Announcing Project Health Insights Preview: Advancing AI for health data

We live in an era with unprecedented increases in the size of health data. Digitization of medical records, medical imaging, genomic data, clinical notes, and more all contributed to an exponential increase in the amount of medical data. The potential benefit of leveraging this health data is enormous. However, with this growth in health data, new challenges arise, including the focus on data privacy and security, the need for data standardization and interoperability. There is a need for effective tools for extracting information that is buried in this data and using it to derive valuable insights, inferences, and deep analytics that can make sense of the data and support clinicians.

Today, I’m excited to announce Project Health Insights Preview. Project Health Insights is a service that derives insights based on patient data and includes pre-built models that aim to power key high value scenarios in the health domain. The models receive patient data in different modalities, perform analysis, and enable clinicians to obtain inferences and insights with evidence from the input data. These insights can assist healthcare professionals in understanding clinical data, like patient profiling, clinical trials matching, and more.

Project Health Insights—leveraging patient data to power actionable insights

Project Health Insights supports pre-built models that receive patient data in multiple modalities as their input, and produce insights and inferences that include:

Confidence scores: The higher the confidence score is, the more certain the model was about the inference value provided.
Evidence: linking model output with specific evidence within the input provided, such as references to spans of text reflecting the data that led to an insight.

Project Health Insights Preview includes two enterprise grade AI models that can be provisioned and deployed in a matter of minutes: Oncology Phenotype and Clinical Trial Matcher.

Oncology Phenotype is a model that enables healthcare providers to rapidly identify key cancer attributes within their patient populations with an existing cancer diagnosis. The model identifies cancer attributes such as tumor site, histology, clinical stage, tumor, nodes, and metastasis (TNM) categories and pathologic stage TNM categories from unstructured clinical documents.

Key features of the Oncology Phenotype model include:

Cancer case finding.
Clinical text extraction for solid tumors.
Importance ranking of evidence.

Clinical Trial Matcher is a model that matches patients to potentially suitable clinical trials, according to the trial’s eligibility criteria and patient data. The model helps with finding relevant clinical trials, that patients could be qualified for, as well as with finding a cohort of potentially eligible patients for a list of clinical trials.

Key Features of the Clinical Trial Matcher model include:

Support for scenarios that are:

Patient Centric: Helping patients find potentially suitable clinical trials and assess their eligibility against the trials criteria.
Trial Centric: Matching a trial with a database of patients to locate a cohort of potentially suitable patients.

Interactive Matching where the model provides insights into missing information that is needed to further narrow down the potential clinical trial list via an interactive experience.
Support for various modalities of patient data such as unstructured clinical notes, structured patient data, and Fast Healthcare Interoperability Resources (FHIR®) bundles.
Support for search across built-in knowledge graphs for clinical trials from clinicaltrials.gov as well as against a custom trial protocol with specific eligibility criteria.

Streamlining clinical trial matching and cancer research

According to the World Health Organization, the number of registered clinical trials increased by more than 4800 percent from 1999 to 2021. Today there are more than 82,000 clinical trials actively recruiting participants worldwide (based on clinicaltrials.gov), with increasingly complicated trial eligibility criteria. However, enrollment in clinical trials is based on manual screening of millions of patients, each with up to hundreds of clinical notes requiring review and analysis by a healthcare professional, making it an unsustainable process. Given this, it is not surprising that up to 80 percent of clinical trials miss their clinical trial enrollment timelines, and up to 48 percent fail to meet clinical trial enrollment targets according to data provided by Tufts University. The Clinical Trial Matcher model aims to solve this exact problem by effectively matching patients with diverse conditions to clinical trials for which they are potentially eligible through analysis of patient’s data and the complex eligibility criteria of clinical trials.

The Oncology Phenotype model allows physicians to effectively analyze cancer patients’ data based on their tumor site, tumor histology, and cancer staging. These models deliver crucial building blocks to realize the goals set out by the White House Cancer Moonshot initiative: to develop and test new treatments, to share more data and knowledge, to collaborate on tools that can benefit all, and to make progress towards ending cancer as we know it.

Providing value across the health and life sciences industry

John’s Hopkins University Medical Center is an early user of Project Health Insights. Dr. Srinivasan Yegnasubramanian is using the Oncology Phenotype model to leverage unstructured data to accelerate Cancer Registry curation efforts for patients with solid tumors.

Pangaea Data is a Microsoft partner working in health AI. “At Pangaea Data we help companies discover 22 times more undiagnosed, misdiagnosed, and miscoded patients by characterizing them through unlocking and summarization of clinically valid actionable intelligence from patient records in a federated privacy-preserving, scalable, and evolving manner. We are exploring using Project Health Insights to augment our own advanced capabilities for characterizing patients.”—Vibhor Gupta, Director and Founder, Pangaea Data.

Akkure Genomics helps patients utilize their own genomic data or DNA to improve their chances of finding a clinical trial. “At AKKURE GENOMICS we leverage Project Health Insights, which empowers our own AI and digital DNA platform capabilities, to help patients get matched to clinical trials based on their individual medical diagnoses, thus boosting enrollment, improving the chances of finding a precision-matched trial and accelerating discovery of new therapeutics and cures.”—Professor Oran Rigby, Chief Engineering Officer and Founder, Akkure.

Built with the end user in mind

Initial models were validated in a research setting through a strategic partnership between Microsoft and Providence to accelerate digital transformation in health and life sciences. These models can enable oncologists to substantially scale up their precision oncology capabilities and generate intelligence and insights useful to clinicians as well as beneficial to patients.

“Microsoft’s ability to structure complex concepts with their natural language processing tools for cancer has contributed significantly to our ability to build research cohorts and discuss cancer treatment options.”—Dr. Carlo Bifulco, Chief Medical Officer, Providence Genomics.

Microsoft will continue to expand capabilities within Project Health Insights to support additional health workloads and enable insights that will guide key decision-making in healthcare.

Microsoft continues to grow its portfolio of AI services for health

Microsoft continues to invest in AI services for the health and life sciences industry. Along with other new offerings in the Microsoft Cloud for Healthcare, we are pleased to announce new enhancements to Text Analytics for Health (TA4H).

The new enhancements include:

Social Determinants of Health (SDoH) and Ethnicity information extraction. The newly introduced SDoH and Ethnicity features enable extraction of social, environmental, and demographics factors from unstructured text. These factors will empower the development of more inclusive healthcare applications. Read more about it in our blog.
Temporal assertions—past, present, and future. The ability to identify the temporal context of TA4H entities whether in the past, present or future.

Customers can now extend TA4H to support custom entities based on their own data. Customers can now also extend the entities extracted by the service.

We are also excited to share that Azure Health Bot now has a new Azure OpenAI template in preview. The Azure Health Bot OpenAI template allows customers to extend their Azure Health Bot instance with Azure OpenAI Service for answering unrecognized utterances in a more intelligent way. This feature will be enabled through the Azure Health Bot template catalogue. Customers can choose to import this template into their bot instance using their Azure OpenAI resource endpoint and key, enabling fallback answers generated by GPT from trusted, medically viable sources that can be provisioned by customers. This feature provides a mechanism for customers to experiment with this capability as preview.1 Read more about this and how to apply responsible AI principles when implementing your own Health Bot instance in this blog.

We look forward to what the coming years will bring for the health and life sciences industry empowered by these new capabilities and the continued innovation we are seeing across AI and machine learning. The potential for improved precision care, quicker and more efficient clinical trials, and thereby drug and therapy availability and medical research is unparalleled. Microsoft looks forward to partnering with you and your organizations on this journey to improve the health of humankind.

Learn more

Project Health Insights Preview
Text analytics for Health
Azure Health Bot
Microsoft Cloud for Healthcare

1 At this time, we are offering the preview for internal testing and evaluation purposes only.

®FHIR is a registered trademark of Health Level Seven International, registered in the U.S. Trademark Office and are used with their permission.
Quelle: Azure

The future of healthcare is data-driven

As analytics tools and machine learning capabilities mature, healthcare innovators are speeding up the development of enhanced treatments supported by Azure’s GPU-accelerated AI infrastructure powered by NVIDIA.

Improving diagnosis and elevating patient care

Man’s search for cures and treatments for common ailments has driven millennia of healthcare innovation. From the use of traditional medicine in early history to the rapid medical advances of the past few centuries, healthcare providers are locked in a constant search for effective solutions to old and emerging diseases and conditions.

The pace of healthcare innovation has increased exponentially over the past few decades, with the industry absorbing radical changes as it transitions from a health care to a health cure society. From telemedicine, personalized wellbeing, and precision medicine to genomics and proteomics, all powered by AI and advanced analytics, modern medical researchers can access more supercomputing capabilities than ever before. This quantum leap in computational capability, powered by AI, enables healthcare services dissemination and consumption in ways, and at a pace, that were previously unimaginable.

Today, health and life sciences leaders leverage Microsoft Azure high-performance computing (HPC) and purpose-built AI infrastructure to accelerate insights into genomics, precision medicine, medical imaging, and clinical trials, with virtually no limits to the computing power they have at their disposal. These advanced computing capabilities are allowing healthcare providers to gain deeper insights into medical data by deploying analytics and machine learning tools on top of clinical simulation data, increasing the accuracy of mathematical formulas used for molecular dynamics and enhancing clinical trial simulation.

By utilizing the infrastructure as a service (IaaS) capabilities of Azure HPC and AI, healthcare innovators can overcome the challenges of scale, collaboration, and compliance without adding complexity. And with access to the latest GPU-enabled virtual machines, researchers can fuel innovation through high-end remote visualization, deep learning, and predictive analytics.

Data scalability powers rapid testing capabilities

Take the example of the National Health Service, where the use of Azure HPC and AI led to the development of an app that could analyze COVID-19 tests at scale, with a level of accuracy and speed that is simply unattainable for human readers. This drastically improved the efficiency and scalability of analysis as well as capacity management.

Another advance worth noting, is that with Dragon Ambient Experience (DAX), an AI-based clinical solution offered by Nuance, doctor-patient experiences are optimized through the digitization of patient conversations into highly accurate medical notes, helping ensure high-quality care. By freeing up time for doctors to engage with their patients in a more direct and personalized manner, DAX improves the patient experience, reducing patient stress and saving time for doctors.

“With support from Azure and PyTorch, our solution can fundamentally change how doctors and patients engage and how doctors deliver healthcare.”—Guido Gallopyn, Vice President of Healthcare Research at Nuance.

Another exciting partnership between Nuance and NVIDIA brings directly into clinical settings medical imaging AI models developed with MONAI, a domain-specific framework for building and deploying imaging AI. By providing healthcare professionals with much needed AI-based diagnostic tools, across modalities and at scale, medical centers can optimize patient care at fractions of the cost compared to traditional health care solutions.

“Adoption of medical imaging AI at scale has traditionally been constrained by the complexity of clinical workflows and the lack of standards, applications, and deployment platforms. Our partnership with Nuance clears those barriers, enabling the extraordinary capabilities of AI to be delivered at the point of care, faster than ever.”—David Niewolny, Director of Healthcare Business Development at NVIDIA.

GPU-accelerated virtual machines are a healthcare game changer

In the field of medical imaging, progress relies heavily on the use of the latest tools and technologies to enable rapid iterations. For example, when Microsoft scientists sought to improve on a state-of-the-art algorithm used to screen blinding retinal diseases, they leveraged the power of the latest NVIDIA GPUs running on Azure virtual machines.

Using Microsoft Azure Machine Learning for computer vision, scientists reduced misclassification by more than 90 percent from 3.9 percent to a mere 0.3 percent. Deep learning model training was completed in 10 minutes over 83,484 images, achieving better performance than a state-of-the-art AI system. These are the types of improvements that can assist doctors in making more robust and objective decisions, leading to improved patient outcomes for patients.

For radiotherapy innovator Elekta, the use of AI could help expand access to life-saving treatments for people around the world. Elekta believes AI technology can help physicians by freeing them up to focus on higher-value activities such as adapting and personalizing treatments. The company accelerates the overall treatment planning process for patients undergoing radiotherapy by automating time-consuming tasks such as advanced analysis services, contouring targets, and optimizing the dose given to patients. In addition, they rely heavily on the agility and power of on-demand infrastructure and services from Microsoft Azure to develop solutions that help empower their clinicians, facilitating the provision of the next generation of personalized cancer treatments.

Elekta uses Azure HPC powered by NVIDIA GPUs to train its machine learning models with the agility to scale storage and compute resources as its research requires. Through Azure’s scalability, Elekta can easily launch experiments in parallel and initiate its entire AI project without any investment in on-premises hardware.

“We rely heavily on Azure cloud infrastructure. With Azure, we can create virtual machines on the fly with specific GPUs, and then scale up as the project demands.”—Silvain Beriault, Lead Research Scientist at Elekta.

With Azure high-performance AI infrastructure, Elekta can dramatically increase the efficiency and effectiveness of its services, helping to reduce the disparity between the many who need radiotherapy treatment and the few who can access it.

Learn more

Leverage Azure HPC and AI infrastructure today or request an Azure HPC demo.

Read more about Azure Machine Learning:

Multimodal 3D Brain Tumor Segmentation with Azure ML and MONAI.
Practical Federated Learning with Azure Machine Learning.

Quelle: Azure