Using AI to improve PAR levels in inventory management in healthcare with OCI technology

April 19, 2024 | 5 minute read
Vaijayanti Joshi
Master Principal Cloud Architect
Sudipta Kumar Panigrahi
Principal cloud Architect
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In healthcare, managing inventory isn’t just a matter of cost savings. It’s a critical component of patient care. Artificial intelligence revolutionizes how healthcare providers manage supplies, from syringes and gloves to life-saving drugs. This blog post explores the impact of AI-powered inventory management in healthcare, outlining how these new technologies can help to identify patterns and trends in medical inventory usage.

The following sections provides a high-level overview of how Oracle can help customers to digitally transform and derive greater business insights by using the following steps:

  • Extract data from Oracle’s suite of software-as-a-service (SaaS) business applications, such as PeopleSoft, or third-party inventory systems, Oracle’s Cerner, or third-party electronic health records (EHR).
  • Utilize Oracle Cloud Infrastructure (OCI)’s platform of services for automating the process of predicting and replenishing inventory using AI.

The challenges of traditional inventory management in healthcare

Healthcare facilities face a unique set of challenges in inventory management, including the need for a wide range of supplies, the critical nature of many products, and the fluctuating demand creates a complex environment. Moreover, traditional inventory methods often lead to overstocking, which ties up capital, or understocking, which can be detrimental to patient care. In such a scenario, the precision and efficiency offered by AI are not just beneficial, but necessary.

Inventory lifecycle: Define, count, replenish, optimize.
Inventory lifecycle: Define, count, replenish, optimize.

 

What is AI in healthcare?

AI in healthcare is revolutionizing the industry by using advanced algorithms and computational techniques to analyze complex medical data, improve diagnostics, personalize treatment plans, enhance operational efficiency, and ultimately, enable delivery of better patient care.

AI is enhancing periodic automatic replenishment (PAR) level medical inventory management in the following ways:

  • Optimized PAR level calculation: AI algorithms can analyze historical consumption data, current patient census, and other relevant factors to dynamically adjust PAR levels for each inventory item. By considering variations in demand patterns, seasonal trends, and other variables, AI helps ensure that PAR levels are accurately set to meet the needs of the healthcare facility while minimizing excess inventory
  • Real-time monitoring and adjustment: AI-powered systems continuously monitor inventory levels and usage patterns in real time. When inventory levels deviate from the predefined PAR levels, AI algorithms automatically trigger reorder requests or adjust PAR levels accordingly. This proactive approach helps ensure that inventory levels are always optimized to meet demand without stockouts or overstocking.
  • Predictive analytics: AI-driven predictive analytics can forecast future demand for medical supplies based on historical data and predictive models. By anticipating fluctuations in demand, AI helps healthcare facilities maintain optimal PAR levels and avoid shortages or excess inventory. Predictive analytics also enable proactive planning and resource allocation to meet future patient needs effectively.
  • Supply chain integration: AI facilitates seamless integration with suppliers and distributors to automate the replenishment process. By analyzing supplier performance, lead times, and pricing data, AI systems can optimize ordering schedules and quantities to enable timely replenishment of inventory items at the appropriate PAR levels. This integration streamlines the procurement process and enhances supply chain efficiency.
  • Demand forecasting and inventory optimization: AI algorithms analyze diverse data sources, including patient demographics, treatment protocols, and historical usage patterns, to forecast demand for specific medical supplies accurately. By aligning inventory levels with anticipated demand, AI-driven PAR level management systems minimize excess inventory, while helping ensure that critical supplies are readily available when needed.
diagram of OCI workflow
OCI’s solutions for data deployments with AI

The OCI solution for inventory optimization can be achieved in 2 different ways using various A.I and Machine learning services. Please Refer to Diagrams below for 2 recommended approaches for the solution.

1) OCI Machine Learning process flow within Autonomous Database

Oracle Machine Learning enables you to solve key enterprise business problems and accelerates the development and deployment of data science and machine learning (ML)-based solutions. Benefit from scalable, automated, and secure machine learning to meet the challenges of data exploration and preparation, as well as model building, evaluation, and deployment. Whether your interests include APIs for SQL, Python, R, or REST, or you prefer no-code user interfaces, Oracle provides support for solution development and deployment.

An example architecture diagram for a solution on OCI
An example architecture diagram for a solution on OCI

2) OCI Data Science process flow with Oracle Accelerated Data Science (ADS) software developer kit (SDK).

OCI Data Science is a fully managed platform for teams of data scientists to build, train, deploy, and manage ML models using Python and open source tools. Data Science integrates with the rest of the OCI stack, including Oracle Functions, Data Flow, Autonomous Data Warehouse, and Object Storage. Oracle Accelerated Data Science (ADS) software developer kit (SDK) is a Python library that's included as part of the Data Science service, which has many functions and objects that automate or simplify the steps in the data science workflow, including connecting to data, exploring, and visualizing data, training a model with AutoML, evaluating models, and explaining models. ADS also provides a simple interface to access the Data Science service model catalog and other OCI services, including Object Storage.

An example architecture diagram for a solution on OCI.

 

Conclusion

Overall, AI-driven PAR level medical inventory management systems offer healthcare facilities the ability to optimize inventory levels, improve supply chain efficiency, and enhance patient care outcomes through proactive and data-driven decision-making. AI enhances PAR level management in healthcare by optimizing inventory levels, improving supply chain efficiency, mitigating risks, and enabling proactive decision-making based on real-time data and predictive analytics.

With Oracle SaaS cloud business applications and Cerner EMR and EHR and inventory Management system like PeopleSoft as data sources, customers can extract and aggregate data to predict optimal inventory level that helps reduce costs and improve patient care outcomes.

To try any of the technologies we’ve mentioned, you can evaluate Oracle Cloud Infrastructure today for free with no commitment.

For more information, see the following resources:

 

 

Vaijayanti Joshi

Master Principal Cloud Architect

Vaijayanti Joshi is a Boston-based Enterprise cloud  Architect for Oracle. She is passionate about technology and enjoys helping customers find innovative solutions to complex business challenges. Her core areas of focus are  healthcare, Machine Learning, and Analytics. When she’s not working with customers on their journey to the cloud, she enjoys biking, swimming, and exploring new places.

Sudipta Kumar Panigrahi

Principal cloud Architect

Sudipta is Principal Cloud Architect based in India with 14 years of Expirience in Oracle Technology


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