Case Study
Case Study: Digital Detection of CMA
- Technology Innovation
The Background:
Cow milk allergy (CMA) significantly impacts infants, presenting through immediate or delayed reactions that vary from respiratory difficulties and skin reactions to behavioural issues like poor sleep.
These diverse and overlapping symptoms make CMA hard to diagnose, particularly because infants cannot communicate their discomfort. Often, standard care fails to diagnose, leaving parents distressed and powerless to alleviate their child’s suffering.
Client's Need
The client, a global leader in baby formula with a comprehensive product range including dairy-free options, is committed to enhancing parental support. Their initiative aims to spotlight CMA and simplify its diagnosis through:
- Development of an App: Designed to help parents accurately record food intake and symptoms, moving beyond mere event logging to sophisticated pattern recognition and data analysis.
- Collaborative Opportunities: Seeking partners with expertise in digital diagnostics to co-develop or license this technology, enhancing their existing support through informative content, community engagement, and tailored resources.
- Data Integration: Enabling direct communication of collected data with healthcare professionals to facilitate faster and more accurate diagnoses, leading to timely dietary adjustments and improved child well-being.
This approach not only supports parents but also integrates advanced digital tools to streamline and improve the diagnostic process for CMA.
IDYL's Approach
IDYL undertook a comprehensive research initiative to create a foundation for developing a digital diagnostic tool, focusing on non-verbal indicators of discomfort in infants, which are crucial for diagnosing conditions like CMA. Our strategic approach included:
- Conducting extensive research on pain assessment in non-verbal individuals, utilizing scientific publications, patent databases, and anecdotal evidence.
- Creating a detailed map of non-standard, non-verbal pain indicators in infants - such as specific body language and sounds - which highlighted the need for sophisticated data capture like image- and noise- recording via the digital tool.
- Identifying and vetting potential academic and commercial partners with expertise in AI-based digital solutions for pain assessment in infants.
IDYL's Solution
Our solution encompassed several actionable steps to support the client’s need for a robust digital tool for CMA detection:
- Compiled a detailed report outlining key features for the new digital tool, emphasising the importance of capturing and analysing specific non-verbal cues like distinct body language and cry patterns associated with pain.
- Provided a vetted list of potential collaborators, complete with a proposed collaboration model, to develop an appropriate device or app.
- Facilitated introductions between the client and potential technology partners, setting the stage for future development and commercialisation of a digital CMA detection tool.
This approach not only addressed the immediate needs of the client but also positioned them at the forefront of innovative, family-centric healthcare solutions.
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