Contract migration synergy is computable. Most deal models don’t compute it.

The synergy stack in a modal PE physician practice rollup contains five or six line items. Cost synergies (back-office consolidation, purchasing, real estate) typically capture 70–85% of announced value within two years.¹ Revenue synergies — contract migration, provider productivity normalization, site-of-service migration, volume expansion — are slower, less reliable, and dominate the realization risk in the deal.

Among the revenue synergies, contract migration is the largest single line item. Acquired practices are typically smaller than the platform, with less leverage in their commercial contracts, lower historical rates, and limited internal capacity to renegotiate. The platform’s contracts are typically better. The diligence model takes the difference, multiplies by the acquired practice’s revenue base, and produces a synergy projection that anchors a meaningful share of the deal’s projected EBITDA expansion.

The methodology used to produce this projection — portfolio-average rate ratios applied to the acquired practice’s revenue base — is the universal standard in current PE healthcare diligence. The methodology produces an answer that is consistently and structurally wrong, by margins that materially affect deal economics. The deterministic alternative is to compute the contractually owed amount on each historical claim under both contracts and sum the differences. The alternative exists, it is computable for any deal where the acquired practice’s claims data is available, and the difference between the methods is large.

We estimate the typical divergence at 40–60% relative to the deterministic answer. The direction is most often biased upward — seller-side broker books and buyer-side models share the methodology and the methodology systematically overstates — but the bias is not universal. In some deals the deterministic method finds more synergy than the standard method, particularly when the acquired practice has specific underperforming payer relationships that the platform can rationalize. The point is not that synergies are smaller than the standard method suggests. The point is that synergies are mismeasured, and the actual answer is what should drive deal pricing.

For deals where contract migration is 30–50% of the synergy stack, mismeasurement at this scale translates to roughly 12–30% mispricing of the total synergy stack, which produces approximately 15–25% mispricing of equity at exit through the standard LBO mechanics. This is the largest single source of avoidable deal-model error we observe across PE physician practice rollups currently in market.

This note develops the analysis. Section 2 describes the standard method. Section 3 develops the four structural sources of its divergence from the deterministic answer. Section 4 describes the alternative. Section 5 demonstrates the gap on a worked example. Section 6 closes with the implications for active diligence and deal structuring.

The portfolio-average rate ratio method works as follows. The diligence team obtains the acquired practice’s audited financials and computes its average commercial rate as a percentage of Medicare. For an ortho practice, this might be 120% of Medicare on the weighted commercial book. The platform’s average commercial rate, computed the same way, might be 145% of Medicare. The ratio (145% / 120% = 1.21) is applied to the acquired practice’s commercial revenue base. If the acquired practice has $30M of commercial revenue, the modeled migration synergy is approximately $6.3M.

The method has three properties that make it the universal standard.

It is intuitive. Rate ratios are how PE buyers think about commercial reimbursement strength. The platform is bigger, so it negotiates better rates. The acquired practice is smaller, so it negotiates weaker rates. The synergy is the gap times the volume. The arithmetic is something a senior associate can do in their head during a management meeting.

It is fast. The data inputs are publicly available or quickly obtained: the acquired practice’s financials produce the denominator, the platform’s average rate is known internally, the multiplication is trivial. The full calculation takes minutes, not days.

It is defensible in the diligence report. The methodology is recognizable to investment committees, to lenders, and to second-round bidders. Everyone uses it. No one questions it.

The method also gets the directional answer correct in simple cases. If the platform has materially better contracts than the acquired practice, the platform will realize some incremental revenue on migrated claims. The direction of the synergy is real. The magnitude is what is mismeasured.

We grant the method its history. It became the standard at a time when claim-level computation was infeasible — the same period during which sampling methodology became the standard for QofE diligence, for the same reasons (data difficult to acquire, computation expensive, rule-system encoding infeasible).² The constraints have lifted; the methodology has not updated.

What the method assumes, and where each assumption fails, is the subject of the next section.

The portfolio-average method assumes that rate differentials between the platform and the acquired practice are uniform — across procedures, across payers, across the existence of the contract relationship, and across time. Each assumption fails, in ways that compound.

Source one: rate differentials are not uniform across procedures.

The Urban Institute’s analysis of FAIR Health commercial claims data found that average commercial markups over Medicare vary widely by specialty — anesthesiology at 250–330%, orthopedics and general surgery at 130–150%, dermatology at approximately 90%, joint replacements specifically at commercial-to-Medicare ratios closer to 2.0x.³ The variation within a single specialty is comparable in magnitude. A commercial contract that pays 150% of Medicare on E&M codes might pay only 110% of Medicare on high-volume surgical codes (where rates anchor more tightly to Medicare and payers resist increases). Another contract might pay 180% on injection codes but 95% on imaging codes performed in office.

The acquired practice’s revenue is not generated uniformly across procedures. It is generated by the specific procedural mix the practice performs. If the practice’s mix is heavily surgical, the average commercial markup is anchored by the surgical rate differential, which is typically smaller than the practice’s overall average. Applying the platform’s average rate advantage (which incorporates higher-markup procedures the acquired practice may perform less of) overstates the realizable uplift on the acquired practice’s actual procedural base.

The empirical pattern is consistent: high-volume codes have smaller rate differentials, low-volume codes have larger ones, and portfolio-average ratios smooth across the gap. The 20% of codes that generate 80% of revenue — the standard Pareto distribution in physician practice claim mix⁴ — are also the codes where rate differentials are smallest. The portfolio-average method weights the synergy as if rate differentials applied uniformly to the practice’s actual volume. They do not.

Source two: rate differentials are not uniform across payers.

Milliman’s 2025 commercial reimbursement benchmarking analysis, using a national database of 62 million commercial members and $276 billion in medical allowed charges, found commercial reimbursement ranging from 140% of Medicare FFS (Alabama) to 277% (Alaska), with state-level variation comparable to specialty-level variation.⁵ Within California alone, MSA-level commercial reimbursement ranges from 160% of Medicare (Madera) to 261% (Vallejo). The Pacific census division averages 201%; East South Central averages 163%. These are population-weighted averages across all payers and all procedures. The variation across specific payers within a single market is comparable in magnitude.

The platform’s “average commercial rate” is not one rate. It is a weighted blend across the specific commercial payers the platform contracts with, in the specific geographies the platform operates in, at the rates the platform has negotiated. When the platform’s UnitedHealth contract is at 150% of Medicare and the acquired practice’s UnitedHealth contract is at 125%, the differential on UHC volume is 20%. When the platform’s Anthem contract is at 140% and the acquired practice’s Anthem contract is at 130%, the differential on Anthem volume is 7.7%. When the platform’s Aetna contract is at 130% and the acquired practice’s Aetna contract is at 135%, the differential is negative — the acquired practice’s Aetna contract is better than the platform’s. The acquired practice does not migrate that contract; it keeps it, or the platform absorbs an effective rate cut on Aetna volume.

The portfolio-average method weights the synergy as if a single rate ratio applied across all payers. The actual computation is payer by payer. The variance across payers is large enough that aggregating across them masks the underlying structure.

Source three: not all contracts migrate.

Most commercial contracts contain anti-assignment clauses requiring payer consent on change of control. The platform cannot unilaterally transfer the acquired practice’s volume onto the platform’s master contracts. Some contracts get terminated and rolled onto platform contracts at the next allowable point. Some remain in place under their existing terms until renewal, which may be one to three years out. Some payer relationships the platform does not have at all — typically regional or specialty-specific payers that have meaningful presence in the acquired practice’s market but not the platform’s — and the acquired practice’s revenue from those payers either continues under existing terms or is lost in the transition.

The MD Clarity industry playbook on post-acquisition revenue cycle integration describes the standard timeline: re-credentialing of acquired providers across platform payer panels takes 90–120 days; the contracting phase to negotiate platform-rate access for each payer adds another 30–60 days; payer-side loading of executed contracts takes another 45–60 days.⁶ The total migration timeline per payer, executed competently, is six to nine months minimum. In practice, with multiple payers running in parallel and varying degrees of payer cooperation, the realistic timeline is 12–24 months for a typical multi-payer migration.

The portfolio-average method assumes all contracts migrate. The actual analysis identifies which contracts migrate, which do not, and on what timeline.

Source four: synergy realization rates from the empirical base.

McKinsey’s research across 200 M&A executives in ten industries found that revenue synergies realize at an average of 23% below announced targets, with the majority of synergies taking 3–5 years to capture rather than the 12–18 month timelines typical in deal models.⁷ BDO’s CFO survey found that 35% of acquirers either missed or fell short of their synergy targets entirely.⁸ McKinsey’s general M&A research distinguishes cost synergies (which capture 70–85% of announced value, because they tie to specific actions like layoffs and consolidation) from revenue synergies (which capture 25–35%, because they depend on customer behavior, contract negotiation, and operational execution that the acquirer does not fully control).⁹

Contract migration is technically a revenue synergy. It depends on payer behavior (whether they consent to assignment, whether they renew on platform terms, whether they push back on rate concessions during change of control), on regulatory timing (credentialing, contract loading, effective dates), and on operational execution by the platform’s managed care team. The empirical base for revenue synergies says 25–35% of announced value, captured over 3–5 years. The modal healthcare PE deal model underwrites contract migration at near-100% of announced value, captured in months 1–12. This is directly contrary to the empirical pattern in M&A across industries. There is no structural reason to expect healthcare PE rollups to outperform the cross-industry base rate. The portfolio-average method does not haircut for realization — it assumes full capture on the modeled timing — which is the largest single source of the divergence from the deterministic answer.

The alternative is the same alternative that § M.01 and § M.02 articulated in different applications: compute the contractually owed amount on each claim under both contracts and sum the differences. The methodology generalizes.

For each historical claim in the acquired practice’s claims data, the analysis identifies the procedure (CPT code with modifiers), the payer, the date of service, and the place of service. The applicable rule system — the acquired practice’s contract on one side, the platform’s contract on the other — produces the contractually owed amount under each. The difference is the per-claim synergy on that procedure-payer-date combination. The sum across the historical claim population, weighted by the migration timing for each payer relationship, is the deterministic synergy projection.

The computational requirements are specific. The analysis requires the acquired practice’s historical claims data, which is acquired in any diligence engagement that performs QofE work and is available without additional friction. It requires the acquired practice’s commercial contracts, which the seller produces in a competent diligence process. It requires the platform’s commercial contracts, which the buyer already has. It requires a rule system that can compute the contractually owed amount under each contract — fee schedules, edits, modifier interactions, place-of-service differentials — at the resolution required for exact derivation. The rule system is the analytical infrastructure that § M.01 argued is feasible to encode under current constraints. The encoding is the engineering work; the computation is mechanical once the encoding exists.

The output is structured differently than the portfolio-average answer. Where the standard method produces a single synergy number, the deterministic method produces a synergy projection broken down by payer, by procedure category, by realization timing, and by the specific source of the differential. The buyer can see that 60% of the projected synergy comes from UHC volume migrating from the acquired practice’s contract to the platform’s contract, with the differential concentrated in surgical codes, materializing in months 6–18 after re-credentialing. They can see that 15% of the projected synergy comes from Anthem migration with a smaller differential, slower realization. They can see that the Aetna relationship would migrate at a negative differential and is therefore left in place. They can see that the regional payer relationship contributes 8% of the acquired practice’s commercial revenue and does not migrate because the platform has no relationship with the payer.

This structure is what the diligence report should output. The single-number synergy projection that the portfolio-average method produces is information-poor relative to the structured projection that the deterministic method produces. The single number is what current LBO models use. The structured projection is what they should use.

We work through the methods on a synthetic acquired practice. The numbers are realistic but stylized; they are not drawn from any specific deal. The purpose is to make the analytical pattern visible, not to characterize any actual transaction.

The acquired practice is a 30-provider multispecialty group with $50M net patient revenue. Composition: 35% Medicare ($17.5M), 40% commercial ($20M), 25% other ($12.5M, including Medicare Advantage, Medicaid, workers’ comp). The commercial book is split across UnitedHealth (37.5%, $7.5M), Anthem (25%, $5M), Aetna (20%, $4M), Cigna (12.5%, $2.5M), and a regional payer (5%, $1M). The acquired practice’s procedural mix is 60% surgical, 40% E&M and ancillary.

Portfolio-average method.

The diligence team computes the acquired practice’s average commercial rate at 120% of Medicare (a representative figure for a midsize multispecialty group without strong commercial negotiating leverage). The platform’s average commercial rate is 145% of Medicare (a representative figure for an established physician practice management platform). The ratio (145% / 120%) = 1.208. Applied to the acquired practice’s $20M commercial revenue base: incremental revenue of $4.17M annually, modeled as captured in year 1.

This is the synergy that anchors the deal model.

Claim-level method.

The analysis decomposes the projection by payer. The platform’s specific contracts produce the following payer-level rates as percentages of Medicare. The acquired practice’s specific contracts produce the comparable rates. Both are realistic for the respective market positions.

The deterministic synergy in year 1: approximately $1.0M, compared to $4.17M under the portfolio-average method. The gap is 76%.

The deterministic synergy at full run-rate (year 3, after all migrations have completed): approximately $2.0M. Compared to the portfolio-average projection of $4.17M assumed in year 1 and persisting, the gap is 52%. Note that the portfolio-average method does not produce a separate “year 3 run-rate” estimate — it produces a single number that is assumed to apply from year 1. The deterministic method makes the timing visible.

The sources of the gap, from largest to smallest:

The Aetna contract differential is negative. The acquired practice’s Aetna contract is better than the platform’s. The platform does not migrate the contract because doing so would reduce revenue. The portfolio-average method assumes positive uplift on Aetna volume; the actual structure produces zero uplift on $4M of revenue.

The regional payer relationship does not migrate. The platform has no contract with this payer. The acquired practice’s $1M of regional payer revenue continues at existing terms (or, in some cases, is lost in the transition because the regional payer chooses not to recontract under the change of control). The portfolio-average method assumes the platform’s rate applies to this revenue; the actual answer is that no platform rate applies.

The Anthem differential is much smaller than the portfolio average suggests. The platform’s overall 145% average is anchored by stronger contracts with other payers; the Anthem-specific differential is only 7.7%, not 21%. Applied to $5M of Anthem volume, the actual uplift is $385K at full run-rate, not the $1.04M the portfolio-average method implies.

Migration timing pushes 50% of the realized synergy from year 1 to year 2 and beyond. Even on contracts that do migrate at meaningful differentials, the realization is gated by re-credentialing (90–120 days), contracting phase (30–60 days), and payer-side loading (45–60 days). For a UHC contract that migrates in month 8 of year 1, only 4 months of differential is realized in year 1. The portfolio-average method assumes year-1 full capture; the actual realization is roughly half.

The combined effect is a deterministic projection of approximately $1.0M in year 1, $1.8M in year 2, $2.0M run-rate from year 3 forward. The portfolio-average projection is $4.17M from year 1 in perpetuity. Across a five-year hold, the cumulative gap is approximately $11M of revenue ($9M of cumulative EBITDA contribution at standard physician practice margins) — material to deal pricing and to the LBO model that priced the deal.

The corrected methodology changes specific things in the deal.

The synergy stack composition shifts. Contract migration, properly computed, is smaller as a share of the total stack than the portfolio-average method suggests. Other synergies — site-of-service migration, provider productivity normalization, volume capacity expansion — become relatively larger contributors to the total. The deal model that gets contract migration right also gets the other synergies’ relative importance right.

The realization timing shifts. Year 1 EBITDA expansion is materially lower than the portfolio-average method projects, with the difference shifting into years 2–3. The LBO model’s debt service capacity in year 1 is correspondingly lower, which affects the financing structure and the lender’s covenant calculations. Deals that look comfortable on year-1 cash coverage under the standard method may be tight on cash coverage under the corrected method.

The risk profile becomes specific. Rather than a single aggregate “contract migration risk” that gets soft-stated in the IC memo, the corrected method produces specific named risks: the UHC migration must complete by month 12 for the year-2 model to hold; the Aetna relationship is at-risk because the acquired practice’s existing contract is better and any change of control conversation could prompt the payer to negotiate down; the regional payer relationship contributes 5% of commercial revenue and has no clear migration path. Each named risk has a corresponding diligence question, a corresponding LOI provision, and a corresponding 100-day plan workstream.

The LOI terms can reflect the structure. Escrow holdbacks tied to specific payer migration milestones — UHC contract executed by month 9, Anthem migration completed by month 15 — are enforceable and produce real protection. Earnouts structured against specific payer realization (not against aggregate synergy capture) align incentives with the deal’s actual mechanics. The standard method’s single-number synergy projection cannot support this structure; the deterministic method’s structured projection can.

The competitive bidding dynamic shifts. In the wave of first-cycle PE exits coming over the next 18–24 months in physician services, deals are typically run as auction processes with multiple bidders. The bidder who applies the deterministic method walks into the bid with a different synergy projection than the bidders applying the standard method. In most cases this is a lower projection; the deterministic bidder bids lower. In some cases — when the deterministic method finds underexploited contract value the standard method missed — the deterministic bidder bids higher and wins the deal. Either way, the bidder using the deterministic method is bidding based on the actual structure rather than on the averaged abstraction.

The methodology is computable for any deal where the acquired practice’s claims data is available. The data is acquired in any competent diligence engagement that performs QofE work. The deterministic computation is engineering work, not research work. The infrastructure exists; the question is whether buyers choose to use it.

The deal that’s getting underwritten this quarter on portfolio-average contract migration synergies is mispricing the asset by 15–25% of equity at exit, in either direction. The corrections are available to any buyer willing to compute claim-by-claim under both contracts.