Large language models can effectively extract stroke and reperfusion audit data from medical free-text discharge summaries.

INTRODUCTION: Audits are an integral part of effective modern healthcare. The collection of data for audits can be resource intensive. Large language models (LLM) may be able to assist. This pilot study aimed to assess the feasibility of using a LLM to extract stroke audit data from free-text medical documentation. METHOD: Discharge summaries from a one-month retrospective cohort of stroke admissions at a tertiary hospital were collected. A locally-deployed LLM, LLaMA3, was then used to extract a variety of routine stroke audit data from free-text discharge summaries. These data were compared to the previously collected human audit data in the statewide registry. Manual case note review was undertaken in cases of discordance. RESULTS: Overall, there was a total of 144 data points that were extracted (9 data points for each of the 16 patients). The LLM was correct in 135/144 (93.8%) of individual datapoints. This performance included binary categorical, multiple-option categorical, datetime, and free-text extraction fields. CONCLUSIONS: LLM may be able to assist with the efficient collection of stroke audit data. Such approaches may be pursued in other specialties. Future studies should seek to examine the most effective way to deploy such approaches in conjunction with human auditors and researchers.

Push or pull? Digital notification platform implementation reduces dysglycaemia.

Pushing selected information to clinicians, as opposed to the traditional method of clinicians pulling information from an electronic medical record, has the potential to improve care. A digital notification platform was designed by clinicians and implemented in a tertiary hospital to flag dysglycaemia. There were 112 patients included in the study, and the post-implementation group demonstrated lower rates of dysglycaemia (2.5% vs 1.1%, P = 0.038). These findings raise considerations for information delivery methods for multiple domains in contemporary healthcare.

Translational artificial intelligence-led optimization and realization of estimated discharge with a supportive weekend interprofessional flow team (TAILORED-SWIFT).

Weekend discharges occur less frequently than discharges on weekdays, contributing to hospital congestion. Artificial intelligence algorithms have previously been derived to predict which patients are nearing discharge based upon ward round notes. In this implementation study, such an artificial intelligence algorithm was coupled with a multidisciplinary discharge facilitation team on weekend shifts. This approach was implemented in a tertiary hospital, and then compared to a historical cohort from the same time the previous year. There were 3990 patients included in the study. There was a significant increase in the proportion of inpatients who received weekend discharges in the intervention group compared to the control group (median 18%, IQR 18-20%, vs median 14%, IQR 12% to 17%, P = 0.031). There was a corresponding higher absolute number of weekend discharges during the intervention period compared to the control period (P = 0.025). The studied intervention was associated with an increase in weekend discharges and economic analyses support this approach as being cost-effective. Further studies are required to examine the generalizability of this approach to other centers.