Tag: Health

AI-powered ambient solution already improving physician productivity, patient throughput, and 88% higher physician satisfaction scores

There has never been a more exciting time to be working in healthcare and life sciences at Microsoft. Our investments across our organization in research and development, world-class talent, and strategic partnerships reflect our CEO Satya Nadella’s vision that many of the next health breakthroughs will come from healthcare and life sciences organizations working in partnership with technology companies like Microsoft. With our partners and our customers, we are just beginning to unlock the power of technology and innovation to advance our shared understanding of human health.

This unique opportunity to make a lasting, positive impact on healthcare is one of the reasons I joined Microsoft. For decades, Microsoft has built a reputation as a trusted partner, now counting more than 168,000 healthcare organizations around the world who rely on us. As we’ve built out platforms for cloud infrastructure, productivity and collaboration, and artificial intelligence, we’ve developed an ecosystem of hundreds of thousands of partners who are building on our technologies and bringing added value to our mutual customers.

Innovation in medical imaging

Being new to Microsoft and a neuroradiologist, one of the areas I am most excited about is how we are helping our customers and partners reimagine healthcare and specifically explore the possibilities in medical imaging. In this area that is so critical to healthcare overall, we’re fortunate to work with some of the leaders in the industry.

GE Healthcare customers in the United States can take advantage of solutions running on Microsoft Azure such as the newly developed Edison™ Datalogue™ Connect, which was designed to provide secure image and data exchange to physicians working in their care settings.

Philips’ Azurion image-guided therapy platform empowers providers through image-guided minimally invasive therapies. Built for HoloLens 2, the work-in-progress Azurion mixed reality platform brings live imaging and other sources of vital data — currently displayed on large external 2D screens — into a 3D holographic environment that can be controlled by the physician.

Nuance, a leader in radiology reporting solutions with its PowerScribe platform, continues to lead the way in bringing ambient technologies to healthcare. We recently announced a strategic partnership with Nuance to accelerate that innovation, bringing together Nuance’s healthcare-optimized AI powered clinical documentation and decision support solutions and Microsoft Azure, advanced conversational AI, and natural language understanding.

Working together with researchers and industry partners, we’re also moving forward to create a broad range of cloud-based tools and solutions that touch many aspects of the development and delivery of effective care, including:

• Azure Quantum solutions at work with Case Western Reserve University to accelerate and improve the accuracy of MRI scanners.
• New opportunities to accelerate medical imagining with GraphCore and the Intelligence Processing Unit (IPU).
• Empowering health team collaboration with Microsoft 365. Intelligent meetings and modern collaboration in a secure platform that integrates with clinical systems, from electronic health records to imaging software.
• Research projects in registration and segmentation. Two of the fundamental problems that clinical researchers and practitioners are dealing with when working with 3D medical imaging modalities.
• The Azure Stack Edge, a new Azure-managed appliance that brings the compute, storage, and intelligence of Azure to the edge for new healthcare scenarios including hardware acceleration with FPGA and GPU.

As part of our ongoing commitment to making health data easier to manage, in October we announced the general availability of the Azure API for FHIR. FHIR is quickly becoming the preferred standard for exchanging electronic health information and enabling the management of PHI data in the cloud. A rapidly growing number of healthcare delivery and healthcare technology companies are already using the Azure API for FHIR to improve interoperability within their own IT systems, including Great Ormond Street Hospital (GOSH) of the NHS, Darena Solutions, Northwell Health, and Humana. With the release of the Azure API for FHIR, Microsoft now provides a fully managed, enterprise-grade service for health data in the FHIR format. Building on this announcement, a few weeks ago we announced the IoMT FHIR Connector for Azure – an open-source tool enabling our customers to more easily ingest data from health and medical devices, including a FHIR framework for Apple HealthKit.

This investment in innovation with our partners and customers puts us on a path to make a meaningful impact in medical imaging. Together we can empower providers with an enterprise imaging platform that enables reliability and security, manages growing patient data with strong controls for privacy and compliance, and provides insights from the data to improve patient care.

Learn more at RSNA

We will be engaging with more than 50,000 clinicians in radiology at the Radiological Society of North America (RSNA) conference in Chicago. Stop by our Microsoft Booth #10745 in the AI Showcase to learn more about our work and see how we are collaborating with partners and customers to reimagine healthcare. Our partners: Agfa HealthCare, Lunit, NucleusHealth, RamSoft, SOPHiA GENETICS, Volpara, 7 Medical and others will be on hand to share their innovative work. In a special session, I’ll be sharing Microsoft’s view on the opportunities to reimagine healthcare and specifically medical imaging. Several of our partners will join to share how they’re using technology, from the cloud to quantum computing to mixed reality and teleradiology, to help radiologists today.

Biomedical researchers are embracing artificial intelligence to accelerate the implementation of cancer treatments that target patients’ specific genomic profiles, a type of precision medicine that in some cases is more effective than traditional chemotherapy and has fewer side effects.

The potential for this new era of cancer treatment stems from advances in genome sequencing technology that enables researchers to more efficiently discover the specific genomic mutations that drive cancer, and an explosion of research on the development of new drugs that target those mutations.

To harness this potential, researchers at The Jackson Laboratory, an independent, nonprofit biomedical research institution also known as JAX and headquartered in Bar Harbor, Maine, developed a tool to help the global medical and scientific communities stay on top of the continuously growing volume of data generated by advances in genomic research.

The tool, called the Clinical Knowledgebase, or CKB, is a searchable database where subject matter experts store, sort and interpret complex genomic data to improve patient outcomes and share information about clinical trials and treatment options.

The challenge is to find the most relevant cancer-related information from the 4,000 or so biomedical research papers published each day, according to Susan Mockus, the associate director of clinical genomic market development with JAX’s genomic medicine institute in Farmington, Connecticut.

“Because there is so much data and so many complexities, without embracing and incorporating artificial intelligence and machine learning to help in the interpretation of the data, progress will be slow,” she said.

That’s why Mockus and her colleagues at JAX are collaborating with computer scientists working on Microsoft’s Project Hanover who are developing AI technology that enables machines to read complex medical and research documents and highlight the important information they contain.

While this machine reading technology is in the early stages of development, researchers have found they can make progress by narrowing the focus to specific areas such as clinical oncology, explained Peter Lee, corporate vice president of Microsoft Healthcare in Redmond, Washington.

“For something that really matters like cancer treatment where there are thousands of new research papers being published every day, we actually have a shot at having the machine read them all and help a board of cancer specialists answer questions about the latest research,” he said.

Curating CKB

Mockus and her colleagues are using Microsoft’s machine reading technology to curate CKB, which stores structured information about genomic mutations that drive cancer, drugs that target cancer genes and the response of patients to those drugs.

One application of this knowledgebase allows oncologists to discover what, if any, matches exist between a patient’s known cancer-related genomic mutations and drugs that target them as they explore and weigh options for treatment, including enrollment in clinical trials for drugs in development.

This information is also useful to translational and clinical researchers, Mockus noted.

The bottleneck is filtering through the more than 4,000 papers published every day in biomedical journals to find the subset of about 200 related to cancer, read them and update CKB with the relevant information on the mutation, drug and patient response.

“What you want is some degree of intelligence incorporated into the system that can go out and not just be efficient, but also be effective and relevant in terms of how it can filter information. That is what Hanover has done,” said Auro Nair, executive vice president of JAX.

The core of Microsoft’s Project Hanover is the capability to comb through the thousands of documents published each day in the biomedical literature and flag and rank all that are potentially relevant to cancer researchers, highlighting, for example, information on gene, mutation, drug and patient response.

Human curators working on CKB are then free to focus on the flagged research papers, validating the accuracy of the highlighted information.

“Our goal is to make the human curators superpowered,” said Hoifung Poon, director of precision health natural language processing with Microsoft’s research organization in Redmond and the lead researcher on Project Hanover.

“With the machine reader, we are able to suggest that this might be a case where a paper is talking about a drug-gene mutation relation that you care about,” Poon explained. “The curator can look at this in context and, in a couple of minutes, say, ‘This is exactly what I want,’ or ‘This is incorrect.’”

Self supervision

To be successful, Poon and his team need to train machine learning models in such a way that they catch all the potentially relevant information – ensure there are no gaps in content – and, at the same time, weed out irrelevant information sufficiently to make the curation process more efficient.

In traditional machine reading tasks such as finding information about celebrities in news stories, researchers tend to focus on relationships contained within a single sentence, such as a celebrity name and a new movie.

Since this type of information is widespread across news stories, researchers can skip instances that are more challenging such as when the name of the celebrity and movie are mentioned in separate paragraphs, or when the relationship involves more than two pieces of information.

“In biomedicine, you can’t do that because your latest finding may only appear in this single paper and if you skip it, it could be life or death for this patient,” explained Poon. “In this case, you have to tackle some of the hard linguistic challenges head on.”

Poon and his team are taking what they call a self-supervision approach to machine learning in which the model automatically annotates training examples from unlabeled text by leveraging prior knowledge in existing databases and ontologies.

For example, a National Cancer Institute initiative manually compiled information from the biomedical literature on how genes regulate each other but was unable to sustain the effort beyond two years. Poon’s team used the compiled knowledge to automatically label documents and train a machine reader to find new instances of gene regulation.

They took the same approach with public datasets on approved cancer drugs and drugs in clinical trials, among other sources.

This connect-the-dots approach creates a machine learned model that “rarely misses anything” and is precise enough “where we can potentially improve the curation efficiency by a lot,” said Poon.

Collaboration with JAX

The collaboration with JAX allows Poon and his team to validate the effectiveness of Microsoft’s machine reading technology while increasing the efficiency of Mockus and her team as they curate CKB.

“Leveraging the machine reader, we can say here is what we are interested in and it will help to triage and actually rank papers for us that have high clinical significance,” Mockus said. “And then a human goes in to really tease apart the data.”

Over time, feedback from the curators will be used to help train the machine reading technology, making the models more precise and, in turn, making the curators more efficient and allowing the scope of CKB to expand.

“We feel really, really good about this relationship,” said Nair. “Particularly from the standpoint of the impact it can have in providing a very powerful tool to clinicians.”

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John Roach writes about Microsoft research and innovation. Follow him on Twitter.