Can AI Turbocharge Your Healthcare Software? Part 2 of 3

As technology improves, healthcare consumers grow increasingly interested in receiving and participating in medical treatment personalized to their unique lifestyles. Together, graph data and artificial intelligence (AI) enable healthcare providers to make this desire a reality.

The Rising Tide of AI and Participatory Healthcare

Today, a growing number of companies market technologies that allow patients to participate in their own healthcare. As a result, healthcare is moving away from the prescriptive care model, which bases diagnoses and treatments on population norms, to a highly participatory model. 

For instance, consider WhenWhere. This company employs image recognition and AI to analyze nutritional labels and determine which grocery store products comply with users’ nutrition plans. Using WhenWhere, a consumer doesn’t need to be a medical expert to interpret dietary data. WhenWhere’s AI-based app empowers anyone to participate in their own nutritional healthcare using only an iPhone.

How AI Facilitates Personalized Healthcare

Historically, healthcare providers have functioned like automobile body shops. When something “breaks” in a patient’s body, the patient checks in for repair. Past doctors have lacked the means to foresee and prevent injury; like mechanics, they merely fixed what was already broken.

However, modern innovation is changing this retrospective element of healthcare. New “sensing” technologies allow patients to track lifestyle factors such as steps, sleep, glucose levels, and calorie consumption. Doctors can then analyze patient medical data with AI to predict which health issues will most likely arise and what lifestyle tweaks will effectively prevent them.

For instance, imagine a patient with insomnia. Let’s suppose that this patient uses a sensing device to track their steps, glucose levels, and caloric consumption for six hours before bed. An AI model could take this medical data and determine that the patient’s insomnia is more severe when, say, their glucose levels are elevated less than two hours before bed. This and similar insights create the opportunity for healthcare providers to personalize treatment plans for individual patients, instead of resorting to less effective, catch-all solutions.

What is Graph Data, and How Does it Benefit Healthcare?

In simple terms, graph data is digital information stored in a structure that highlights data points and the relationships between them. We elaborate on this topic in our eBook, but, in this context, it’s more important to understand the benefits of graph data than the minutiae of how it works.

One benefit of graph data is readability. Unlike a columns-and-rows tabular dataset, a graph dataset is flexible; it displays data in a structure that resembles a spider’s web—with strings symbolizing relationships and junctions representing data points. In a healthcare context, these information “webs” make it easy for doctors to visualize the connections between patients.

For example, imagine that two patients, John and Jane Doe, share a mold allergy. A graph database containing their patient medical data might display two junctions: one labeled “John Doe” and the other “Jane Doe.” In this case, there would also be a junction marked “mold allergy,” with two strings connecting it to John and Jane. This setup ensures that John and Jane’s shared allergy is visible even at a cursory glance, ensuring that their doctor notices their connection.

So, graph data more accessible than tabular data. But that’s not all; graph data is also more manageable. Due to its flexible structure, graph data enables the easy addition of new information without the hassle of re-architecting an entire database. By contrast, tabular data requires the insertion and organization of new columns and rows to display additional information: a complicated, time-consuming process.

Overall, graph data provides a utilitarian balance between structure and flexibility. It presents significant advantages in comparison to traditional, tabular data. Consequently, it has innovative, cost-effective applications in personalized healthcare, particularly in combination with AI.

Stay tuned for part three next week, which will conclude our exploration of AI and graph data applications in healthcare. If you can’t wait, download our eBook “Turbocharge Your Healthcare Software with Graph Data and Artificial Intelligence” below!


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