What We Do

monARC is revolutionizing how research is done by transforming the data generated during routine clinical care and daily living into evidence for developing new treatments and better healthcare while reducing time and cost for  life science companies and healthcare providers.

How We Work With Researchers

  • monARC partners with healthcare systems and patients directly to collect real-world data from a multitude of sources, including electronic health records, wearables, devices, patient-reported outcomes.

  • monARC uses natural language processing and machine learning to organize and analyze data into research grade real world evidence.

  • monARC collaborates with partners advancing new medical treatments to provide the information necessary to develop therapies more efficiently.

1Real World Data for External Control Arms & Pragmatic Trials  2Natural Language Processing/Machine Learning

1Real World Data for External Control Arms & Pragmatic Trials

2Natural Language Processing/Machine Learning


Real-World Data Trials

Real-World Data (RWD) is data relating to patient health status and / or the delivery of health care routinely collected from a variety of sources

RWD includes all health data outside of a traditional randomized controlled trial (RCT). Sources of RWD may be electronic health records, insurance claims data, registry data, and patient generated data from apps and other connected technologies.

monARC’s RWD Platform can be used to conduct a variety of novel trial designs using RWD to generate regulatory-grade evidence.


External Control Arms

monARC’s RWD Platform can generate external control arms (ECAs) that model the placebo or standard of care arm in clinical trials. The ECA is generated from patient data routinely captured during standard of care in EHRs combined with other RWD sources such as registries and claims databases.


  • Accelerated phase 2 trials for early decision making, greater insight into outcomes for traditionally underrepresented patient groups

  • Reduced cost through trial population sizes and repurposing assessments already collected in clinical care

  • Faster, cheaper trials with more generalizable data to accelerate both regulatory and reimbursement decisions

  • Time and cost savings in excess of 50% can be realized using ECAs instead of traditional control arms


Comparative Effectiveness Studies

monARC’s RWD Platform is ideally suited to conduct comparative effective research (CER) to identify optimal treatment approaches, including for specific patient subgroups, and inform a more efficient and effective healthcare system. CER compares at least two active interventions and is carried out in setting that reflect usual care; the patients, settings and conditions are real-world.


  • Accelerate reimbursement decisions for new therapies

  • Improve treatment decisions for patients, and in particular those sub-groups not typically represented in traditional RCTs