggesting and considering a variety of hypotheses and finding key evidence to support potential answers, diagnoses and possible treatment options.

“Every day, physicians and scientists around the world add more and more information to what I think of as an ever-expanding, global medical library,” said C. Martin Harris, M.D., Chief Information Officer of Cleveland Clinic. “Cleveland Clinic’s collaboration with IBM is exciting because it offers us the opportunity to teach Watson to ‘think’ in ways that have the potential to make it a powerful tool in medicine. Technology like this can allow us to leverage that medical library to help train our students and also find new ways to address the public health challenges we face today.”

Watson Will Learn From Medical Students

Students will help improve Watson’s language and domain analysis capabilities by judging the evidence it provides and analyzing its answers within the domain of medicine. Through engagement with this education tool and Watson, medical students and Watson will benefit from each other’s strengths and expertise to both learn and improve their collaborative performance.

The collaboration will also focus on leveraging Watson to process an electronic medical record (EMR) based on a deep semantic understanding of the content within an EMR.

The shift is clearly away from memorization and towards critical thinking where medical training programs will help student to use powerful discovery and language analysis tools like Watson to help them evaluate medical case scenarios and find evidence to help them carefully rationalize decisions. The physicians will rely on their own experience and expert critical thinking skills to read the evidence and make the final judgments.

“The practice of medicine is changing and so should the way medical students learn. In the real world, medical case scenarios should rely on people’s ability to quickly find and apply the most relevant knowledge. Finding and evaluating multistep paths through the medical literature is required to identify evidence in support of potential diagnoses and treatment options,” said Dr. David Ferrucci, IBM Fellow and Principal Investigator of the Watson project.

Over time, the expectation is that Watson will get “smarter” about medical language and how to assemble good chains of evidence from available content. Students will learn how to focus on critical thinking skills and how to best leverage informational tools like Watson in helping them learn how to diagnose and treat patients.

IBM and Cleveland Clinic will discuss the role of Watson for the future of healthcare and healthcare education this week at the Cleveland Clinic Medical Innovation Summit being held October 29-31, 2012 in Cleveland, OH.

I sat down recently at the IBM InterConnect event in Singapore to conduct a fascinating mid-year employee performance review for IBM’s Watson technology with Watson GM Manoj Saxena.  You can see the fruits of our discussion in the video below.

Live @ Information On Demand 2012: Craig Rhinehart On Predictive Healthcare

I made it back to Austin late last night, mostly no worse for the wear.

There were a number of key announcements made at Information On Demand 2012 over the course of the past few days in Las Vegas.

One of those that I mentioned in one of my keynote post summaries was IBM Patient Care and Insights, new analytics software based on innovations from IBM Labs that helps healthcare organizations improve patient care and lower operational costs by considering the specific health history of each individual patient.

This is a fascinating new capability with profound implications for healthcare providers.

The new IBM solution provides the core capabilities for devising predictive models of various health conditions, which can be used to identify early intervention opportunities to improve the patient’s outlook by minimizing or avoiding potential health problems.

It features advanced analytics and care management capabilities to help identify early intervention opportunities and coordinate patient care.

Providing Individualized Care

At the core of IBM Patient Care and Insights, developed by IBM’s software, research and services teams, are similarity analytics that help drive smart, individualized care delivery.

Born in IBM Research, IBM similarity analytics is a set of core capabilities and algorithms that allow healthcare professionals to examine thousands of patient characteristics at once — including demographic, social, clinical and financial factors along with unstructured data such as physicians’ notes — to generate personalized evidence and insights, and then provide care according to personalized treatment plans.

By way of example, physicians can make personalized recommendations to improve a patient’s outcome by finding other patients with similar clinical characteristics to see what treatments were most effective or what complications they may have encountered.

They can also perform patient-physician matching so an individual is paired with a doctor that is optimal for a specific condition. With this solution, caregivers can better tap into the collective memory of the care delivery system to uncover new levels of tailored insight or “early identifiers” from historical/long term patient data that enable doctors and others to help manage a patient’s healthcare needs well into the future.

Craig Rhinehart, director for IBM’s ECM Strategy and Market Development organization, sat down with Scott Laningham and I earlier this week to describe the challenges facing health care, and how the IBM Patient Care and Insights can help improve health care by delivering dynamic case-based, patient-centric electronic care plans and population analysis.

Go here for more information on IBM Patient Care and Insights and IBM Intelligent Investigation Manager.