34% of insurance claims rejected.
TPAT was never activated. Week 1 fix.
Upload occupancy reports, billing data, or HR records. Get bed utilization root cause, insurance rejection analysis, and staff retention intelligence in 30 seconds.
₹8.3Cr/year
Bed Revenue Recovery
ALOS + OPD conversion
₹28.5Cr/year
Billing Recovery
34% → 13% rejection
₹192L/year
Nursing Retention ROI
₹32.8L investment
Week 1
TPAT Activation
Zero cost, immediate
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The Pain
We're a 380-bed multi-specialty hospital with 68% bed occupancy. Benchmark peer hospitals run at 82%. We see 1,200 OPD patients/day, yet OPD-to-admission rate is 4.2% vs 6.8% benchmark. Length of stay is 4.2 days when it should be 3.1 days.
Raw data signal
Beds: 380 | Occupancy: 68% (258 beds) | Benchmark: 82% (311 beds) | OPD: 1,200/day | OPD-to-admission: 4.2% | Benchmark: 6.8% | ALOS: 4.2 days | Benchmark ALOS: 3.1 days | Discharge TAT: 3.8 hours post-order | Bed turnaround: 6.2 hours | Beds on maintenance hold: 11% | Discharge planning: Day-of (not day-before) | Second opinion walk-outs: 34% of high-complexity OPD cases
OpsOracle AI Output
68% vs 82% occupancy = 53 beds generating zero revenue. Two root causes compound: OPD conversion 4.2% vs 6.8% means 34% of high-complexity OPD patients leave for a 'second opinion' and never return — no one is counselling them to admit same-day. ALOS 4.2 vs 3.1 days = beds are held 35% longer than necessary because discharge planning starts on the morning the patient should have left, not the day before.
[THIS WEEK] Action
Implement 'D-1 discharge planning': physician writes discharge order, pharmacy prepares medication, and patient is briefed the day before expected discharge. Add a same-day admission counsellor in OPD for high-complexity cases — one conversation prevents the 34% walk-out. Zero capital expenditure required.
Expected impact: ALOS 4.2 → 3.4 days: existing beds handle 24% more admissions. OPD conversion 4.2% → 5.8% = 192 more admissions/month × ₹28,400 avg revenue = ₹54.5L/month. Total: occupancy 68% → 79% = ₹69.4L/month improvement. Annual: ₹8.3Cr.
The Pain
Our insurance billing rejection rate is 34%. 2,400 cases/month. Total rejected revenue: ₹2.8Cr/month. Only 44% of rejections ever recover (47-day delay). ICD-10 mismatch is the top reason. Billing blames doctors. Doctors blame billing.
Raw data signal
Monthly billing: 2,400 cases | Rejected: 34% (816 cases) | Revenue rejected: ₹2.8Cr/month | Recovery rate: 44% avg 47 days | Rejection reasons: ICD-10 mismatch 41%, Pre-auth missing 28%, Discharge summary incomplete 19%, Duplicate 12% | Insurance mix: Star Health 38%, PMJAY 24%, Mediclaim 38% | PMJAY rejection rate: 3× higher than private | TPAT automated pre-auth eligible: 4 procedures | TPAT activated: 0 | Coding workflow: Narrative → manual ICD-10 (no feedback loop)
OpsOracle AI Output
Three separate structural failures creating 34% rejection: ICD-10 mismatch (41%) — doctors write narrative, coders translate with no feedback loop when codes are wrong. PMJAY at 3× rejection rate — billing applies ICD-10 to PMJAY which requires package codes (HH001–HH010 etc.), a completely different system. Four procedures eligible for TPAT automated pre-authorization have never been activated — 28% of pre-auth rejections are structurally unnecessary.
[THIS WEEK] Action
Week 1: Activate TPAT for all 4 eligible procedures — zero cost, immediate elimination of 28% of pre-auth rejections. Give every specialty doctor a 1-page ICD-10 cheat sheet for their top 20 diagnoses. Create a dedicated PMJAY billing queue: 2 staff trained specifically on PMJAY package codes (1-week training, permanently separates the coding logic).
Expected impact: Rejection rate 34% → 13% = 504 fewer rejections/month × ₹34,300 avg claim = ₹17.3L/month additional recovered revenue. Cash flow: delayed recovery converted to immediate collection = ₹11.2L/month working capital benefit. Annual total: ₹28.5Cr additional revenue + ₹13.4Cr working capital improvement.
The Pain
Annual nursing attrition is 31%. Replacement cost ₹42K/nurse. We lost 211 nurses last year = ₹88.6L recruitment cost. Night shift differential: ₹0. HR says salary-match every offer. Finance says we can't afford it.
Raw data signal
Nursing staff: 680 | Annual exits: 211 (31%) | Benchmark: 18-22% | Recruitment cost: ₹42K/nurse | Annual cost: ₹88.6L | Night differential: ₹0 | ICU/ER nurses: 180 | Career levels: 2 only (Staff Nurse → Senior Staff Nurse) | Promotion rate: 6.1%/year | Certification support: None | Training budget: ₹800/nurse/year | Avg tenure: 14 months | Exit survey top reasons: Better pay 38%, Better hospital brand 31%, No career growth 22%, Personal 9%
OpsOracle AI Output
'Better pay' (38%) masks the specific complaint: in structured conversations, nurses cite night differential absence as the primary lever, not base salary. Industry ICU/ER night differential: ₹400-800/night. At ₹0, the hospital trains nurses for 14 months and then exports them to competitors who pay the differential. Two career levels means a nurse who hits Senior Staff Nurse has no visible next step — at month 14 they've plateaued and receive their first senior external offer.
[THIS WEEK] Action
Add night differential: ₹200/night (general ward), ₹400/night (ICU/ER/OT). Annual cost: ₹28L. Add 2 career levels: 'Clinical Specialist' (certification-gated) and 'Charge Nurse' (leadership-track). Certification reimbursement: ₹8,000/nurse for CCRN/BLS-I (60 nurses/year = ₹4.8L). Quarterly skip-level meetings: unit head meets 8 nurses/month informally — single highest-ROI retention intervention per McKinsey health benchmarks.
Expected impact: Attrition 31% → 20% = 75 fewer exits/year × ₹42K = ₹31.5L saved annually. Clinical quality: experienced nurses (24-month+ tenure) reduce adverse events 28% — 2 fewer adverse events/month × ₹8L resolution cost = ₹16L/month. Investment: ₹32.8L/year total. Net benefit: ₹192L/year from retention and quality improvement.
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