Problems › We Need a Business Plan for the Bank › HealthTech & Digital Health
A lender is not reading for ambition. They are reading for whether the downside case still services the debt. This page works through it for digital health companies specifically — including an unedited excerpt from a real analysis of a digital health company.
A lender is not reading for ambition. They are reading for whether the downside case still services the debt. 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 debt service coverage will stay a matter of opinion.
Plans written for lenders fail on the same thing: an optimistic single case with no visible arithmetic. The reader is trying to establish whether they get paid back if things go moderately wrong, and a plan with only a good case gives them nothing to test.
What survives scrutiny is a base case with stated assumptions, a downside that is genuinely bad rather than politely reduced, and a clear line from operating performance to debt service in both. The upside case matters least.
The second failure is inconsistency — a revenue line that does not reconcile to the headcount plan, or working capital that does not move with sales. Lenders read these documents for a living and find those quickly.
These three together are the signature. One on its own usually points somewhere else.
✓ You need the document by a deadline set by someone else
✓ The projections exist in a spreadsheet nobody outside the business has stress-tested
✓ There is no downside case, or it is the base case minus ten percent
The move that usually makes it worse. Writing the plan to be persuasive rather than to be checkable, which is the fastest way to lose a reader who checks for a living.
It is for you if you run or finance a digital health company and you need the document by a deadline set by someone else. 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 · Cost Reduction & Efficiency · sample company profile
The move. Monetize the largest three-condition outcomes dataset to subsidize outcomes risk and generate 13% growth without increasing at-risk share.
The leak it closes. Reduces dependence on 38% at-risk PMPM revenue by adding non-at-risk, high-margin revenue stream
The assumption it rests on. State privacy laws do not mandate patient-level consent for de-identified data before 2029 — the engine put the probability at 0.7.
| Investment required | $1.8–2.4M over 18 months |
| Expected return | 2.3–3.8× on $2.1M midpoint investment within 36 months |
| Revenue, year 1 | $0.8–1.2M ARR (3–4 deals) |
| Revenue, year 2 | $2.4–3.6M ARR (9–12 deals) |
| Revenue, year 3 | $4.2–6.8M ARR (15–20 deals) |
| Exit criteria | Kill move if fewer than 2 deals ≥$150k ACV close by Month 12 OR if any state privacy statute requiring patient-level consent for de-identified data is enacted before Month 18; reallocate remaining budget to Clinical Coaching Capacity Marketplace node |
This is one move out of a full analysis. Read a complete report — every page, no email required.
This question routes to Business Plan Studio, 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.
Short enough to be read and complete enough to be tested. The financials and the assumptions behind them carry the decision; narrative beyond what is needed to explain them adds risk rather than confidence.
Whether the cash flow services the debt under a case that is not the good one, and whether the numbers reconcile internally. Almost everything else is context for those two.
Match the term of the facility, monthly for the first year. Detail beyond the horizon of the loan signals unfamiliarity rather than rigour.
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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