For MSOs, billing aggregators & payer organizations

Your backlog isn't going to clear itself. And hiring won't get you there fast enough.

You're processing thousands of claims a week. Your queues are growing faster than your team. Every new client adds volume your back office wasn't sized to absorb. The board wants margin. Operations wants headcount. Neither is going to win.

Hired Billing Support is the backend processing engine for healthcare operations at scale. Embedded in your systems, accountable to your SLAs, elastic enough to double when your volume does.

— Operations dashboard · Queue status

Queue
Depth
AHT
Claims processing
2,847
14m
Eligibility verification
1,204
4m
Prior authorization
892
22m
Denial management
634
18m
Payment posting
1,560
3m
SLA breach risk this week3 of 5 queues at risk
The capacity problem

You know exactly where the bottleneck is. You can't hire your way out fast enough.

The pipeline is full. Your sales team is closing new accounts. Operations is the constraint.

Team leads are working evenings. Average handle time is creeping up. SLA breach reports are landing on someone's desk every week. Two of your best processors gave notice last month, and replacements are two months out.

You can hire — but training a new processor to full productivity is 90-120 days. You can buy software — but software doesn't work the queues.

What you actually need is throughput. Today. Not next quarter.

01
Volume scaling faster than headcount.
Every signed client adds queue depth your back office didn't budget for. Queue depth costs SLA performance.
02
AHT is creeping up.
When processors are stretched, accuracy drops. When accuracy drops, rework spikes. Rework destroys margin faster than anything else.
03
Hiring cycles are 90-180 days.
Recruit, screen, hire, train, ramp. Client launches don't wait that long. Neither does existing volume.
04
Unit economics are compressing.
Operational cost per claim goes up. Reimbursement per claim goes down. Margin is the meat in the middle — getting thin.
05
Standardization breaks at scale.
What worked at 2,000 claims/week stops working at 8,000. SOPs drift. Quality varies. Reporting gaps appear.
06
Your best people are doing the wrong work.
Senior staff fill in on routine processing because junior processors aren't keeping up. Judgment work goes unattended.
What's actually happening behind the scenes

You don't have a hiring problem. You have a capacity elasticity problem.

Traditional operations scale the way furniture is built — slow, fixed, expensive. You commit to headcount, real estate, training infrastructure, and management overhead, and you bear the cost whether volume is at peak or trough.

But healthcare volume isn't smooth. It spikes on new client launches. It surges at the start of every benefit year. It dips, then bursts. Your fixed cost structure can't move with it.

What you actually need is operations that flex — capacity that comes online in days, scales up or down without termination cost, and slots into your existing systems. That's not BPO. That's an operational layer.
— Our model

We act as your backend processing engine. Embedded. Elastic. Accountable.

We don't replace your leadership, your systems, or your client relationships. We provide the throughput layer underneath them — staffed by trained healthcare specialists, supported by AI-assisted workflows, and accountable to your SLAs.

You'll have a dedicated team lead from our side who interfaces with your operations manager. You set the SOPs. We execute them. Queue depth, AHT, accuracy, and SLA performance — visible in a shared dashboard, updated in real time.

When your volume doubles next quarter, we scale with you in days. When it drops, we scale back without severance cost.

What we run

Backend processing. Complete coverage.

All deliverable under a white-label arrangement if your client relationships require it.

Claims processing at volume

Submission, adjudication support, payer follow-up, exception handling.

Eligibility & benefits verification

Real-time and batch processing across all major payers.

Prior authorization processing

Pre-cert submission, payer follow-up, status tracking at scale.

AR resolution

Aging bucket clearance, payer escalation, root-cause analysis.

Denial management & appeals

Categorization, routing, appeal authoring, overturn tracking.

Payment posting & reconciliation

ERA/EOB posting, exception research, daily reconciliation.

Credentialing support

Application processing, follow-up, maintenance cycles.

Data entry & document processing

Demographics, charge entry, document indexing at throughput volume.

Quality assurance & audit

Sample-based QA on all processed work. Accuracy tracking.

Reporting & analytics

Queue reporting, SLA dashboards, root-cause analysis.

How we integrate

We slot into your operation. Not the other way around.

— 01

We work in your systems.

Your platform. Your ticketing. Your reporting tools. No migration. No duplication.

— 02

We follow your SOPs.

Your QA standards. Your escalation rules. Your client-specific protocols. Documented and executed against.

— 03

We report into your ops leadership.

Dedicated team lead. Daily syncs. Weekly SLA review. Monthly capacity planning.

— 04

We flex with your volume.

Scale up in days. Scale back without cost. Operations becomes a variable cost, not fixed.

AI + human at scale

AI compresses the volume. Humans handle the exceptions.

At MSO and aggregator scale, AI matters more — not less. We use AI-assisted workflows for claim scrubbing, eligibility batching, denial categorization, and document classification. Throughput per processor goes up 2-4x on routine work.

The exceptions — denied claims needing root-cause analysis, payer escalations, complex appeals, edge-case eligibility — get a trained human every time. Because automated decisions on exceptions are how aggregators lose clients.

The ratio matters. AI does the volume work. Human time is preserved for the work that decides whether your client renews.

2-4x
Throughput per processor on routine work via AI
98%
SLA compliance across processing categories
7-14d
Capacity scaling for new client launches
95%+
Quality scores on sampled work
By the numbers

What scaling with us looks like.

40-60%
Lower operational cost per claim versus equivalent in-house operations.
-28%
AHT reduction via AI-assisted workflows on routine processing.
14d
Average time to scale capacity for a new client launch.
The honest comparison

Why not just build it in-house?

You can. Here's what it looks like.

Build in-house
Embed HBS
6–12 months to stand up
14–30 days to live
Fixed cost: salaries, real estate, infrastructure
Variable cost — scales with volume
Hiring risk, training cost, attrition risk
We absorb those
Capped at your local labor market
Global trained healthcare specialist pool
You manage the operation
You manage the relationship. We manage execution.
New client launch waits for hiring
New client launch starts day one
The question isn't whether to build operations — it's which part to own vs which part to lease. Most leadership we work with chooses to own client relationships and strategy. They lease the throughput layer. That's the math that wins.
Let's plan capacity

Tell us the volume. We'll show you the model.

Current volume, projected growth, top three bottlenecks. We'll come back with a capacity model and integration plan — typically within 7 business days.

Mutual NDA · BAA on every engagement · SOC 2 aligned
Chat with HBS Support