Can AI Turbocharge Your Healthcare Software? Part 1 of 3

How can artificial intelligence (AI) turbocharge your healthcare software? The key is “graph data.” In this three-part series, Tom Blue, Ryan Wright, and Tim Oates explore the evolution of healthcare as it relates to AI-powered technology and graph data.

The Truth Has Been Right In Front of Us

Today, scientific research suggests that health is self-determined, not predestined. Scientists estimate that genetics determine only 20% of health, while personal choice determines the rest. Further, modern technology allows healthcare providers to collect valuable data on the interplay between personal choice and patient health. The next significant advancement in healthcare will come from using AI to leverage this data. 

Twin studies (conducted on monozygotic twins) have proven that even genetically identical individuals diverge in appearance and personality. As twins age, they make different choices and, accordingly, their physical, mental, and emotional health grows distinct. We discuss this phenomenon in more depth in our eBook, but the most important takeaway is this:

Health is not a fate predestined by one’s genes or environment. Instead, health is a skill cultivated through personal choice. And AI can help unlock information about choices and their impacts on our health.

Changing the Healthcare Paradigm

Armed with the knowledge that personal choice informs patient health, modern healthcare providers utilize AI and graph data to guide their patients to choices that will yield maximally beneficial outcomes.

As a result, the healthcare industry is rapidly outgrowing the age of symptom suppression. In the past, a doctor might prescribe a pill to suppress symptoms instead of addressing their root cause. Today, however, informed doctors leverage cutting-edge technology like AI and graph data to diagnose and treat the root causes of their patients’ health problems, not only their surface-level symptoms.

The Healthcare of Tomorrow

Historically, healthcare has been necessarily generalized. Medical diagnostics and treatments originate from studies spanning hundreds, if not thousands of individuals. In such an impersonal, manual context, personal nuance is easily overlooked.

For example, imagine that three patients visit a doctor from the past. They all report debilitating migraines. As a result, the doctor prescribes the same treatment to all three patients: a pill purported to alleviate migraine pain, even though their migraines stem from entirely different root causes.

By contrast, a doctor leveraging modern technology will provide far more personalized healthcare. This doctor uses AI and graph data to analyze the aforementioned three patients’ medical histories and determine that they suffer from migraine-inducing glucose toxicity, sinus inflammation, and jaw tension, respectively. The doctor then directly treats the root causes of their migraines, not only their general symptoms.

Applications of Graph Data and AI

Ultimately, the key to achieving personalized healthcare is using AI to analyze graph data. Consider the average person; they contain gigabytes upon gigabytes of medical data. For reasons we explain in our eBook, this data is most useful in graph form.

Sophisticated AI techniques can now isolate useful, actionable information by analyzing patient medical data in graph data form. (For more information on why this is a pretty incredible breakthrough, read this blog by one of our Senior Data Scientists or check out the technical paper). 

For instance, AI can cross-match data points from a patient’s genome and family history against existing medical knowledge to determine their susceptibility to heritable diseases. This and similar analyses yield insight into patients’ unique circumstances, allowing healthcare providers to treat them more effectively as individuals.

Our eBook, “Turbocharge Your Healthcare Software with Graph Data and Artificial Intelligence,” covers this topic in more depth. Click below to download it. Also, stay tuned for part two next week, in which we further explore AI applications in healthcare software.

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