Tag: predictive modeling

Delivering on the Promise of Healthcare Analytics: Troubleshooting, Intelligent Design and Predictive Modeling

Lauran Hazan
Lauran Hazan

Guest post by Lauran Hazan, director of healthcare analytics, STANLEY Healthcare.

Across nearly every industry, Lean process improvement and analytics have radically changed the way that businesses operate. Now, with the advent of big data and accompanying business insights, we’ve moved beyond troubleshooting problems to data-driven design and predictive analytics. The impact of these processes and technologies is felt at every level of the manufacturing supply chain. What happens when all of these innovations hit healthcare?

We’re already seeing many of them in action in hospitals across the world, which are now able to analyze the movement of patients, clinicians and equipment, thanks to RTLS and RFID – among the first Internet of Things (IoT) technologies. The central value proposition of IoT analytics and data visualizations in healthcare is that by providing clinicians and other users with actionable insight into their everyday processes, they will be empowered to understand and modify their behavior, and improve efficiency and the patient experience.

We know this technology works – revealing inefficient workflows, missing or insufficient levels of equipment, patients who have been waiting too long, and more. But acting on these insights to generate change requires more than technology. It needs visionary leadership to create cultural change, grounded in objective data and the real-time feedback it provides.

It’s no easy feat, and we’ve seen industrial engineers working to create change in healthcare for years. What’s different now is the data, which moves us beyond gut instinct or individual experience. Analytics in healthcare – based on objective and comprehensive IoT data – supports a constructive conversation about change, and can be used by staff at all levels to study the impact of an experimental process improvement. Hospitals can enable highly skilled workers to lead from within, rather than managing them top-down. They can leverage the experience and scientific mindset of clinical staff to identify new areas for growth, experiment to improve, measure success and continue to innovate with each new win.

That last point is perhaps the most important. For us to truly change healthcare, hospitals must develop a continuous cycle of improvement. This is what it means to be a Lean hospital in today’s data-empowered industry. Once the organization changes a practice or habit, it can study the impact of that change and then uncover other opportunities to improve further. The next set of practice changes may involve different measurements and metrics as the process of discovery continues.

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