Hospital Finance & Revenue Analytics

Revenue per bed and revenue per doctor are the two fastest ways to compare performance across units, specialties, and time without drowning in gross billing totals. A high gross bill does not help if occupied beds or doctor sessions do not convert to collected cash. Most hospitals compute these ratios in month-end MIS, long after scheduling and payor mix decisions are locked.

FireAI joins inpatient and day-care billing with bed-day equivalents and doctor attribution from your HIS so ratios update as charges post. You can slice revenue per bed by ward class, specialty, and payor category, and revenue per doctor by department, session type, and consultant, using the same definitions finance and medical leadership agree on.

What FireAI tracks:

• Revenue per occupied bed day and per licensed bed (where bed master is available)
• Revenue per doctor per day and per session, with optional case-mix adjustment by department
• Trend lines for both metrics over rolling 30, 60, and 90 days
• Variance vs hospital target or peer group average by unit
• Split of revenue attributed to procedures, room charges, and professional fees where data supports it

How FireAI solves it: A 220-bed multispecialty hospital in Delhi used FireAI to find that two surgical units had similar gross billing but revenue per bed differed by 19% because one unit held patients longer in recovery without converting to billable step-down beds. Adjusting discharge planning and bed flow lifted net revenue per bed in that unit within one quarter without adding beds.

What you can ask FireAI:

• "What was revenue per bed last month by specialty?"
• "Show revenue per doctor for Cardiology this quarter vs last quarter"
• "Which wards are below target revenue per bed despite high occupancy?"

Payor mix shapes margin, working capital, and how hard your billing team must work. A shift toward cash-paying patients improves immediacy but may signal insurance rejection fatigue. Heavy TPA volume improves covered lives but stretches insurance claim settlement cycles. Without a single hospital billing analytics view, leadership often discovers mix shifts only when EBITDA misses.

FireAI classifies revenue and receivables by payor type: government schemes, private insurance, TPAs, corporate tie-ups, and direct out-of-pocket payments. Dashboards show mix by department, specialty, and elective vs emergency paths so you can align packages, deposits, and pre-auth workflows with how patients actually pay.

What FireAI tracks:

• Share of billed value and collected cash by payor category month over month
• TPA vs direct insurer vs self-pay split for inpatient vs outpatient
• Discount and contractual adjustment rates by payor
• Bad debt and write-off rates correlated with payor segment
• Alerts when mix deviates more than a threshold vs plan (for example TPA share up 5 points)

How FireAI solves it: A Bengaluru provider group saw TPA share rise from 41% to 53% in six months. FireAI tied the shift to two corporate panels and one insurer network expansion. They renegotiated advance deposits for elective surgery and tightened pre-auth cutoffs, which stabilized days in AR for that segment within 60 days.

What you can ask FireAI:

• "What is payor mix by value this quarter vs last quarter?"
• "Which departments have the highest out-of-pocket share?"
• "Show TPA revenue trend for Orthopaedics over the last 6 months"

Insurance claim settlement speed is a direct driver of working capital. Every day a claim sits in query, resubmission, or payor review is a day of outstanding collections that could have cleared. Hospital billing analytics rarely connects claim status timestamps to finance KPIs in one place, so teams chase spreadsheets instead of root causes.

FireAI ingests claim IDs, submission dates, query dates, resubmission dates, and settlement dates from your billing or revenue cycle system. It computes median and P90 insurance claim settlement time by insurer and TPA, flags claims breaching internal SLA, and segments delays into query response lag, authorization gaps, and coding issues.

What FireAI tracks:

• Days from bill freeze to first submission, and from submission to settlement
• Query rate and average days to respond and resubmit
• Rejection reasons grouped by insurer and department
• TAT comparison across TPAs and direct insurers
• Trend of pending claim value older than 30, 60, and 90 days

How FireAI solves it: A Chennai hospital found median settlement for one TPA was 41 days vs 24 days for a peer insurer with similar volume. Drill-down showed 62% of delays were coding queries on implant charges. A focused training block for billers on implant documentation cut median TAT to 29 days in eight weeks.

What you can ask FireAI:

• "What is median insurance claim settlement time by insurer this quarter?"
• "Which TPAs have the highest query rate on inpatient claims?"
• "Show claims pending more than 45 days by value"

Outstanding collections are where hospital billing analytics meets cash reality. Aging reports from ERP or HIS often list totals but do not tie outstanding collections to payor behavior, denial patterns, or department billing quality. Finance needs aging by payor, not only by bill date, to prioritize follow-up and release working capital.

FireAI builds AR aging buckets (0-30, 31-60, 61-90, 90+) by payor class and by department. It surfaces which TPAs or insurers drive the tail, which units post late charges, and how outstanding collections correlate with claim query cycles. Optional links to patient deposits and partial payments give a clearer net collectible view.

What FireAI tracks:

• Outstanding amount and share in each aging bucket by insurer, TPA, corporate, and self-pay
• Movement of bills between buckets week over week
• Denial and short-pay value rolled into the next follow-up cycle
• Concentration: top 20 accounts or bills representing 80% of overdue value
• DSO-style metrics segmented by payor where payment history allows

How FireAI solves it: A Hyderabad hospital saw outstanding collections over 90 days spike for one TPA. FireAI showed 71% of that tail was tied to disputed package tariffs for two procedures. Finance opened a tariff reconciliation task force with the TPA and moved Rs 2.4 Cr into active settlement within 45 days.

What you can ask FireAI:

• "What is outstanding balance aging by payor this week?"
• "Show 90+ day AR for TPAs only and rank by value"
• "Which departments contribute most to overdue self-pay?"