ENHANCED CLINICAL TRIAL ENROLLMENT

Written by Nick Brown on . Posted in USE CASES.

Situation

85% of clinical trials end with less than expected enrollment, while 19% are terminated or fail to meet accrual goals at all. This scenario has significant impact on the viability of trials, leading to insufficient results, loss of time and financial resources and ultimately a reduction in innovation.

Problem

One of the biggest challenges associated with clinical trial enrollment is identifying candidates due to the vast amounts of siloed and unstructured data needed. In fact, over half of eligibility criteria is unstructured, making it incredibly complex to search and access. Attempting to make sense of this content, combined with searching across data silos such as patient records and genomics data is incredibly time intensive, with key insights and candidates missed.

Solution

A leading U.S. cancer research center is turning to Vyasa to solve this problem. By establishing an intelligent data fabric with Vyasa Layar, the research center has created a unified data platform to search and identify relationships between clinical trials and its patient population to improve trial enrollment and viability. With Vyasa, the research center can:

  • Unify siloed data sources including patient records, genomics files and clinical trials into a single platform without moving or replicating files.
  • Search across multiple data sources and quickly identify insights with the power of deep learning.
  • Explore data in natural language. No coding or data science experience required.
  • Discover relationships in their data through dynamic knowledge graphs.
  • Visualize their data in smart spreadsheets and real-time dashboards.

With Vyasa, organizations empower users across skill sets to leverage deep learning and gain new value from their data. In turn, users have experienced a 25% improvement in productivity around data analysis tasks, all without compromising on accuracy.

ACCELERATING DATA ACCESSIBILITY FOR PEDIATRIC ONCOLOGY

Written by Nick Brown on . Posted in USE CASES.

Situation

The NIH’s Childhood Cancer Data Initiative is set to revolutionize the data landscape around pediatric cancers and treatments. While important progress is being made, hospitals and oncology departments still face an uphill battle with the resources they have available to understand patient illnesses and gain the insights they need.

Problem

The healthcare industry faces a number of data challenges, all of which are impacting pediatric oncology. These include:

  • Data silos across departments and institutions.
  • Limited access to the latest research and trials on pediatric cancer and treatments.
  • Tools for quickly accessing insights in complex data. Ex: published research, biomedical images, drug-like compounds, genomics files.

Solution

Advancements in deep learning combined with a novel data architecture developed by Vyasa are changing the way hospitals, researchers and oncologists can collect and analyze data related to pediatric cancer. Known as an intelligent data fabric, Vyasa enables organizations to unify disparate data sources regardless of cloud or on-premise storage location and without moving or replicating the data. Once connected, Vyasa applies deep learning on the data making it easily searchable and accessible. With an intelligent data fabric, users improve search accuracy by 97% while decreasing manual analysis time by 25% through the ability to:

  • Collaborate and share data across departments, and identify trends across patient populations.
  • Easily search and extract insights from genomics data, including processing VCF and BAM files.
  • Harmonize multiple data sources together, including structured and unstructured content. (ex: scientific literature & images).
  • Discover terms and insights not listed in ontologies with Vyasa’s named entity recognition (NER).

The Vyasa intelligent data fabric comes with a suite of low code interfaces that enable users to search and visualize their data in natural language, accellerating access to insights for physicians, oncologists, researchers and more.

Smarter Manufacturing with an Intelligent Data Fabric

Written by Nick Brown on . Posted in USE CASES.

Situation

As Industry 4.0 continues to transform manufacturing, a sector that was already ripe with information is experiencing a spike in data production from the factory floor to corporate offices. As a result, the market for big data analytics in manufacturing is projected to reach 4.55B by 2026 as the sector continues to adopt “smart industry” initiatives.

Problem

While Industry 4.0 poses a tremendous opportunity for manufacturing, the industry faces a number of data challenges – one of the biggest being data silos occurring horizontally and vertically across organizations. This scenario creates a lack of visibility across the organization that can limit productivity and access to key insight needed to influence tasks such as quality assurance, production monitoring, incident analysis, forecasting and competitor intelligence.

Solution

Advancements in deep learning and data management are revolutionizing the way the manufacturing industry can harness and operationalize its data. Vyasa has combined these capabilities into its Layar intelligent data fabric. With Layar, manufacturing professionals can connect their data regardless of storage location or file structure. Once connected, Layar applies deep learning on the data sources creating a unified platform without moving or replicating any of the data. This enables content to be easily accessible and searchable allowing users to:

  • Leverage natural language processing to explore their data in a low-code environment.
  • Extract insights from complex documents and images for streamlined quality assurance, incident analysis and more.
  • Improve competitor and customer intelligence by connecting external sources such as websites or social media feeds to internal data.
  • Visualize sensor and production data from the factory floor in intuitive charts and graphs. With an intelligent data fabric, manufacturers can overcome data silos and accelerate their access to data and insights; all while creating a foundation for smart industry initiatives.

With an intelligent data fabric, manufacturers can overcome data silos and accelerate their access to data and insights; all while creating a foundation for smart industry initiatives.

SMARTER RESEARCH: AI-POWERED SYSTEMATIC LITERATURE REVIEW

Written by vyasa on . Posted in USE CASES.

More than 90% of clinical-trial compounds fail to demonstrate sufficient efficacy and safety. To mitigate this issue, many look to third-parties to conduct extensive research via systematic literature review (SLR) to gather the relevant information needed to validate the investment and approach to a trial. While providing valuable insight, each SLR can cost over $140,000.

Problem

SLRs are largely conducted via manual processes – with researchers needing to compile, review and analyze large sets of unstructured content. As a result, these processes are incredibly time consuming due to the amount of data and grey literature that needs to be consumed and reported on to make SLRs successful. Due to the nature of this work, insights can be missed and timelines can be delayed which hold up productivity for the end customer.

Solution

Advancements in deep learning text analytics are enhancing systematic literature reviews by improving the accessibility and searchability of unstructured content. Vyasa has developed high-performing deep learning models that understand context and can identify key terms that improve the accuracy and time spent on systematic literature reviews. We then take this a step further with our Layar data fabric, a novel data architecture that unifies content sources into a single platform. With the Vyasa platform, users can:

  • Integrate siloed data sets into a single, searchable platform.
  • Search unstructured content in natural language.
  • Explore their data via highly-visual knowledge graphs, dashboards and smart spreadsheets.
  • Export unstructured content into structured formats.

By leveraging Vyasa, users can improve query accuracy by 97% while reducing research times by as much as 90%, leading to smarter, more efficient systematic literature reviews.

ENHANCED POPULATION DATA, SMARTER HEALTH PLANS

Written by vyasa on . Posted in USE CASES.

The sharing of population data between healthcare providers and payers is critical to maintaining quality care and qualified health plans. However, this data is often messy, consisting of numerous unstructured formats and growing in exponential values. Providers often track hundreds of distinct line items ranging from geographic location to patient ethnicity and spoken language to clinician notes from each individual visit. All of which gets shared with healthcare payers for analysis.

Problem

A single patient generates up to 80 megabytes in imaging and EMR data1 alone. With large healthcare payers ranging in 20-40 million customers, manually extracting insights from this content isn’t sustainable.

Analyzing all of this data is required for effective risk assessment, to understand health trends in a given population and adjust plans to deliver the best care possible. Without the right tools this leads to hundreds of hours of research time wasted, dozens of high-paid workers burning out from tedious tasks and missed insight caused by human error.

Solution

Advancements in deep learning are revolutionizing how healthcare payers manage, access and extract insights from their most important content. Today’s deep learning models are trained to understand groups of text and the nuance that comes with written language, such as semantically similar terms for the same topic.

Analysts at healthcare payers can apply powerful deep learning solutions from Vyasa to unstructured documents to extract insights such as: cancer screening results, written clinician notes, wellness tests, patient behaviors, etc.

By leveraging deep learning models in their text analytics, healthcare payers can improve research accuracy by 97%. Relevant and accurate data is delivered in milliseconds, meaning research times can be cut by as much as 90%. With access to more efficient processes, and more accurate insight available, healthcare providers can better understand customer needs, stay ahead of the latest healthcare trends and ultimately create smarter, more attractive plans.

1 Huesch, D., and Mosher, T. J. (2017). Using It or Losing It? The Case for Data Scientists Inside Health Care. NeJM Catalyst. Available online at: https://catalyst.nejm.org/case-data-scien- tists-inside-health-care/ (Accessed Jun 20, 2018).

ENHANCED PATIENT JOURNEYS WITH REAL-WORLD DATA

Written by vyasa on . Posted in USE CASES.

Understanding the patient journey has been an ongoing challenge for professionals in healthcare and the life sciences. This information can influence paths of care, the discovery of adverse effects, introduce opportunities to innovate around therapeutics and much more.

Today’s digital world presents new opportunities for patients to connect and share information with care providers and support groups – all of which creates valuable insight to influence the patient journey.

Problem

Effective patient journey mapping requires analyzing multiple data streams – all of which are being updated on a daily basis. These data streams include:

  • Social media posts
  • Forum discussions
  • Reported patient data
  • Wearable monitoring

Managing these real-world data sources is incredibly time and labor intensive. In most cases, professionals must monitor multiple platforms and pull relevant information into a single source before analysis can even take place.

Solution

Advancements in deep learning and data management are revolutionizing how professionals in healthcare and the life sciences manage, access and extract insights from real-world data. Vyasa Synapse leverages deep learning to enable researchers and healthcare professionals to ask complex questions across data sources and receive results in a spreadsheet format. Spreadsheets are updated in real-time, providing users with the most up-to-date patient journey data.

By leveraging Synapse, researchers can improve research accuracy by 97%. Relevant and accurate data is delivered in milliseconds, meaning research times can be cut by as much as 90%. With access to more efficient processes, healthcare providers and life science companies can more accurately map the patient journey – leading to higher-quality care, enhanced product innovation and ultimately a healthier population.

BEYOND THE VISUAL: EXTRACTING INSIGHT FROM MEDICAL IMAGERY

Written by vyasa on . Posted in USE CASES.

The adage “a picture is worth a thousand words” isn’t just a reflection on traditional photographs. In fact, as much as 90% of healthcare data comes from imagery.1 Within each image hides key insights from disease type to patient demographics to dimensions, voxel size and repetition time. This data can influence diagnoses from healthcare providers, rare disease research, clinical trial design and much more.

Problem

While rich in insight, managing and analyzing medical imagery is incredibly complex. Images come in a variety of modalities, are produced by different departments such as radiology and pathology and are utilized differently across the healthcare organization – from providers to researchers to analysts.

In addition, image files are much larger than traditional data sources – making them difficult to share while taking up valuable storage. As a result, healthcare organizations spend far too much time and financial resources on imagery – from tasking highly-skilled professionals with tedious image analysis to managing the IT infrastructure needed to utilize these assets.

Solution

Most think of applying deep learning for image recognition, but these solutions provide much greater value when applied to analyzing metadata within medical imagery. Vyasa is changing the way healthcare organizations approach managing and from their content. Advanced deep learning image analytics built by Vyasa enable users to:

  • Unify and catalog image assets across the organization.
  • Enhance image detection and classification.
  • Make images easily searchable via simple annotation.

With a streamlined approach to image analytics with Vyasa, users can easily filter their image libraries to just their most relevant content, quickly identify cohorts for clinical trials, improve research accuracy for pathology and radiology reports and much more.

1. GE (2018) Beyond Imaging:the paradox of AI and medical imaging innovation https://www.gehealthcare.com/article/beyond-imagingthe-paradox-of-ai-and-medical-imaging-innovation#_ftn- ref1

DON’T MOVE YOUR DATA. UNIFY IT.

Written by vyasa on . Posted in USE CASES.

It’s never been easier to share, store and replicate data thanks to our increasingly digital working environments. While this scenario has made activities like collaboration and remote work seamless, the deluge of new data produced within organizations is creating strains on IT systems and resulting headaches for the professionals in charge of managing them. Considering that 64.2ZB1 of data was created or replicated in 2020 alone, it’s no wonder data has not only become the lifeblood, but also the thorn in the side of enterprise IT departments.

Problem

Over 80% of organizational data is dark2. IT teams are suffering from dark data if they’re challenged with:

  • Multiple data silos
  • Redundant or obsolete files
  • Unstructured content such as images, PDFs, presentations decks, etc.

Largely inaccessible, yet rich in insight, simply sitting on this data can lead to missed intelligence that can influence product development, sales strategies or competitor research that can drive successful businesses. Tasked with solving this problem, IT teams traditionally turn to unsustainable solutions that require expensive and time consuming data migrations.

Solution

A new data architecture is changing the way IT professionals can approach data management. Known as the data fabric, IT teams can unify data sources across their environment, regardless of whether files are stored on premise or in the cloud. No need to duplicate your data or storage and no data lake required.

Vyasa takes this a step further with its Layar deep learning data fabric. Combining the data fabric architecture with novel approaches to deep learning, IT teams that integrate their data within Layar make their content easy to search, explore and visualize, regardless of file format.

Listed as the #1 strategic technology trend for 2020 by Gartner3, data fabrics are poised to eliminate the need for costly data migrations that can impact productivity and create new data management issues for IT teams.

1 Reinsel, D and Rydning, J. (2021). IDC Global DataSphere https://www.idc.com/getdoc.jsp?containerId=IDC_P38353

2 IBM (2015, November 23). The Future of Cognitive Computing. https://www.ibm.com/blogs/cloud0archive/2015/future-of-cognitive-computiving/

3 Gartner (2021). Gartner Top Strategic Technology Trends for 2022 https://www.gartner.com/en/information-technology/insights/top-technology-trends

TACKLING DATA SILOS POST HEALTHCARE M&A

Written by vyasa on . Posted in USE CASES.

The health services industry is ripe with mergers & acquisitions (M&A). In fact, 2021 deal volumes have exceeded levels from the past three years.1 While healthcare M&A can have significant benefits to patients including access to a larger breadth of services or decreased costs, it often causes major pain points for IT teams tasked with maintaining operational efficiency. Research firm Deloitte notes that IT system integration accounts for 70% of organizational synergies.2

Problem

Healthcare organizations produce and collect mountains of data every day that is subsequently stored in various formats and silos. When healthcare providers merge, IT teams are faced with swamps of data consisting of valuable insight from content such as:

  • Patient records
  • Clinician notes
  • Published research
  • Financial records
  • Medical imagery

Privacy & security protocols as well as interoperability issues make accessing and unifying this data a challenge.

Solution

Advancements in deep learning are revolutionizing the way healthcare organizations can manage, access and extract insights from their most important content. With Vyasa’s Layar data fabric, those in charge of managing data across the healthcare network can unify data sources regardless of file format or where it’s stored and without requiring a data lake. Deep learning models built within Layar automatically catalog the integrated data making it easily accessible.

By leveraging a deep learning data fabric architecture, healthcare organizations can make access to insights a matter of seconds or minutes, instead of days or weeks. Research and analysis is accelerated, as is the accuracy of outcomes, leading to safer clinical trials, improved disease research and a healthier patient population.

1 PwC (2021). Health services deals insights: 2021 midyear outlook https://www.pwc.com/us/en/industries/health-industries/library/health-services-deals-insights.html

2 Deloitte (2018). Health care mergers and acquisitions | The IT factor https://www2.deloitte.com/us/en/pages/life-sciences-and-health-care/articles/healthcare-it-mergers-and-acquisi- tions-technology.html

MODERNIZING GOVERNMENT DATA INFRASTRUCTURE

Written by vyasa on . Posted in USE CASES.

The impact of the COVID-19 pandemic has spurred innovation in state and local governments. A critical part of these efforts is modernizing IT systems that can support remote operations and collaboration across departments. As municipalities look critically at their current infrastructure, it’s clear that digital transformation will play a key role in years to come.

Problem

As part of the IT modernization process, governments have been undertaking massive projects to digitize records, turning difficult to manage physical documents into unstructured and siloed data sources. These data sources include:

  • Permits & certificates
  • Public health records
  • Court & legal filings
  • Budget forecasts
  • Environmental analysis

While a step in the right direction, this issue doesn’t make records any easier to search or manage. Tackling data accessibility problems will be key to making a more resilient and collaborative government sector a reality.

Solution

A new data architecture is changing the way government IT professionals can approach modernizing their data infrastructure. Known as the data fabric, IT teams can securely unify data sources across departments of government, regardless of whether files are stored on premise or in the cloud. No need to duplicate your data or storage and no data lake required.

Vyasa takes this a step further with its Layar deep learning data fabric. Combining the data fabric architecture with novel approaches to deep learning, IT teams that integrate their data within Layar make their content easy to search, explore and visualize, regardless of file format. This makes researching and accessing legal files, public health records, permits and certificates and more a matter of minutes instead of hours or days.

Listed as the #1 strategic technology trend for 2020 by Gartner1, data fabrics are poised to eliminate the need for costly data migrations that can consume budgets and create new data management issues for IT teams.

1 Gartner (2021). Gartner Top Strategic Technology Trends for 2022 https://www.gartner.com/en/information-technology/insights/top-technology-trends

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Unifying Data. Accelerating Insights.

Applications

Layar

Cortex

Synapse

Axon

Signal