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AI in healthcare: How intelligent is your healthcare supply chain?

Brain and data chip

When you think of artificial intelligence in healthcare, you may imagine computers generating medical diagnoses in milliseconds, or wearables alerting you to health concerns based on real-time data.

While these uses are real, perhaps less evident is the growing impact of artificial intelligence on the operation of the healthcare supply chain.

Although supply chain isn’t always as headline-grabbing as algorithms beating out physicians, when it comes to shaping the everyday provision of healthcare, it’s crucial. Harnessing and analyzing data in the pursuit of supply chain optimization and improved patient outcomes may prove the biggest game-changer of all.

The supply chain benefits of capturing healthcare data

A healthcare supply chain is complex, and it's not just the regulatory and compliance issues. It’s about meeting the practical needs of patients at a time of emotional and physical stress.

"With a hospital, you're not only having to deal with patients in terms of their healthcare, but the other side of the supply chain, related to people having to stay overnight in a hospital," explains Brian Carrier, vice president of supply chain innovation for UPS’s global logistics division.

That’s why the more data is captured across the supply chain, the better the service delivery to the patient—whether the item in question is a medical kit or an evening meal.

Improvements in healthcare data capture

Before we can think about using data, we need to collect it. The challenge is capturing all the data that’s flowing through the healthcare system at any given time.

Ongoing improvements in the deployment of technologies are proving fruitful. From RFID and bar code scanners to tablets and wearables, these physical devices enable increasingly robust data collection throughout the supply chain. AI and analytics can then be applied to its real-time movement.

"As products and services are being consumed, that information can come back into an inventory planning system that understands not only consumption trends but enables items to be immediately replenished,” says Carrier. 

This facilitates the move toward a more predictive healthcare supply chain.

Applying data-driven insights to hospital inventory management

Anticipating replenishment triggers can save the supply chain a significant amount of time.

With traditional warehousing distribution, replenishment may take days or weeks. By prompting the movement of goods, including cold chain products, from centralized distribution centers to forward-stocking locations closer to the point of actual need, the replenishment of stock can be expedited.

It’s not just about understanding when you need to restock; it’s about knowing what you already have in stock and identifying areas where you may be holding excess inventory.

Hospitals have historically faced an uphill challenge when it comes to inventory management. According to the 2017 Cardinal Health Hospital Supply Chain Survey, almost four in five respondents were manually counting inventory somewhere in their supply chain. Notably, fewer than one in five respondents (17%) said their hospital had an automated system in place for real-time inventory tracking.

The situation is improving. Industry data points to increased adoption of AI in inventory management driven by a growing appetite among healthcare leaders for technology-driven solutions. According to 2018 research from Nuance Communications, the vast majority (90%) of healthcare CIOs are interested in deploying AI in their organization and almost two-thirds (64%) already have active AI deployments or are planning them.

Applying AI-driven data analysis to hospital inventory management remains an area ripe for development. While the challenge of tracking inventory flows across a hospital’s supply chain is tangible, the opportunity for securing operational efficiencies and improving patient experience is equally real.

Improving data governance in healthcare

Whatever healthcare sector you’re in, the raw data created by your organization can emerge unstructured, unruly, and—left to its own devices—incapable of organizing itself. With a staggering 175 zettabytes of data projected to be generated by 2025 according to research from Seagate and IDC, getting data governance right is a challenge.

The reality is that for data to be actionable at scale, it needs careful management. And, when it comes to healthcare, industry experts insist it will take significant work to get the industry’s data into better shape.

Speaking at the UPS 2019 US Healthcare Forum, Dr. Robert Handfield, professor of supply chain management at North Carolina State University, stressed challenges in the handling of healthcare data among manufacturers, distributors, healthcare providers, patients, and payers. “Generally, the systems are pretty broken,” Handfield stated. “If your data isn’t good, then all the algorithms you use on that data won’t produce a very useful outcome.”

Even here, artificial intelligence and machine-based learning can offer solutions, according to Handfield. AI technologies can help a healthcare organization develop data taxonomies that produce a cleansed and structured database that operates with other players in the supply chain, he said.

Striking the balance on healthcare data sharing

Having the data in good enough condition for sharing is key, but it cannot be shared without conditions. 

Ensuring compliance with the significant data privacy provisions of HIPAA legislation is a long-standing concern for the healthcare sector. "As we continue to capture data throughout the healthcare supply chain, there have to be strong data governance policies so we're not sharing information that shouldn't be shared," Carrier says.

There’s also sensitivity around the potential for inadvertently sharing data with competitors. This is a double-edged sword: companies must be savvy about who they’re sharing information with, but they shouldn’t be resistant to the idea of sharing itself. For AI-driven healthcare to establish a meaningful foothold, organizations need to develop more—not fewer—data-sharing partnerships within the supply chain, Carrier argues.

If you need any convincing, look no further than the continued efforts of technology powerhouses such as Google, Amazon, and Apple to disrupt the healthcare industry through data-driven approaches. These industry titans are masters at collecting data, organizing it intelligently, and analyzing it at scale: they intuitively understand that the more data they capture, the more opportunity they have to structure that data and generate insights through AI. 

Consider Google’s recent partnership with the Mayo Clinic. Designed to drive efficiencies in the Mayo Clinic’s supply chain and support the delivery of home healthcare monitoring, the 10-year strategic partnership between the two organizations places cloud-based analysis of data at its heart.

Applying AI predictively in the healthcare supply chain 

The future belongs to those who predict it.

A primary use for artificial intelligence in healthcare rests on capturing real-time supply chain data, leveraging that information to develop insights to predict what may happen in the future, and then prescribing solutions for those expected events.

Predictive analytics may alert you when a medical vehicle in your fleet needs preventative maintenance. It can raise warnings of future material storages as well as weather-related or even political disruption.  Being able to pre-emptively respond to developments in the trade war between the United States and China is an example of how predictive visibility into supply chain disruption can help mitigate risk for healthcare suppliers.

Dr. Robert Handfield welcomes the use of predictive analytics in supply chain management. The more pre-emptive information an organization has about the impact of trade disruption in its supply chain, the better. “Do you start pulling shipments forward? Do you reroute again? Do you look at secondary sources of supply? In many cases, people are using insights from AI solutions to drive the difference between staying in markets or ultimately exiting in some cases,” he states.

From the delivery of temperature-sensitive prescription medication to the coordination of home healthcare services among providers, predictive analytics improves healthcare inventory management by anticipating exactly what product is needed, where, and when.  

Avoid “one-size-fits-all” when integrating artificial intelligence into healthcare

As defined by Gartner, artificial intelligence applies “advanced analytic and logic-based techniques to support and automate decisions, to offer interpretation of events, and to take actions.” 

That’s one big definition, by any measure, covering a wide range of applications dependent on business need.

However, what works for one company and one supply chain may not work for a similar business down the road. Customization will become central to the future of artificial intelligence in healthcare, argues Kristen Daihes, partner at AI consultancy Opex Analytics. Also speaking at the 2019 UPS® Healthcare Forum, Daihes said the need for company-specific AI is “completely changing the paradigm in terms of thinking about creating custom apps.” She predicts rapid growth in the number of custom AI solutions within healthcare. 

Advancing automation in healthcare

How do you take ordinary, repeatable tasks and automate them through software solutions?

Instances of automation in healthcare have been around for many decades. But the advent of big data is taking healthcare into a new era of computer-driven automation, with large sections of traditionally backroom and customer service functions in the crosshairs of change.  

In addition to customer service (think chatbots), this new wave of automation is disrupting areas such as route planning for vehicle navigation, medical delivery for specimen logistics, and control tower environments responsible for supply chain monitoring.

The influence of AI extends directly into the distribution center itself. By revealing a much more detailed understanding of how products are being ordered and used, AI enables healthcare logistics providers to get a step ahead. “It’s allowing us to do more kitting or prep work for items like surgical kits, so that some of the tasks being done downstream can actually be done in the warehouse environment," Brian Carrier says.

For Carrier, the key to successfully deploying AI in healthcare rests on a commitment to collaboration and knowledge share. “At UPS, we’re committed to delivering true supply chain optimization by offering a comprehensive and uniform solution that touches all aspects of the healthcare industry,” he says.

“At the same time, we know we can’t do this alone. That’s why we’re working closely with our technology and supply-side partners to ensure we’re introducing into our logistics network the most cutting-edge solutions available so that our customers and partners are able to fully benefit from everything that AI in healthcare has to offer.”

And don’t worry about being late to the game. According to a study of healthcare leaders conducted by GE Healthcare, two-thirds of organizations are only beginning to develop their analytics plans. 

AI in healthcare: A brave new world

Smartly done, the development of artificial intelligence in healthcare represents a win-win for everyone— driving supply chain efficiencies, lowering operating costs, and leading to improved patient outcomes.

Find out more about how UPS is helping build the future of healthcare logistics and how we can help you.

Find Out More

  • UPS Healthcare Shipping & Logistics

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  • UPS Supply Chain Solutions

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  • Integrate UPS Technology

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