As healthcare organizations have transitioned from paper to electronic documentation over the past 10 years, they have faced the challenge of knowing how best to use the wealth of information that is increasingly available through healthcare IT solutions to reduce costs and drive efficiencies. The volume of data that many healthcare systems and hospitals are now able to access, track, analyze, and share has reached the level of true big data—with data flowing in and out of clinical, operational, and financial transactional systems, data flowing between patients and providers as patients become more engaged in their care, and data being collected from devices used at the point of care and at home with patients.
The promise of effectively use of big data is compelling. A recent McKinsey & Company report indicates that if current innovations in the use of big data to reduce U.S. healthcare costs were scaled up, the cost savings could be formidable. The researchers suggest that $300 billion to $450 billion in reduced healthcare spending is possible, which could even be a conservative estimate. This projection is largely based on healthcare organizations being able to harness big data to mold sustainable business models that deliver high-value care tailored specifically to patient needs at the right time, with better information about patient needs collected at the point of care and beyond.
Early results of the Pioneer ACO program support this premise, indicating that the organizations capable of achieving shared savings are those that are best able to identify and engage high-risk patients at the right time in the right manner.
On a grand scale, much of the conversation about cost reduction through use of business intelligence has focused on these big data strategies that offer providers robust information and help them stay connected to their patients and reduce cost in light of healthcare reform and value-based strategies.
Yet the simple truth is that not all healthcare organizations can keep step with those organizations that are already beginning to use big data for business intelligence. They face practical limitations because the cost of creating a big data program can be very high, and implementation can be extremely complex. For larger healthcare organizations that have the means, these challenges are not insurmountable. But a comprehensive big data strategy is likely to be out of reach for a smaller community hospital. These organizations should be asking a fundamental question: What incremental strategies are available to us today to begin to make effective use of business intelligence?
Before shopping for a big data solution, healthcare organizations should review their strategic portfolios and determine if existing business requirements necessitate big data strategies, such as creation of accountable care networks. Smaller community hospitals may see better returns from more traditional analytics to identify cost drivers and reduce cost in specific areas, such as high-cost service department operations and supply chains.
Overbuying business intelligence tools that can provide more than business objectives currently require can potentially create more costs than savings. Big data solutions will ultimately save billions across large populations served by integrated delivery systems, but there are still cost-saving business intelligence solutions that can be effective on a smaller-scale for organizations that are not yet pursuing value-based reimbursement strategies.
Erin Mastagni is a senior consultant, ECG Management Consultants, Inc., Plano, Texas.
Publication Date: Thursday, March 27, 2014