Improving hospital operational efficiency through data science boils down to applying predictive analytics to improve planning and execution of key care-delivery processes, chief among them resource utilization (including infusion chairs, operating rooms, imaging equipment, and inpatient beds), staff schedules, and patient admittance and discharge. When this is done right, providers see an increase in patient access (accommodation of more patients, sooner) and revenue, lower cost, increased asset utilization, and an improved patient experience.
Hospitals are starting to see gains from predictive analytics, especially on the operational side. OR block scheduling is an area where the ROI can be significant. Now, with the explosion of smart devices, computational power in the cloud, and the growing pervasiveness of data science and machine learning algorithms, an entirely different realm of operational optimization is suddenly possible.
Most healthcare providers are waking up to the fact that their operations need a data-driven, scientific overhaul much the same way as auto manufacturing, semiconductor manufacturing and all other asset-intensive, “flow”-based systems have experienced. The good news is that there are tools, software and resources that can be used to bring about this transformation.