By Christine Kern, contributing writer
Data standardization and optimization drives informed decision making and cost reduction.
Predictive analytics and the use of data standardization and optimization to drive better decision making and reduce costs are among the priorities of the Best 50 healthcare supply chain leaders for 2017, according to Global Healthcare Exchange (GHX) survey. The results further revealed top performing supply chain organizations will look to improve price accuracy between supplier and provider partners, as well as standardize business processes and data across the organization.
“Healthcare’s supply chain continues to be at a pivotal juncture, taking increased advantage of advanced technology to deliver affordable value-based care by accelerating efficiencies and improving access to quality data for decision making,” said Bruce Johnson, president and CEO of GHX. “Improving operational performance and driving down costs through supply chain automation is one of our top priorities. The focus today on accurate data is central to our mission. The linkage between data on products used in patient care as recorded in electronic health records with accurate and comprehensive information about those products in item masters is key to solving the cost/quality equation.”
The following areas were identified as top healthcare supply chain initiatives planned for 2017:
And according to Eugene Schneller, professor at the W.P. Carey School of Business at Arizona State University, predictive analytics is a key component to monitor for supply chain improvement. He told Health Facilities Magazine the development of new tools that allow providers to predict which patients with require more intense interventions than others is driving the shift from demand forecast to predictive analytics in supply chain.
Predictive analytics in the supply chain is coming, says Jean-Claude Saghbini, vice president of inventory management solutions for Cardinal Health. “We are collecting massive amounts of data and we are looking at creating more predictive algorithms from that data,” he said. “Rather than analytics being retrospective, we are trying to infer what will happen in the future.”