Komodo Health

In today's healthcare landscape, the explosion of available data presents both opportunities and challenges. While this wealth of information can lead to better outcomes for patients, organizations are inhibited by a fragmented insights process, with teams working in silos and conflicting results emerging from different departments.

Join us for an eye-opening webinar that analyzes the broken healthcare data insights workflow and introduces a game-changing solution for teams looking to realize the full value of real-world data: MapLab.

Key Challenges:

• 7-month average time across the industry to obtain actionable insights
• Inconsistent data sources used across teams can lead to misalignment
• Burdensome workflows and "swivel chair" processes create process complexity
• Complex analytics are difficult for business leaders to digest for fast decision-making
• Siloed teams hinder a collaborative insight workflow

Our Solution: MapLab Enterprise

Discover how MapLab bridges the gap between data scientists and business leads across the enterprise, offering a unified platform for seamless collaboration. Learn how this innovative solution:

• Starts with streamlined data that is cleaned, certified, normalized, and ready for use
• Empowers data scientists to focus on high-value analytics rather than tedious data cleaning
• Delivers actionable insights from day one to speed decision-making for a competitive edge

Live Demo: Experience the power of MapAI, our NLP assistant, as we demonstrate how any user can effortlessly define a patient cohort and get instant insights. Watch as this user then seamlessly transitions the analysis to a data scientist for more complex analytics, showcasing the versatility of our platform across various user environments - from no-code to high-code. Don't miss this opportunity to transform your healthcare data workflow and gain a competitive edge in the industry.

Speakers:
Laurent Bride - Chief Technology Officer, Komodo Health
Daniel Cohen - Principal Product Strategy Manager, Komodo Health
Lucy Bostwick - Product Manager, Developer Experience, Komodo Health
Julia Goebel - Chief Marketing Officer, Komodo Health

Reliable data projections is a cornerstone of analytics in the Life Sciences industry, from market share tracking, territory sizing, to incentive compensation and more. Since projections rely heavily on claims, which are not a random sampling, the foundation of these analyses often suffer from biases and gaps. This makes projecting data difficult and time-consuming, often leading to incorrect results. 

Understanding true market dynamics requires a diverse set of deeply characterized sources, as well as coverage profiles of each contributing source to enable the calculation of reliable insights. 

Join Dr. Paul Gurney, Senior Vice President, Data Product Management at Komodo Health, in an upcoming webinar to learn about Komodo’s best-in-class projection methodology and real-world example outputs.

In this session, Dr. Gurney will share:

• Our methodology behind the most robust projections available (74% higher than legacy aggregators), and how it differs from traditional approaches
• Avoiding and accounting for areas of low visibility when projecting subnationally
• Creating outputs on metrics of interest beyond NRx and TRx

Diseases like NASH traditionally require an invasive liver biopsy to positively identify patients. When no therapies exist on the market, healthcare providers often hold off on performing a biopsy, resulting in a significant number of undiagnosed patients. By harnessing large clinical encounter data sets, we can begin to understand the patient populations at each stage and severity of disease, leading to better guidance of treatment development and delivery.

Presenters:

• Dr. Aswin Chandrakantan, Chief Medical Officer & SVP Corporate Development, Komodo Health

Rare diseases contribute a significant burden of illness due to the challenges associated with diagnosis. For the ~8,000 adult-onset or age-neutral rare diseases, patients can wait years between symptom onset and diagnosis leading to disease progression and poor outcomes. Biopharma companies struggle to efficiently engage and educate providers on rare diseases because most providers will see less than a handful of patients with a given rare disease over the course of decades of practice. Powered by large, provider identified clinical encounter data sets, machine learning models can now be used to compliantly “identify” providers who are managing patients with undiagnosed rare diseases.

Presenters:

• Aswin Chandrakantan, M.D., Head of Product, Chief Medical Officer, Komodo Health
• Neeraja Krishnaswamy-Bhagavatula, Alnylam