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5 min read

AI and Healthcare: Personalizing the Future

Written by
Tim Edgar
Published on
Est read time
5 min read

Defining AI and Collaborative Filtering

At its core, artificial intelligence seeks to replicate human cognition—learning from experiences, adapting to new information, and making informed decisions. And one methodology that has gained significant traction in the field of AI is collaborative filtering.

Collaborative filtering is about understanding and predicting the preferences of users based on the behaviors of similar users. It's the genius behind Amazon's product recommendations and Netflix's movie suggestions. For instance, if you've recently purchased an Asimov novel on Amazon, you might find recommendations for similar science fiction books the next time you log in. It's not merely about promoting products; it's about anticipating needs and enhancing user experience.

Drawing from my time at Bing, search engines also employ a form of collaborative filtering. By understanding user behaviors and deriving trends, search engines predict and suggest what a user might be looking for. It's not about retrieving vast amounts of data but presenting the most relevant information to the user. Bing, for instance, analyzes patterns, identifying commonalities to predict what users might search for next. It's a delicate balance of offering relevant matches while fostering trust in the recommendations.

Venteur's AI-Driven Approach

Transitioning from movies and books to healthcare might seem a stretch, but the principles remain the same. At Venteur, we harness AI to recommend health plans tailored to individual needs, much like how Netflix might suggest a movie. However, the stakes are undeniably higher.

Our ability to hyper-personalize stems from an approach similar to collaborative filtering. We don't just look at data in isolation. We analyze patterns across the vast landscape of public healthcare data and answers from the people we’re helping, seeking out individuals with similar profiles—those with parallel medical histories, diagnoses, or life events. By doing this, we can provide insights into potential healthcare trajectories, helping users visualize what's on their horizon, be it two, five, or even 10 years down the line.

For example: Consider a life-altering event like becoming a parent. The medical expenses associated with such a milestone can be overwhelming. Venteur’s system analyzes data from families who've walked this path before, offering a glimpse into potential expenses. This isn't just about numbers—it's about providing predictability in an otherwise uncertain journey, and then serving up a recommended plan that makes sense.

The magic behind our approach is twofold. First, we leverage the power of AI to sift through vast amounts of data, identifying patterns and making correlations. This aids in understanding what's possible for an individual, ensuring the recommendations are tailored and relevant.

Second, we incorporate a lot of mathematical modeling. The world of healthcare is rife with possibilities, and by aligning these with insurance products and understanding the associated risks, we ensure that our recommendations are not only personalized but also optimized for value.

Historically, health plan recommendations were often based on simple metrics, like the number of doctor's visits in a year. But Venteur's model is more dynamic. We ask probing questions, like "how do you view your health today?", allowing our AI to gain deeper insights into individual needs.

Building Trust in the Age of AI

AI's potential in medical classification problems is well-documented, but it's not without its biases.

I recall an AI computer vision model trained to detect potential cancerous growths in melanoma, which showcased impressive accuracy. However, upon further investigation, it was discovered that the model's results were influenced by certain controls used during its training.

Specifically, a study showed that the AI algorithm appeared more likely to interpret images with rulers—used frequently in images presenting malignant melanoma (MM)—as malignant. The algorithm inadvertently was trained to recognize such findings as malignant since images presenting a MM had rulers more often than benign lesions (Source here; trigger warning, there’s images of cancerous growths).

The promise of AI is alluring - especially when there’s real AI products helping real people get real healthcare solutions like the story I referenced in the intro. However, in healthcare, the bar for trust is set exceptionally high. And rightly so. A misjudged movie suggestion from Netflix might result in two hours lost, but a misguided health plan recommendation or a missed diagnosis? The consequences are far-reaching.

This is an immense responsibility for people building new AI models today. Not just In healthcare, but in general. The question isn't just about how accurate our AI is, but how much our users trust it and how we show our work. Every recommendation we make is backed by robust data, algorithms, and insights. But beyond raw data, we should aim to demystify the AI process.

For Venteur’s AI products, we try to address questions like, "why did you recommend this plan?" or "what factors within the algorithm influenced this suggestion?" By simplifying complex processes and offering transparent insights, we bridge the gap between machine precision and human trust.

We believe in giving our users a clear mental model of our recommendations, helping them understand not just the "what" but also the "why" behind each suggestion. It's about fostering trust, whether in the logical aspects of our technology or in the Venteur brand itself.

Venteur’s Mission and How it Informs our AI Model

Our mission extends beyond technology. We're driven by the belief that every individual deserves personalized healthcare decisions that are not only based on vast data but also tailored to their unique circumstances.

This personalization brings depth to our recommendations, making them resonate more with individual needs. It's not just about saving costs but ensuring quality, understanding, and empowerment.

With AI as our ally, we strive to make healthcare decisions clearer, simpler, and more tailored than ever before, ensuring that each recommendation is a reflection of our commitment to a person’s well-being.

As we stand at the crossroads of technology and healthcare, the journey ahead is filled with promise. AI, with its vast capabilities, has the potential to redefine how we perceive healthcare. However, at Venteur, we believe that technology should serve humanity, not the other way around.

We believe that personalizing your healthcare decisions isn't just about convenience—it's about maximizing value. Every dollar spent on healthcare should be utilized to its fullest potential, and our AI-driven approach is designed to ensure just that. Being in the driver's seat lets you shape your healthcare choices to fit your life. It feels so much more personal than just going with a one-size-fits-all plan someone in HR hands you.

Venteur's AI-driven approach is rooted in a simple belief: healthcare decisions should be personalized, informed, and empowering. We're not just offering plans; we're providing a roadmap, guiding individuals towards a healthier, more secure future.

Our commitment is unwavering: To harness the power of AI in the service of humanity, ensuring that every individual feels empowered, informed, and confident in their healthcare decisions. 

Join us as we shape the future, one decision at a time.

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