Risk management
AI can help health care providers effectively manage risk and build resiliency for specific geographies or groups of providers, and proactively alert and course-correct for disruptions automatically. The technology can analyze current and historical data involving inventory levels, patient case levels, geopolitical events and weather, for example.
Some organizations rely on prepopulated dashboards fed by this data, and alerts and course corrections for disruptions can be triggered automatically. GenAI can be queried to produce risk assessments, scenario simulations and mitigation strategies on demand — in response to shortages or widespread calamities, such as another pandemic — to help planners manage and mitigate the risks proactively.
Sourcing
The health care industry has more sophisticated IT systems today, with vast amounts of data. As mentioned, being able to accurately analyze data around cost, quality and outcomes can help physicians determine the best supplies or drugs to use depending on patient needs.
By considering these factors as well as supplier performance and risk profiles, GenAI algorithms can provide recommendations or rankings for making informed decisions for a health care system’s sourcing strategy. Furthermore, supply chain functions can use this information to facilitate product standardization and utilization efforts among categories of supplies.
Equipment optimization and inventory management
US providers struggle to effectively optimize how well costly diagnostic or treatment equipment — such as MRIs, CTs, PET scans and proton therapy — is used across a particular market, geography or even within a health system. Providers can take a page from the playbook of manufacturers to leverage AI to get the most out of their equipment. GenAI could more effectively match supply with demand and automatically schedule patients to optimize the utilization of these limited resources.
And in the operating room, GenAI can help revolutionize how preference cards — used by physicians to note which supplies and instruments are needed for a surgery — are managed. Instead, imagine a system in which GenAI makes recommendations for preference card updates based on historic usage patterns, the patient profile/demographic, case types, physician and forecast – and then automatically updates the preference cards based on the approvals from reviewers. In the process, a hospital optimizes inventory and ensures that all supplies are ready and on time for every case.
Distribution and logistics
GenAI can continually update and optimize delivery or pickup routes based on changing factors like traffic conditions, weather and the priority of deliveries. This could be a boon for the US health care sector, which is responsible for about 5% of national greenhouse emissions¹, partly because of its transportation footprint within the supply chain, including the driving by patients and employees. AI can also play a role in more effectively routing patients to the most appropriate and closest care setting, which would be a win-win for the patients and the providers.
And thanks to advances in telemedicine adoption during the pandemic, that setting may be in the patient’s home — however, many providers lack the proper infrastructure. They may struggle to determine how to get the right supplies and caregivers to the patient, as well as fulfill the reverse logistics for lab samples and returned equipment. GenAI can be leveraged to recommend logistics partners and create routes based on time, criticality, distance, cost and other variables.
Get started today
While GenAI is a powerful tool with certain limitations, it is not a strategy. Focus on the business value and define a roadmap to shape and impact the organization, guided by three steps:
1. Assess data quality, integrity and management.
Supply chain has a lot of systems in play without much standardization. But data is the core of a successful GenAI platform, and many health care organizations are relatively immature in these capabilities while also facing greater regulation than many other sectors, making strong governance a high priority. How are you cleansing, synthesizing and maintaining data? Do you have the data integrity and common structure to build advanced analytics on that data?
2. Create a matrix of possibilities.
Proofs-of-concept can focus on use cases with low complexity but high value, deployed in an iterative and agile manner. Narrowly tailor these quick wins with an understanding that they require experimentation and won’t be perfect right from the start. However, know that the truly transformative power of GenAI can typically be unlocked when you design processes around it, not vice versa.
3. Build out the infrastructure with your people in mind.
In parallel with your proofs-of-concept and quick wins, work toward a more strategic view, building the capabilities that can be shared across the organization. Drive training and development on GenAI with strong change management, so that your staff and caregivers understand that the technology is augmenting, not replacing, their jobs.
