Penn Medicine
$23.8M
Annualized financial impact at a single site
- 8,632
- Bed-days saved, annualized
Care that sees ahead.
Healthleap is the AI platform that surfaces at-risk inpatients earlier, starting with malnutrition, one of the most underdiagnosed conditions in the hospital.
The current state is unacceptable.
- up to 50%
- of inpatients are at risk of malnutrition
- <9%
- are diagnosed
- ~5,500
- malnourished patients missed per year at a typical 1,000-bed hospital
Screen all inpatients daily
- Scans every chart
- Every adult, every day
- Scores each patient
- Risk score written into the EHR
- Improves CDI and coding
- Upstream identification at admission or during the stay
Healthleap analyzes every adult inpatient's chart each day and writes a nutrition-risk score into the EHR flowsheet each morning. The registered dietitian's routine doesn't change, but instead of chasing false flags, the day starts with the highest-acuity patients already at the top of the list. Healthleap sits upstream of CDI, closing the identification gap that comes before it.
Scored and sorted by severity of nutrition risk
Each morning, every adult inpatient is scored and sorted by nutrition-risk severity. The sickest patients sit at the top of the list in the dietitian's EHR, not buried in chart review.
Today's priority list
- 10.94
Bed 7B
64 F · 4 days LOS
- 20.89
Bed 3A
71 M · 2 days LOS
- 30.81
Bed 12C
58 F · 6 days LOS
- 40.76
Bed 9D
47 M · 1 day LOS
We fit upstream of CDI and coding
Healthleap identifies at-risk patients on day one, before CDI and coding workflows can engage. We don't replace them; we feed them a cleaner, earlier signal.
Patient admitted
Risk isn't continuously captured and surfaced.
Healthleap identifies risk
Daily prioritization, written into the EHR.
CDI and coding engage
Existing investment, fed an earlier signal.
Reimbursement & quality capture
Severity, LOS, and incremental revenue follow.
Validated against the clinical reference standard.
Co-authored research with leaders from Cedars-Sinai, Johns Hopkins, and Stanford, and published in Applied Clinical Informatics. To our knowledge, Healthleap offers the only commercially available, peer-reviewed AI malnutrition screening tool.
- 88%higher sensitivity
- Than nurse-administered malnutrition screening
- 4 daysearlier identification
- Of patients with malnutrition
- 0.95AUROC
- during stay; 0.92 on day one
Cedars-Sinai
$11M
Annual impact
- 39%
- More diagnoses, same staffing
Every 1,000 additional reimbursable malnutrition cases captured is roughly $5M in incremental reimbursement.
Malnutrition is just the start
The same EHR-native daily screening intelligence we use for malnutrition is already being extended to other conditions that hospitals consistently struggle to identify early and address in time.
Malnutrition
Risk identification, validated against the clinical reference standard.
Delirium & toxic metabolic encephalopathy
Identification designed to surface patients who may be missed.
Aspiration pneumonia
Identifying patients at elevated aspiration risk before complications develop.
Congestive heart failure readmissions
Flagging the patients most likely to return before they leave.
Pressure injuries
Earlier identification of at-risk patients for prevention protocols.
Close gaps. Improve care.
Strengthen margins.
- Measurable incremental reimbursement, contractual ROI floor.
- Measurable improvement in O:E ratios, length of stay, and severity capture.
- Prioritize the highest-risk patients, not false flags
The AI in Healthleap stands for actionable insights.
See what you've been missing.
Healthleap helps leading health systems find malnutrition-risk inpatients earlier than admission screening, using peer-reviewed daily EHR-based prioritization, with measurable impact on reimbursement, length of stay, and severity capture.