Delayed flow is a risk to patient safety: A mixed method analysis of emergency department patient flow

Pryce, Alex; Unwin, Maria; Kinsman, Leigh; McCann, Damhnat

doi:10.1016/j.ienj.2020.100956

Cited by 19 publications

(15 citation statements)

References 35 publications

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“…Regarding transportation, the proportion of patients who arrived by ambulance ranged from 8-43%, with the majority arriving by public or private transport (see Additional le 3). Three studies (Göransson et al 2103 [19], Pryce et al (2021) [11] and Strum et al 2019 [13]) reported that 69-91% self-presented or walked-in (with referral status not reported) as a mode of arrival, but the means of transport were not speci ed.…”

Section: Resultsmentioning

confidence: 99%

Patients’ pathways to the emergency department: a scoping review

Nummedal

King

Uleberg

et al. 2023

Preprint

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Background Emergency Department (ED) crowding is a common healthcare issue. The causes are multifactorial, and some causes may be found by analyzing patient trajectories prior to ED visits. The aim of this scoping review is to identify and examine studies that describe patient trajectories prior to ED arrival. Methods The scoping review was performed according to the JBI Manual for Evidence Synthesis and the PRISMA-SCR checklist. A literature search was done to identify studies describing where patients come from and/or their pathway of care before the ED visits. The search was conducted in MEDLINE, Embase, and the Cochrane Library from inception up to March 17th, 2022 and updated on December 5th, 2022. Two reviewers independently screened the records at all stages of the review process. Results Out of 6,465 records screened, 14 papers from Australia, Canada, Haiti, Norway, Sweden, Switzerland, Belgium, Indonesia and the UK met the inclusion criteria. Four studies reported on where patients originated from. Seven studies reported on who referred them. Ten reported how patients were transported and five reported if alternative care or advice was sought prior to visiting an ED. Data were sparse for these categories of information; not all studies reported the full spectrum of patients within each category. Conclusion There are knowledge gaps when it comes to describing patients’ pathways to the emergency department. The data reported provided limited insight, and the lack of uniform data prohibits comparisons across studies. Further studies that comprehensively describe patient trajectories prior to an ED visit are paramount to help understand the reasons for the increased patient influx and ED crowding.

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Section: Resultsmentioning

confidence: 99%

Patients’ pathways to the emergency department: a scoping review

Nummedal

King

Uleberg

et al. 2023

Preprint

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“…Previous studies showed that delayed ED care impacts patient safety, mainly due to longer time to triage and time to care provision. 28 Improved preparedness may lead to better downtime workflow and fewer delays in care, thereby limiting the impact of a cyberattack on patient safety.…”

Section: Discussionmentioning

confidence: 99%

Hacking Acute Care: A Qualitative Study on the Healthcare Impacts of Ransomware Attacks Against Hospitals

Boven

Kusters

Tin

et al. 2023

Preprint

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Background and objectives: Cyberattacks against healthcare institutions are an increasing threat and have the potential to impair health outcomes. Current research is limited and focuses mainly on the technical consequences, whereas little is known about the healthcare staff experiences and the impact on emergency care, both during the incident and in the recovery phase. This study aims to explore the impact of a sample of large ransomware attacks against hospitals between 2017 and 2022 on acute care delivery and patient care during the recovery phase. Methods: This interview-based qualitative study assessed the experiences of emergency healthcare professionals and Information and Communication Technology (ICT) staff and investigated the challenges faced when struck by a major hospital ransomware attack. The semi-structured interview guideline was based on current literature and cybersecurity expert consultation. Transcripts were anonymized and information tracing back to participants and/or their organizations was removed for privacy purposes. Results: Nine participants were interviewed, including emergency healthcare providers and ICT-focused staff. Five themes were constructed from the data: impact and challenges regarding patient care continuity, challenges during the recovery process, personal impact on healthcare staff, preparedness, and lessons identified and future recommendations. Conclusions: According to the participants of this qualitative study, ransomware attacks have a significant impact on emergency department (ED) workflow, acute patient care and the personal wellbeing of healthcare providers. Preparedness for such incidents is often limited and many challenges are encountered during the acute and recovery phase of the attack. Proactive preparedness efforts are essential to improve contingency planning and to develop response strategies for hospital ransomware attacks.

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“…A variety of factors with potential influence on the diagnostic process will be extracted from both the EHR and other standalone databases at the 2 sites. Existing literature has provided information on factors related to patients, providers, and system-level parameters and the interactions of these parameters in the ED (eg, patient-per-provider ratio and patient length of stay), which can be explored further [8,13,[24][25][26][27][28][29]. Several additional variables that can be extracted from the EHR will be under consideration.…”

Section: Quantitative Variablesmentioning

confidence: 99%

“…ED crowding is a complex issue related to both system-level and patient-level factors (complexity and acuity) [28] and is associated with an increased risk for patient safety, including treatment delays, reduced quality of care, and increased morbidity and mortality [24]. Prolonged ED length of stay correlates with increased patient mortality [27]. High workloads, lack of control, and communication failures may lead to patient safety risks [29].…”

Section: Quantitative Variablesmentioning

confidence: 99%

Monitoring Diagnostic Safety Risks in Emergency Departments: Protocol for a Machine Learning Study

Enayati¹,

Sir²,

Zhang³

et al. 2021

JMIR Res Protoc

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Background Diagnostic decision making, especially in emergency departments, is a highly complex cognitive process that involves uncertainty and susceptibility to errors. A combination of factors, including patient factors (eg, history, behaviors, complexity, and comorbidity), provider-care team factors (eg, cognitive load and information gathering and synthesis), and system factors (eg, health information technology, crowding, shift-based work, and interruptions) may contribute to diagnostic errors. Using electronic triggers to identify records of patients with certain patterns of care, such as escalation of care, has been useful to screen for diagnostic errors. Once errors are identified, sophisticated data analytics and machine learning techniques can be applied to existing electronic health record (EHR) data sets to shed light on potential risk factors influencing diagnostic decision making. Objective This study aims to identify variables associated with diagnostic errors in emergency departments using large-scale EHR data and machine learning techniques. Methods This study plans to use trigger algorithms within EHR data repositories to generate a large data set of records that are labeled trigger-positive or trigger-negative, depending on whether they meet certain criteria. Samples from both data sets will be validated using medical record reviews, upon which we expect to find a higher number of diagnostic safety events in the trigger-positive subset. Machine learning will be used to evaluate relationships between certain patient factors, provider-care team factors, and system-level risk factors and diagnostic safety signals in the statistically matched groups of trigger-positive and trigger-negative charts. Results This federally funded study was approved by the institutional review board of 2 academic medical centers with affiliated community hospitals. Trigger queries are being developed at both organizations, and sample cohorts will be labeled using the triggers. Machine learning techniques such as association rule mining, chi-square automated interaction detection, and classification and regression trees will be used to discover important variables that could be incorporated within future clinical decision support systems to help identify and reduce risks that contribute to diagnostic errors. Conclusions The use of large EHR data sets and machine learning to investigate risk factors (related to the patient, provider-care team, and system-level) in the diagnostic process may help create future mechanisms for monitoring diagnostic safety. International Registered Report Identifier (IRRID) DERR1-10.2196/24642

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Delayed flow is a risk to patient safety: A mixed method analysis of emergency department patient flow

Cited by 19 publications

References 35 publications

Patients’ pathways to the emergency department: a scoping review

Patients’ pathways to the emergency department: a scoping review

Hacking Acute Care: A Qualitative Study on the Healthcare Impacts of Ransomware Attacks Against Hospitals

Monitoring Diagnostic Safety Risks in Emergency Departments: Protocol for a Machine Learning Study

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