Hospitals need blockchain for supply chain automation
“A supply chain is the network of all the individuals, organizations, resources, activities and technology involved in the creation and sale of a product, from the delivery of source materials from the supplier to the manufacturer, through to its eventual delivery to the end user.”
At hospitals though, the customers aren’t really customers but patients and the sales people aren’t really sales people but doctors and nurses. The real product everybody wants at a hospital or clinic is their own improved health, a satisfying experience and affordable pricing.
An injured patient in a hospital emergency room or a sick child at their local urgent care clinic isn’t thinking about a doctor or nurses shift cycles or lack of sleep or preferences in the equipment and drugs all around them. A child might think some cutting edge new lamp is cool, but if it’s not a satisfying experience or the hospital staff weren’t friendly, it’ won’t matter.
How can all these inventory and resource challenges make a difference in health outcomes, patient experiences and healthcare costs.
Hospitals don’t operate like Walmart or Amazon or Nike. Supply and demand means something entirely different. And the changing expectations and outside mobile influences of patients as consumers doesn’t always sit well with a highly regulated industry like healthcare.
Supply chain management, inventory management, medical device asset management has become a key security issue, optimization and efficiency issue, and a cost problem for hospitals.
Reimbursements and Costs
Supply chain management costs consistently rank as some of the largest expenses for healthcare and hospital systems. A recent survey of healthcare executives and administrators in the USA revealed that productivity improvements and healthcare supply chain cost reductions are their top priorities. Value-based reimbursement models are being implemented by many hospitals because of the Affordable Care Act built-in incentives for healthcare providers to coordinate patient care with a “pay-for-value” model, based on performance and results, rather than the traditional “pay-for-service,” where the cost of care was often paid upfront.
Many hospitals and healthcare leaders are looking at how they can provide an improved patient experience while reigning in costs.
And with this, many healthcare leaders are looking to the healthcare supply chain as one of the biggest areas for savings opportunities . Integrating blockchain for supply chain automation can help alleviate some security pressures, audit trail regulations, and ensure things like total landed supply costs—the total cost of getting a product from manufacturer to the hospital or physician’s office.
This is no easy task and a lot of “i trust this person but I don’t” makes this a perfect use case for blockchain. Supply chains go beyond the initial purchase price and include things like logistics costs, duty fees, taxes, insurance, and other fees.
A more holistic and end-to-end secure approach to the healthcare supply chain is not only about improving efficiency and reducing costs to the hospitals and healthcare systems, but about providing better healthcare in the least lead time and at lower costs to the patient.
Efficient & Secure Data Integration, Automation and Analytics
Integrating, aggregating , transforming, automating and and analyzing data along the supply chain trail can provide better indicators of product need, cost optimization strategies and helping to reduce waste, stabilize inventory and, ultimately, bring costs down.
Tracking and securing smart medical devices with IoT sensors mean technologies like Radio Frequency Identification (RFID) and computerized provider order entry (CPOE) systems integrated with AI and blockchain, can help automate the entire supply chain process.
Better data analysis that’s secure and automated data ensures improved efficiency levels, maximizing inventory costs and utilization, all of which can help reduce costs, an automated, technology-based supply chain can help improve patient outcomes by supporting a higher level of patient care.
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Data is the lifeblood of many organizations, but it can be hard to get your hands on when data protection protocols are in place. With synthetic data you needn’t worry about access time-consuming roadblocks because this new technology bypasses them entirely!
Consider one financial institution with rich streams containing strategic information for decision makers–it was so highly protected that gaining internal use rights was practically impossible without outside help from specialists who knew how break through these security measures
Synthetic data is the answer to all of your problems. It can protect you from a myriad security threats and vulnerabilities by eliminating any chance that hackers might have at accessing company or customer information without consent, while also maintaining privacy for everyone involved in its creation!
What is synthetic data ?
AI-based synthetic data has been used to bring statistical properties and patterns of real world events into a different context.
The goal is reproduce the probability distribution or sampling theorem, but this time with new variables that weren’t present in any previous datasets
Now you can train machine learning and computer vision models faster than ever! With synthetic data, companies are able to quickly create large sets of training examples. This is a huge advantage for those who need access and time on their hands – it’s like having your own personal data set ready at any moment.
The power to analyze large datasets with speed and accuracy, without the need for third party data sets that are prohibitively expensive.
Synthetic data gives companies access to mimicked real world synthetic datasets and images made from high-quality analyzed real world sources at a low cost – enabling them not only see how their business would perform but also make informed decisions about where it needs improvement or success!
Why is it so hard ?
While the benefits of synthetic data are compelling, realizing them can be difficult. Generating synthetic files requires an organization to do more than just plug in AI tools that analyze their own datasets; this complex process needs people with specialized skill sets and understanding about machine learning and deep learning algorithms who have advanced knowledge on how these technologies work together as well specific frameworks tailored for each task at hand.
What’s next ?
Synthetic data is a complex and often tricky area to work in. Organizations should be sure that the value will outweigh any drawbacks before getting involved with it, which can include pitfalls from doing so wrong or having incorrect assumptions when creating synthetic datasets for use within your company’s operations.
Data-driven leaders who want their organization to be successful should make it a priority and never abandon the idea of using data and analytics as an integral part in decision making.
Engineers at the University of Waterloo combined two existing deep-learning AI techniques to identify players by their sweater numbers with 90-per-cent accuracy.
“That is significant because the only major cue you have to identify a particular player in a hockey video is jersey number,” said Kanav Vats, a Ph.D. student in systems design engineering who led the project. “Players on a team otherwise appear very similar because of their helmets and uniforms.”
Player identification is one aspect of a complicated challenge as members of the Vision and Image Processing (VIP) Lab at Waterloo work with industry partner Stathletes Inc. on AI software to analyze player performance and produce other data-driven insights.
The researchers built a data set of more than 54,000 images from National Hockey League games—the largest data set of its kind—and used it to train AI algorithms to recognize sweater numbers in new images.
Accuracy was boosted by representing the number 12, for instance, as both a two-digit number and two single digits, 1 and 2, put together, an approach known in the field of AI as multi-task learning.
“Using different representations to teach the same thing can improve performance,” Vats said. “We combined a wholistic representation and a digit-wise representation with great results.”
The research team is also developing AI to track players in video, locate them on the ice and recognize what they are doing, such as taking a shot or checking an opposing player, for integration in a single system.
Check out some cool artificial intelligence companies in San Diego, including Pagarba Solutions.