Feasibility of Automating Patient Acuity Measurement Using a Machine Learning Algorithm

• Published in: Journal of nursing measurement 2016-01, Vol.24 (3), p.419-427
• Main Authors: Brennan, Caitlin W., Meng, Frank, Meterko, Mark M., D'Avolio, Leonard W.
• Format: Article
• Language: English
• Subjects: Administration, Management, and Leadership; Algorithms; Artificial Intelligence; Electronic Health Records; Humans; Natural Language Processing; Nursing; Nursing Education; Nursing Process - standards; Patient Acuity; Pilot Projects; Predictive Value of Tests; Professional Issues and Trends; Reproducibility of Results; Workload
• ISSN: 1061-3749; 1945-7049
• DOI: 10.1891/1061-3749.24.3.419

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.