Why data integrity is key to achieving value in healthcare

Value is extremely hard to define when it comes to healthcare, according to University of Washington computer science professor Ankur Teredesai, co-founder and chief technology officer of Seattle-based KenSci, a company that provides a risk prediction platform powered by artificial intelligence and machine learning. Teredesai, who co-chaired this year's Association for Computing Machinery's KDD Conference, held in August in Anchorage, says the entire premise of value in healthcare is based on the ability to measure performance metrics while simultaneously establishing baselines for reducing unwarranted variation. "Data is central to cost prediction and estimating unwanted variation," he adds, noting that eventually, providers will use data and AI-driven decision-making for optimizing schedules and assessing patient risk. At the KDD Conference, Teredesai encouraged healthcare communities to think beyond the size of data and focus on the complexity and integrity of data sources. "Advances along these lines will help bend the utilization curve much before we see doctors being replaced by AI," he says.