Until recently, healthcare, pharmaceutical, and medical-technology companies have focused the bulk of their AI investments on clinical uses. AI has been employed to help diagnose breast cancer, identify potential patients for clinical trials, and accelerate the processes used to determine whether a device or a new drug has the potential to work or fail.
Now, medical centers are investing in AI for backend uses, such as speeding up contract generation, management, and review.
To do so effectively, companies should structure their data for AI-powered analysis, said David Gould, the chief customer officer at EncompaaS, a technology company that helps organizations prepare their data for implementing AI tools. Company leaders should also recognize that adopting AI is a continuous process that requires investing in employee upskilling, healthcare supply chain experts told Business Insider.
Prepare data for effective and accurate algorithm input
Creating uniform data sets is an essential step for any organization seeking to build a large language model or deploy an effective chatbot, Gould said.
He said that the data that’s used to train any given LLM has to be accurately classified or organized into a database.
“Structured data, by its nature, is classified,” Gould said. This includes information that’s already in a healthcare system’s database, like customer ID numbers, diagnostic codes, and prices for supplies.
But unstructured data — like a trove of PDFs outlining contracts, for instance — also needs to be properly prepared so that an AI algorithm knows which information to extract and what to do with it.
Because contracts from different vendors aren’t written with a uniform structure, a person has to train machine-learning AI to look for specific things if they want to extract necessary data from a specific table, for instance, said Gould.
Imagine a PDF that’s a contract. AI software “can only tell you that it’s a document — it doesn’t tell you what that context is, whether it’s a contract, an amendment, or a notice,” Gould told BI.
“The algorithm only works well if the data matches what the algorithm is looking for,” he said. “Saying, ‘OK, chatbot, look all across my enterprise for information and see what you can find’ is not effective, because the processing cost is going to become very, very expensive. And that’s where you get drift and bias, because the data is not really ready.”
Preparing data may also mean reckoning with historical information that wasn’t properly classified or recorded, and ensuring that it’s updated with the correct metadata for AI-based analysis. That can be time-consuming, Gould said.
First, data needs to be classified correctly and also within the appropriate context. That might mean identifying and fixing documents’ previously mistaken tags. The data also needs to be stored in a way that maintains compliance with privacy and other safety regulations, like HIPAA, which protects patient privacy.
Consider the impacts on personnel
Automating laborious manual tasks can be transformative, particularly for roles in procurement and compliance, said Matt Parker and Jacob Thompson of SpendMend, an AI tool for pharmacy procurement, at a recent webinar on AI integration.
“In the world of pharma and healthcare, the people that are being asked to do this work are highly educated, expensive, and ambitious,” said Gould. Yet they have historically been tasked with “cutting and pasting of spreadsheet contracts to spreadsheets” for many hours a day. By automating these tasks, AI implementation can cut down on hours of work.
Still, some employees may need retraining or additional training because AI often changes job functions, Jeremy Strong, the vice president of supply chain at Rush University Medical Center, told BI. Ensuring there’s a plan in place to address AI upskilling and acknowledging the significance of changes to employees’ job functions can help to manage the transition, Strong said.
The better employees get at asking precise questions, the more AI algorithms can improve at providing accurate answers, said Gould.
He gave the example of figuring out how many contracts with a specific type of clause will expire in 30 days. Typically, this process would take weeks, if not months, with a records manager or an entire records department looking through thousands of contracts. But with AI, an employee can learn how to ask a powerful and precise question that captures this information more quickly.
