Like Amazon, the massive healthcare organization is deploying natural language processing across large swaths of its business. One of the biggest responsibilities of the organization is to authorize (or deny) payments for doctor-recommended medical procedures. This process of getting pre-approval can be costly, which is why Unitedhealthcare is using machine learning to streamline and automate as much of this process as possible. Unitedhealthcare has also turned to natural language processing to sort the more than one million calls they get to their customer service line each day. Their goal is to deliver a better customer experience to all of their 115 million customers.
McKesson's investment in AI is less about managing their patients and more about managing their business. For several years, McKesson has maintained a partnership with a global professional services firm focused on bringing about digital transformation. According to Emerj, this partnership was designed to bring smart contracts and Artificial Intelligence to several business processes, including:
• Customer payments, such as hospital stays and prescription drugs;
• Patient information gather, storage, and processing;
• Automated contract management;
• Vendor payments and reimbursements.
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AmerisourceBergen, the world’s most profitable pharmaceutical company, is bringing artificial intelligence to its benefit verification process. In 2018, the pharmaceutical conglomerate announced that one of their subsidiaries had launched an AI-powered electronic benefit verification solution that would leverage a dataset of “health coverage and payer data collected from millions of manual verifications” to evaluate each incoming benefit verification. Today, their system can predict outcomes and process data in real-time with only a tiny fraction getting forwarded to a human clinician for further investigation.
Cardinal Health recently released a platform designed to support oncology professionals by making available a robust set of AI-powered capabilities including:
1. Tools to help deliver coordinated, comprehensive, high-quality cancer care for patients in all treatment settings;
2. Actionable and effective insights that help patients become active participants in their treatment plan;
3. Resources to help develop palliative care plans that respect the values and desires of the patient and his or her family.
Additionally, the platform uses machine learning to identify patients at risk of 30-day mortality who would have been missed by conventional predictive analytic approaches. According to their self-published case study, the deployment of this platform led to an 80% increase in patients referred to palliative care and an increase in those getting flagged for depression.