# Tailoring Clinical Decision Support Systems to Nurse Anesthetists: A Rapid Review of Current Evidence.

N. Milleville, S. Pruvot, G. Carin, Paul Quindroit

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N. Milleville, S. Pruvot, G. Carin, Paul Quindroit. Tailoring Clinical Decision Support Systems to Nurse Anesthetists: A Rapid Review of Current Evidence. Stud Health Technol Inform., 2025, Stud Health Technol Inform, 327, pp.12-16. 10.3233/SHTI250264. hal-05111102

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doi:10.3233/SHTI250264

# Tailoring Clinical Decision Support Systems to Nurse Anesthetists: A Rapid Review of Current Evidence

Nicolas MILLEVILLEª, Sabine PRUVOTª, Gaëtan CARINª and Paul QUINDROITᵇ,<fnref n="1" />

ORCID ID: Paul Quindroit https://orcid.org/0000-0003-0387-2413

**Abstract.** Certified registered nurse anesthetists (CRNAs) rely on various information sources to make clinical decisions. Clinical decision support systems (CDSS) can enhance decision-making, but few are tailored specifically for CRNAs. This rapid review examines the current evidence on CDSS use in anesthesia care. A rapid review was conducted following PRISMA guidelines. Databases Medline CINAHL and Embase were searched for studies focusing on CDSS for CRNAs. Six studies were included. No CDSS was designed specifically for CRNAs. The identified systems, both rule-based and AI-based, improved decision-making, particularly in risk prediction and intraoperative management, but were not adapted to the specific needs of CRNAs. While CDSS can enhance anesthesia care, systems designed for other professionals may not fully meet CRNA requirements. Tailored CDSS are needed to address their unique decision-making processes. Developing CRNA-specific CDSS could optimize decision-making and improve patient outcomes in anesthesia practice.

**Keywords.** Clinical decision support system, nurse anesthetists, rapid review

## 1. Introduction

In the fast-paced and high-stakes environment of the operating room, anesthesia providers, including certified registered nurse anesthetists (CRNAs), rely on a multitude of information sources to guide their decision-making process. Clinical decision support systems (CDSS) integrated into the electronic health record (EHR) have emerged as a promising tool to enhance decision-making. A CDSS is designed to improve healthcare delivery by enhancing medical decisions with targeted clinical knowledge, patient information, and other health information [1]. These systems can be broadly categorized into two types: knowledge-based or rule-based and non-knowledge-based or artificial intelligence-based. Knowledge-based CDSS use rules (IF-THEN statements) to evaluate data and produce outputs based on literature, practice, or patient-directed evidence [1]. Non-knowledge-based CDSS leverage artificial intelligence (AI), machine learning (ML), or statistical pattern recognition to make decisions without explicit programming based on expert knowledge [1].

<footnote>ªCHU Lille, Hôpital Huriez, Pôle d'Anesthésie Réanimation, F-59000 Lille, France</footnote>

<footnote>ᵇUniv. Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales, F-59000 Lille, France</footnote>

<footnote n="1">¹ Corresponding Author: Quindroit Paul ; E-mail: paul.quindroit@univ-lille.fr.</footnote>
CRNAs, as integral decision-makers in anesthesia care, face unique challenges, such as managing anesthesia induction and maintenance, monitoring intraoperative physiological parameters, and responding to perioperative complications [2]. Studies have demonstrated the potential of CDSS in supporting these tasks [1].

Despite the known benefits, there is a shortage of research specifically targeting the use of CDSS for CRNAs compared to those aimed at other health professionals. Dunn Lopez et al. conducted an integrative review of CDSS for hospital bedside nurses, finding that such systems generally have positive effects on outcomes, although more research is needed to address the specific needs of different nursing specialties [3].

To address this gap, we conducted a rapid review of studies related to CDSS tailored to the decision-making processes of CRNAs. This review aims to synthesize the current evidence on the effectiveness of CDSS in supporting CRNAs, identifying both benefits and areas requiring further investigation to optimize the use of these systems in anesthesia practice.

## 2. Methods

We conducted a rapid review by applying the processes and methods of a systematic review in a streamlined and accelerated manner [4]. The rapid review was carried out and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [5]. The search was limited to two databases, Medline via PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Embase, using the keywords: "clinical decision support system" AND "anesthesia" AND "nurses" and their related terms.

Articles focused on CDSS for CRNAs were considered for inclusion if they met the following criteria: (a) published in a peer-reviewed journal; (b) either a primary study (including qualitative, quantitative, and mixed methods), a review, or a descriptive report; (c) written in English or French; and (d) met the previously described definition of CDSS. There were no geographical or publication timeframe limitations imposed. Publications were excluded if they did not describe the CDSS or if they did not target CRNAs specifically. One researcher screened the titles and abstracts, and the full texts were appraised by two researchers and discussed before inclusion. Data from the identified publications were extracted, and the articles were exported to an Excel file for screening and data extraction.

## 3. Results

The search strategy yielded 363 results, from which 55 were duplicates. We completed a full-text evaluation of eleven papers, from which six were retained (Figure 1).
<figure><img src="image_2.png" /><figcaption>Figure 1. PRISMA flowchart of identified, excluded and included literature</figcaption></figure>

The characteristics of the included studies are described in Table 1. Among the six articles, five were written by authors from the USA [6–10] and one from the Netherlands [11], and they were published between 2010 and 2023. Additionally, all the CDSS were implemented in hospital settings. However, none of the CDSS were specifically designed for CRNAs; generally, they are intended for anesthesiologists, medical interns, and nurses.

<table><caption>Table 1. characteristics of the included studies</caption><thead><tr><td>Ref.</td><td>Year</td><td>Study design</td><td>Aim of the study</td><td>Type of CDSS</td></tr></thead><tbody><tr><td>[10]</td><td>2023</td><td>Qualitative</td><td>Evaluate the concordance between machine learning-generated postoperative predictions and clinician-generated risk rankings for acute kidney injury, delirium, pneumonia, deep vein thrombosis, and pulmonary embolism, and identify their associated risk factors.</td><td>AI based</td></tr><tr><td>[9]</td><td>2015</td><td>Review</td><td>Discuss the challenges in developing and implementing anesthesia information management systems aimed at improving anesthesia provider performance.</td><td>Rule based</td></tr><tr><td>[8]</td><td>2023</td><td>Quantitative</td><td>Describe how an automated EHR application using patient data improved decision-making and reduced cognitive burden for choosing between monitored anesthesia care and moderate sedation.</td><td>AI-based</td></tr><tr><td>[7]</td><td>2021</td><td>Qualitative</td><td>Describe the development and user feedback analysis of a robust, real-time CDSS application that provides patient-specific dosing information and alerts to prevent medication errors in the operating room.</td><td>Rule based</td></tr><tr><td>[6]</td><td>2015</td><td>Quantitative</td><td>Evaluate the primary hypothesis that a supplemental CDSS alert for critically low systolic blood pressure reduces the duration of intraoperative hypotension.</td><td>Rule based</td></tr><tr><td>[11]</td><td>2010</td><td>Quantitative</td><td>Evaluate the impact of patient-specific automated reminders on the timely administration of postoperative nausea and vomiting prophylaxis during general anesthesia.</td><td>Rule based</td></tr></tbody></table>
## 4. Discussion

This rapid review highlights the effectiveness of CDSS in enhancing decision-making, reducing cognitive load, and improving guideline adherence. However, a significant gap remains, as most systems are designed for anesthesiologists or general nursing staff, underscoring the need for CRNA-specific tools.

The studies included in this review consistently demonstrate that CDSS can significantly enhance decision-making processes in anesthesia care. Both rule-based and AI-based systems have shown improvements in areas such as postoperative risk prediction, intraoperative management, and medication dosing. For instance, an AI-based system evaluated in a 2023 study was effective in aligning machine-generated postoperative risk predictions with clinician assessments, aiding CRNAs in anticipating complications like acute kidney injury and pneumonia [10].

Additionally, CDSS have been shown to reduce cognitive load and improve adherence to clinical guidelines. Automated decision aids, such as those for administering prophylaxis for postoperative nausea and vomiting, have been associated with improved patient outcomes and greater operational efficiency [11]. These findings align with the broader literature on the benefits of CDSS, supporting their value in enhancing anesthesia care [12,13].

However, the implementation of CDSS comes with challenges. Developing systems that are user-friendly and integrate seamlessly with existing EHR while providing real-time, actionable insights is complex. Studies from 2015 and 2021 discussed the difficulties in ensuring that CDSS are robust, effective, and do not contribute to alert fatigue, which can undermine their utility.

A significant gap identified in this review is the lack of CDSS specifically designed for CRNAs. While the general benefits of CDSS are evident, CRNAs face unique clinical scenarios and decision-making processes that may not be fully addressed by systems intended for anesthesiologists or general nurses. There is a critical need for research focused on developing and evaluating CDSS tailored to the specific needs of CRNAs. Such research could provide insights into optimizing these systems to support CRNAs more effectively in their specialized roles.

## 5. Conclusions

This rapid review underscores the potential benefits of CDSS in enhancing decision-making, reducing cognitive load, and improving patient outcomes in anesthesia care. However, it also highlights a significant gap in research and development for CRNA-specific systems.

The evidence suggests that while CDSS can offer substantial advantages for general anesthesia providers, there is a crucial need for systems designed with the specific workflows and decision-making challenges of CRNAs in mind. Future research should prioritize the development and evaluation of CRNA-specific CDSS to ensure these tools are optimally effective and user-friendly. It is therefore essential to analyze CRNAs' decision-making processes to better tailor CDSS to their needs [14].

Investing in CRNA-specific CDSS is likely to lead to significant improvements in patient safety and operational efficiency in anesthesia practice. Tailoring these systems to the unique needs of CRNAs will help maximize their potential benefits, ultimately enhancing the quality of care provided in the operating room.
## References

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