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Does a public online IVF prediction tool help set patient expectations? A mixed methods evaluation study
Bronwyn K Brew1, Natasha Donnolley1,2, Oisin Fitzgerald3
1National Perinatal Epidemiology and Statistics Unit, Centre for Big Data Research in Health and School of Clinical Medicine, Discipline of Women's Health, UNSW, Kensington, NSW, Australia.
An online In Vitro Fertilization (IVF) success calculator, using real-world data, effectively adjusted patient expectations. The tool helped users align their predictions with calculated success rates, with many finding it trustworthy and helpful.
Area of Science:
- Reproductive Medicine
- Health Informatics
- Patient Education
Background:
- Web-based In Vitro Fertilization (IVF) prediction tools are available but lack evaluation regarding their impact on patient expectations and perceived trustworthiness.
- Patient demand for transparency and participation in healthcare decisions is increasing.
Purpose of the Study:
- To evaluate the impact of a public online IVF success prediction calculator, the YourIVFSuccess Estimator, on patient expectations.
- To assess patient perceptions of the tool's usefulness and trustworthiness.
Main Methods:
- A pre-post evaluation design was employed, surveying 780 online users of the Australian YourIVFSuccess Estimator.
- Participants completed online surveys before and after using the tool to assess changes in their IVF success expectations.
Main Results:
- The YourIVFSuccess Estimator significantly aided consumer expectations, with half of users adjusting their predictions after use.
- 26% of participants had their IVF success expectations confirmed, and 91% found the tool trustworthy.
- Positive perceptions were linked to the tool's independent funding and use of real-world data.
Conclusions:
- Publicly accessible IVF prediction tools utilizing real-world data are valuable for aligning patient expectations regarding IVF success rates.
- Country-specific prediction tools informed by national data sources are recommended due to international variations in patient characteristics and IVF practices.
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