Related Experiment Video
Updated: Oct 31, 2025
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Nonlinear association of nurse staffing and readmissions uncovered in machine learning analysis
Olga Yakusheva1,2, James T Bang3, Ronda G Hughes4
1Department of Systems, Populations, and Leadership, School of Nursing, University of Michigan, Ann Arbor, Michigan, USA.
Objective:
Several studies of nurse staffing and patient outcomes found a curvilinear or U-shaped relationship, with benefits from additional nurse staffing diminishing or reversing at high staffing levels. This study examined potential diminishing returns to nurse staffing and the existence of a "tipping point" or the level of staffing after which higher nurse staffing no longer improves and may worsen readmissions.
Data Sources/Study Setting:
The Readiness Evaluation And Discharge Interventions (READI) study database of over 130,000 adult (18+) inpatient discharges from 62 medical, surgical, and medical-surgical (noncritical care) units from 31 United States (US) hospitals during October 2014-March 2017.
Study Design:
Observational cross-sectional study using a fully nonparametric random forest machine learning method. Primary exposure was nurse hours per patient day (HPPD) broken down by registered nurses (nonovertime and overtime) and nonlicensed nursing personnel. The outcome was 30-day all-cause same-hospital readmission. Partial dependence plots were used to visualize the pattern of predicted patient readmission risk along a range of unit staffing levels, holding all other patient characteristics and hospital and unit structural variables constant.
Data Collection/Extraction Methods:
Secondary analysis of the READI data. Missing values were imputed using the missing forest algorithm in R.
Principal Findings:
Partial dependence plots were U-shaped, showing the readmission risk first declining and then rising with additional nurse staffing. The tipping points were at 6.95 and 0.21 HPPD for registered nurse staffing (nonovertime and overtime, respectively) and 2.91 HPPD of nonlicensed nursing personnel.
Conclusions:
The U-shaped association was consistent with diminishing returns to nurse staffing suggesting that incremental gains in readmission reduction from additional nurse staffing taper off and could reverse at high staffing levels. If confirmed in future causal analyses across multiple outcomes, accompanying investments in infrastructure and human resources may be needed to maximize nursing performance outcomes at higher levels of nurse staffing.
Related Concept Videos
01:30Current Trends in Nursing II
01:28Current Trends in Nursing I
01:11Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
01:10Documentation of Nursing Diagnosis
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
01:30National Nursing Organizations II
The AACN emphasizes a healthy work environment through six standards to achieve an optimal patient outcome. The standards are appropriate staffing, meaningful recognition, collaboration, authentic leadership, effective communication, and decision-making. In addition, AACN provides certification programs, webinars, journals, and...
01:25Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...