This workbook was made to translate delivery work into numbers a funder or trustee can inspect.
It separates method, survey fields, outcome calculation, and improvement notes so the reporting logic is easy to follow.
Workbook: 05_Impact_Measurement_Framework.xlsx
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Outcome Measurement Model
An impact workbook that connects project activity to survey evidence, outcomes, and capacity cost.
- Outcome measurement
- Survey design
- Capacity costing
Overview
Source sheet: 00_Read_Me
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| xTend — Impact Measurement Framework | ||||
| Structured directly from 'XTend Model for Impacts vs Costs.docx' | ||||
| This workbook encodes the methodology described in the source document. It does NOT invent QALY, income-uplift, or healthcare-saving numbers — those cells remain empty pending survey data collection. Once participant survey data is captured against the templates below, the model can be populated and SROI computed. | ||||
Methodology
Source sheet: 01_Methodology
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| Methodology — Verbatim from Source Document | ||||
|---|---|---|---|---|
| Impact area | Metric | Data collection | Analysis approach | Notes from source |
| 1. Economic impact on patient | Increased earning potential | Survey participants about employment status before and after receiving the prosthetic | Calculate average income increase attributable to improved mobility and ability to work; use local average income data for context | |
| 1. Economic impact on patient | Economic productivity | Obtain data on participants' hours worked or productivity levels | Estimate economic contributions based on productivity improvements; use data from similar initiatives as a benchmark | |
| 2. Health benefits | Healthcare cost savings | Gather data on healthcare usage (doctor visits, hospital stays) before and after prosthetic adoption | Calculate cost savings from reduced healthcare needs using local healthcare cost data | Source notes: maybe not applicable → Quality |
| 2. Health benefits | Physical and mental health improvements | Use standardized health surveys or indices (e.g., SF-36) to measure improvements | Translate qualitative health improvements into estimated economic impacts based on health economics literature | |
| 3. Social outcomes | Social integration | Survey participants on social activities and community participation pre- and post-prosthetic use | Use literature estimates to assign economic value to increased social participation, such as reduced isolation or improved mental health | |
| 4. Quality of life | Quality of Life Enhancement | Employ quality of life assessment tools (e.g., WHOQOL-BREF) for before and after comparisons | Use cost-utility analysis to quantify changes in QALYs and assign a monetary value based on willingness-to-pay thresholds | |
| 5. Model the impact | Functional model + scenarios + sensitivity | Construct spreadsheet/stat software; include demographics, prosthetic costs, measured changes | Run scenarios on prosthetic lifetime, replacement rates, broader effect ranges; sensitivity analysis on most influential variables | |
| 6. Aggregate and report | Net impact per prosthetic + scenario analysis | Sum economic benefits per category, adjusting for costs | Net economic value generated per prosthetic = total benefits − costs; present with clear visuals and narrative | |
Survey
Source sheet: 02_Survey_Template
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| Participant Survey Template — pre/post arm fitting | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| ID | Country | Region | Age | Sex | Employment pre | Hours/wk pre | Income pre (local) | Income pre (£) | WHOQOL-BREF pre |
| Six- / twelve-month follow-up | |||||||||
| ID | Follow-up date | Employment post | Hours/wk post | Income post (local) | Income post (£) | Doctor visits | Hospital stays | WHOQOL-BREF post | Notes |
Outcome Calculation
Source sheet: 03_Impact_Calc
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| Impact Calculation — fill once survey data is captured | ||
|---|---|---|
| Variable | Value | Source / formula |
| Number of participants surveyed | [TBD] | Count from 02_Survey_Template |
| Mean income pre-fitting (£/yr) | [TBD] | Average of 'Income pre (£)' column |
| Mean income post-fitting (£/yr) | [TBD] | Average of 'Income post (£)' column |
| Mean income uplift (£/yr) | [derived] | Post − Pre |
| Mean hours worked uplift (hrs/wk) | [TBD] | Post − Pre |
| Mean doctor visits avoided (per yr) | [TBD] | Pre − Post |
| Local healthcare unit cost (£) | [TBD] | From local health system data |
| Healthcare cost saved (£/yr) | [derived] | Visits avoided × unit cost |
| QALY gain per participant per year | [TBD] | WHOQOL-BREF mapping to QALY |
| £ per QALY (willingness-to-pay) | [TBD] | Decide on UK/local threshold |
| Annual value of QALY gain (£) | [derived] | QALY × £/QALY |
| Useful life of arm (years) | [TBD] | Field follow-up |
| Discount rate | [TBD] | Trustee decision (e.g., HMT Green Book) |
| PV factor | [derived] | (1 − (1+r)^-n) / r |
| Lifetime PV of benefits per participant (£) | [derived] | Sum of benefits × PV factor |
| Fully-loaded cost per arm (£) | [TBD] | From budget actuals once available |
| SROI ratio (£ social / £ cost) | [derived] | PV benefits ÷ cost |
Improvement Notes
Source sheet: 04_Optimisation_Notes
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| Cost-Optimisation Notes (from source doc, sections 'Nature of Impact Function' & 'Methodology') | |||
|---|---|---|---|
| Function shape — Linear | Direct, proportional relationship between cost per arm and impact. Possible if higher costs translate to better quality and outcomes. | ||
| Function shape — Concave | Diminishing returns: early spending lifts impact materially; gains taper. Common if early quality improvements are large and later ones marginal. | ||
| Function shape — Convex | Excessive spending on high-end materials overshoots optimum. Decreasing impact as cost rises. | ||
| Additional variables to track | Time to build per arm; labour costs; material waste; supply-chain reliability. | ||
| Modelling approach | Define functional-form hypothesis; collect historical cost & impact data; fit multiple functions; expand to multivariate regression with interaction effects. | ||
| Optimisation | Use discrete-choice optimisation (Integer Programming, Branch & Bound) to find optimal cost or configuration scenario. | ||
| Validation | Implement recommended change → collect data on subsequent impact → continually refine & validate. | ||