Feasibility of Automating Patient Acuity Measurement Using a Machine Learning Algorithm

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 process...

Full description

Saved in:
Bibliographic Details
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
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1061-3749
1945-7049
DOI:10.1891/1061-3749.24.3.419