Feasibility of Automating Patient Acuity Measurement Using a Machine Learning Algorithm
Caitlin W Brennan1, Frank Meng, Mark M Meterko
1Nursing Department, National Institutes of Health Clinical Center, Bethesda, Maryland, USA.
Background And Purpose:
One method of determining nurse staffing is to match patient demand for nursing care (patient acuity) with available nursing staff. This pilot study explored the feasibility of automating acuity measurement using a machine learning algorithm.
Methods:
Natural language processing combined with a machine learning algorithm was used to predict acuity levels based on electronic health record data.
Results:
The algorithm was able to predict acuity relatively well. A main challenge was discordance among nurse raters of acuity in generating a gold standard of acuity before applying the machine learning algorithm.
Conclusions:
This pilot study tested applying machine learning techniques to acuity measurement and yielded a moderate level of performance. Higher agreement among the gold standard may yield higher performance in future studies.
