Using telecommunication dialogue and nursing documentation to predict the risk of emergency room visit in a web-based...

The effectiveness of telehealth care programmes in reducing mortality among patients with chronic conditions has been well established. Valuable insights into patients' conditions can be gleaned through daily telecommunication between patients and nurse case managers. We hypothesized that using natural language processing can predict acute deterioration in patients with chronic conditions in telehealth care programme based on the nursing records and speech dialogues occurring during daily telecommunication.

We conducted a retrospective study utilizing audio recording transcripts from telecommunication sessions between patients and nurse case managers at our telehealth care centre, along with nursing notes as input data. Pre-trained transformer-based neural network models were constructed to predict emergency room (ER) visits within a 2-week timeframe. The case group included 94 patients with 585 speech recordings and nursing records, while the control group included 36 patients with 396 speech recordings and nursing records. Our results showed that employing transcripts and a bidirectional encoder representations from transformers (BERT)-base model with a sliding window for predicting ER visits yielded moderate accuracy 0.75 (interquartile range: 0.742, 0.773). The inclusion of long short-term memory in the model did not significantly enhance accuracy. Notably, combining nursing records and transcripts as inputs exhibited superior performance, achieving an overall accuracy of 0.892 (interquartile range: 0.891, 0.893) by the six models.

Our study demonstrates the feasibility of predicting ER visits using telehealth dialogue transcripts and nursing notes with pre-trained transformer models. The incorporation of nursing notes significantly enhances the model's performance, providing a valuable method for improving predictive accuracy in telehealth care.