Source: Emerj | March 26, 2019
Author: Niccolo Mejia
Although one might assume healthcare companies are investing the most amount of money into AI for R&D efforts, such as clinical trials, drug discovery, and surgical robotics, the top healthcare companies don’t seem to be publicizing their efforts if they are.
These could be for a variety of reasons, as we outline in our article How to Discover the AI Initiatives of Fortune 500 Companies, but we might assume it’s to prevent their competitors from garnering competitive intelligence on those companies.
Instead, it’s much more common to see healthcare companies advertising their AI initiatives for white-collar automation and business intelligence, and that’s certainly the case for McKesson, UnitedHealth Group, and Cardinal: three of the largest healthcare companies in the world.
In this article, business leaders can learn about the current AI initiatives and projects at these three top healthcare companies. We list some of their initiatives below:
- McKesson partnered with Genpact to allow for internal machine learning-based big data analytics and acquired Change Healthcare to offer healthcare companies white-collar automation such as contract management, order to cash, source to pay, and more.
- UnitedHealth Group’s Optum subsidiary offers the OptumIQ predictive analytics platform to healthcare companies with the promise of helping them forecast sales, market demand, and pricing accuracy.
- Cardinal Health offers predictive analytics for monitoring operations, estimating the resources patients might use and insurance claims management through its Vitalsource GPO service.
Our overview starts with McKesson and their AI initiative with Genpact for big data analytics.
McKesson is a world leader in healthcare supply chain management solutions, health information technology, and oncology. The company is currently ranked sixth place in the Fortune 500.
Partnership with Genpact: White-Collar Automation
They have been improving their data management and analytics systems using machine learning since 2016 and partnered with Genpact to optimize numerous processes within their business, such as:
- Order to cash: customer sales orders for goods and services and payments for those orders, such as prescription drugs for patients or medical tools for hospitals.
- Record to report: collecting and delivering relevant and timely information. This could include telepathology and teleradiology information or a lab test where results may be communicated.
- Contract management: this could include automated reminders when contracts with supply companies or pharmaceutical companies could end. Analytics systems may also consider active contracts when recommending the best next step for the business leader to take.
- Master data management: the management of the company’s big data and how data is stored within the company. In healthcare, this mainly refers to patient EHR data that would be held in the client company’s database.
- Source to pay: the process of managing company spending and where funds can or should be allocated. This is essentially using predictive analytics to budget while considering patient data and possibly discerning future resource utilization by those patients.
Acquisition of Change Healthcare: Claims Management and Predictive Analytics
McKesson also acquired the AI firm Change Healthcare, which seems to have given them access to predictive analytics solutions for value-based care and reimbursement, or those services priced according to the value received by the patient. Change Healthcare’s machine learning-based analytics software could also assist McKesson with ensuring payment accuracy and improving revenue by reducing missed payments and chargebacks.
Claims Management Software
Most recently, Change Healthcare announced a solution called Claims Lifecycle Artificial Intelligence. The company added the software to its Intelligent Healthcare Network and its financial solutions in order to optimize claims processing for providers and payers. They claim the solution is made for optimizing the entire lifecycle of a claim, from when the claim is first filed to when it is paid or rejected.
According to the infographic from McKesson below, the software can detect claims that will be denied during the claims’ submission. The machine learning model would compare the claim to past accepted claims to verify its accuracy: