Identifying Gaps in Literature: Future Research Directions on AI Adoption in Supply Chains and Its Outcomes

Over the last ten years, many people have shown interest in using AI to improve supply chain management (SCM). Supply chains cover many steps — from buying and choosing suppliers, to managing inventory, and delivering products. AI can change these steps by offering predictions, doing routine jobs automatically, and helping make decisions quickly. This is very important in healthcare where getting supplies like medicines and devices on time can affect patient care.

A review of research papers from 2012 to 2023 shows that AI’s role in supply chains is growing. Researchers use models like MACO (Motivation, Application, Capability, Outcome) to explain why organizations start using AI, how they use it, what technology they need, and what results they get. AI helps healthcare supply chains by reducing waste, managing inventory better, and reacting faster to problems like shortages or sudden high demand.

Even with these improvements, the use of AI in healthcare supply chains is still new. Many studies say more research is needed to understand how healthcare groups can use AI well. This is important for doctors, administrators, and IT managers who handle complicated buying processes and supply risks.

Key Motivations and Outcomes of AI Adoption in Supply Chains

Research divides the main reasons for using AI into three groups: economic, technological, and social. Economically, organizations want to cut costs and work better. Technologically, new tools like machine learning and data analysis make AI easier and more reliable to use. Socially, people want supply chains to be open and more sustainable.

The effects of using AI include saving money, competing better, and making organizations stronger. This helps with new ideas and better operations, which matter a lot in healthcare where supply chains affect how well services run. In the US, AI helps keep supplies steady by predicting shortages and helping find other sources.

But, healthcare supply chains are complicated by rules and safety needs. Generic AI supply chain models may not solve all these problems. More research is needed on how AI can help US healthcare meet rules from groups like the FDA and stay safe while also making supply chains stronger.

Gaps in Current Literature Relevant to US Healthcare Supply Chains

• Lack of Healthcare-Specific AI Supply Chain Models
  Most studies look at AI in factories or stores where rules and safety are very different from healthcare. There is little research on how AI can help with medical supply buying, managing, or risk with rules like FDA guidelines in mind.

• Long-Term Outcomes and Impact Assessment
  Many studies focus on short-term benefits like saving money or working better. Few studies look at how AI affects supply chain strength and healthcare service over many years in the US.

• Integration Challenges and Organizational Factors
  The research talks a lot about technology but not much about company culture, staff readiness, and how to handle change, which are very important in healthcare. More study is needed on how administrators and IT managers can get their groups ready for AI.

• Ethical and Transparency Concerns
  AI raises questions about decision-making openness, responsibility, and patient data privacy, especially in buying and supply decisions. Research should create rules to make sure AI is used responsibly in healthcare supply chains.

• Scalability of AI Solutions in Varied Healthcare Settings
  There is not much knowledge about how AI tools can be used in different sizes and types of US healthcare groups, from small clinics to big hospitals. This makes it hard to create solutions that balance cost, difficulty, and usefulness.

• Interplay with Other Emerging Technologies
  AI often works together with other technologies like blockchain for tracking or IoT for real-time monitoring. Research on how these combined tools affect medical supply chains in US healthcare is still limited.

• Risk Management and Supply Chain Resilience
  AI helps find and reduce supply risks, but studies say there is a need for new risk management plans that use AI better in healthcare. This became important during supply problems like those seen in the COVID-19 pandemic.

AI and Workflow Automation in Healthcare Supply Chains

Automation is a key part of AI adoption and directly helps medical administrators and IT managers. Using AI to automate supply chain tasks can lower paperwork, improve accuracy, and speed up responses.

• Automated Procurement Processes: AI systems can pick suppliers by checking cost, delivery time, and past compliance. This saves time and reduces mistakes in ordering.

• Inventory Management: AI predicts demand using past data, patient info, and seasonal changes. This helps healthcare providers keep just the right amount of stock without too much or too little.

• Order Tracking and Supplier Communication: AI chatbots or assistants can handle supplier messages, order confirmations, and track problems without needing people. This lets staff focus on more important jobs.

• Risk Alerts and Real-Time Adjustments: AI watches for delivery delays or quality issues and can quickly change plans, like switching suppliers or speeding shipments.

In healthcare, these automated systems can stop delays that hurt patient care, reduce staff work, and make supply chains easier to see and manage.

Implications for US Medical Practice Administrators and IT Managers

Medical administrators and IT managers in the US need to think carefully about how to adopt AI. Research suggests taking a careful approach to find which supply chain parts benefit from AI and where people should still watch closely.

• Organizations should check if their technology and staff are ready to avoid problems in adoption.

• Plans must include following healthcare rules and protecting patient data.

• Trying out AI tools in small projects for buying or inventory can give useful experience before big use.

• Teams from clinical, admin, and IT should work together to make sure AI helps patient care.

• Buying teams should look for AI systems that are open in how they make decisions and include ethical protections.

Future Research Directions Specific to US Healthcare

• Build AI supply chain models made for healthcare that include rules and clinical needs.

• Do long-term studies to see how AI affects supply resilience and healthcare results.

• Study ways to manage organizational changes to help use AI in healthcare places.

• Look at ethical rules and oversight to keep AI use responsible.

• Find AI answers that can work in many healthcare types, including rural and less served areas.

• Check how AI works with other tech like IoT and blockchain in healthcare supply chains.

• Create risk management systems that use AI to spot and handle supply problems early.

By working on these research topics, people can better understand what AI can and cannot do in US healthcare supply chains.

Key Insights

Using AI in healthcare supply chains in the United States can bring benefits but also has challenges and unanswered questions. The gaps in research show a need for studies focused on healthcare-specific models, long-term effects, organizational issues, ethics, scalability, and combined technology solutions. For medical administrators, owners, and IT managers, understanding these points is important to make good choices about AI investments that help keep supply chains strong, efficient, and compliant.