2020
DOI: 10.1097/nna.0000000000000892
|View full text |Cite
|
Sign up to set email alerts
|

Development of a Nursing Assignment Tool Using Workload Acuity Scores

Abstract: OBJECTIVE To determine a just and consistent practice for creating nursing assignments. BACKGROUND Traditional methods of assigning patients to nurses may lead to unbalanced nursing workload. This article describes the ongoing, hospital-wide effort to evaluate and implement a nursing assignment tool based on electronic health record (EHR) functionality and auto-calculated nursing workload scores. METHODS EHR… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

1
17
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 22 publications
(22 citation statements)
references
References 9 publications
1
17
0
Order By: Relevance
“…Additionally, excessive workload also increases the rate of staff turnover, diminishes work performance, and increases the risks inpatient care (Bakhamis et al, 2019). These results prove previous findings, which emphasize the use of lean health care, analytics, and AI to improve the efficient use of resources and the safety of patients (Meyer et al, 2020; Needleman, 2013). Our study also raises new questions about the need to understand how AI‐driven digital health technologies contribute to strengthening compassionate nursing care.…”
Section: Discussionsupporting
confidence: 89%
“…Additionally, excessive workload also increases the rate of staff turnover, diminishes work performance, and increases the risks inpatient care (Bakhamis et al, 2019). These results prove previous findings, which emphasize the use of lean health care, analytics, and AI to improve the efficient use of resources and the safety of patients (Meyer et al, 2020; Needleman, 2013). Our study also raises new questions about the need to understand how AI‐driven digital health technologies contribute to strengthening compassionate nursing care.…”
Section: Discussionsupporting
confidence: 89%
“…A patient's nursing need/severity is related to the amount of nursing work, and the presence of an appropriate number of nurses affects the incidence of adverse reactions, including infection, pressure sores, falls, and prescription errors [ 35 ]. An optimal nurse-to-patient ratio is important to improve the quality of care and patient outcomes [ 36 ]. In Korea, the Ministry of Health and Welfare's 2019 medical delivery system improvement policy contains content such as improvement of the evaluation and compensation system for intensive care of critically ill patients in tertiary hospitals [ 37 ].…”
Section: Discussionmentioning
confidence: 99%
“…Real-time physiologic risk score calculations can integrate EMR data with streaming telemetry-based monitoring to calculate up-to-date severity scores (Rothman et al, 2013). Along with recent research using EMR data to access patterns and orders to better estimate workload (Meyer et al, 2020), there is potential to use more comprehensive informatics models to generate acuity measures for pre-assignment staffi ng and implement automated alerts for infants experiencing a high variance throughout a given day. Continued efforts on system interoperability may offer access to scheduling software, allowing insight into available shift lengths for scheduled nurses across the unit and clinical factors such as operating room schedules (Ivory, 2015;Lehne et al, 2019).…”
Section: Staffi Ng Factorsmentioning
confidence: 99%
“…Real-time physiologic risk score calculations can integrate EMR data with streaming telemetry-based monitoring to calculate up-to-date severity scores (Rothman et al, 2013). Along with recent research using EMR data to access patterns and orders to better estimate workload (Meyer et al, 2020), there is potential to use more comprehensive informatics models to generate acuity measures for pre-assignment staffing and implement automated alerts for infants experiencing a high variance throughout a given day.…”
Section: Using Data To Inform Pre-shift Staffing Factorsmentioning
confidence: 99%