The Research and Development arm of MEMOTEXT

Our goal is to develop new algorithms and analytics strategies to bring personalized digital engagement to all patients. At the heart of every data point in healthcare is a patient. Our mission is to harness and activate the utility of that data to empower patients on a personal level to reach their health goals. Using machine learning and AI technologies, we create personalized digital health interventions to support, educate, and sustain behavior change in patients.

Supervised Learning

• Predicting the onset of chronic disease, complexity, changes in health status

• Creating vulnerability/risk scores and understanding drivers for risk

Unsupervised Learning

• Clustering to determine distinct patient archetypes based on clinical, demographic, adherence, behavioral data

Rule-based Learning

• Mapping and analyzing clinical and medication pathways

• Deriving clinical rules to uncover risk and identify opportunities to offer additional supports to patients

We uncover specific learnings to tailor our digital engagement programs to serve the needs of the patient and our client partners.

We build interventions that learn over time using 2-way communication and react based on the individual’s unique behavior while providing valuable insights (e.g. risk flagging) to all healthcare stakeholders. Human behavior is complex and highly unique. These methodologies allow us to describe and label unknowns by predicting what will happen for specific patient cases (e.g. disease trajectories). Our R&D team also creates new clinically-relevant metrics to quantify health behaviors and outcomes (e.g. our  ‘Balanced Adherence Metric’ (BAM) initiative).


Connect with us to collaborate and stay in the loop