Operationalizing Prediction Situation

As healthcare organizations continue to implement new and advanced predictive analytics throughout their systems, identifying potential at-risk patients - sepsis patients, or patients with high potential for readmission, for example - becomes standard procedure; operationalizing the data these analytic tools discover now becomes the focus.

Prediction is a substantial advancement in healthcare operations. However, predicting patient status by itself is not impactful, nor worthwhile, without an associated operational protocol that intervenes based on that prediction. Knowing you may have ten patients at risk for sepsis is a huge leap forward, now how do I ensure these patients remain safe and healthy? Someone must do something, in the appropriate order, in a prescribed time-frame. Is this being done?

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Situation

There is perhaps no easier way to slow the throughput of your hospital than by stumbling through an ineffective discharge process. Inefficient and drawn-out discharges arise naturally out of competing staff priorities and process variations. These effectively reduce the physician’s and nurses’ ability to readily coordinate discharges. Each disjointed operational and logistical variation becomes the responsibility of yet another care giver to manage - distracting everyone from the joy of practicing medicine.