How Automation Can Help with Healthcare’s “Messy” Data Problem

But most of the data that the industry is processing is human-generated, meaning it’s messy and riddled with errors, and often manually inputted from Excel spreadsheets into various disparate platforms and technology systems. It’s extremely challenging to efficiently streamline the processing of healthcare’s messy, inconsistent data, which is why we’ve seen that it’s been so hard to pull insights from healthcare’s stores of data, not to mention solve the industry’s interoperability problems. Fortunately, new approaches to automation that are specifically designed to work with healthcare’s unique “messy” data, can help cut down on superfluous administrative costs and wasted time, and lead to more efficient care, fewer errors, and compliance with new legislation—like the No Surprises Act, which went into effect on January 1, 2022 and requires plans to make provider roster data updates in 48 hours. If we’re ever going to tackle the challenges that plague the industry, healthcare leaders need to look to this new era of automation tools that are purpose-built to learn and understand health data problems. Here are three considerations for assessing the ability of a given automation solution to efficiently deal with a health plan’s “messy” data: What is required upfront to power these tools is the codification of the decision-making processes in place at the plan under the current manual data entry infrastructure.