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Life is fleeting, but data is forever

Our medical histories carry immense value, long after we're gone. Why aren't we using them?

Over the past several decades, the way medical care is documented has dramatically changed. While a patient in 1950 might have accumulated only a few folders of paper records documenting their care, an average patient today will likely accumulate 80 megabytes of health data per year. What's more, that is likely to increase exponentially each year.

With the transformation of medicine from analog to digital, plus the rise of new data-generating devices for health tracking and genomic information, we can look forward to a new world in which virtually every aspect of a patient's medical history can be communicated, stored, and manipulated. For each patient, this huge body of data represents a sort of digital cadaver, a treasure trove of useful medical information and insights that could become invaluable in developing patient treatments in the future.

Please read: EHRs prove invaluable in Israel vaccination campaign

One use for this tsunami of health information is to create big data pools that, when analyzed, can help governments, organizations, and individual policymakers follow trends in patient care and improve workflow. Communities could bring these pools of information together into population health databases and learn a great deal about the ways conditions evolve across large bodies of people.

These approaches are already being used. However, as organizations better learn to take advantage of such databases, we might be able to do more with the massive amounts of data being compiled. Rather than reaching abstract conclusions from broad populations, data could be broken down in person-by-person models that offer information previously unavailable to healthcare providers. Call them digital patients.

In the future, there may be a new, more personalized way to make use of a patient's personal data that offers even more benefits. Rather than analyzing digital health data on a broad level, data from the moment a child is born to the present day could be compiled to create a fully fleshed picture of their medical status. This dataset—a digital body, perhaps—offers clinicians a new view into a patient's health status and makes it easier to anonymously compare individual patients on a one-to-one basis.

Once this set of data is available and complete, it becomes a valuable tool in the healthcare of the individual patient. Thanks to data analytics and techniques such as swarm learning, this information could be used to expand the overall body of medical information and knowledge, allowing (anonymized) insights gleaned from individual patient treatments to be applied on a much broader scale.

This body of data will also enable practitioners to make more accurate predictions about their patients' future health. And note that it isn't about making use of new forms of data collection or diagnostics; all of this data is already being collected. Rather, the idea is to establish more effective ways to use data to improve patient outcomes.

A wide range of future benefits

For example, with a fully fleshed profile in place, doctors will be able to target messages to specific people with specific conditions, such as type 2 diabetes, and offer them advice on how to care for themselves on an individual level. Eventually, this data may be used to help people avoid experiencing such illnesses entirely.

Another way this model could help patients would be to allow providers to try out new interventions and see how they work at a level that would be difficult to accomplish with a living, breathing human being. For example, researchers would be able to model how an individual might respond to treatments based on their specific DNA, conditions, and behaviors across their lifetime. There is virtually no way to create a human model of such depth and complexity at present.

While traditional providers might be able to guesstimate how the efforts they make will succeed, they aren't able to make decisions based on all of the digital information generated by that patient across their entire care history.

For a number of reasons, creating these digital patients is still a difficult feat at present. The biggest obstacle? All of the data must be in a compatible format that can be mixed, matched, and analyzed effectively. Unfortunately, the healthcare industry is still far from finding a single universal solution for sharing and using data from varied sources. While HL7 FHIR (Fast Healthcare Interoperability Resources) has gotten a lot of traction in recent years, it has achieved nothing like the consensus that allows financial institutions to share data freely, nor has it reached a level of practical use that would motivate vendors to adopt it as a primary standard.

So why aren't we doing this?

Another major hurdle to building a comprehensive digital patient is that the organization holding the patient's data must agree to share it for the purpose for which it is intended. Even if we design a health data format that works for virtually everyone in the healthcare ecosystem, administrative relationships would need to be established to allow a huge range of data to be shared freely between partners. Creating workable communication agreements between private, government, and not-for-profit organizations would at best be a major challenge and perhaps even nearly impossible as things stand today.

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Once a solution to the data sharing problem is found, medical and biological science will likely find that the technology to make use of the information is ready and waiting. High-performance computing is enabling genomics sequencing to be faster than ever, and the rise of HPC as a service is making this level of performance available to a much broader audience. Large in-memory computing capabilities are allowing digital patients to be held in memory close to the processers, so that the entirety of a patient's data can be viewed at once, regardless of how much data has been accumulated.

Ultimately, though, it's worth making the effort to pull and accumulate a lifetime's worth of data to create this digital patient, one that clinicians can use to gain a detailed understanding of how an individual's life choices, medical treatments, and environmental factors impact their health. As individual patient data continues to flow in at mammoth speeds, it will offer an increasingly clear picture of how a person's medical position changes over time.

In fact, the digital patient could (and should) keep giving long after the person dies, whereby we can continue to learn from the digital cadaver and the body of data left behind. Future generations will be able to use this data to test new theories, evolve medical practices, and find ways to rapidly advance and improve patient outcomes.

We are all going to die. Our data can live forever.

This article/content was written by the individual writer identified and does not necessarily reflect the view of Hewlett Packard Enterprise Company.