The Role of Real-Time, Self-Service Analytics in Healthcare

Self-Service analytics in healthcareEnterprise analytic platforms play a valuable role because of their ability to collect big data that can be warehoused and mined across the healthcare organization. They are often used successfully to solve complex problems, such as:

• Risk stratify capitated patient populations,
• Provide insight into supply chain improvements, and
• Ferret out revenue cycle bottlenecks,

to name a few. One has to ask why, despite the power and capabilities of these centralized, top-down platforms are hospital departments still missing sufficiently actionable, data-driven insights?

One of the main reasons is that monolithic enterprise platforms are not typically designed to empower departmental end users to quickly pose questions and get answers. Instead, these complex, centrally managed systems can impart significant lag times between requesting a report and obtaining results, lengthening a key usability metric called “time to initial insight”. Moreover, the initial reports produced by any analytics systems and the insights they may provide frequently prompts additional questions in order to uncover the actionable details necessary and sufficient for optimal decision-making, and that also build trust and organizational alignment. Unfortunately, obtaining such details via these systems often requires time consuming, iterative report changes that don’t support rapid and continuous clinical quality improvements.

One approach that has demonstrated effectiveness is complementing the enterprise analytic platforms top-down, strategic decision making capability with a cloud-based, departmental, self-service analytics solutions. This approach empowers service line leaders and clinical department staff to access the bottom-up understanding and real-time feedback needed to achieve continuous clinical quality improvement. Because both enterprise and departmental systems each have an important role to play, leveraging the strengths of both can ensure the entire organization benefits. More about the hybrid departmental approach to data analytics in our next blog.


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