March 4, 2024

Weekly Business Insights 03/04/2024

Generative AI: 20 Ways It Can Boost A Company’s Bottom Line. Managing healthcare supply chain through artificial intelligence (AI). The Application of Artificial Intelligence in Health Care Resource Allocation Before and During the COVID-19 Pandemic.


Generative AI: 20 Ways It Can Boost A Company’s Bottom Line

Generative artificial intelligence’s ability to create content, art and even code has opened up a world of opportunities for businesses seeking to leverage the technology’s potential. From startups to industry giants, organizations are exploring innovative ways to monetize generative AI, both in terms of creating public-facing content and lowering overall production costs.

As the adoption of generative AI accelerates, businesses are using it as a resource to learn, predict and make decisions without requiring explicit programming. For example, these algorithms are helping retailers improve automation by analyzing and processing large and tedious amounts of data to identify patterns and make informed predictions or decisions based on this information. 

Generative AI isn’t a panacea, but it’s exceptionally good at generating context with limited prompts. We’re seeing huge benefits for companies that use it to automate the process of managing metadata in their data catalogs. The previously onerous task of adding manual context and metadata to a dataset can be neutralized immediately, giving data teams more time to deliver value.

The world of industrial AI is full of messy, missing and misleading data. Generative AI can help create synthetic data that can be used as a proxy for research and machine learning training. Data privacy issues are also becoming increasingly stifling, and synthetic data generated by generative AI can help in that area.

Managing healthcare supply chain through artificial intelligence (AI): A study of critical success factors

A recent report by Grand View (2020) estimates that the global AI market will grow at a CAGR of 57 percent between 2017 and 2025. There is also a surge in the academic literature on the potential of AI across different subjects, healthcare supply chain being one of them.AI technology is being used for different therapeutic and research purposes in healthcare, and a few include managing chronic disease and drug discovery.

In the context of HSC, AI-based HSC can contribute to product delivery, tracking, inventory sharing, and resource pooling among stakeholders. It can help validate product legitimacy, track counterfeit products, and authenticate medical devices.

Studies have also discussed the immense potential of AI in verifying and setting producers’ price eligibility and improving healthcare data management. In their recent study, deliberated the applications of AI-enhanced medical in HSCs.

In a nutshell, by integrating disparate procurement, clinical systems, and financial administration throughout the HSC arrangement, AI-based HSC offers a practical solution to the challenges currently experienced by the HSC industry, have found AI capable of creating supply chain surplus in healthcare via improvements in operational efficiency and patient quality.

An adequate supply chain reduces cost and risk, improves quality, speed, and dependability, ensures flexibility, and enhances sustainability. Although attaining an effective supply chain remains a challenge across industries and businesses, the involvement of patient safety and related health outcomes generate greater complexity in the healthcare supply chain (HSC), which gets aggravated by the presence of multiple participants falling under different jurisdictions. Operational issues like lead time and disruptions like disasters and strikes pose risks to the supply chain. Besides these, issues on service interruptions, shorter product life, and climate variability also challenge supply chain efficiency.

The Application of Artificial Intelligence in Health Care Resource Allocation Before and During the COVID-19 Pandemic: Scoping Review

This scoping review synthesized evidence on AI in health resource distribution, focusing on the COVID-19 pandemic. The results suggest that the application of AI has the potential to improve efficacy in resource distribution, especially during emergencies. Efficient data sharing and collecting structures are needed to make reliable and evidence-based decisions. Health inequality, distributive justice, and transparency must be considered when deploying AI models in real-world situations. In the health care system, resource distribution is an essential issue for policymakers, as resources are always scarce.

Health care resource distribution is determined by the supply-demand relationship, logistics, and governance structure. Using theCOVID-19 response as an example, the severity of the pandemic can determine the health care resources required in each location, but the resources might not be distributed according to need. AI can be applied to study supply-demand, logistics, and patient characteristics, but the ethics and implications of the use of AI in policymaking remain important issues.

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