Procurement teams rarely lose arguments because they chose the “wrong” dressing. They lose because the model is not auditable.
This guide gives you a step-by-step way to build a defendable total cost of ownership (TCO) / cost-in-use model wound dressings comparison for silicone foam dressings vs. alternatives (gauze, hydrocolloids, other foams, and two-layer regimens). You’ll end with a calculator that clinical leaders can sanity-check, finance can price, and supply chain can source.
If your internal debate keeps stalling at silicone foam vs gauze cost, the rest of this guide is designed to move the discussion to the variables that usually decide TCO: change frequency, nursing minutes, and waste.
Key Takeaway: If your model doesn’t separate wear time, nursing time, and waste, it will understate the economic value of longer-wear dressings and overstate the importance of unit price.
What you’ll build (outputs)
By the end, you’ll have:
A one-page TCO comparison table (per 100 dressing-days, per patient-week, or per 1,000 patient-days).
A calculator structure you can drop into Excel/Sheets.
A KPI dashboard and “what to request from vendors” checklist.
Optional module: a pressure injury (PI) avoidance sensitivity case for high-risk units.
Prerequisites (inputs you need)
Input: pick a scope that matches how your team approves products.
Your evaluation setting: inpatient prevention (ICU/med-surg) vs chronic wound treatment (home health/outpatient).
Your comparator set (what you’re replacing):
Gauze-based regimens
Hydrocolloids
Generic foam dressings
Two-layer regimen (primary + secondary)
Your unit economics:
Loaded nursing labor rate ($/hour)
Your delivered unit price by dressing type (or an RFP target range)
Output: a blank calculator tab with named inputs.
Done when: you can list every input you’ll need to ask a vendor for.
Step 1) Define the unit of analysis (the “denominator”)
A TCO model fails when the denominator shifts mid-way.
Choose one:
Per patient-week (best for chronic wounds and outpatient protocols)
Per 1,000 patient-days (best for PI prevention programs)
Per 100 dressing-days (best for cross-setting normalization)
Input: care setting + how your VA/contracting team reports.
Action: lock the denominator and write it at the top of your spreadsheet.
Output: a header row used by all later steps.
Done when: everyone agrees what “one unit” of comparison means.
Pro Tip: If stakeholders can’t agree on one denominator, build two views that reuse the same inputs. Don’t rebuild the model.
Step 2) Choose “alternatives” that map to real protocols (not just product categories)
You’re comparing protocols, not materials.
Create 2–4 protocol bundles, for example:
Protocol A (Silicone foam): one silicone foam dressing per change
Protocol B (Two-layer SoC): primary filler + secondary cover
Protocol C (Gauze-based): gauze + fixation/secondary, higher change frequency
Protocol D (Hydrocolloid): hydrocolloid as primary cover (where clinically appropriate)
Input: your current SoC and what you want to standardize.
Action: name each protocol in plain language.
Output: a protocol list with 1–2 lines describing each.
Done when: a clinician can confirm it reflects how dressings are actually used.
Step 3) Build the core consumption model (dressing wear time nursing time cost)
(If you need a single audit phrase for finance, this step is your: dressing wear time nursing time cost bridge.)
This is the backbone. Everything else is a modifier.
This step is the heart of a cost-in-use model wound dressings comparison: how wear time (changes/week) converts into both supply consumption and labor touches.
3.1 Set change frequency bands (conservative / moderate / aggressive)
Because you asked for scenario modeling, use three bands:
Conservative: assumes more frequent changes
Moderate: your “expected” protocol
Aggressive: assumes extended wear time when clinically appropriate
For conservative reference points, you can anchor to coverage/typical frequencies like CMS LCD Surgical Dressings (L33831), which describes typical covered dressing frequencies by type (e.g., foam covers up to 3×/week in certain use cases) in CMS’s LCD L33831.
Input: for each protocol, set changes_per_week for each band.
Action: add a table:
Protocol | Conservative changes/week | Moderate changes/week | Aggressive changes/week |
|---|---|---|---|
Silicone foam | |||
Two-layer SoC | |||
Gauze-based | |||
Hydrocolloid |
Output: change frequency table.
Done when: the table passes a 30-second clinician sanity-check.
3.2 Convert change frequency into dressing units used
At minimum, use:
units_per_change(how many dressings used per change)changes_per_week
Formula:
units_per_week = units_per_change × changes_per_week
If you want one more level of realism, add:
secondary_items_per_change(fixation, barrier film, secondary covers)
Input: units/change per protocol.
Action: calculate weekly consumption for each SKU class.
Output: units/week per protocol.
Done when: you can explain, line by line, why Protocol B uses more items than Protocol A.
3.3 Use published “benchmark deltas” carefully
When you need a benchmark to show stakeholders that your deltas are plausible, use a published example and then re-run the numbers using your own unit prices.
Example: a peer-reviewed RCT comparing a conforming silicone foam dressing to a two-dressing regimen reported lower estimated 4-week costs in the silicone foam arm (UK setting), while clinical performance was comparable in that trial. See the 2024 RCT and cost-effectiveness analysis (PMC full text).
Input: published delta (direction + magnitude).
Action: use it as a reasonableness check, not as your default.
Output: a benchmark note in your appendix.
Done when: your model still works if you remove the benchmark entirely.
Step 4) Add nursing labor (minutes per change dominates more often than you think)
Labor usually scales with number of changes, not with unit price.
This is often where “silicone foam vs gauze cost” flips from a unit-price debate into a labor-touch debate.
4.1 Define nursing time per change
Instead of claiming one number, make this a user-entered input:
minutes_per_change(include removal + skin assessment + application + documentation)
Input: minutes/change (range).
Action: model total nursing minutes per week:
nursing_minutes_per_week = changes_per_week × minutes_per_change
Output: nursing minutes/week per protocol.
Done when: you can show how a 1-change/week reduction translates into hours saved per 1,000 patient-days.
4.2 Convert minutes to dollars
labor_cost_per_week = nursing_minutes_per_week ÷ 60 × loaded_labor_rate
Input: loaded labor rate ($/hour).
Action: calculate labor cost by protocol and band.
Output: labor cost table.
Done when: finance can replace your rate with their internal rate without touching the formulas.
Step 5) Add waste and rework (leakage, strike-through, premature changes)
Waste is where “it depends” becomes measurable.
5.1 Create two waste inputs
waste_rate= % of dressings opened but not ultimately used (contamination, wrong size, premature change)rework_rate= % of changes that require an extra unplanned change (leakage/strike-through, edge roll, poor seal)
Input: waste + rework rates per protocol (banded if needed).
Action: adjust effective units and changes:
effective_units = units × (1 + waste_rate)effective_changes = changes × (1 + rework_rate)
Output: adjusted units and adjusted labor.
Done when: the waste module changes both supply cost and labor cost.
5.2 Tie waste to a mechanism (so clinicians trust it)
You don’t need a perfect equation. You need a defensible explanation.
For example, a clinical review on minimizing dressing-change pain notes the temptation to overestimate wear time, which can increase risks of periwound maceration, leakage, and strike-through (leading to earlier or unplanned changes). See Minimising wound-related pain at dressing change (2008, PMC).
Input: mechanism narrative + one citation.
Action: include a 2–3 line note in the model explaining what drives rework.
Output: a “why this variable exists” annotation.
Done when: a clinician can point to the mechanism and say “yes, that happens.”
Step 6) Optional: pressure injury prevention silicone foam cost effectiveness (sensitivity case)
This module is only appropriate if you’re evaluating prophylactic dressings in high-risk settings.
6.1 Decide if PI avoidance belongs in the base case
For most procurement reviews, keep PI avoidance as a sensitivity case unless your committee explicitly approves prevention economics.
Input: committee preference.
Action: mark this module “optional.”
Output: a toggle.
Done when: base-case TCO stands on wear time + labor + waste alone.
6.2 Use published prevention economics as your reference
One cost-effectiveness analysis of multi-layered silicone foam dressings for ICU prevention reported cost-effectiveness for sacral prevention and more marginal results for heels, with ICERs that depend on baseline PI risk and treatment costs (German ICU context). See the 2020 cost-effectiveness study (PMC).
Inputs (as ranges, not absolutes):
baseline PI incidence rate
relative risk reduction (RRR) range
cost per PI event (your internal estimate)
Action: model “expected PI events avoided” and subtract the avoided cost.
Output: PI avoidance delta ($) per denominator.
Done when: the PI module can be turned off without breaking the model.
⚠️ Warning: PI prevention evidence is setting-specific. Treat effect sizes as ranges and document your baseline risk assumption.
Silicone foam dressing total cost of ownership (TCO) calculator: compute TCO
Step 7) Add unit prices and compute total TCO
Now that quantities and labor are set, unit prices are simple multiplication.
7.1 Cost categories (keep them separate)
Supply cost (dressings + secondary items)
Labor cost (nursing time)
Waste/rework cost (already embedded if you adjusted units/changes)
Optional avoided cost (PI module)
Formula:
TCO = supply_cost + labor_cost + rework_cost − avoided_costs
Input: unit prices by SKU class.
Action: compute TCO per protocol and scenario band.
Output: a single comparison table.
Done when: you can explain what percentage of TCO comes from labor vs supplies.
Step 8) Build a one-slide procurement summary (what stakeholders actually need)
This is how you get approval.
Include:
Decision: which protocol wins under moderate assumptions?
Top 3 drivers: which variables move the answer most?
Sensitivity: what must be true for the alternative to win?
Implementation: what changes in practice are required (training, sizing, change protocol)?
For long-horizon framing, you can cite that budget impact models exist and can show multi-year savings when switching from multi-product regimens to a single silicone foam dressing regimen in specific populations; see the PubMed record for “Potential cost savings of a wound bed-conforming silicone foam…” (2025).
Input: your final table.
Action: convert it into an exec summary.
Output: one-slide narrative.
Done when: a value analysis committee member can repeat your logic in one minute.
KPI dashboard (what to measure after the switch)
Track these KPIs for 8–12 weeks post-implementation:
Dressing changes per patient-week (by unit)
Nursing minutes per dressing change (spot-audit)
Unplanned change rate (leakage/strike-through)
Waste rate (opened-not-used)
Skin injury / MARSI incidence (if tracked)
Pressure injury incidence (if the program is prophylactic)
Stockout rate and line-item fill rate (supply continuity)
Sample SLA language (vendor + supply reliability)
Use this as a starting point in OEM/ODM contracting.
Fill rate: “Supplier will maintain ≥98% line-item fill rate, measured monthly.”
Lead time: “Standard lead time ≤ X weeks; expedite process defined for clinically critical SKUs.”
Traceability: “UDI/lot traceability within 24 hours for any requested batch.”
Change control: “No material or process changes without written notification and validation evidence.”
Where silicone foam tends to win (and where it doesn’t)
This is procurement reality.
Silicone foam often wins when
Change frequency can be reduced without compromising clinical goals.
Nursing time is constrained and labor is a first-class cost driver.
Skin is fragile and adhesive trauma increases rework or patient discomfort.
Exudate management failures drive unplanned changes.
Alternatives can win when
Exudate is minimal and a lower-cost cover is clinically acceptable.
Wear time cannot be extended due to wound characteristics or protocol.
The unit requires daily visualization and full change anyway.
Neutral manufacturer examples (for sourcing, not claims)
When you evaluate OEM/ODM options, you’ll want traceable SKUs, IFUs, and a clear spec pack.
As an example of how manufacturers present silicone foam options, you can review the product specifications for SLK Medical and specific silicone foam formats like a bordered flexible foam dressing (example: silicone foam dressing border flex). Use these pages as catalog references, not as evidence of economic outcomes.
(Video) Quick application demo (for stakeholder alignment)
If your committee wants a short visual on application and removal, here’s a widely used educational manufacturer demo:
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Common failure modes (and how to prevent them)
Failure mode: The model shows savings, but the unit doesn’t change practice.
Fix: train on change-frequency criteria and document “change only when…” rules.
Failure mode: Wear time assumptions are over-optimistic.
Fix: use conservative/moderate/aggressive bands and track unplanned change rate.
Failure mode: Waste is invisible.
Fix: add a simple opened-not-used tally for 2 weeks.
Failure mode: PI module causes arguments.
Fix: keep PI avoidance as sensitivity case; document baseline risk and scope.
Next steps
If you want, I can turn this model into a one-page, stakeholder-ready calculator template (Excel/Sheets layout) plus an RFP input checklist (unit price fields, packaging/MOQ, compliance docs, lead times) tailored to your OEM sourcing process.
References
(Links are cited inline in the sections above.)
Clinical performance and cost-effectiveness study (2024, PMC)
Potential cost savings model (2025, PubMed)
Cost-effectiveness of multilayer silicone foam dressings (2020, PMC)
CMS LCD Surgical Dressings (L33831)
Minimising wound-related pain at dressing change (2008, PMC)
Wound management for the 21st century (2016, PMC)
