Tag: Health & Life Science

SMARTER RESEARCH: AI-POWERED SYSTEMATIC LITERATURE REVIEW

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.


BEYOND THE VISUAL: EXTRACTING INSIGHT FROM MEDICAL IMAGERY

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

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

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