Understanding Patient Journeys Starts with Real-World Data
Understanding the patient experience continues to be an elusive endeavor for those in the healthcare and life sciences.
Known as patient journey mapping, organizations from providers to payers to pharmaceutical companies spend significant time and financial resources to better understand these individuals which can influence paths of care, product innovation and other opportunities to improve healthcare outcomes.
To effectively build a patient journey organizations need access to massive amounts of data, something that the healthcare industry has a wealth of, but little ability to fully operationalize.
Wrangling Patient Journey Data
The healthcare industry has long faced data challenges. With over 80% of organizational data being siloed and the healthcare industry poised to generate over 30% of the world’s data, it’s clear that this issue isn’t ending any time soon.
For those looking to build patient journeys, this means the majority of valuable insight is locked within silos and buried within unstructured content such as records and physician notes which are challenging to search and collect information from. But what’s included in these reports is only half the story.
Effective patient journey mapping requires going beyond internal data to analyze valuable insight from real-world sources updating on a daily basis. For example, valuable experiences are shared constantly through platforms such as social media, forum discussions and wearables.
Managing these real-world data sources is incredibly time and labor-intensive given the variety and volume of content posted. In most cases, professionals must monitor multiple platforms and pull relevant information into a single source before analysis can even take place.
Real-World Analysis in a Single Environment
New data management architectures can revolutionize the way the industry monitors and collects real-world data. Known as the data fabric, organizations can create a single environment for accessing information by connecting their siloed data sources regardless of location and without having to move our copy files. Similar to connecting to storage on-premise or in the cloud, data fabrics can also connect to external sources like social media feeds to gain instant access to real-world developments.
Vyasa takes this a step further with its Layar data fabric. In addition to overcoming data silos, Layar features pre-built deep learning models that catalog, tag and analyze the context of internal and external data. This creates an environment where users can create a single source of truth around a specific topic.
For example, a pharmaceutical company monitoring adverse effects of a new therapeutic or a healthcare provider better understanding alternative treatments for a disease can leverage Vyasa to conduct this analysis within a single platform. Today, users leverage Vyasa’s Canonical Data Fabric to access the latest scientific from Clinical Trials and published research around emerging diseases as well as monitor the market through new patent filings.
One Step Closer to Completing the Journey
Understanding the patient journey is critical to improving care – whether you are a healthcare provider, pharmaceutical manufacturer or insurance provider. Advancements in deep learning and data management mean these organizations don’t have to turn to outdated approaches for patient journey mapping. While gaining a full view of the patient journey may always be just out of reach, deploying a data fabric approach is one step closer to understanding the most important aspect of healthcare – the health and wellbeing of patients.