A McKinsey report estimates that in about 10 years, Big Data can capture more than $300 billion annually in new value with two-third of that in the form of reductions to national healthcare expenditure.
Big Data is proving to be a huge asset in tackling community healthcare issues in efforts to reduce the costs associated with emergency care and makes it prevention-focused. In clinical research and care delivery, Big Data can be leveraged as a powerful tool to find solutions to Alzheimer’s disease and certain types of cancer and provide a low-cost approach to personalized medicine. In health policy, planning and implementation, initiatives such as using cellphone data to track disease origination and spread can lead to key insights on where to spend valuable economic resources to control diseases and epidemics.
Here are the six key challenges faced by healthcare companies while implementing Big Data:
- Fragmented data
- Big Data is all about real or near real-time
- Data is driving the processes
- “Scale-up” is shifting to “scale-out”
- Software as a service (SaaS) and Infrastructure as a Service (IaaS)
- Data privacy concerns
In this white paper, we address these challenges through the following classified sample use cases for Big Data in healthcare, which improves urban healthcare and accelerates value-based strategies.
We highlight how healthcare organizations should evaluate Big Data needs, and move toward a data-driven, hypothesis-generating approach to forward clinical research frontiers. One of the most persistent problems in healthcare is care transitions between sites of care. Clinical information modeling and continuous data integration and access enabled by Big Data are fundamental to solving this. By leveraging Big Data, healthcare organizations can create value-based outcome-driven efficient care delivery that benefits all stakeholders.
- Clinical Research
- Care Delivery
- Health Policy
- Clinical Outcomes and Data Safety
- Fraud, Waste and Abuse
- Value-based strategies