The prospect of integrating disparate sources of information into a multifaceted canvas of patient experiences is a tantalizing one, yet basic concerns with the usability of electronic health records, the availability of health information exchange, and a chronic lack of time, knowhow, and funding have all contributed to keeping big data on the back bench.
However, a new wave of commitment to health data interoperability, paired with advances in data standardization and a growing recognition that the EHR is no longer enough for cost-effective, high quality care, have started to make big data analytics a great deal more accessible to providers across the care continuum.
In turn, these accomplishments have raised stakeholders’ hopes that big data is about to produce a seismic shift in the way providers make decisions, interact with their patients, and power through their daily workflows.
Analytics is becoming a priority area for health IT investment as healthcare organizations get on board with the data-driven mentality of systemic reform – forty percent of respondents to a recent IDC Health Insights poll said budgets are loosening up for health IT projects – and big data is becoming a major criteria for financial and clinical success.
The majority of providers are still looking for tools and technologies that will enable them to participate in basic big data activities, like risk stratification, population health management, and reducing operational costs. However, cutting-edge developers are already reaching past this first stage of adoption towards a future in which big data is no big deal.
What are some of the emerging strategies, technologies, and infrastructure projects that are pushing the boundaries of how big data fits in to the healthcare landscape?
Imaging analytics opens up new diagnostic vistas
Since the beginning of the diagnostic imaging era, interpreting x-rays, CAT scans, and MRIs has largely remained under the purview of skilled clinicians who specialize in catching abnormalities and reporting on findings.
But as computing power increases and analytics algorithms start to become intelligent enough to analyze patterns in digital images, these test results are taking on a whole new meaning for the diagnostic process – and expanding the potential for using this data for additional aspects of patient care.