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FREE TOOL · HOSPITAL

Episode & Leakage Simulator

Model the economic impact of structured AI across the three episode phases — pre-admission (ERAS, pre-auth), day-of (digital check-in), and post-discharge (readmission prevention, leakage recovery, RPM). Spain peer-reviewed evidence (CARME, Diz-Ferreira, Aguilar-Rodríguez, González-Arévalo).

Numbers vary materially by line. Pick the line you want to model first — readmission baseline, leakage baseline, LOS, and ERAS impact all auto-fill from peer-reviewed Spain evidence.

Volume + revenue

Episodes per year for this service line at your hospital or group.

Spain privado median for this service line. Adjust to your DRG mix.

Phase A — Pre-admission

Spain: 5-15% (González-Arévalo 2009 Can J Anaesth). Same-day cancellations included.

0%25%9%

Industry typical 1-5 days when handled manually. Each day beyond 2 adds ~0.5pp of preventable cancellations (clearance expires, patient drops). Direct contracts with insurers can drop this to <1 day.

1d7d3d

Industry typical 5-15%. About 40% of denials become effective cancellations (the rest get appealed/recovered in the same window). High denials suggest documentation or coding gaps at admission.

0%25%10%

ERAS = Enhanced Recovery After Surgery. Pilot = one service line. Full = systemic protocol across surgical services. Diz-Ferreira 2025 Spain meta: cardiac surgery −1.24 LOS days, −25% complications full protocol.

Phase B — Day of

% of admissions using digital check-in (pre-arrival forms + ID + insurance verification). Spain post-COVID: 20-50% in urban privado. Compounds the cancellation lever — 0% adoption = 1.0× effect, 90% adoption ≈ 1.9× (day-of adherence and no-show prevention).

0%90%40%

Phase C — Post-discharge

EU/Spain median for this service line. Auto-set from service line; adjust if your number differs.

3%30%18%

% of downstream episodes (rehab, follow-up procedures, specialist consults) going out-of-network.

5%50%15%

% of RPM-eligible patients (HF, COPD, post-surgical) currently enrolled in remote monitoring. EU/US best-in-class: 60-70%. The intervention only counts headroom up to ~95% — higher current enrollment = smaller additional uplift.

0%80%30%

Choose the maturity of the AI layer touching your post-discharge journey. Each level adds capabilities.

No uplift modeled. Manual phone calls only; numbers stay at the baselines above.

Total economic impact (annual)

0

Sum of the four drivers below at the chosen service line, ERAS level, and intervention level.

Readmission avoidance

0

0 readmissions avoided × €4,500 average cost per readmission.

Leakage retention

0

0 downstream episodes retained × your revenue per episode.

Capacity recovery

0

0 bed-days ≈ 0 additional surgeries possible without expanding capacity.

Cancellation reduction

0

Reducing same-day / pre-op cancellations recovers 0 additional episodes × revenue per episode.

Additional RPM enrollments

+0

Eligible cohort × your selected intervention uplift. CARME 2011 reported −67.8% HF readmissions for the enrolled cohort — that clinical impact is reported separately, not double-counted into readmission avoidance.

Total economic impact by driver

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Model your own scenario in under a minute.

Free account. No card required. Save scenarios, compare 2-4 side by side, export PDF for your team. Calibrated for Spain privado hospitals (CARME, Diz-Ferreira, Aguilar-Rodríguez).

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