No-Show Prediction
“Which patients will miss their appointment?”
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 patients will miss their appointment?
Patient no-shows cost the U.S. healthcare system $150B annually. A mid-size health system with 200 providers loses $3.2M per year to empty appointment slots. Generic reminder systems treat every patient the same. The real signal is in the relationships: which provider, which day, which referral chain, which past visit patterns predict who will actually show up.
How KumoRFM solves this
Graph-learned clinical intelligence across your entire patient network
Kumo connects patients, appointments, visit history, and providers into a single relational graph. It learns that patients referred by certain providers to specific specialists on Monday mornings have 3x higher no-show rates. The model captures temporal patterns (seasonal trends, day-of-week effects) and social patterns (patients in the same household missing together) that rule-based systems cannot detect.
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
PATIENTS
| patient_id | age | zip_code | insurance |
|---|---|---|---|
| P2001 | 34 | 10001 | Medicaid |
| P2002 | 67 | 10025 | Medicare |
| P2003 | 45 | 10013 | Commercial |
APPOINTMENTS
| appt_id | patient_id | provider_id | scheduled_date | type |
|---|---|---|---|---|
| A001 | P2001 | DR101 | 2025-03-15 | Follow-up |
| A002 | P2002 | DR205 | 2025-03-16 | New patient |
| A003 | P2003 | DR101 | 2025-03-15 | Annual exam |
VISIT_HISTORY
| visit_id | patient_id | date | status | provider_id |
|---|---|---|---|---|
| V001 | P2001 | 2025-01-10 | No-show | DR101 |
| V002 | P2002 | 2025-02-05 | Completed | DR205 |
| V003 | P2003 | 2025-02-20 | Completed | DR101 |
PROVIDERS
| provider_id | name | specialty | location |
|---|---|---|---|
| DR101 | Dr. Kim | Internal Medicine | Main Campus |
| DR205 | Dr. Rajan | Cardiology | West Wing |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(APPOINTMENTS.STATUS = 'No-show', 0, 1, days) FOR EACH APPOINTMENTS.APPT_ID WHERE APPOINTMENTS.SCHEDULED_DATE >= '2025-03-15'
Prediction output
Every entity gets a score, updated continuously
| APPT_ID | PATIENT_ID | SCHEDULED_DATE | NO_SHOW_PROB |
|---|---|---|---|
| A001 | P2001 | 2025-03-15 | 0.71 |
| A002 | P2002 | 2025-03-16 | 0.12 |
| A003 | P2003 | 2025-03-15 | 0.08 |
Understand why
Every prediction includes feature attributions — no black boxes
Appointment A001 -- Patient P2001, Follow-up with Dr. Kim
Predicted: 71% no-show probability
Top contributing features
Past no-show rate (last 12 months)
3 of 5 appts
35% attribution
Days since appointment was booked
28 days
22% attribution
Provider's Monday no-show rate
18%
17% attribution
Distance from patient ZIP to clinic
8.2 miles
14% attribution
Insurance type
Medicaid
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 200-provider health system recovering 20% of no-show slots through predictive overbooking and targeted outreach saves $3.2M annually. Kumo learns patient-provider-schedule interaction patterns that flat reminder systems cannot capture.
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.
