The crucial role of data quality in healthcare

At Digital Health Rewired24, experts emphasised the critical importance of data quality as artificial intelligence (AI) plays an increasingly vital role in healthcare. This intersection between data quality and AI integration is central for practice managers, influencing patient care delivery and organisational efficiency

CREDIT: This is an edited version of an article that originally appeared on Digital Health

“Analytics is all about asking questions. In order to ask and answer questions, the analysts and those taking action need a stable source of data,” Joanna Peller, Palantir health lead at Palantir Technologies told the audience on the AI, Data and Analytics stage Tuesday.

In November, Palantir won the contract to operate the NHS’s new Federated Data Platform. Peller noted that the FDP would provide an infrastructure for deploying data models but doesn’t provide the models themselves. “But if the data is bad, the data is bad, and that is where the organisations need to start looking at accuracy,” she added.

Peller was joined by Ming Tang, chief data and analytics officer at NHS England, Bruno Botelho, deputy COO and director of digital operations and innovation at Chelsea and Westminster Hospital NHS Foundation Trust and Wes Baker, director of strategic analytics, economics, and population health management at Mersey Care NHS Foundation Trust.

“All of you are sitting on a lot of data and you need to get your teams to start thinking about the boring stuff, the timeliness and completeness of the data,” Tang told the session.

Botelho addressed the audience against a backdrop of a nearly 30-minute video showing a junior doctor writing a patient summary, a task that is one of the many that Chelsea and Westminster have been using AI to accelerate.

“We are working with the junior doctors on how to leverage that tool and create a space where the information is available and pre-populated based on the inpatient population,” he said. “Through LLMs (large language models) or AI we can consolidate the information and make it available to physicians in a few seconds, with a letter generated allowing the doctor to just proofread, make more concise, rewrite or add information,” he said.

Part of embedding AI within systems will mean ensuring that the health workforce feels comfortable using it, speakers concluded. In particular, this will require eschewing the blame culture that is found in parts of the health service and accepting risk and failure.

“If mistakes happen it’s about learning from those mistakes,” Baker said. “Culture will be really important as you are embedding AI tools.”

As AI becomes increasingly integrated into healthcare systems, prioritising data quality, embracing cultural shifts, and ensuring workforce comfort with AI tools will be paramount for successful implementation and transformation.