Clinical Trial Enrollment
“Which sites will meet enrollment targets?”
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
By submitting, you accept the Terms and Privacy Policy.
Loved by data scientists, ML engineers & CXOs at
A real-world example
Which sites will meet enrollment targets?
80% of clinical trials fail to meet enrollment timelines. Each day of delay costs a sponsor $600K-$8M in lost patent life. A Phase III trial with 150 sites where 40% underperform wastes $50M in site management costs alone. Site selection today relies on investigator surveys and historical spreadsheets, missing the network dynamics between investigators, referring physicians, and patient populations.
How KumoRFM solves this
Graph-learned clinical intelligence across your entire patient network
Kumo builds a graph connecting studies, sites, investigators, and patient catchment areas. It learns that sites where the principal investigator has co-published with the medical monitor and has referring relationships with 5+ PCPs in high-prevalence ZIP codes enroll 2.3x faster. The model captures investigator network effects, competing trial cannibalization, and seasonal patient availability patterns.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
STUDIES
| study_id | therapeutic_area | phase | target_enrollment |
|---|---|---|---|
| STU001 | Oncology | Phase III | 1200 |
| STU002 | Cardiology | Phase II | 450 |
SITES
| site_id | study_id | institution | region | activated_date |
|---|---|---|---|---|
| SITE01 | STU001 | Mass General | Northeast | 2025-01-15 |
| SITE02 | STU001 | Mayo Clinic | Midwest | 2025-01-20 |
| SITE03 | STU002 | Cleveland Clinic | Midwest | 2025-02-01 |
INVESTIGATORS
| investigator_id | site_id | name | publications | prior_trials |
|---|---|---|---|---|
| INV01 | SITE01 | Dr. Chen | 47 | 12 |
| INV02 | SITE02 | Dr. Patel | 23 | 6 |
| INV03 | SITE03 | Dr. Lopez | 31 | 9 |
PATIENTS
| patient_id | site_id | screened_date | enrolled | screen_fail_reason |
|---|---|---|---|---|
| PT01 | SITE01 | 2025-02-10 | Y | |
| PT02 | SITE01 | 2025-02-15 | N | Exclusion criteria |
| PT03 | SITE02 | 2025-02-20 | Y |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(PATIENTS.*, 0, 90, days) FOR EACH SITES.SITE_ID WHERE PATIENTS.ENROLLED = 'Y'
Prediction output
Every entity gets a score, updated continuously
| SITE_ID | STUDY_ID | PREDICTED_ENROLLED_90D | TARGET_PCT |
|---|---|---|---|
| SITE01 | STU001 | 34 | 85% |
| SITE02 | STU001 | 12 | 30% |
| SITE03 | STU002 | 28 | 93% |
Understand why
Every prediction includes feature attributions — no black boxes
Site SITE02 -- Mayo Clinic, STU001
Predicted: 12 patients in 90 days (30% of target)
Top contributing features
Investigator prior trial enrollment rate
0.4x avg
29% attribution
Competing trials at same institution
3 active
24% attribution
Screen failure rate (first 30d)
62%
20% attribution
Referral network size (connected PCPs)
2 PCPs
15% attribution
Patient catchment prevalence
Low
12% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A Phase III trial sponsor identifying underperforming sites 60 days earlier saves $50M in reallocation costs and accelerates enrollment by 4 months. Kumo captures investigator networks and competing trial dynamics that spreadsheet-based site selection misses.
Related use cases
Explore more healthcare use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
