Research & Patient Journey Insights
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Find patients with rare, undiagnosed, or misdiagnosed conditions, including subgroups.
I’m working in a rare disease – any additional patients are worth finding.
My patient population is hard to find because there’s no standard coding for it, so I can’t easily search for patients in a database.
There’s lots of disagreement around how to define the population I care about, but providers know cases when they see them.
I think my population is significantly underdiagnosed and / or misdiagnosed, and we’re missing patients.
I need to understand the size of the market for my medication but I don’t fully trust existing estimates.
Identify patients that respond particular treatments to improve access and guideline adherence.
I suspect my treatment might work better than others for a subgroup of patients, but I don’t know how to find them.
Guidelines leading to my treatment aren’t well followed – I need help improving adherence.
My treatment is effective but significantly underpenetrated. I need to highlight ‘best candidates’ to begin moving the needle.
I think there’s real unmet need – patients who don’t respond well to available treatments – but I’m not sure how to isolate and define that patient group.
Predict risk of specific outcomes: disease progression, complications, and catastrophic events.
It would be really impactful to know which patients are at high risk of disease progression, in advance.
I know some patients experience complications after surgery and would take action beforehand with insight on who’s at greatest risk.
I’m trying to make the case for earlier treatment to reduce progression and catastrophic event risk – I need a way to actually pinpoint cases where this risk is highest.
I operate in a risk-sharing environment that would benefit from greater precision at the patient level.
Isolate patients who are more likely to generate higher utilization, and utilization growth, over time.
A patient subgroup whose utilization will increase dramatically could benefit from earlier intervention.
To make a strong case around reimbursement, I need to demonstrate my treatment can help impact utilization for patients resistant to other medications.
I suspect there’s unmet need and high utilization from interactions between my condition and comorbidities, but I don’t know how to disentangle or pinpoint effects.
Accelerate recruitment by focusing on patients most likely to meet enrollment critieria
I’ve activated sites for my trial but struggling to achieve enrollment targets without a better sense of who might be most appropriate for inclusion.
I don’t have a good ‘heatmap’ for where to find trial candidates. If I did, I could select the right sites.
My inclusion / exclusion criteria are impossible to apply at scale. If I had a sense of which patients were most likely to meet them, the study team could focus on that group.
I’m not sure if additional sites, or more patients at my existing sites, will help me reach my enrollment goals faster.
Artificial Intelligence: Friend or Foe?
An Evaluation of Real-World Use of Biologics in Rare Systemic Vasculitides During Routine Clinical Care in the US
MAPS Podcast: Using AI to Accelerate Diagnosis and Treatment
A new approach to identifying patients with elevated risk for Fabry disease using a machine learning algorithm
AI Uncovers Other Patients Like You to Support Doctors
Hard to Define, Harder to Find Patients: Using AI & Real-World Data to Understand Treatment Resistant Depression
AI Applications for Clinical Development: From Identifying Patients to Amplifying Trial Endpoints
