We understand the REAL patient journey

Researching real-life cancer treatment journeys. Revealing hidden patterns using Machine Learning & Artificial Intelligence to advance clinical research and improve patient quality of life

Cutting Edge AI, Machine Learning And Proprietary Medical Neural Networks Are Transforming How Partners Understand Patient Journeys

Why research with Belong?

  • Highly engaged patients​
  • Disruptive analytics technologies​
  • Unparalleled Real World Data​

Belong.Life changes the cancer curves:
Understanding what patients REALLY care about, what they REALLY do, and what REALLY worked for them

4200 +
CLINICAL TRIALS MATCHES

Helping patients find new pathways

1 +
ML & AI ALGORITHMS

NLP, OCR & Neural Networks 

1 +
RESEARCH PARTNERS

Accelerating cancer research

Belong.life can help you accelerate cancer research

by understanding real-life treatment journeys, analyzing critical decision points, conducting surveys and providing you with competitive BI.

Recent studies

Poster PRESENTATION

Implementation results at the gynecological cancer care in JGH​

See the latest study that was conducted by McGill University and the Jewish general hospital in Toronto, Canada, using the Belong.life platform

Identifying the patient journey and treatment lines patterns in metastatic pancreatic cancer​

Data from patients using the Belong PPN was able to provide insights into the treatment journeys of MPC patients in Israel, the four firstline treatment prevalence and the following lines of treatment. Continuous evaluation of RWE and PRO from the Belong PPN would allow comparative effectiveness research of cancer treatments and lead to improved evidence-based care and patients’ health-related quality of life.

Mapping PD-1 inhibitors’ side effects using patient reported outcomes (PRO)

Data from the Belong PPN successfully captured the spectrum of PD-1 inhibitors’ reported AEs. Analyzing PRO of post approval treatments could provide deeper understanding of treatment patterns, expose limiting AE that bear implications on cost of treatment, indicate patients’ quality of life (QOL), improve management recommendations and provide a comprehensive outlook of patients’ outcomes

New Cancer Patient Survey Shows Emotional Impact of Illness-Related Fatigue Greater than Physical Effect

A real-world data survey was designed to evaluate, from the patients’ perspective, the effects of fatigue on their treatments as well as their emotional and physical state.

FATIGUE PREVALENCE AND ADHERENCE TO TREATMENT: A REAL-WORLD DATA SURVEY AND MATHEMATICAL MODEL APPLICATION

Fatigue is a common symptom reported by cancer patients (pts) and has been previously documented to affect patient’s quality of life.
A real-world data survey was designed to evaluate, from the pts perspective, the fatigue effect and treatment adherence. The survey was created in a digital format. This was sent randomly and replied anonymously by users of the Belong app a dedicated social network for cancer pts and their caregivers. Belong leveraged both push notifications as well as DPROs (Digital patients reported outcome feature) which appeared on user’s apps dashboards for their increased engagement.

A TARGETED SURVEY OF BELONG.LIFE ADVANCED BREAST CANCER PATIENTS, FOCUSING ON PATIENT’S REPORTED OUTCOMES, REAL WORLD EVIDENCE AND INSIGHTS IN REDUCING THE BURDEN OF FINANCIAL TOXICITY.

In medicine, financial toxicity (FT) is a term used to describe economic problems a patient has related to the cost of medical care. Financial toxicity is an emerging and growing concern for many cancer patients

PUBLICATIONS

ASCO Annual meeting 2019

Real-world data (RWD) and patients reported outcomes (PRO) in breast cancer (BC): Physical, emotional side effects (S/E), financial toxicity (FT), and complementary usage (CM) relations

ASCO ANNUAL MEETING 2019

Creating the real-world medical record: Using digital patient-generated data to create an updated picture of patients outcomes.

ASCO ANNUAL MEETING 2019

Unveiling the real-world outcomes of breast cancer (BC) patients with taxanes-induced neuropathy using a digital patient-powered network (PPN).