The nursing process and total health cost variability: an analysis using machine learning.

AIMS

To find out whether the information that the nursing process provides (functional patterns and the NANDA-NIC-NOC taxonomy), presented through clinical histories, influences predictions of total healthcare costs.

BACKGROUND

The nursing process, is not included in the systems that calculate expenditure in the Spanish healthcare system. Such an omission can result in suboptimal resource allocation.

METHODS

Analytical and retrospective observational study of a population of 1,691,075 people over the age of 15. The explanatory variables were age, sex and nursing process data, with total healthcare cost as the outcome variable. A bivariate analysis and a multiple regression were performed for the multivariate analysis. To improve prediction accuracy and account for non-linear relationships, the analysis was completed using two machine learning models.

RESULTS

58% (n = 980,437) of the population presented some data from the nursing process, for individuals with an assessed pattern, the average cost was €2304.17 compared with €950.93 for those who had none; with a nursing diagnosis, the average cost was €1,666 versus €840 without it. Having created the best model for the analysis using neural networks and XGBOOST, an average coefficient of determination of R = 21.45% was obtained.

CONCLUSIONS

The variability in total healthcare costs can be explained in more than 21% of cases by the model created, including sex, age, and the information related to the nursing process.

IMPLICATIONS FOR HEALTH POLICY

Demonstrating the influence of nursing care on total patient costs will facilitate its inclusion in management programs, promoting the use of nursing data in risk adjustment models and healthcare planning.