Personal Electronic Health Record (PEHR)

centralizes Medical Records in the patient's unique personal EHR
SMART PEHR platform is taking clinical trials and observational studies
to the next generation - Real World Trials.
SMART PEHR Clinical Data Warehouse (CDW) is a real time database that harmonizes and consolidates data from a variety of clinical sources to present a unified view of a single patient.
SMART PEHR makes a difference through analytics and Machine Learning (ML) and Artificial Intelligence (AI)-driven applications, based on diverse data from across the care continuum.
SMART PEHR provides an opportunity to change how research studies are conducted, enabling a single point of connection to any number of data sources.
Predictive Care Analytics providers (AI and ML) are connected with SMART PEHR Platform, using big data to spot warning signs and anomalies in patterns so that preventive actions can be taken.
SMART PEHR aggregates Real World Data, patient data from different electronic medical record (EMR) systems, eCOA, hospital records, connected diagnosis labs, DNA sequencing, imaging institutions, devices, wearables, ePRO, telehealth and home healthcare visits.
Life sciences and pharmaceutical companies can gain great value by using Real-World Data : SMART PEHR connects the world’s health data to improve patient outcomes, linking study data with RWD.

SMART Personal EHR
patient-centric platform provides interoperability and bridges AI, ML and NLP analytics in Real-Time, accelerating the discovery of Real-World Data in either centralized or decentralized clinical trials.

DCT +(AI + ML + NLP)= Safer CT

DCT +(AI + ML + NLP)= Shorter CT
DCT +(AI + ML + NLP)= Cost-effective CT


  • Pharmaceutical companies that are sponsoring clinical trials Customers.
  • Hospitals that are participating in clinical trials.
  • Scientific investigation organization that is running or participating in clinical trials.
  • CROs (Contract Research Organization) that provides clinical trial management services for the pharmaceutical, biotech, and medical device industries.

  • New Decentralized clinical trials (DCTs).
  • Traditional centralized clinical trials with the challenge to process the data with AI and ML, to get Genomics diagnosis or to connect a few centralized clinical trials into one global Trial (few countries).
  • Finished clinical trials with the challenge to process the data with AI and ML
  • Observational studies where researchers observe the effect of a risk factor, diagnostic test, treatment or another procedure.
  • Features


  • Monitoring and data review costs are reduced
  • We Improve clinical trial efficiency and patient enrollment
  • We Accelerate evidence generation and insights
  • Eliminate some of the administration costs that result with in-person visits
  • Enhanced patient retention
  • Improved transparency for researchers
  • Predicting patient utilization patterns
  • Decentralized approach to faster recruitment
  • Optimize Time, Cost, Risk, and Patient Centricity