Targeted Approach Optimizes Clinical Study Recruitment of Medically-Eligible Patients Affected by Rare Diseases
July 29, 2021 (SAN FRANCISCO) – Real Chemistry’s IPM.ai, the leader in uncovering undiagnosed and misdiagnosed patients via artificial intelligence (AI) and machine learning (ML), today announced the release of its newest product offering, which is aimed at revolutionizing patient recruitment for clinical studies of rare and specialty diseases. Life science customers can use The Real Chemistry Clinical Trial Recruitment System to efficiently and effectively find medically-eligible patients within their healthcare ecosystems. This technology-based approach is designed to accelerate clinical investigations and lead to faster patient outcomes with less risk.
"With 90% of clinical evaluations for rare diseases failing to meet patient recruitment goals, a new approach is needed," said IPM.ai Co-Founder and President Ron Elwell. “The traditional strategies used for high prevalence, highly diagnosed diseases have proven ineffective for uncommon conditions, which is why our transformative approach rewrites the clinical recruitment playbook for specialty medicines. Utilizing the same proven technology we have successfully applied to other stages of the commercialization process, we have created a more effective and proactive methodology for patient recruitment that prioritizes bringing the study to the right patients instead of simply hoping the right patient finds the study."
IPM.ai is the partner of choice for more than 60 of the world’s foremost precision medicine pioneers due to its unique fusion of people, processes, technology and data to drive optimal outcomes. IPM.ai’s HIPAA-certified system of insight applies AI and ML against a real-world and granular-level data universe of more than 300 million de-identified patient journeys, 65 billion anonymous social determinant of health signals, and the ability to incorporate first-party data sources of any type at any scale. Unlike conventional solution providers, IPM.ai collaborates with clients to define the ideal patient, then build condition-specific models to discover undiagnosed and misdiagnosed patients. This proprietary intelligence improves clinical study recruitment efforts by enabling engagement with healthcare professionals whose patients have the lowest propensity for screening failure and the highest likelihood of successful enrollment. By linking ideal study populations with treating healthcare providers, inferred specialists and key opinion leaders/influencers, sponsors can optimize study protocols, site selection and principal investigator determination, further enhancing patient recruitment and retention.
IPM.ai provides healthcare and life sciences companies with a smarter way to make critical decisions that enables them to identify, engage and activate patients who could benefit from new therapies and standards of care for rare and specialty diseases. It makes the clinical promise of precision medicine a reality for companies of all sizes across the spectrum of therapeutic areas at every stage of the product life cycle, from drug development and clinical study to product launch and commercial operations.
About Real Chemistry and IPM.ai
Real Chemistry is a global health innovation company making the world a healthier place by empowering patients to access the right treatment at the right time, equitably and cost-effectively. We leverage best-in-class, data-driven tech-enabled and digital solutions to deliver clear, concise communications, engagement, activation and value across a full range of healthcare stakeholders – patients, payors, providers, caretakers and regulators.
IPM.ai, part of Real Chemistry, is an Insights as a Service (IaaS) provider that empowers the world’s leading life sciences companies to better understand and improve the lives of patients through the commercialization of precision medicine for specialty and rare diseases. IPM.ai’s system of insight optimizes drug development, clinical study, product launch and commercial operations by utilizing granular-level longitudinal analytics, artificial intelligence and machine learning in conjunction with a real-world data universe of over 300 million de-identified patient journeys and 65 billion anonymized social determinants of health signals.
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