Readmission Prediction
“Which patients will be readmitted within 30 days?”
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A real-world example
Which patients will be readmitted within 30 days?
CMS penalizes hospitals up to 3% of Medicare reimbursements for excess readmissions. For a 400-bed hospital processing 25,000 discharges per year, each avoidable readmission costs $15,000 on average. A 15% reduction in 30-day readmissions saves $5.6M annually in penalties and direct care costs. Traditional LACE scores miss the cross-patient signals hidden in shared providers, medication overlaps, and procedure histories.
How KumoRFM solves this
Graph-learned clinical intelligence across your entire patient network
Kumo builds a heterogeneous graph across patients, encounters, diagnoses, procedures, and medications. It learns that patients sharing the same attending physician with specific procedure-diagnosis combinations have correlated readmission risk. The model captures medication interaction patterns across the patient network, not just individual patient features. One PQL query replaces months of manual feature engineering across siloed EHR tables.
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 | gender | insurance |
|---|---|---|---|
| P1001 | 72 | M | Medicare |
| P1002 | 58 | F | Commercial |
| P1003 | 81 | M | Medicare |
ENCOUNTERS
| encounter_id | patient_id | admit_date | discharge_date | type |
|---|---|---|---|---|
| E5001 | P1001 | 2025-02-10 | 2025-02-16 | Inpatient |
| E5002 | P1002 | 2025-02-20 | 2025-02-23 | Inpatient |
| E5003 | P1003 | 2025-02-25 | 2025-03-02 | Inpatient |
DIAGNOSES
| diagnosis_id | encounter_id | icd10_code | description |
|---|---|---|---|
| D001 | E5001 | I50.9 | Heart failure, unspecified |
| D002 | E5002 | J44.1 | COPD with acute exacerbation |
| D003 | E5003 | N18.6 | End-stage renal disease |
PROCEDURES
| procedure_id | encounter_id | cpt_code | description |
|---|---|---|---|
| PR001 | E5001 | 93306 | Echocardiogram |
| PR002 | E5002 | 94640 | Nebulizer treatment |
| PR003 | E5003 | 90935 | Hemodialysis |
MEDICATIONS
| rx_id | patient_id | drug_name | start_date | active |
|---|---|---|---|---|
| RX01 | P1001 | Furosemide 40mg | 2025-02-10 | Y |
| RX02 | P1002 | Albuterol inhaler | 2025-02-20 | Y |
| RX03 | P1003 | Epoetin alfa | 2025-01-15 | Y |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(ENCOUNTERS.*, 0, 30, days) FOR EACH PATIENTS.PATIENT_ID WHERE ENCOUNTERS.TYPE = 'Inpatient'
Prediction output
Every entity gets a score, updated continuously
| PATIENT_ID | DISCHARGE_DATE | READMIT_30D | PROBABILITY |
|---|---|---|---|
| P1001 | 2025-02-16 | True | 0.74 |
| P1002 | 2025-02-23 | False | 0.18 |
| P1003 | 2025-03-02 | True | 0.89 |
Understand why
Every prediction includes feature attributions — no black boxes
Patient P1003 -- 81yo Male, ESRD
Predicted: True (89% readmission probability)
Top contributing features
Number of inpatient encounters (last 90d)
4 visits
31% attribution
Active high-risk medication count
7 drugs
24% attribution
Shared-provider readmission rate
34%
18% attribution
Diagnosis complexity (HCC score)
3.2
15% attribution
Days between last two admissions
12 days
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 400-bed hospital reducing 30-day readmissions by 15% saves $5.6M per year in CMS penalties and direct care costs. Kumo learns cross-patient signals from shared providers, medication overlaps, and procedure patterns that LACE scores miss entirely.
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.
