Precision Medicine Trends

What it means for the future of clinical trials

Precision medicine has broad applications in optimizing the design and execution of clinical trials, and it plays an increasingly important role as pressure grows on the pharmaceutical and medical device industry to run more efficient, cost-effective and productive trials. Ultimately, precision medicine helps deliver measurable value from therapeutic interventions across health systems.

It’s also about taking steps to ensure patient engagement, retention and treatment adherence over the course of a study. To determine how precision medicine supports these objectives, Informa Pharma Intelligence conducted an online survey for NTT DATA between Nov. 20 and Dec. 3, 2019. Here’s what that research revealed.

female doctor with old patient

NTT DATA and Informa Pharma Intelligence discuss the role of precision medicine in advancing cancer research and treatment and the future of clinical trials.


Study Highlights


81% of respondents say understanding patient populations is the most important step in developing a precision medicine program


67% of respondents believe that oncology is the seedbed for precision medicine


49% perceive patient awareness, access and enrollment as the biggest obstacles to precision medicine in clinical trials

life sciences lab equipment

Master Opportunities, Mitigate Risk

Survey Highlights:

  • Each patient is unique as evidenced by individualized treatment
  • Geographic location is the most significant factor in defining clinical trial inclusion
  • Patient awareness, access and enrollment are significant obstacles in precision medicine and clinical trials
  • Emails, text and voice communication is used the most to improve patient retention
  • Patient genetic composition has the greatest impact on disease progression.
  • While costs are a concern, staying ahead of the technology curve is more critical


hcls insights solution

New Insights, Untapped Potential

HCLS Insights Powered by Nucleus, our advanced analytics platform, provides the necessary tools for trial site identification, investigator and physician targeting, biomarker progression, and more. This provides a foundation for improved patient outcomes, greater degrees of quality, and cost efficiencies required for success.

Use Cases

Clinical trial site selection based on patient availability, location, trial site location and investigator credentialing

  • Use of cognitive automation and semantic NLP in extracting real world diagnostic data and medical IOT data for clinical trial site selection.
  • Use of machine learning algorithms for predicting location of clinical trial sites through multivariate analysis of biomarkers, patient journeys, physician and hospital data

Physician targeting, patient care coordination and segmentation using EMR, prescription and investigator data sets

  • Use of cognitive automation and semantic NLP in ingesting and harmonizing consumer, prescription and diagnostic data, clinical investigator data sets for targeted segmentation of physicians.
  • Use of major machine learning models to score physicians, patient journeys and investigators for clinical trials based on clinical trial efficacy and brand adoption propensity.

Discovery of specific biomarkers through search of novel information relevant to potential biosimilars

  • Deploying autonomous self-organizing semantic and sentiment engines to aid biomarker discovery has potential to impact pharmaceutical research.
  • Deploying advanced machine learning models for the discovery of biomarkers/phenotypes and related pharmacogenomic entities for predicting patient response

Precision Medicine Trends

What’s the role of precision medicine in the future of clinical trials?
Our research may surprise you. See what respondents had to say.