Authored by: Angie Adams
When a medical practitioner is with a patient, they read the patient’s medical history, ask questions and do their best to evaluate, analyze and recommend treatment plans using available information and tools. But what if the information or tools are flawed or incomplete?
A person is more than the sum of their parts. The same may be said for patients. Medical records have years and years of data spread across many providers and specialists. It’s likely, then, that when a person sees a clinician, their healthcare practitioner will have only a fraction of the data needed to complete their full health picture.
In the past, practitioners lacked the right tools and simply did the best with what insights they had. But today’s advancements in interoperability and artificial intelligence have created a new reality to support practitioners in delivering whole-person healthcare.
Making difficult and sometimes unlikely connections
Whole-person healthcare has become a buzzword. But really it’s about looking at a patient’s entire health rather than at only whatever issue landed them in your exam room. Because oftentimes, the issue in front of you isn’t what it appears to be.
For example, imagine a patient is diagnosed with an autoimmune disease in their 40s. Medical practitioners know that the patient didn’t wake up one day with the condition; it developed over time. And during that time, the patient may have visited many specialists, each treating their piece of the puzzle without any visibility into the other practitioners’ work.
The patient visited an otolaryngologist for dry eyes; a rheumatologist for joint problems; and a gastroenterologist for acid reflux and irritable bowel syndrome. Working in silos, the providers couldn’t see how their respective pieces were connected to the whole, but if they could, it may have resulted in a faster diagnosis and preventing further advancement of the disease.
Interoperability is a resource that helps pull data together to spot connections more easily. But still, with vast amounts of data to digest, it’s difficult to consider everything. This is why AI may prove a valuable tool.
AI’s potential role in identifying patterns and making connections
A patient’s medical history may contain data spread across various specialists’ records and geographic locations disparately collected over many years. Getting up to speed on this data is a huge undertaking. But in the future, AI could be trained to recognize data patterns and call out relevant insights to providers. It could digest large amounts of data, searching for logical insights to support providers in making more-informed decisions.
Of course, as with any new tool, there are always potential risks. And one of the risks with AI is the potential for bias. Addressing this issue requires clinicians’ involvement to ensure no inherent bias exists in the training models. Furthermore, as we become more dependent on AI, guarding against these problems will become increasingly important.
A future focused on whole health
Every person is unique. Each has different health histories, factors that influence their health situations, and potential risks. And when practitioners have the data they need and can take advantage of tools like interoperability and AI, they have a better chance of spotting important connections that allow faster diagnosis and treatment.
Instead of healthcare delivery occurring in fragmented experiences with multiple specialists as it does today, future patient encounters will be with practitioners who work in synergy while seeing the whole picture. This approach benefits patients as well as practitioners who can use technology to address the whole patient rather than individual, fragmented parts.