SOLVE: HEALTHCARE – Intel and Partners Demonstrate Real Uses for Artificial Intelligence in Healthcare

When used together, the terms “healthcare” and “artificial intelligence” (AI) often conjure up images of “robot surgeons.” That’s one use case – and a promising one – but there are other, less photo-worthy AI examples that warrant our attention, such as the use of data “lakes” to develop insights on large patient populations in order to help predict, pre-empt and prevent people from getting sicker than they already are.

Or using AI to gain a better understanding of the human genome to more precisely deliver patient care. Or using AI to detect the massive waste, fraud and abuse in healthcare spending today – estimated at 3 to 10 percent1 of the more than $3 trillion spent in the U.S. annually2.

AI applications like these are starting to make an impact on our healthcare system today and are poised to revolutionize everything from the back office to the doctor’s office, from the emergency room to the living room. Intel together with several of our partners is demonstrating this progress today at our inaugural SOLVE: Healthcare event.

Read on to learn more about some of the innovative companies applying AI to improve the delivery of healthcare. The solutions demonstrated here today represent a fraction of the companies Intel is working with on new use cases. Visit the Intel newsroom to learn more.

AccuHealth*: Remote Patient Monitoring

Responsible for 86 percent the annual health care expenditure in the US3, chronic illness is one of the most pressing challenges facing our health care system today. The average cost of an ER visit ranges anywhere between $50 to $3,0004 and the average cost of a day in the hospital starts as $1,8005. Costs soar even higher with each care-related procedures and treatments performed. For every ER visit and hospital bill, there is often a chronically ill patient who may be in a life-threatening situation To help prevent these acute events, keep chronic illnesses in check, and avoid the associated costs, AccuHealth, a Chilean-based startup, has developed a patient-centric healthcare model that shifts reactionary facility-based care to preventative home-based remote care.

Using wearable sensors linked to a smart Intel-based monitoring device that connects to the AccuHealth virtual hospital remote monitoring center, patients perform 3-5 minute “check-ups” throughout the day from the comfort of their homes or office. Patient data is sent in real time to a data center where powerful processors apply data mining and predictive modeling to identify anticipate any changes of concern which can be addressed before they escalate to acute and costly care. In the event of any alarming shifts, caregivers can proactively intervene before an ER visit or hospitalization are required.

To date, AccuHealth has monitored over 15,000 patients. The data indicates a 42 percent decrease in emergency room visits, and over a 30 percent savings for participating insurance companies. To date, AccuHealth has successfully implemented this solution in Chile.

Broad* Institute: Developing a Genome Analysis Toolkit to Better Understand Disease

The human genome includes 3 billion base pairs of DNA. By analyzing and understanding vast amounts of genomic data across tens of thousands of people, researchers are gaining insights into how diseases form — and what we can do to fight them.

Broad Institute developed the Genome Analysis Toolkit* (GATK) to analyze the rapidly growing body of genomic data, predicted to be multiple exabytes’ worth by 2025. Intel helped optimize code and develop the reference architecture called BIGStack. This open-source, AI-enabled toolkit can rapidly tackle vast amounts of data (Broad scientists generate 24 terabytes of data per day, and more than 57,000 researchers around the world use the GATK) while providing some of the most consistent, accurate results available to researchers and clinicians.

The design of many GATK tools has evolved with recent advances in machine learning, enabling the development of new and improved analytical capabilities, which in turn has enabled researchers, clinicians and industry users to make novel discoveries and pave the way for precision medicine. Ultimately, this may translate into faster treatment identification by clinicians for diseases such as cancer, Alzheimer’s, Type 2 diabetes, and schizophrenia, as well as faster biomarker targeting by pharmaceutical companies.

Diaceutics*: Using AI to Improve Patient Diagnostics

Today, it takes 15 years and $3 billion dollars to bring a drug to market. Diaceutics, an Ireland-based diagnostic commercialization service provider, works with pharmaceutical companies to improve patient outcomes and strategically integrate diagnostic testing with targeted drug treatments. Currently, the company works with 29 of the world’s top 30 pharmaceutical companies to inform and guide precision medicine development and launch.

Collaborating with Intel and using Intel Xeon processors, Diaceutics can leverage its vast, global database of patient test data, using AI – in real time – to analyze big data reserves, recognize patterns and identify new patient subsets. The goal: to inform, expedite and improve diagnoses, treatment and outcomes for patients with similar characteristics.

One example: in the U.S. alone, an estimated 78,000 cancer patients are not properly tested each year, meaning that they are missing out on potentially lifesaving medications. Diaceutics’ AI and machine learning, however, can quickly analyze biomarkers and disease patterns for a variety of tumors and cancer types that can lead to at-risk patients being identified, diagnosed and treated more quickly and accurately. With expertise in the laboratory, diagnostics and pharmaceuticals, the company is transforming an industry model for diagnosing and treating patients, further unlocking the promise of precision medicine.

Doctor Hazel*: An App to Detect Skin Cancer

According to the Skin Cancer Foundation, skin cancer is the most common cancer in the U.S. Half of the population will be diagnosed with some form of skin cancer by the time they reach age 65. Without early detection, the five-year survival rate falls to 62 percent when the disease reaches the lymph nodes and to 18 percent when it metastasizes to distant organs6. However, when melanoma is found early, the survival rate is extremely high.

Software developers Mike Borozdin and Peter Ma, used a high-powered endoscope camera and the Intel® Movidius™ Neural Compute Stick, a tiny, fanless, deep-learning USB drive designed for high-performance AI programming, to create an artificial intelligence-powered app that in the future may used to detect skin cancer – in real time – by simply analyzing a photo of a mole.

Dubbed Doctor Hazel, the app sorts through a database of images, classifies the mole in question and instantly lets the person know if the mole looks benign or potentially cancerous. If the app raises a red flag, the person is advised to follow up with a dermatologist. The result: earlier detection, quicker treatment, and better patient outcomes.

GE Healthcare*: Using AI to Improve Imaging

Hospitals and health care systems often rely on medical imaging – MRIs, CT scans, PET scans, bone scans, etc. – to diagnose and determine treatment for patients.

GE Healthcare, a leader in healthcare imaging, is using applied intelligence to improve and speed up the imaging process, while also reducing costs for hospitals and health care systems using their equipment. GE has partnered with Intel on AI in medical imaging workflows and clinical diagnostic scanning to find ways to detect a disease in the early stages, transforming data into actionable insights. The aim: quicker, more informed clinician decisions and better patient outcomes. This includes reduced patient risk and dosage exposure – with faster image processing – to expedite a patient’s time to diagnosis and treatment.

By using Intel’s Xeon processors, GE anticipates improving radiologists’ reading productivity compared to the prior generation by reducing first image display to under two seconds and full study load times to under eight seconds. Additionally GE anticipates lowering the cost of ownership of the equipment by up to 25 percent.

With outstanding ability to extract and interpret data across healthcare IT systems, devices and imaging equipment, GE delivers a one-two punch of insight and actionability to help inform decision making and improve outcomes.

ICON* PLC, Michael J. Fox Foundation* (MJFF), and Teva Pharmaceutical Industries Ltd*.: Transforming Clinical Trials

In 2013, sparked by a request from former Intel CEO and President, Andy Grove, Intel IT partnered with the Michael J. Fox Foundation to improve research and treatment associated with Parkinson’s disease. The collaboration included a multiphase research study using an AI platform, to gain insights from patient data collected with wearable and mobile technologies. The result of this study? The Intel® Pharma Analytics Platform.

To date, MJFF and Teva Pharmaceutical have used the Intel® Pharma Analytics Platform to digitize clinical trials. Side by side with a traditional clinical trial that relies on burdensome clinic visits and paper diaries, the Intel® platform provides an edge-to-cloud AI solution that uses remote monitoring. This allows clinical data from sensors and wearable devices to be efficiently and continuously captured. It also applies machine learning techniques to develop objective measures for assessing symptoms and quantify the impact of therapies. Most recently, ICON PLC, a global provider of drug development solutions and services, has added the platform to further enhance its existing offerings.

So far, the platform has been used in over more than a dozen clinical trials, comprising more than 1.2 million hours of data collection from more than 1,000 patients. Trial results demonstrate increased compliance and patient retention. Building on Intel’s AI and analytics leadership, the Intel Pharma Analytics Platform provides powerful web-based analytics, running on high-performance Intel Xeon processor-based servers and Intel® Solid State Drives that can handle the massive volumes of streaming sensor data.

Intermountain Healthcare*: Pursuing Precision Medicine

Though many healthcare innovations originate in large academic institutions, Intermountain Healthcare, a Utah-based healthcare system that includes 22 hospitals and 185 clinics, shows that cutting-edge research can also take place within a large integrated health network.

Intermountain’s Precision Genomics group has more than 4 million samples in its biorepository that researchers are working to digitize, sequence and add to their data set so they can share throughout the network and with other partners via their Oncology Precision Network (OPeN).

Clinicians provide a personalized approach to testing, diagnosing and treating cancer. After a patient’s DNA has been sequenced, the results are discussed by Precision Genomics’ molecular tumor board, an expert panel of scientists, physicians, nurses, and pharmacists from inside and outside the Intermountain network. AI augments this capability allowing them to take on more cases more quickly and at a lower cost. The board meets in person and through teleconferencing to discuss treatment that best suits each individual patient.

Already, Intermountain believes they have doubled the overall survival rate of patients in its precision medicine program by providing more targeted treatment based on each individual’s specific cancer.

Mayo Clinic*: Merging Heterogenous Data Sources for Individualized Care

Today, medical data comes not just in the form of test results – from such things as CT scans, heart monitors or blood pressure cuffs – but also from wearable fitness devices and Internet-of-Things, home-based technologies. Until recently, there has been no way to merge these data fields to benefit patients. Leveraging contemporary architectures, including high-performance multi-core and many-core processors, Mayo has the computing power to bring these heterogeneous data pools together in a cohesive manner to analyze it and search for patterns indicating better ways to diagnose and treat individuals. The Mayo Clinic sees AI as the bridge they need to turn big data into real, actionable information to move past treating populations to treating individuals and bringing about the best possible outcome for each and every patient.

Montefiore Health System*: Predicting and Assessing Risk in Acute Patients

Staying in the intensive care unit can be quite costly, and the burden rises the longer a patient stays in the ICU and if they require ventilation. Longer stays also come with a higher the risk of complications.

To reduce patients’ time in ICU and the risk of hospital acquired conditions (HACs), cutting-edge hospitals are turning to data. To this end, Montefiore, a nationally ranked health system, the largest in the Bronx, has created an AI platform called PALM, which stands for Patient-centered Analytics and Learning Machine, that streams and mines data in near real time.

A constant theme is finding ways to predict when a patient is about to take a turn for the worse so caregivers can intervene before an acute event. For example, clinicians have used PALM to predict which patients in the ICU are at risk for respiratory failure. They are also using PALM to predict sepsis and other HACs before they become problematic.

Eventually, Montefiore caregivers hope to extend this prediction model to patients outside of the hospital, using a variety of data sources – including genetics, socioeconomic data, etc. – to determine who will develop chronic conditions as much as two years before they occur.

OptumLabs*: Using Data Science and Collaboration to Solve Health care’s biggest problems

Partnering with more than 25 thought-leading health care institutions, including Mayo Clinic, AARP*, American Cancer Society, and UC Health, OptumLabs is passionate about discovering solutions that help address health care’s greatest challenges. OptumLabs uses a rich data set of over 200 million de-identified patient lives, including administrative claims, medical records and self-reported health information, to generate actionable insights and build predictive AI models that can lead to earlier patient diagnosis, lower costs, and better patient outcomes.

With the help of state-of-the-art data science and evolving AI-driven technologies, such as those powered by powerful Intel Xeon processors and other tools, OptumLabs and its partners have done a lot of impactful work. This includes creating new kinds of patient clusters for those with heart failure and COPD and developing a unique dashboard of key performance indicators that are able to help organizations identify, benchmark and set performance targets to respond to the drivers of the national opioid epidemic.

OptumLabs has also been using machine learning to predict Alzheimer’s and dementia diagnosis years before diagnosis. Alzheimer’s Disease is a growing problem that is projected to affect more than 10 million Americans by 2040, and there is currently no cure in sight. OptumLabs has partnered with the Global CEO Initiative on Alzheimer’s Disease, and others, to identify predictive signals earlier and find people for clinical trials and other interventions. Initial models can identify signals of dementia four to eight years earlier than the first diagnosis, and the next phase of work strives to improve these results by using more nuanced clinical data and newer deep learning models.

Princeton Neuroscience Institute*: Treating Mental Disorders with AI Technologies

It used to be that the standard treatment for mental disorders such as clinical depression, anxiety, addiction (to smoking, for example) and PTSD, took place on the couch, with clinicians using cognitive behavioral therapy to help their patients overcome issues.

Today, however, researchers at the Princeton Neuroscience Institute are analyzing functional MRI (fMRI) technology to decode the brain and deliver treatment in the form of real-time neurofeedback for such afflictions.

The neurofeedback from the decoding is used to update stimulus provided to the subject – in the form of visuals, audio and/or instructions to perform certain tasks – which then modifies a patient’s brain states.

AI technologies include high-performance computing Intel Xeon processor clusters and an open source brain-imaging analysis kit called BrainIAK (brainiak.org) that provides a performance-optimized Python library that can scale-up laptop-based decoding to MPI clusters in the cloud.

Stanford University*, UC Berkeley*, Intel, and Lucile Packard Children’s Hospital*: Ushering in the Next Generation of Medical Imaging

Arterys*, a company originating from scientists at Stanford University, has developed a new AI assistant for radiologists. This work focuses on quantitative medical imaging analysis in the areas of cardiac and oncologic imaging (lung, liver, etc.) and tracking changes over time. This permits rapid and more quantitative results from imaging exams across a range of clinical indications.

Now, the same team at Stanford has collaborated with researchers at UC Berkeley to develop fast image reconstruction algorithms (compressed sensing) that “smart” sample the electromagnetic field to deliver high resolution/low noise images with speed, allowing for improved deep learning-based segmentation of tumors.

Stanford, collaborating with Intel, is currently using a small Intel Xeon processor-based server cluster for fast computation of images in its imaging applications to shorten exams, and make the exams more feasible for children. Today images from this technique can be reconstructed in about one minute compared to over 45 minutes before, while the radiologist and technologists wait. As applied to pediatrics, this faster processing time saves the youngest and most vulnerable patients from the added risk of intubation and/or sedation usually required for the traditional imaging process.

University of California San Francisco* (UCSF): Detecting Disease Before Onset

Early in 2017, UCSF partnered with Intel to create a deep learning analytics platform designed to deliver clinical decision support and predictive analytics capabilities that would enable delivery of the right care to the right patient at the right time – ideally before the onset of disease. The collaboration brings together Intel’s leading-edge computer science and deep learning capabilities with UCSF’s clinical and research expertise to create a scalable, high-performance computational environment to support enhanced frontline clinical decision making for a wide variety of patient care scenarios.

The UCSF Center for Digital Health Innovation under its SmarterHealth* Initiative has created the logical nexus of data, analytics, technology, methodology and scalable commercial partners in order to make artificial intelligence practical and useful at the point of care to the benefit of all patients worldwide.

Further, by integrating these advanced methodologies into medicine and health we are demystifying the integration of man and machine; the outcome is the ability to harness incredible technological advances by blurring the line between medicine and health, actually personalizing the delivery of medicine, defining the ability to predict health trajectories, and while lowering costs to all stakeholders.

SmarterHealth and their core enhanced patient data sets have already been utilized to create novel and useful algorithms to help practitioners “triage” trauma patients in order to optimize personalized clinical diagnosis. Other initiatives include developing point of care image data reconstruction and recognition intended to both inform clinicians about how to capture the “right image” while using deep learning and artificial intelligence to not only map individual physiological structures but to also use serial deployed imaging to help clinicians and health providers to actually predict patient trajectories at the point of care.

Finally, by engaging leading academic and technology partners around the world through the SmarterHealth Artificial Intelligence in Medicine Consortium we intend to co-develop and disseminate these actionable capabilities for the betterment of all patients throughout the world.

1https://www.datameer.com/company/datameer-blog/role-big-data-preventing-healthcare-fraud-waste-abuse/

2https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsHistorical.html

3https://www.cdc.gov/chronicdisease/overview/index.htm

4http://health.costhelper.com/emergency-room.html

5http://www.realtorsinsurancemarketplace.com/how-much-does-it-cost-to-spend-a-day-in-the-hospital/

6https://www.cancer.org/cancer/melanoma-skin-cancer/detection-diagnosis-staging/survival-rates-for-melanoma-skin-cancer-by-stage.html

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