Research: ‘Trending’ EHR Terms Could Help Hospitals Forecast Patient Surges

Similar to how social media posts can be tracked and aggregated by “hashtags,” detecting words or phrases that are trending in electronic health records (EHRs) can help clinical teams forecast and plan for surges in patients, according to new research.

For the study, published recently in Nature Digital Medicine, researchers from King's College London, King's College Hospital NHS Foundation Trust (KCH), and Guy's and St Thomas' Hospital NHS Foundation Trust (GSTT), used natural language processing (NLP) algorithms to translate the electronic notes made by doctors into a standardized, structured set of medical terms that could be analyzed by a computer.

Just like tracking and grouping social media posts by hashtags, during key stages of the COVID-19 pandemic last year, the researchers detected words or phrases that trended in patients’ medical records at KCH and GSTT. For instance, they tracked the number of patient records containing keywords for symptomatic COVID-19, such as “dry cough”, “fever” or “pneumonia.”

Throughout the pandemic, hospital doctors have entered patient symptoms and test results into EHRs, which are used to track the spread of COVID-19 at a national level, the researchers noted. However, these records often contain incomplete and unstructured data, that is difficult to access and analyze, they said.

By analyzing the text as a “bag of words,” the researchers were able to produce real-time maps of trending signals—symptoms that were most frequently recorded by doctors—and these signals closely mirrored patterns of positive laboratory tests reported by each hospital. Clear spikes were visible in March 2020, for instance, during the first wave of COVID-19 cases, and in subsequent waves, the researchers noted. For the data tracking and aggregating, they used CogStack, an information retrieval and extraction platform.

The study indicates that these signals provide a real-time situational report of reflecting current activity levels in a hospital and up to four days advance warning for hospitals helping them to prepare for surges in COVID-19 admissions, they concluded.

The study authors also reported a strong association between the trending signals and regional tracking of COVID-19 admissions in London hospitals. In addition, they found that as new COVID-19 symptoms emerged nationally, these symptoms were also recorded more frequently by doctors at KCH and GSTT.

 Dr. James Teo, clinical director of AI at King's College Hospital and Guy's and St Thomas' Hospital, said, "By teaching computers how to read and understand doctors' notes, we hope to reveal important patterns and trends that could help in the fight against COVID-19 and other diseases.”

He added, “Tracking word trends in electronic health records offers an additional method for studying disease and healthcare activity, in a way that is very easy and cost-effective to run. While this method was shown to be effective in two individual hospital Trusts, the approach could be scaled up to a regional or even national level with the right privacy safeguards.”