New ROI Calculator: estimate MoniDose savings for your organisation in under a minute
We have published an interactive ROI calculator that welfare regions, care homes, and home-care organisations can use to estimate annual savings from automated medication dispensing — with their own numbers. The calculation is based on published research and Finnish labour-cost assumptions. Outputs can support investment cases or procurement preparation.
Medication errors and the administrative load on nurses are two central problems that automated medication dispensing addresses. But how big would the savings actually be in your organisation — not in generic case studies, but exactly where you work?
The newly published ROI calculator answers this. Enter five inputs and see an immediate estimate of how much time and money MoniDose can save per year. The calculator is free, available at monidose.fi/en/roi-calculator, and requires no registration.
Why a ROI calculator?
In care-sector procurement, the investment case must be defensible in numbers. General marketing claims like "saves time and money" are not enough for a procurement decision — decision-makers need concrete figures that reflect their own unit's reality.
We built a calculator that gives a fast, range-based estimate — not a false-precision single number, but a solid pointer for budget planning, early procurement preparation, or investment justification. The calculation rests on published research, not marketing claims.
What the calculator measures
The calculator asks for five inputs — all things typically known or easily checked:
1. Facility type. Three options: care home, home care, or assisted living. Affects assumptions about how automation saves time in different environments.
2. Number of residents / patients. How many people would be served by the pouch-roll system. Range 1–200.
3. Number of nurses involved in medication rounds. Who participates in manual medication distribution today. Range 1–30.
4. Average hourly cost of a nurse. Gross pay + on-costs (roughly 1.2× multiplier). In Finland, a registered nurse's average gross pay according to KT 2025 statistics is around €33/h, but total cost to the employer is ~€40/h. The calculator defaults to 40, adjustable 15–60 €/h.
5. Current medication error rate. Research shows manual dispensing error rates vary from 1% to 20%. If you do not know your own figure, the 5% default is a reasonable starting point for Finnish conditions.
What the results show
On the right side the calculator displays the estimated annual benefit via four metrics:
Daily time saved (hours). How much nursing time is freed up per day. Shown as a range (e.g. "3.3 – 4.7 h") because the actual figure depends on workflow.
Estimated daily and monthly savings (€). The monetary equivalent of the freed nurse time. Calculated by multiplying the time saved by the organisation's hourly cost.
Prevented medication errors per year. A 38–53% reduction range from current errors — based on comparing manual and automated dispensing accuracy.
Total annual savings. Time savings + the cost of prevented errors. A prevented error costs roughly €138 in additional treatment costs.
The calculator also shows an 8-component breakdown of the savings: avoided routine visits, automatic documentation, pharmacy workflow automation, real-time alerts, reminders that reduce calls, remote connection via the device, route and shift optimisation, and reduced supervisor load.
Step-by-step guide
1. Open the calculator. Go to monidose.fi/en/roi-calculator.
2. Read the top guide. The page has a "How to use the calculator" block with five steps laid out. After that the inputs are intuitive.
3. Select the facility type. From the dropdown, choose care home, home care, or assisted living — whichever best describes your organisation.
4. Adjust the sliders. Set resident count, nurse count, hourly cost, and current error rate. All values update in real time — the metrics on the right change as you move a slider.
5. Try scenarios. See how savings change if resident count doubles or if the error rate is higher. This builds intuition for how sensitive the calculation is to each input.
6. Explore the component breakdown. Lower on the page an 8-component breakdown shows how much time each mechanism saves. This clarifies where the total savings come from.
How to read the results correctly
Two common mistakes worth avoiding:
Do not read a single number as absolute. Results are always shown as a range (e.g. "€15,000 – €21,000") because actuals depend on implementation decisions, the organisation's baseline, and culture. The midpoint is a good working assumption, but the real figure can fall above or below.
Account for implementation costs. The calculator does not yet include MoniDose purchase cost, training, deployment, or maintenance. These need to be estimated separately when calculating investment payback. You get these figures during the demo, tailored to your organisation.
What the numbers are based on
Key calculation assumptions and their sources:
30–42 minutes saved per patient per day — McKinsey (2023) home-care automation report and the Leeds pilot study 2024. The lower bound reflects a conservative estimate, the upper bound optimal implementation.
38–53% medication error reduction — aggregated multi-dose dispensing studies 2015–2024 (e.g. Sinnemäki et al., Suomen Lääkärilehti).
~€138 per prevented error — Fimea assessment reports on treatment add-on costs. Severe errors cost many times more — this is the average across error types.
1800 hours per full-time nurse year (FTE) — Finnish Working Hours Act standard with leave.
4 medication rounds per day — average in home care and assisted living. Organisation-specific number may be 2–6.
The calculation is no more complex than four basic arithmetic operations multiplied by the baseline assumptions. The formulas are publicly visible in the calculator's source code — no black box.
What you can do with the results
Investment case for leadership. If you are justifying a purchase, you need concrete savings figures. The calculator's annual numbers are a good starting point for budget discussions.
Procurement preparation. When preparing a procurement decision, the calculator's numbers can feed into the pre-study. They do not replace a market survey or tender, but they indicate the order of magnitude.
Internal discussions. Care-home leadership, home-care managers, or welfare-region service leads can use the calculator to build shared understanding of what automation would actually do in their unit.
Rough cost impact during service procurement. When selecting a service provider, it is useful to know the potential in the device itself before comparing prices.
What the calculator does NOT do
In the interest of honesty, what the tool is not:
It is not a quote. Actual purchase cost, deployment, training, and maintenance are separate.
It is not a clinical effectiveness analysis. It measures time savings and cost impact, not patient safety directly.
It does not account for organisation-specific variables like current IT readiness, space, or cultural-change challenges. Those should be assessed separately.
Next steps
If the numbers look promising and you want a tailored estimate for your organisation:
1. Book a free demo from the button at the bottom of the calculator. We walk through MoniDose's functionality and discuss how it would fit your needs. 2. For individual users or family members wondering whether MoniDose fits a single person, we recommend our suitability assessment. The ROI calculator looks at organisational benefit — the suitability assessment looks at the individual. 3. If you need a PDF report for an investment case, request it during the demo. We prepare a tailored estimate for your organisation.
Try the calculator
The calculator is live at monidose.fi/en/roi-calculator, free to use. Takes less than a minute.
If you want to give feedback or ask about the calculation background, write to info@monidose.fi.
MoniDose is under development as a Class I medical device under EU MDR 2017/745. Certification is in progress. CE marking has not yet been granted. The calculator's estimates are based on published research and average assumptions — actual results vary, and the final impact is always assessed at deployment.