As the information age propels forward at exponential speed, the key player remains data. It is estimated that there will be more than 1.6 zettabytes of healthcare data by the end of 2019. That is 1,600,000,000 terabytes. The amounts are staggering to consider. While technology continues to make strides in capturing and storing the ever-increasing data landscape the challenge of putting all those bytes to use remains a significant need.

Organizations are trying to leverage the mountains, lakes, stacks, libraries, and warehouses of data to solve the vast array of clinical, operational, and financial needs they face every day. This comes with several challenges.

The sheer amount of data being collected at every level and from every device causes its own challenges; from storage solutions and capacity to data structuring.  Add to that the ever-present challenges of privacy and security and IT departments already face a heavy lift to keep up.

At the same time the business struggles to deal with spiraling costs and the push towards healthcare price transparency.  As one might expect, complying with regulation and the transitions to value-based care drives a very real financial impact. Healthcare organizations are looking for business solutions through managed care networks, or acquisitions and mergers, while also looking for technology solutions that rely on standardization and integration across disparate systems. Data is at the forefront when looking for ways to improve processes to reduce costs while maintaining the ability to provide safe, quality healthcare to those in its care.

From a clinical perspective the number of systems care-providers interact with continues to grow and do not always communicate with each other, creating more work and requiring additional resources. AI is not trusted to perform at an acceptable level above humans in patient care settings, due partly to the sheer amount of data that is needed to adequately train the models.

In the face of these challenges, incredible advancements are being made across the entire spectrum by harnessing the power of the data and technologies now available.

Artificial intelligence is already helping to predict diagnosis and present providers with likely care pathways for specific ailments during admissions. During any patient encounter, data is being leveraged at nearly every level from providing drug interaction warnings to monitoring for adverse outcomes, such as HAI likelihood. It is also commonplace in automating medical coding for billing, significantly reducing the tedious manual job of administrators combing through provider notes.

We are even starting to see more advanced care delivery interactions leveraging virtual consultations with digital AI care practitioners, such as Akos AdviNOW technology. This seems a natural extension from someone trying to self-diagnose using online resources and can increase the number of patient’s a provider can see.  All of this is in addition to the vast number of personal devices now providing information directly to the patient to make informed decisions on their personal healthcare.

Deep knowledge and vast amounts of data are required to really answer meaningful questions. I expect that this will drive a consolidation of aggregated health data. This could take the form of handful of large analytical companies – think google – that aggregate all the data and become the sole providers of analytical intelligence as they buy up smaller companies with interesting solutions, merely needing larger training data. Ultimately, I think the data will be sourced from the government, as the largest single payer of healthcare in the United States, or from the largest EHR vendors. This would enable individual researchers and information technology companies to create more and more solutions to tackle very specific domain questions and use cases. Thought leaders for these targeted answers will create standard integration APIs and provide their knowledge solutions from a Data as a Service (DaaS) model from a common DaaS marketplace. Hospitals and systems will subscribe to the analytical / AI services most beneficial to their practices. EHRs will look to license the solutions and expertise to integrate into their systems and provide more robust all-inclusive solutions, allowing care providers to use technology they are already familiar with.

The information age continues to get brighter and the positive impact passionate innovators can have with the data now digitized and available is nearly limitless.  The future of having actionable information at the right time to reduce pressures faced by providers while improving both efficiency and clinical outcomes will continue to solve problems on a use-case by use-case basis; improving quality of care and providing better patient outcomes.


About the Author

Joel Ewald has been with IPS as a Senior Database Architect since 2014, working to leverage current capabilities and standardize software delivery to provide actionable information when it is needed. He has over 15 years of experience in software development, focused primarily on the Microsoft database stack for both transactional OLTP and data warehousing solutions supporting business intelligence products. At IPS, he has provided decision support tools for, OR, ED, Lab, Infection Control, Anesthetics,  Patient Flow, Nurse Staffing, and Pharmacy. Prior to joining IPS team, he worked in logistics project management, and public sector administration and public safety software technology sectors.

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