# Data Analytics and Administrative Decision-Making in Nursing Management: A Systematic Review

## TL;DR

This review explores how data analytics improves nurse managers' decisions, enhancing patient care and healthcare efficiency.

## Contribution

It systematically evaluates the role of data analytics in nursing management decision-making across four analytics levels.

## Key findings

- Data analytics improves administrative decisions in patient care, staffing, and crisis management.
- Descriptive, predictive, and integrated analytics are most commonly used in nursing management.
- Education and infrastructure are critical for effective data-driven nursing management.

## Abstract

This systematic review aimed to investigate the impact of data analytics on nurse managers' administrative decision-making process and roles.

The growing integration of data analytics in health care has accelerated the shift toward data-driven decision-making in nursing management, aiming to optimize patient care quality and enhance organizational performance within digital healthcare environments. Nurse managers play a pivotal role in leveraging data analytics to support evidence-based management, facilitating more informed, efficient, and strategic administrative decision-making.

This systematic review was conducted in accordance with PRISMA guidelines. A comprehensive search strategy was employed to identify relevant studies published from 2019 through 2024 using four electronic databases—PubMed, CINAHL, MEDLINE, and Embase. A total of 2051 studies were screened, and 83 studies were eligible for full-text screening according to the established inclusion and exclusion criteria. Eight different quality assessment tools were applied. Data tabulation and narrative synthesis were employed.

Twenty-one studies representing eight different study designs were included in the review. There were diverse applications of data analytics across four analytics levels: descriptive (n = 4), diagnostic (n = 2), predictive (n = 9), and prescriptive (n = 1). Additionally, integrated approaches combining two levels of analytics were identified (n = 5).

The integration of data analytics into nursing management has the potential to enhance an administrative decision-making process across diverse nursing management roles, particularly in four key areas: improving patient care quality, strategic management, nurse staffing and work engagement, and nursing management during health crises.

Strengthening nurse managers' analytical and digital competencies through targeted education and continuous training is essential. Ensuring supportive infrastructure can enable more informed, efficient, and evidence-based management, ultimately leading to improved healthcare quality and operational performance. Future research should explore the long-term impact and broader applicability across diverse healthcare settings.