What if we could look at a patientโs complete medical history, the statistics from their smartwatch, plus the records of people most like them in the world to drive understandings and predictions about their health?
This idea of recommending oneโknown as Precision Medicineโwould provide retrospection analysis, insight into conditions and predictive analysis of future requirements based on a patientโs current lifestyle. Precision medicine would allow doctors and medical staff to determine the best course of action and predict how to help patients down the road or steer them away from potentially hazardous paths.
Current medical diagnostics are constrained by individuals compared to the โaverage patientโ and brief analysis of surface symptoms. This could be significantly improved by introducing more dataโspecifically records of patients from around the world. By identifying a group of people a patient is most related to โbased on climate, diagnoses, and medicinesโ doctors can use this information to make quicker and better diagnoses, and potentially predict health outcomes.
The challenge? This process would be very data-intensive, and concerns about patient data privacy would need to be addressed. The fact is that, currently, the bandwidth and technology infrastructure required is not in place to support this level of data-intensive analysis in real-time. At best, a patient compared to others within the hospital they go to, and even that process would take an exceptional amount of time to compute and determine a recommendation.
Part of the reason is that processor technologyโessentiallyโstopped getting better a while ago. Up until now, computers have always evolved to keep up with our increasing demands, but their fundamental design is based on the same architecture that was put in place more than 60 years ago, and that architecture is starting to hit its limits.
The challenge in computing has always been building enough memory to keep up with increasing computing requirements. Memory has always been a scarce resource. However, HPE envisages a future where computers are designed to put the data at the core. Utilizing Memory-Driven Computing, HPE foresees that systems around the globe would be able to assimilate patient data, anonymously, and allow medical professionals to access information from a distributed matrix of anonymized data in real-time.
Role of composable infrastructure in improving patient care
HPE Synergy is the worldโs first platform architected for composable infrastructure. Whereas traditional infrastructure management involves compute, storage, and fabric silos with their software management tools, Synergy integrates software-defined intelligence into the hardware itself. With HPE Synergy, compute, storage and fabric are provisioned together enabling application and service delivery through a single interface that precisely composes (and recomposes) infrastructures into any combination at near-instant speeds.
HPE Synergy is ideal for a DevOps approach, as well as traditional IT, as it eliminates time-consuming provisioning processes that often delay projects for weeks or months. The composable infrastructure allows the EMIS-hosted infrastructure teams to provide new services and capabilities rapidly to the software developers.
Takeaways
Healthcare providers can now move more quickly to deliver potentially life-saving healthcare services. The aim is to improve efficiency, improve the service and improve patient care. If healthcare providers can build facilities quicker, then they can save money and put new ground-breaking functionality in front of patients.
HPE Synergy has enabled a new โshare your ownโ records service, where patients can choose to give a care provider 24 hours access to a particular part of their medical record. The quicker innovations like this can be brought into the healthcare system the more significant the benefit for everyone. Ultimately, this HPE Synergy deployment shows that composable infrastructure is winning. The software-defined flexibility and DevOps push for new services that this enables is improving patient care and also saving money.
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Nabanita Maji
Hewlett Packard Enterprise
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