Get More From Clinical Analytics with BI Systems Support

Clinical decision support systems are critical components to helping healthcare companies make decisions, improve patient care and population health, and control costs. However, analyzing financial, operational and quality control data is also key to achieving those goals. Business intelligence (BI) efforts in healthcare provide a foundation of information and key insights on which to build.

Healthcare providers are seeing the value in BI tools as well. And a new report from Transparency Market Research estimates that the global market for business intelligence in healthcare IT will hit $3.91 billion by 2023, up from $1.52 billion in 2014.

What Is Driving Healthcare BI Utilization?

With the wide adoption of electronic health records (EHRs), massive amounts of data are being generated. With the right tools and the right team to analyze this data, healthcare organizations have the power to manage the health of communities much more effectively and improve outcomes for patients. This helps healthcare companies meet new regulations as well as the market shifts toward value-based care over fee-for-service models.

Harnessing the power of big data holds great potential in cost savings for the healthcare industry. In fact, reported at the end of last year that big data could save around $400 billion in healthcare costs, and that includes more than just clinical decision support.

Evaluating department data (emergency, surgical and pharmacy), physician quality, performance enhancement tracking, and patient outcomes can provide proper benchmarks and guide organizational decision-making as well as clinical decision making. Data analytics from healthcare BI efforts can include enterprise, predictive, ACO, healthcare data integration and warehousing, and population health analytics.

But there are a few challenges to effectively implementing healthcare BI systems, resulting in low BI ROI, including: data silos; underestimating existing capabilities; a lack of automated data; too many tools and infrastructures; an unclear BI vision; and more.

Learn more about how to implement a BI system that will provide a measurable return on investment in our white paper.

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