Using Artificial Neural Networks to Determine Patient Location in the Postoperative Recovery Area

Abstract

This paper considers the use of artificial neural networks (ANNs) as a method for determining the location of postoperative recovery area (PACU) patients. It is our hope that these methods, called location determination algorithms, could be used to eventually replace the requirement in postoperative recovery area regulations that patients be placed within line-of-sight of recovery area staff. This change would permit recovery area staff to participate in other activities, such as providing care to additional patients or creating patient preparation for transfer, possibly lowering the cost of postoperative care. Two models have already been described that are based on a similar concept of keeping track of patient flow through a hospital area. These neural networks, after being trained to predict destination based on arrival information, were used to facilitate improving patient flow and to develop a more accurate arrival time estimation model. We present here a proof-of-concept artificial neural network that can accurately determine a patient's location in the postoperative recovery area.