How xetova is Using AI to Strengthen Healthcare Systems


AHSA® - October 7, 2020 - 0 comments

If you’ve heard about Artificial Intelligence (AI) and are still wondering what it really means, you are not alone. AI is when computer systems have the ability to think and act like human beings with a whole lot more accuracy, flexibility and productivity.

There is a lot of information flowing around demonizing AI stating that it is a replacement of human labor and that companies are purely overstating their abilities to use it. Despite what is being said, both positive and negative, we should not underestimate the power of AI in healthcare, especially in relation to transforming healthcare supply chains, managing existing disease burdens, fighting COVID-19 and preparing for future pandemics.

Transforming Supply Chain Management with ML

Machine Learning (ML) is a section of AI that allows computers to consume information, interpret it and apply it without any human intervention. In areas where data-based solutions are needed, ML processes the information and extracts patterns that provide useful insights.

At xetova, we are using and Machine Learning in our digital procurement platform which helps healthcare organizations tap into the power of AI through solutions that facilitate efficiency, collaboration inclusion and investment in Africa.

In light of COVID-19, xetova developed an AI driven COVID-19 response management platform with powerful tools important in the journey towards building health security and achieving Universal Health Coverage (UHC).

We use statistical time series forecasting models and recurrent neural networks to forecast product demand, receiving client requests and tracking bids and orders.

What Healthcare Supply Chain Challenges are we Solving?

    1. Demand Forecast of Essential Healthcare Products: Through Machine Learning, we combine data sourced from various healthcare stakeholders to predict the demand of essential healthcare products them helps in managing the sourcing of products.
    1. Transportation Management and Timely Delivery of Products: Healthcare supply chain efficiency is largely dependent on timely delivery of products. Through machine learning, we assess buyer requirements then ensure products are delivered on time based on market demand.
    1. Inventory Management through Stock Level Analysis: Operational efficiency is very important especially during purchase orders. We use stock level analysis to identify products that are no longer useful in the market place. This helps in preventing overstocking of items that are not needed.
    1. Price Analysis: We compare costs in the supply chain and retail profit margins to establish the best combination of pricing and customer demand.

Benefits of integrating ML into Supply Chains

Machine Learning in supply chain management helps organizations operate more efficiently in many ways through;

    • Automating tedious tasks leaving more time to focus on strategic business activities.
    • Optimizing product flow without overstocking.
    • Reducing operational costs by improving quality and reducing waste.
    • Improving consistency in supplier relationship management through simpler and faster administrative methods.
    • Deriving actionable insights for quick decision making.

Artificial Intelligence is already changing the future of healthcare. Our aim is to add value to national governments and healthcare stakeholders focused on providing reliable, affordable and good quality healthcare products through supply chain solutions that will build health security in Kenya and beyond.

Related posts

Post a Comment

Your email address will not be published. Required fields are marked *