IPM.ai’s unparalleled ability to demonstrate empirical results in finding patients is rooted in our unique combination of longitudinal RWE data assets, combined decades of in-house consulting, technology, and domain expertise, and cutting edge machine learning infrastructure. Whether you are looking to identify patients yet to be diagnosed in rare disease, identify patients ready to progress to your 2nd or 3rd line therapy, or identify patients at risk of non-adherence, IPM.ai can help. Our process focuses on developing the business rules to isolate the patient population of interest, leveraging ML/AI to predict the lookalike audience at scale, associating physicians to the patient via several different attribution techniques, and then engaging in an omni-channel environment.
Activate your patient finding lookalike model through personal promotion by including a Rep Triggers program. Triggers ensure your field force delivers the right message to the right physician at the right time. Leads are piped to your reps through your CRM system in a timely manner. You can also choose to create a true surround sound environment if you leverage non-personal promotion activation via digital advertising with our parent company, Swoop.
Patient experiences vary drastically as they move through continuum of care which results in multiple different pathways. The patient experience can depend upon many factors including age, gender, co-morbidities, and disease severity. Layer on provider and payer dynamics, and very different journeys can exist to the end result of treatment decision. Yet, many manufacturers continue to search for the homogeneous ‘patient journey.’
Patient journey and patient segmentation uncover these various dynamics and provide you with the full view of all stakeholders and highlight the relevant leverage points to act. IPM.ai leverages its experienced team of consultants and integrated RWE claims data to complete the analytics.
Patient journey looks across the continuum of care to address various questions related to both the Patient (demographics, co-morbidities, prior treatment, diagnostic testing, signs and symptoms) and the Provider (Place of service, HCP specialty and payer makeup). A Patient journey helps us paint a picture of the patient’s experience at various critical moments in their journey.
Patient segmentation takes it a step further and divides the patients into groups based on patient treatment history, provider makeup and the patient behavior. Patient segmentation helps in understanding the lifestyles and preferences associated with each segment and plan resources and delivers messaging that is best suited for each of the segment.
Who is actually important in patient treatment decisions, and how far ‘upstream’ does this influence occur? Biopharma organizations have historically used a combination of field intelligence, primary research and secondary data to answer this very question, yet many organizations still have only a cursory understanding of who is truly important. IPM.ai places the patient at the center of this analysis, and draws on years of patient-provider interaction data to effectively map referral patterns and identify key opinion leaders and influencers in a given disease state. Our insights into healthcare professional networks will fuel your clinical trial site identification, access program development, peer-to-peer marketing efforts and field resource planning / deployment activities.
Pharma forecasting and market assessment (sizing) approaches have traditionally relied heavily on literature reviews and primary market research. However, with our longitudinal RWE patient level data, IPM.ai is able to ground such exercises in real, observable patient volumes and real, observable treatment patterns. This data, coupled with an experienced team plus the ability to dive deeply into diagnosis, treatment and share analogs, positions IPM.ai to best help you develop empirical market assessments and forecasts across a range of therapeutic areas.
What People Are Saying
Thanks for taking the time to explain your system. It’s incredibly exciting and particularly necessary for the dynamics of our disease state given the multiplicity of HCP specialties that are involved in diagnosis and treatment of patients that suffer from AHPs.
— Aaron Beitner, Associate Director, Brand Management Alnylam Pharmaceuticals 
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IPM.ai is dedicated to enabling improved patient care and lowering the cost of care through technology.
IPM.ai’s Data Asset
IPM.ai RWE healthcare data asset is an expansive collection of de-identified patient level transactions in the United States, made up of 280MM+ unique patients compromising 8+ years of history. Constructed of longitudinal medical, hospital, and prescription events, the database brings together disparate sources and compiles the information on IPM.ai’s state of the art technical infrastructure. Our data aggregation platform was built to balance data centricity with clinical and commercial value. This revolutionary method operates as the perfect nexus of efficiency and insight: the union of expertise in architecture and data amplifies the ability to execute analytics at a rapid rate without compromising quality.