Problems › Where Should We Invest Next? › Healthcare Providers
Capital allocation goes wrong when panels expand under value-based contracts before cost-per-episode data exist to show which panels return the next dollar. What makes this harder for healthcare providers is structural: downside risk has been accepted on 38,000 lives without the cost-per-episode data needed to price it. Any credible answer therefore has to hold cost per episode and payer mix in the same view, which is exactly where most internal analysis stops because the two live in different systems.
Capital allocation goes wrong when panels expand under value-based contracts before cost-per-episode data exist to show which panels return the next dollar. What makes this harder for healthcare providers is structural: downside risk has been accepted on 38,000 lives without the cost-per-episode data needed to price it. Any credible answer therefore has to hold cost per episode and payer mix in the same view, which is exactly where most internal analysis stops because the two live in different systems.
Most providers allocate by history and by advocacy: panels that grew last year receive the next hires, and the medical director who argues for more attributed lives secures the increment. Neither step checks whether the added panel size improves contribution per provider once downside risk on the existing 38,000 lives is already accepted.
The analysis that helps ranks each panel on two things — what it returns on incremental investment measured by cost per episode, and how durable that return stays under the current payer mix. A panel that lowers cost per episode quickly but loses attribution inside two years is a different proposition from one that holds contribution per provider steady across a longer contract, and treating them as comparable is how providers fund the wrong growth.
The output should be a sequence with a stopping rule, not a budget split. Which panel receives the next provider first, what cost-per-episode threshold funds the one after, and the observation in the attributed lives data that would show the sequence is wrong.
These three together are the signature. One on its own usually points somewhere else.
✓ Budgets are set by last year’s panel sizes plus a percentage, with no recalculation against current cost per episode.
✓ CFO and medical director cannot produce a ranked list of panels by contribution per provider on the next dollar.
✓ Investment cases are defended by the number of attributed lives added rather than by measured cost per episode under the value-based contract.
The move that usually makes it worse. Spreading new provider slots evenly across panels to keep internal balance, which leaves the panel with the highest cost per episode underfunded while risk on the 38,000 lives remains unpriced.
It is for you if you run or finance a healthcare provider and budgets are set by last year plus a percentage. It is the situation where the numbers are available but nobody has put them in an order that produces a decision.
It is not for you if Percision is the wrong tool if you already know the answer and only need execution capacity, or if the business is pre-revenue — then the constraint is evidence about the market, not analysis of your own figures. Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library. Also wrong if you need facilitation, politics, or someone to sit with a lender or buyer. Those are human jobs.
Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library.
Below is an excerpt from a real run of this analysis on a healthcare provider. It is a sample profile rather than a customer, and it is unedited engine output — this is the format you get, on your own numbers.
The subject is Cedar Ridge Health Partners, a sample company profile used for testing rather than a customer — 38,000 attributed lives under value-based contracts.
Excerpt from a real Percision run · Quick Market Scan · sample company profile
The move. Sell coordinated care bundles directly to self-insured employers using existing clinic density and ASC capacity.
The leak it closes. Bypasses commercial payer take-rate (estimated 15–20% of premium) and prior-authorization friction, reducing denial leakage on these lives to near zero
The assumption it rests on. At least two of the five largest self-insured employers in the two metros will sign a 3-year direct contract within 18 months — the engine put the probability at 0.7.
| Investment required | $2M over 36 months ($800K Year 1, $700K Year 2, $500K Year 3) |
| Expected return | 6.0–9.0× on $2M investment |
| Revenue, year 1 | $0 incremental (pilot setup and first contract negotiations) |
| Revenue, year 2 | $4–6M incremental (2–3 employer contracts, 4,000–6,000 covered lives) |
| Revenue, year 3 | $12–18M incremental (5 employer contracts, 10,000–15,000 covered lives) |
| Exit criteria | Terminate pilot and redeploy 4 FTEs if fewer than 2 employer contracts signed by Month 18 OR if operating margin on employer channel falls below 6% for two consecutive quarters |
This is one move out of a full analysis. Read a complete report — every page, no email required.
This question routes to Growth Portfolio Framework, one of 29 engagements the platform runs. For healthcare providers it works through cost per episode, payer mix, panel size and contribution per provider, then produces the sequence rather than a list of options — which move first, what it funds, and the observation that would say the sequence is wrong.
You watch the analysis get built before paying anything. Read a complete report here if you would rather see the depth first.
Price the durability explicitly. A return that decays needs a stated half-life; once each option carries one, options with different horizons become comparable rather than a matter of taste.
Usually the strongest, because that is where a marginal dollar compounds. Fixing the weakest is worth doing when it is a constraint on the strongest, and not otherwise.
Then decide on reversibility. When two options return similarly, take the one you can stop, because the value of the information you buy exceeds the difference in the estimates.
Materially, yes. Downside risk has been accepted on 38,000 lives without the cost-per-episode data needed to price it — which changes both the diagnosis and the order of the fixes. The metrics that decide it here are cost per episode, payer mix, panel size, and an answer built on industry-general benchmarks will usually point at the wrong one first.
Less than most people expect. Your last twelve months of revenue and cost split the way you already split it, plus whatever you hold on cost per episode and payer mix. The analysis is explicit about what it is assuming where your data stops, which is more useful than waiting for numbers you may never have.
Percision is the wrong tool if you already know the answer and only need execution capacity, or if the business is pre-revenue — then the constraint is evidence about the market, not analysis of your own figures. Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library. Also wrong if you need facilitation, politics, or someone to sit with a lender or buyer. Those are human jobs.
Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library.
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