Kristin Molina: What we see is hospitals more and more are needing to do more with less. And especially with what we’ve seen with the rise of the pandemic and overcrowding, that it’s not just about adding more beds or staff or other resources.
But it’s really about how to optimize and give the best care to each patient in the right care setting – and being able to anticipate and predict increases in demand, and that you can have the staff and resources available as you anticipate peaks in demand.
If you don’t expect to have that demand, you’re not having extra resources or staff around when it’s not needed. But of course, with COVID and all that, it just seems to be a peak on peak, sadly.
But really, managing patient flow requires an enterprise view across all the parts of the hospital, and the hospital network, and even what’s happening outside the four walls of the hospital.
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So that’s where really the combination of bringing together clinical and operational data across different care settings and different systems, so that these care teams can have that full picture, and the situational awareness of what’s going on in their unit, or their department or even at the enterprise level.
And so that’s where we see being able to use predictive analytics, kind of driven by machine learning and AI, is really allowing health systems to have actionable insights into what that next best health action should be so that they can optimize the transitions of care and kind of unlock any bottlenecks in patient flow.
Source: Mobihealthnews