Problems › Where Should We Invest Next? › HealthTech & Digital Health
Capital allocation goes wrong when the loudest line gets funded rather than the one with the best return on the next dollar. This page works through it for digital health companies specifically — including an unedited excerpt from a real analysis of a digital health company.
Capital allocation goes wrong when the loudest line gets funded rather than the one with the best return on the next dollar. Digital health companies carry a specific bind here — outcomes risk is being signed faster than the company can learn whether it can carry it — a 12-month measurement window against an 11-month sales cycle. Until that is priced, at-risk revenue share will keep moving for reasons nobody can attribute, and the debate about return by line will stay a matter of opinion.
Most businesses allocate by history and by advocacy: the lines that got money last year get it again, and the person who argues best gets the increment. Neither has anything to do with where the next dollar earns most.
The analysis that helps ranks each line on two things — what it returns on incremental investment, and how durable that return is. A line that returns well but decays in eighteen months is a different proposition from one that returns modestly for a decade, and treating them as comparable is how businesses end up funding decline.
The output should be a sequence with a stopping rule, not a budget split. Which one first, what it funds next, and the observation that would say the sequence is wrong.
These three together are the signature. One on its own usually points somewhere else.
✓ Budgets are set by last year plus a percentage
✓ Nobody can rank the lines by return on incremental investment
✓ Investment decisions are defended by strategic importance rather than by arithmetic
The move that usually makes it worse. Spreading capital evenly to keep the peace, which underfunds the one thing that would have compounded.
It is for you if you run or finance a digital health company 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 digital health company. 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 Vantabridge Health, a sample company profile used for testing rather than a customer — $62M ARR, 340,000 enrolled members.
Excerpt from a real Percision run · Customer Value Architecture · sample company profile
The move. Reduce downside exposure from $7.1M outcomes shortfall to $4.2M while maintaining upside participation in 34 health-plan contracts.
The leak it closes. $2.9M gross profit protected annually through downside cap (difference between $7.1M shortfall at 38% vs $4.2M shortfall at 25%)
The assumption it rests on. Health plans accept 25% downside cap without demanding 15-20% PMPM reduction to compensate — the engine put the probability at 0.7.
| Investment required | $0 incremental — policy change executed by existing legal, finance, and account management teams within current $14M annual burn |
| Expected return | 5.2× on zero incremental investment — derived from $4.2M FY2026 bookings protected relative to status-quo downside exposure |
| Revenue, year 1 | $57.8M ARR (25% at-risk share = $15.5M at-risk revenue vs $23.6M status quo) |
| Revenue, year 2 | $61.4M ARR (assuming 80% contract renewal at 25% cap) |
| Revenue, year 3 | $68.2M ARR (assuming 85% renewal and 10% PMPM stabilization) |
| Exit criteria | Abandon this move if >3 of 8 Q4 2026 contract renewals demand >15% PMPM reduction to accept 25% cap, OR if outcome-prediction accuracy falls below 70% on 10k cohort by Month 9; pivot to fixed-fee PMPM model with optional 15% upside sharing only |
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 digital health companies it works through at-risk revenue share, engagement rate, gross margin and logo churn, 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. Outcomes risk is being signed faster than the company can learn whether it can carry it — a 12-month measurement window against an 11-month sales cycle — which changes both the diagnosis and the order of the fixes. The metrics that decide it here are at-risk revenue share, engagement rate, gross margin, 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 at-risk revenue share and engagement rate. 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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