⚡ Quick Summary
A recent study explored community preferences for the AI-assisted screening app AiSTi, designed for detecting STI-related anogenital skin conditions. Key findings revealed that users prioritize low cost, clinician verification, and high accuracy in their app preferences.
🔍 Key Details
- 📊 Participants: 411 individuals, median age 32 years
- 🧩 Key attributes examined: data type, AI accuracy, clinician verification, result details, speed, professional support, cost
- ⚙️ Methodology: Discrete choice experiment (DCE) with random parameters logit (RPL) and latent class analysis (LCA)
- 🏆 Predicted uptake: 72% based on current app attributes
🔑 Key Takeaways
- 💰 Cost is the most influential factor in user preferences, accounting for 24.1% of choices.
- 👩⚕️ Clinician verification of results is crucial, influencing 20.4% of preferences.
- 🤖 AI accuracy is also a significant factor, with 19.5% of participants prioritizing it.
- ⏱️ Speed of results is important, contributing to 19.1% of user preferences.
- 📊 Two distinct user groups were identified: ‘all-rounders’ (88%) and ‘cost-focused’ (12%).
- 📈 Optimizing AI accuracy to 80-89% with clinician verification could boost uptake to 90%.
- 🌟 An ideal app would be low-cost, clinician-verified, highly accurate, and provide rapid results.
📚 Background
The World Health Organization (WHO) emphasizes the importance of digital technologies in combating sexually transmitted infections (STIs). In response, the Melbourne Sexual Health Centre (MSHC) developed the AiSTi app to facilitate the detection of common STI-related anogenital skin conditions. Understanding community preferences for such digital tools is essential for their successful implementation and uptake.
🗒️ Study
Conducted between January and March 2024, this study utilized a discrete choice experiment (DCE) to gauge community preferences regarding the AiSTi app. Participants were surveyed about various app attributes, including data type, AI accuracy, clinician verification, and cost. The analysis employed random parameters logit (RPL) and latent class analysis (LCA) models to interpret the data.
📈 Results
The study revealed that the median age of participants was 32 years, with a diverse representation of sexual orientations. The RPL model indicated that the most significant attribute influencing user preferences was the cost of the app (24.1%), followed closely by clinician verification (20.4%) and AI accuracy (19.5%). The LCA identified two main user groups, highlighting the varying priorities among potential users.
🌍 Impact and Implications
The findings from this study have significant implications for the development and marketing of AI-assisted health applications. By focusing on user preferences for low-cost, clinician-verified, and highly accurate results, developers can enhance the likelihood of widespread adoption. This could lead to improved health outcomes by facilitating earlier detection and treatment of STI-related conditions.
🔮 Conclusion
The study underscores the importance of aligning digital health solutions with community preferences. An AI-assisted screening app that is affordable, clinician-verified, and accurate has the potential to significantly increase user uptake. As we move forward, it is crucial to continue exploring user needs to optimize the effectiveness of such health technologies.
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What Do People Want from an AI-Assisted Screening App for Sexually Transmitted Infection-Related Anogenital Lesions: A Discrete Choice Experiment.
Abstract
BACKGROUND: One of the World Health Organization (WHO) recommendations to achieve its global targets for sexually transmitted infections (STIs) is the increased use of digital technologies. Melbourne Sexual Health Centre (MSHC) has developed an AI-assisted screening application (app) called AiSTi for the detection of common STI-related anogenital skin conditions. This study aims to understand the community’s preference for using the AiSTi app.
METHODS: We used a discrete choice experiment (DCE) to understand community preferences regarding the attributes of the AiSTi app for checking anogenital skin lesions. The DCE design included the attributes: data type; AI accuracy; verification of result by clinician; details of result; speed; professional support; and cost. The anonymous DCE survey was distributed to clients attending MSHC and through social media channels in Australia between January and March 2024. Participant preferences on various app attributes were examined using random parameters logit (RPL) and latent class analysis (LCA) models.
RESULTS: The median age of 411 participants was 32 years (interquartile range 26-40 years), with 64% assigned male at birth. Of the participants, 177 (43.1%) identified as same-sex attracted and 137 (33.3%) as heterosexual. In the RPL model, the most influential attribute was the cost of using the app (24.1%), followed by the clinician’s verification of results (20.4%), the AI accuracy (19.5%) and the speed of receiving the result (19.1%). The LCA identified two distinct groups: ‘all-rounders’ (88%), who considered every attribute as important, and a ‘cost-focussed’ group (12%), who mainly focussed on the price. On the basis of the currently available app attributes, the predicted uptake was 72%. In the short term, a more feasible scenario of improving AI accuracy to 80-89% with clinician verification at a $5 cost could increase uptake to 90%. A long-term optimistic scenario with AI accuracy over 95%, no clinician verification and no cost could increase it to 95%.
CONCLUSIONS: Preferences for an AI-assisted screening app targeting STI-related anogenital skin lesions are one that is low-cost, clinician-verified, highly accurate and provides results rapidly. An app with these key qualities would substantially improve user uptake.
Author: [‘Soe NN’, ‘Latt PM’, ‘King A’, ‘Lee D’, ‘Phillips TR’, ‘Fairley CK’, ‘Zhang L’, ‘Ong JJ’]
Journal: Patient
Citation: Soe NN, et al. What Do People Want from an AI-Assisted Screening App for Sexually Transmitted Infection-Related Anogenital Lesions: A Discrete Choice Experiment. What Do People Want from an AI-Assisted Screening App for Sexually Transmitted Infection-Related Anogenital Lesions: A Discrete Choice Experiment. 2024; (unknown volume):(unknown pages). doi: 10.1007/s40271-024-00720-8