Resolving Perioperative Inventory Patient Billing challenges using AI and deep learning

Kiran Veernapu

doi:10.5281/zenodo.14787192

Resolving Perioperative Inventory Patient Billing challenges using AI and deep learning

Author(s)	Kiran Veernapu
Country	United States
Abstract	Medical supplies and equipment needed during a surgical procedure in operating rooms are referred to as perioperative inventory. These medical supplies are highly specialized, sterile, and swiftly available for a surgical need. Perioperative inventory is typically maintained at higher stock levels to avoid delays and interruptions to the scheduled surgeries. Surgeons and nursing staff use perioperative inventory cards known as preference cards to identify the item needed during the surgery. While usage of the preference card system in the operating room has been great practice, there are challenges in using this approach. The surgeries need a lot of small to large items, healthcare organizations are partnering with suppliers to bring the items needed for the surgeries. Nurses record the usage of the items. This process results into errors, leading to several mistakes in the patient invoice. Effective management of perioperative inventory is essential for maintaining operational efficiency, minimizing costs, and ensuring patient safety in healthcare institutions. Perioperative inventory refers to the stock of medical supplies, medications, surgical instruments, and other essential items required before, during, and after a surgical procedure [1]. The complexity of inventory management in the perioperative setting is heightened by factors such as fluctuating demand, uncertainty in surgical schedules, and the critical need for accuracy and timeliness in maintaining inventory levels. This paper explores the application of deep learning algorithms in managing perioperative inventory, highlighting their potential to optimize stock levels, reduce waste, improve patient care, and enhance the overall operational workflow. Key algorithms, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning (RL), are discussed, along with their role in predicting demand, automating replenishment, and making intelligent inventory decisions.
Keywords	Peri-Operative Inventory, Inventory Issues, AI, Deep Learning, Healthcare, Supply Chain, Inventory Management.
Field	Engineering
Published In	Volume 3, Issue 4, April 2022
Published On	2022-04-06
Cite This	Resolving Perioperative Inventory Patient Billing challenges using AI and deep learning - Kiran Veernapu - IJLRP Volume 3, Issue 4, April 2022. DOI 10.5281/zenodo.14787192
DOI	https://doi.org/10.5281/zenodo.14787192
Short DOI	https://doi.org/g83tc6

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Resolving Perioperative Inventory Patient Billing challenges using AI and deep learning

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