Data Mining will Become Essential to Reduce the Disease Burden and Empower Healthcare Stakeholders

An increasing number of people, from patients to payers to biotech companies to providers, are playing a role in medical decision-making, and pharmaceutical companies are looking for more effective ways to communicate the value of innovative treatments they offer. One of the ways they are doing so is by looking to evidence-based programs that provide opportunities for action to convince these stakeholders of a treatment’s value. A comprehensive data set can help analytics teams see millions of patient journeys and quickly identify patients and providers who can benefit from novel therapies.

Frost & Sullivan recently published an executive brief, The Most Effective Way to Identify Better Outcomes When Using Limited Patient Populations, which examines how examining predictable patterns in the patient journey can help commercial teams identify patients who can benefit from innovative treatments.

“We believe that smarter, more innovative use of data and analytics is essential for reducing the global burden of disease,” said Komodo Health Chief Medical Officer Aswin Chandrakantan, MD. “Rich and nuanced data is the key to predictive solutions that can empower a multitude of healthcare stakeholders, including life sciences companies, healthcare payers and providers, and patient advocacy groups, to create a more cost-effective, value-driven healthcare system.”

Komodo combines the world’s most comprehensive view of patient encounters with innovative algorithms and clinical expertise to track the unique patient journeys of more than 300 million individuals.

Komodo’s expertise is important in this multibillion-dollar industry because a company may have only limited experience with a disease or be entering a completely new therapeutic area. For this reason, a succinct alert map of patient encounter data is a valuable asset. Commercial teams should evaluate a mapping vendor based on:

  • Broad visibility: a deep data ecosystem that captures diverse signals for large populations and an array of clinical encounters.
  • Accuracy: data modeling that eliminates false negatives and positives in alerts.
  • Speed: The frequency of alerts on patient treatment at optimal phases.
  • Evolution: New features in alerting software and new data sources that continually elevate the system.
  • Expertise: Years of experience in identifying highly complex patient cohorts and translating clinical patient journeys.

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