It has been a challenge for the healthcare industry to move into the electronic health records era. Today, however, roughly 30 per cent of the world’s stored data is generated in the healthcare industry.
Having “gone digital,” the industry is now facing a more nebulous challenge: to extract actionable insights — meaning — out of the numbers, and to apply these insights in their clinical and operational processes.
The insights that come out of advanced data analytics helps healthcare organizations in many critical ways, including but not limited to:
- Better understanding of disease states;
- Ability to get new medications to patients in a shorter time;
- Identification and development of new treatments;
- Empirical evidence of drug safety and efficacy for regulators; and
- Ability to decide on best strategies for commercializing treatments.
The rewards coming out of turning data analytics into data insights are potentially game-changing for healthcare organizations: patients are healthier, care costs less and the processes around it are more transparent, and staff and patient satisfaction levels rise. These potential benefits are not lost on healthcare decision-makers, two-thirds of whom say analytics is one of their top three priorities.
At the same time, the analytics road is not an easy one for healthcare bodies to take. On top of informational siloes and interoperability woes, there are issues around staffing, security, and workflow integration.
Big data itself is complex, and requires organizations to get serious — or at least more serious — about how they collect, store, analyze, and present data. All of this must be accomplished within strict — sometimes severe — budgetary constraints.
IBM reference architecture
IBM’s reference architecture for healthcare and life sciences defines a platform for delivering high performance for big data workloads while lowering the total cost of IT ownership, consists of key infrastructure components from IBM’s high-performance compute and storage portfolio.
The architecture defines a highly flexible, scalable, and cost-effective platform for accessing, managing, storing, sharing, integrating, and analyzing big data, and supports an expanding ecosystem of leading industry partners.
Among the key platform capabilities:
- Scale to support exponentially growing volumes of big data;
- Flexibility to support evolving analytics applications built on Spark, Hadoop, Docker, and other frameworks;
- Simplified integration of biomedical data across storage silos;
- Storage, management, and analysis of unstructured data;
- Metadata capture and storage for searchability, repeatability, and auditability of data and workflows;
- Easy collaborations across geographic boundaries;
- Security for protected health information and protection of intellectual property rights; and
- Easy and cost-effective IT administration
IT organizations can use the reference architecture as a high-level guide for overcoming data management challenges and processing bottlenecks frequently encountered in personalized healthcare initiatives and other compute- and data-intensive biomedical workloads.
Analytics is no mere fad in healthcare. The market is expected to reach almost $30 billion USD by 2022, up from $7.2 billion USD in 2016.
The IBM white paper, “A reference architecture for high performance analytics in healthcare and life science,” is for healthcare and life sciences professionals who want to find out more about a healthcare and life sciences platform that delivers high performance for big data workloads while lowering the cost of IT ownership.
