The terms “big data” and “analytics” (particularly the new forms of analytics brought by artificial intelligence or “AI”) have become ubiquitous in recent years across many industries, including healthcare. The amount of healthcare data generated each day—clinical data from electronic health records, claims data, imaging data, genomics data, socioeconomic data, patient-generated data—is growing exponentially. Processing, securely storing, and making use of all of this existing, untapped data for new insights is still unrealized in much of healthcare, but has enormous potential for improving healthcare delivery.
What are some ways that big data and new analytic methods can be used by health systems and providers to improve care and outcomes?
- Health systems can use it to perform analytics that relies on previously separate, multiple data streams, including structured and unstructured data, to gain a complete picture of patient health and risk factors.
- Health systems can use it to build risk models that predict the likelihood of a patient experiencing undesirable outcomes such as acquiring an infection while hospitalized or being readmitted to a hospital after discharge.
- Health systems can use it to gain insights that are actionable so that that resources and targeted interventions are delivered to patients most effectively, which can improve patient care at reduced costs.
Evaluate and Assess
There are an endless number of use cases beyond those just mentioned here. Turning the promise of big data and analytics from vision into reality is a massive undertaking. We see this in our work with countries and health systems, which are eager to adopt big data solutions and intrigued by the potential use of big data and new forms of analytics for clinical care but are not set up to make it possible. An important first step for developing big data and analytics capacities in and across systems is to evaluate and assess the current state of the IT infrastructure with the health system in order to understand what is needed to support the use of big data and analytics across the health system. Following this assessment, investment is often needed in the servers and data processing capabilities that perform data ingestion, extraction, loading, and storage.
Health systems will need not just the tools and technology to become data-driven, but also the mindset. This is a fundamental shift in how data is used—moving from traditional business intelligence (using data to see what happened) to looking forward (using data to predict what will happen). Leadership efforts and involvement of end-users during any transitional process is key to ensuring success. It will also be important to establish cross-functional teams to develop a roadmap that defines what an organization’s IT and big data future looks like, and linking workflows and interventions appropriate to that roadmap.
Intel is working with a range of hospitals, health systems, and governments, who want to upgrade IT infrastructure to take advantage of advances in big data and analytics in healthcare. We are also working with ecosystem partners to develop end-to-end solutions that take full advantage of emerging technologies such as machine learning and Artificial Intelligence and will accelerate the wide-spread integration of big data capabilities for providers, payers, pharmaceutical companies, and others.
Rising costs of care, aging populations, increasing rates of chronic conditions—these are universal challenges that health systems and countries face today. Big data and analytics have great potential to improve care delivery while lowering costs, but this will require providing clear guidance to health systems around IT requirements, as well as health system leadership and organizational efforts that embrace moving towards this new way of informing and delivering clinical care.