Bending the patient safety curve: how much can AI help?

Interventions to improve patient admission and discharge practices in adult intensive care units: A systematic review

OBJECTIVES

To identify and synthesise interventions and implementation strategies to optimise patient flow, addressing admission delays, discharge delays, and after-hours discharges in adult intensive care units.

METHODS

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. Five electronic databases, including CINAHL, PubMed, Emcare, Scopus, and the Cochrane Library, were searched from 2007 to 2023 to identify articles describing interventions to enhance patient flow practices in adult intensive care units. The Critical Appraisal Skills Program (CASP) tool assessed the methodological quality of the included studies. All data was synthesised using a narrative approach.

SETTING

Adult intensive care units.

RESULTS

Eight studies met the inclusion criteria, mainly comprising quality improvement projects (n = 3) or before-and-after studies (n = 4). Intervention types included changing workflow processes, introducing decision support tools, publishing quality indicator data, utilising outreach nursing services, and promoting multidisciplinary communication. Various implementation strategies were used, including one-on-one training, in-person knowledge transfer, digital communication, and digital data synthesis and display. Most studies (n = 6) reported a significant improvement in at least one intensive care process-related outcome, although fewer studies specifically reported improvements in admission delays (0/0), discharge delays (1/2), and after-hours discharge (2/4). Two out of six studies reported significant improvements in patient-related outcomes after implementing the intervention.

CONCLUSION

Organisational-level strategies, such as protocols and alert systems, were frequently employed to improve patient flow within ICUs, while healthcare professional-level strategies to enhance communication were less commonly used. While most studies improved ICU processes, only half succeeded in significantly reducing discharge delays and/or after-hours discharges, and only a third reported improved patient outcomes, highlighting the need for more effective interventions.

IMPLICATIONS FOR CLINICAL PRACTICE

The findings of this review can guide the development of evidence-based, targeted, and tailored interventions aimed at improving patient and organisational outcomes.

Maximizing the Ability of Health IT and AI to Improve Patient Safety

This Viewpoint discusses how health information technology (IT) and artificial intelligence (AI) can be used to transform patient safety.

Establishing a Validation Infrastructure for Imaging-Based Artificial Intelligence Algorithms Before Clinical Implementation

With promising artificial intelligence (AI) algorithms receiving FDA clearance, the potential impact of these models on clinical outcomes must be evaluated locally before their integration into routine workflows. Robust validation infrastructures are pivotal to inspecting the accuracy and generalizability of these deep learning algorithms to ensure both patient safety and health equity. Protected health information concerns, intellectual property rights, and diverse requirements of models impede the development of rigorous external validation infrastructures. The authors propose various suggestions for addressing the challenges associated with the development of efficient, customizable, and cost-effective infrastructures for the external validation of AI models at large medical centers and institutions. The authors present comprehensive steps to establish an AI inferencing infrastructure outside clinical systems to examine the local performance of AI algorithms before health practice or systemwide implementation and promote an evidence-based approach for adopting AI models that can enhance radiology workflows and improve patient outcomes.

The World Federation of Chiropractic Global Patient Safety Task Force: a call to action

BACKGROUND

The Global Patient Safety Action Plan, an initiative of the World Health Organization (WHO), draws attention to patient safety as being an issue of utmost importance in healthcare. In response, the World Federation of Chiropractic (WFC) has established a Global Patient Safety Task Force to advance a patient safety culture across all facets of the chiropractic profession. This commentary aims to introduce principles and call upon the chiropractic profession to actively engage with the Global Patient Safety Action Plan beginning immediately and over the coming decade.

MAIN TEXT

This commentary addresses why the chiropractic profession should pay attention to the WHO Global Patient Safety Action Plan, and what actions the chiropractic profession should take to advance these objectives. Each strategic objective identified by WHO serves as a focal point for reflection and action. Objective 1 emphasizes the need to view each clinical interaction as a chance to improve patient safety through learning. Objective 2 urges the implementation of frameworks that dismantle systemic obstacles, minimizing human errors and strengthening patient safety procedures. Objective 3 supports the optimization of clinical process safety. Objective 4 recognizes the need for patient and family engagement. Objective 5 describes the need for integrated patient safety competencies in training programs. Objective 6 explains the need for foundational data infrastructure, ecosystem, and culture. Objective 7 emphasizes that patient safety is optimized when healthcare professionals cultivate synergy and partnerships.

CONCLUSIONS

The WFC Global Patient Safety Task Force provides a structured framework for aligning essential considerations for patient safety in chiropractic care with WHO strategic objectives. Embracing the prescribed action steps offers a roadmap for the chiropractic profession to nurture an inclusive and dedicated culture, placing patient safety at its core. This commentary advocates for a concerted effort within the chiropractic community to commit to and implement these principles for the collective advancement of patient safety.

Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review

The integration of artificial intelligence (AI) and machine learning (ML) in healthcare has become a major point of interest and raises the question of its impact on the emergency department (ED) triaging process. AI's capacity to emulate human cognitive processes coupled with advancements in computing has shown positive outcomes in various aspects of healthcare but little is known about the use of AI in triaging patients in ED. AI algorithms may allow for earlier diagnosis and intervention; however, overconfident answers may present dangers to patients. The purpose of this review was to explore comprehensively recently published literature regarding the effect of AI and ML in ED triage and identify research gaps. A systemized search was conducted in September 2023 using the electronic databases EMBASE, Ovid MEDLINE, and Web of Science. To meet inclusion criteria, articles had to be peer-reviewed, written in English, and based on primary data research studies published in US journals 2013-2023. Other criteria included 1) studies with patients needing to be admitted to hospital EDs, 2) AI must have been used when triaging a patient, and 3) patient outcomes must be represented. The search was conducted using controlled descriptors from the Medical Subject Headings (MeSH) that included the terms "artificial intelligence" OR "machine learning" AND "emergency ward" OR "emergency care" OR "emergency department" OR "emergency room" AND "patient triage" OR "triage" OR "triaging." The search initially identified 1,142 citations. After a rigorous, systemized screening process and critical appraisal of the evidence, 29 studies were selected for the final review. The findings indicated that 1) ML models consistently demonstrated superior discrimination abilities compared to conventional triage systems, 2) the integration of AI into the triage process yielded significant enhancements in predictive accuracy, disease identification, and risk assessment, 3) ML accurately determined the necessity of hospitalization for patients requiring urgent attention, and 4) ML improved resource allocation and quality of patient care, including predicting length of stay. The suggested superiority of ML models in prioritizing patients in the ED holds the potential to redefine triage precision.

Patient Safety and Artificial Intelligence in Clinical Care

This Viewpoint offers 3 recommendations for health care organizations and other stakeholders to consider as part of the Health and Human Services’ artificial intelligence safety program.

Impact of a deep learning sepsis prediction model on quality of care and survival

Sepsis remains a major cause of mortality and morbidity worldwide. Algorithms that assist with the early recognition of sepsis may improve outcomes, but relatively few studies have examined their impact on real-world patient outcomes. Our objective was to assess the impact of a deep-learning model (COMPOSER) for the early prediction of sepsis on patient outcomes. We completed a before-and-after quasi-experimental study at two distinct Emergency Departments (EDs) within the UC San Diego Health System. We included 6217 adult septic patients from 1/1/2021 through 4/30/2023. The exposure tested was a nurse-facing Best Practice Advisory (BPA) triggered by COMPOSER. In-hospital mortality, sepsis bundle compliance, 72-h change in sequential organ failure assessment (SOFA) score following sepsis onset, ICU-free days, and the number of ICU encounters were evaluated in the pre-intervention period (705 days) and the post-intervention period (145 days). The causal impact analysis was performed using a Bayesian structural time-series approach with confounder adjustments to assess the significance of the exposure at the 95% confidence level. The deployment of COMPOSER was significantly associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality (95% CI, 0.3%-3.5%), a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance (95% CI, 2.4%-8.0%), and a 4% (95% CI, 1.1%-7.1%) reduction in 72-h SOFA change after sepsis onset in causal inference analysis. This study suggests that the deployment of COMPOSER for early prediction of sepsis was associated with a significant reduction in mortality and a significant increase in sepsis bundle compliance.

How can artificial intelligence decrease cognitive and work burden for front line practitioners?

Artificial intelligence (AI) has tremendous potential to improve the cognitive and work burden of clinicians across a range of clinical activities, which could lead to reduced burnout and better clinical care. The recent explosion of generative AI nicely illustrates this potential. Developers and organizations deploying AI have a responsibility to ensure AI is designed and implemented with end-user input, has mechanisms to identify and potentially reduce bias, and that the impact on cognitive and work burden is measured, monitored, and improved. This article focuses specifically on the role AI can play in reducing cognitive and work burden, outlines the critical issues associated with the use of AI, and serves as a call to action for vendors and users to work together to develop functionality that addresses these challenges.