What data should we share with Mandolin to estimate ROI (backlog days, denial rate, BPO spend, FTE hours, time-to-therapy)?

Most infusion and specialty-drug leaders don’t lack ROI—they lack clean, believable math that finance and operations can sign off on. To estimate Mandolin’s ROI on your backlog days, denial rate, BPO spend, FTE hours, and time-to-therapy, you don’t need a data warehouse. You need a small, focused set of operational numbers that reflect the reality of how work moves today across fax, portal, and phone.

Below is exactly what to pull, why it matters, and how to approximate it if your systems aren’t perfectly instrumented. This is the same lens I used running benefits verification, prior auth, and appeals teams for multi-site AICs and buy-and-bill programs.

---

Quick Answer: To estimate ROI accurately, Mandolin typically needs:

• Your current volume (referrals/prescriptions, documents, patients)
• How long each step takes (FTE hours per task, backlog days, time-to-therapy)
• Your cost structure (internal FTE cost, BPO spend)
• Your downstream leakage (denial rate, write-offs, missed infusions)

From there, we model:

• How much labor Mandolin agents replace or absorb
• How many days of backlog and time-to-therapy you can eliminate
• How much revenue you unlock by avoiding avoidable denials and capacity caps

---

At-a-Glance: The Core Data Mandolin Uses to Estimate ROI

Here’s the short list of what’s most helpful to share.

Category	Data Point	Why It Matters for ROI
Volume & Throughput	Daily/weekly referrals or new prescriptions	Sets the baseline workload Mandolin will handle
	Documents per day (faxes, portal downloads, scans)	Ties to Mandolin’s 24x speed impact (minutes per doc)
	Active patients per month on therapy	Links operational performance to revenue and access
Backlog & Timing	Average backlog days (intake → EHR, Rx readiness)	Quantifies “stuck work” and delayed starts
	Average time-to-therapy by drug or program	Measures patient impact and revenue timing
Labor & Cost	FTEs by function (intake, BV, PA, appeals, claims status)	Establishes current labor footprint
	Fully-loaded FTE cost (or salary ranges)	Converts time saved into dollars
	BPO/vendor spend for these workflows	Captures external cost Mandolin can replace or reduce
Quality & Revenue Leakage	Initial denial rate (medical & PA-related)	Quantifies lost revenue and rework Mandolin can prevent
	% of denials overturned vs written off	Shows true financial impact of upstream improvement
	Missed or delayed infusions due to admin issues	Connects workflow gaps to patient access and revenue
Operational Mix	Payer mix & top payers	Informs complexity of portal/fax/phone work
	Drug mix (high-cost biologics, buy-and-bill vs SP)	Clarifies financial impact of each avoided delay/denial

You don’t need every field perfectly. Reasonable ranges are enough to build a credible ROI model.

---

1. Volume & Throughput: How Much Work Is on the Table?

Mandolin’s ROI scales with volume. The more referrals, prescriptions, and documents you move through portals and faxes, the more value a “back office full of your best employee” delivers.

Data to Share

• Referrals / new prescriptions per day or month

  • By service line if you can (e.g., neurology, GI, rheum)
  • Bonus: % that are buy-and-bill vs white bag / brown bag

• Documents per day

  • Faxed referrals
  • Clinical notes / lab reports
  • Payer portal exports or screenshots
  • PA forms, letters, and supporting documentation

• Active patients on therapy per month

  • Particularly for high-cost biologics and infusion regimens

How Mandolin Uses This

• Mandolin has published results like:
  • 24x increase in speed: going from 20 minutes per document manually to ~3 minutes with an under-2-hour turnaround.
  • Zero backlog: eliminating a 4-day prescription backlog for a team processing 200–300 new prescriptions per day.
• When we know your volume, we can:
  • Estimate how many minutes per document / per Rx Mandolin can give back.
  • Model how that translates into FTEs worth of capacity and more patients moved through intake, BV, and PA without adding staff.

If you don’t have this perfectly:
Share your best counts for “typical days” and “busy days,” or pull a one-week sample. Directionally correct is far better than perfect but missing.

---

2. Backlog Days and Time-to-Therapy: How Stuck Is Work Today?

In specialty drugs, backlog and time-to-therapy are the real heartbeat metrics. They show where portal, fax, and phone work is piling up.

Data to Share

• Backlog days

  • Average days between referral arrival and full EHR entry / “ready-to-work” status
  • Average days of prescription backlog (how long a new Rx waits before anyone touches it)
  • Oldest item in queue on a typical day (e.g., “We often have referrals sitting 3–4 days before full intake”)

• Time-to-therapy

  • Average days from referral received → first infusion or drug dispense
  • If possible, break out:
    • Intake/EHR entry time
    • Benefits verification time
    • Prior auth turnaround
    • Scheduling lag

• SLAs or targets

  • Any internal goal like “All new referrals entered into the EHR within 24 hours” or “PA submitted within 48 hours of referral”

How Mandolin Uses This

Mandolin’s agents automate the steps that usually drive delays:

• Reading faxed referrals and clinical notes “regardless of formatting or source” and entering them into your EHR.
• Navigating payer portals for eligibility and benefits.
• Performing benefits investigations and calling payers.
• Compiling and submitting prior auths, plus managing portal checks or payer calls for status.

With your backlog and timing data, we can:

• Quantify days of backlog eliminated when intake and BV/PA prep are handled automatically.
• Model time-to-therapy compression, e.g., shaving 3–5 days off starts for key drugs.
• Tie those days directly to revenue pull-forward (so finance sees the cash impact) and earlier patient access (so clinical leaders see the access impact).

If you don’t track this formally:
Estimate from recent cases or ask your team:

• “On average, how many days do referrals sit before we fully key them into the EHR?”
• “Once a referral hits, how long before a patient gets their first infusion?”

---

3. FTE Hours and Staffing: How Much Human Time Is Going Into the Messy Middle?

Mandolin doesn’t sell “dashboards”; it replaces the manual labor of navigating portals, reading faxes, and making calls. To estimate ROI, we need to understand your current human effort.

Data to Share

• FTE counts by function

  • Intake / referral management
  • Benefits verification (medical/pharmacy)
  • Prior authorization (and medical policy review)
  • Claims statusing and appeals
  • Any dedicated “payer portal team”

• Time per workflow

  • Average minutes per:
    • Referral intake and EHR entry
    • Benefits investigation (per patient, per drug)
    • PA prep and submission
    • Claims status check and basic appeal initiation
  • If you have time studies or SOP estimates, share those.

• Fully-loaded FTE cost

  • Rough all-in annual cost (salary + benefits + taxes), even ranges like:
    • Intake/BV: $55k–$75k
    • Senior auth/appeals: $70k–$95k

How Mandolin Uses This

With FTE counts and minutes-per-task, we can:

• Convert today’s workflow into total labor hours per week/month.
• Use Mandolin’s benchmark improvements (e.g., 24x faster document handling, under-2-hour turnaround) to:
  • Estimate FTE-equivalent capacity Mandolin gives back.
  • Model where you can repurpose headcount away from data entry and portal refreshes into higher-value work (patient calls, complex appeals, program building).

If you only know schedules, not time-per-task:
Share team size, hours per day, and rough percent of time spent on these workflows. We’ll help reverse-engineer a reasonable current-state baseline.

---

4. BPO Spend and External Vendors: Where Are You Buying Capacity Today?

Many centers plug staffing gaps with outsourced vendors. Mandolin’s ROI model includes this external spend because agents can absorb that work without you scaling a BPO contract.

Data to Share

• Current BPO / vendor relationships for:

  • Benefits verification
  • Prior auth submissions / follow-up
  • Claims status or basic denials management

• Pricing model

  • Per-case or per-Rx fees
  • Per-FTE or monthly retainer contracts

• Volume processed externally

  • % of BV / PA / claims statusing that’s outsourced
  • Average monthly charges or an annual budget number

How Mandolin Uses This

We use BPO data to:

• Quantify hard-dollar savings if Mandolin replaces some or all outsourced workflows.
• Show how much volume you can repatriate without hiring, because AI agents absorb the work that used to require external staff.
• Combine internal FTE and vendor spend into a total cost per Rx / per patient, then compare that to a Mandolin-enabled cost per Rx.

If you don’t have a clean breakdown:
Share last year’s invoices or a best-guess split across services (e.g., “We spend about $X/year mostly on PA support and BV overflow”).

---

5. Denial Rate and Revenue Leakage: What’s the Cost of Errors and Delays?

The biggest hidden ROI often isn’t staffing—it’s avoidable denials, rework, and write-offs when something small goes wrong in BV, policy matching, or PA submission.

Data to Share

• Baseline denial metrics

  • Initial denial rate on claims tied to specialty drugs or infusions
    • Ideally broken out by: PA-related, eligibility/benefits, coding, other
  • % of denials that are:
    • Appealed and overturned
    • Written off or abandoned

• Financial impact

  • Dollar value of denied claims per month/quarter for these drugs/services
  • Write-off rate (e.g., “We write off ~X% of denied specialty claims annually”)

• Operational drivers

  • Common root causes: missing labs, wrong policy used, expired auth, misaligned coding, benefit misreads, missed re-auths

How Mandolin Uses This

Mandolin’s agents:

• Read and interpret clinical notes, labs, and medical policies—“regardless of formatting or source.”
• Assemble complete PA packages aligned to payer-specific requirements.
• Perform ongoing claims statusing and help initiate appeal workflows by checking portals or calling payers and interpreting remits.

With your denial and write-off data, we can:

• Estimate how many denials are preventable with more precise BV, policy alignment, and complete submissions.
• Model reduction in denial rate and the incremental revenue from:
  • Fewer write-offs.
  • Less rework and fewer repeat submissions.
• Tie these improvements back to time-to-therapy (fewer “pause and re-do” moments) and staff workload.

If you lack granular denial breakdowns:
Start with:

• Overall denial rate for infusion/specialty claims.
• Your best estimate of what % is PA/benefit-related (most teams can roughly call this out).
• A recent month or quarter’s total write-offs for this segment.

---

6. Operational Mix: Payers, Sites, and Drug Economics

Finally, the flavor of your work matters. A high-Medicare, high-340B mix with lots of biologics behaves differently from a commercially heavy neurology program.

Data to Share

• Payer mix

  • % Medicare, Medicaid, commercial, exchange
  • Top 5 payers by volume

• Drug mix

  • Major infused biologics and high-cost specialty agents
  • Whether you’re primarily:
    • Buy-and-bill
    • White bagging
    • Specialty pharmacy-dispensed

• Site & pricing nuances

  • Sites in 340B or GPO arrangements
  • Use of site-specific fee schedules
  • Use of co-pay assistance or foundation support

How Mandolin Uses This

Mandolin’s platform:

• Performs full benefits investigations via portals and phone calls.
• Calculates patient out-of-pocket with actual operational nuance:
  • Real-time benefits
  • Site-specific fee schedules
  • Co-pay assistance
  • GPO and 340B pricing
  • Drug acquisition costs

With your mix data, we can:

• Prioritize high-impact programs where each avoided delay or denial carries large financial stakes.
• Show how better, faster OOP estimates impact:
  • Patient acceptance and adherence.
  • Revenue predictability and margin protection on buy-and-bill.

---

How Precise Does This Data Need to Be?

Perfection isn’t the goal; credibility is.

To model ROI on backlog days, denial rate, BPO spend, FTE hours, and time-to-therapy, Mandolin typically needs:

1. Directional volume:
   • “We process 200–300 new prescriptions a day and 250 documents/day.”
2. Reasonable time estimates:
   • “Intake is 10–12 minutes per Rx; docs are ~20 minutes each.”
3. Order-of-magnitude costs:
   • “We have 5 FTEs and spend ~$X/year on BPO support.”
4. Baseline performance:
   • “Backlog is usually 3–4 days; denial rate is around Y%; time-to-therapy is about Z days.”

From there, we plug in Mandolin’s proven impacts—like 24x document speed, under-2-hour turnaround, and zero-backlog results—to produce a conservative and an aggressive scenario. Both are fully traceable back to your inputs, so finance and operations can pressure-test assumptions.

---

Final Verdict: The Minimum Data to Start an ROI Conversation

If you want a concise checklist of what to share with Mandolin to estimate ROI across backlog days, denial rate, BPO spend, FTE hours, and time-to-therapy, focus on:

1. Workload & Flow

   • Referrals/new Rxs per day or month
   • Documents per day
   • Active patients on therapy

2. Speed & Backlog

   • Average backlog days for intake and prescriptions
   • Average time-to-therapy (referral → first treatment)

3. Cost & Capacity

   • FTE counts by function + approximate fully-loaded cost
   • BPO/vendor spend for BV, PA, and claims status/appeals

4. Quality & Revenue

   • Initial denial rate and write-off patterns
   • Known admin-driven delays or missed infusions

5. Context

   • Payer mix, drug mix, buy-and-bill vs SP, and any 340B/GPO dynamics

With just those pieces, Mandolin can build a grounded ROI model showing how AI agents taking over portal, fax, and phone work translate into fewer backlog days, lower denial rates, reduced BPO spend, reclaimed FTE hours, and shorter time-to-therapy—measured in minutes, days, and dollars, not buzzwords.

---

Next Step

Get Started