In no other industry has big data been embraced with such enthusiasm as it has in the healthcare industry. Where other sectors have been stumped not only with how to handle vast quantities of data, but also in how to analyze and apply the information that is derived from all that data, healthcare IT leaders have forged ahead. That’s not to say that their approaches to managing and analyzing big data don’t vary widely, but CIOs – both on the provider side and the payer side – understand that using the data available to them enables better patient outcomes and, in turn, facilitates revenue and cost management and improve security for personally identifiable information (PII) and other protected health information (PHI).
Generally, the gating factors in how CIOs approach big data in terms of management and analytics is guided by the size of the organization and the resources they have available to them. For larger organizations, it makes sense to build and host a custom analytics environment, but smaller healthcare organizations have been adept at accessing tools that offer analytics as a service, or even population management as a service.
Whichever way healthcare organizations choose to approach the data-driven revolution there are is a critical issue that must be kept front and center in the minds of IT leaders. In healthcare – especially as it pertains to patient care – real time data is the most valuable data and, dare I say, the only data that matters. Latency in accessing, analyzing, or applying data to outcomes can, quite literally, be a killer. Moreover as more data can be collected from connected devices and the data sources, especially imaging data, becomes richer and more complex, the likelihood that latency will become a mitigating factor in data analysis and application is high.
To mitigate, or even safeguard, against latency and manage the overabundance of data in a way that enables application to patient care and improved outcomes, the storage infrastructure and architecture behind the scenes needs to be in perfect health. It goes without saying that there are no useful healthcare analytics without data being in the cloud. Whether it’s a private cloud, a public cloud, or a hybrid deployment, a cloud-based infrastructure is the foundation for future-focused healthcare provision. From being able to share data between groups within an organization or being able to scale capacity to meet demand during peak demand periods, such as open enrollment, and then scale it back once peak demand has ended, is a boon for healthcare organizations. With the reductions in CAPEX and the ability to closely manage OPEX, there’s the opportunity to direct those savings into the next -generation of flash storage. While flash is still a premium storage media, the cost of entry is significantly less than it was just a few years ago and the benefits, particularly when managing latency is a key requirement, are far greater than the costs.
When this storage infrastructure is assembled what happens for medical teams, researchers, actuaries, and insurers is that they are empowered. Researchers can investigate diseases at the genomic level, leading to physicians being able to deliver precision medicine and treatment tailored to the individual, which leads to better patient outcomes in everything from reductions in patient readmission to survival rates and life expectancy. By being able to analyze data from smart devices that are part of Patient Centered Medical Homes (PCMH), doctors can get ahead of emergent conditions that are part of chronic illnesses, such as congestive heart failure and diabetes, and head-off hospital admissions, either entirely or at least catch them at a less expensive point of entry than the emergency room.
Healthcare analytics is one of those fields where the promise of technology to markedly improve the quality of life is coming to fruition. While more progress has been made in this field of endeavor than most, I am firm believer that we’re still only at the beginning of the journey in terms of what could be possible. From using big data to track epidemics to creating research exchanges to hasten the development of vaccines against diseases like Ebola and Zika, to the continued development of Patient Centered Medical Homes and wearable devices, there is a seemingly endless source of data to mine, analyze, and put to work for better healthcare outcomes.
