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The intra- and inter-hospital patient transfer is an important aspect of patient care which is often undertaken to improve upon the existing management of the patient. It may involve transfer of patient within the same facility for any diagnostic procedure or transfer to another facility with more advanced care. The main aim in all such transfers is maintaining the continuity of medical care. As the transfer of sick patient may induce various physiological alterations which may adversely affect the prognosis of the patient, it should be initiated systematically and according to the evidence-based guidelines. The key elements of safe transfer involve decision to transfer and communication, pre-transfer stabilisation and preparation, choosing the appropriate mode of transfer, i.e., land transport or air transport, personnel accompanying the patient, equipment and monitoring required during the transfer, and finally, the documentation and handover of the patient at the receiving facility. These key elements should be followed in each transfer to prevent any adverse events which may severely affect the patient prognosis. The existing international guidelines are evidence based from various professional bodies in developed countries. However, in developing countries like India, with limited infrastructure, these guidelines can be modified accordingly. The most important aspect is implementation of these guidelines in Indian scenario with periodical quality assessments to improve the standard of care.

Background and purpose Patients with acute ischaemic stroke and a large vessel occlusion who present to a non‐endovascular‐capable centre often require inter‐hospital transfer for thrombectomy. Whether the inter‐hospital transfer time is associated with 3‐month functional outcome is poorly known. Methods Acute stroke patients enrolled between January 2015 and December 2022 in the prospective French multicentre Endovascular Treatment of Ischaemic Stroke registry were retrospectively analysed. Patients with an anterior circulation large vessel occlusion transferred from a non‐endovascular to a comprehensive stroke centre for thrombectomy were eligible. Inter‐hospital transfer time was defined as the time between imaging in the referring hospital and groin puncture for thrombectomy. The relationship between transfer time and favourable 3‐month functional outcome (modified Rankin Scale 0–2) was assessed through a mixed logistic regression model adjusting for centre and symptom‐onset‐to‐referring‐hospital imaging time, age, sex, diabetes, referring hospital National Institutes of Health Stroke Scale score, Alberta Stroke Programme Early Computed Tomography Score, occlusion site and intravenous thrombolysis use. Results Overall, 3769 patients were included (median inter‐hospital transfer time 161 min, interquartile range 128–195; 46% with favourable outcome). A longer transfer time was independently associated with lower rates of favourable outcome (p < 0.001). Compared to patients with transfer time below 120 min, there was a 15% reduction in the odds of achieving favourable outcome for transfer times between 120 and 180 min (adjusted odds ratio 0.85; 95% confidence interval 0.67–1.07), and a 36% reduction for transfer times beyond 180 min (adjusted odds ratio 0.64; 95% confidence interval 0.50–0.81). Conclusions A shorter inter‐hospital transfer time is strongly associated with favourable 3‐month functional outcome. A speedier inter‐hospital transfer is of critical importance to improve outcome.

Aim The purpose of this study was to explore the latent conditions of cooperation and conflict in intra‐hospital patient transfers (i.e. transfers of patients between units in a hospital). Design Secondary qualitative analysis of 28 interviews conducted with 29 hospital staff, including physicians (N = 13), nurses (N = 10) and support staff (N = 6) from a single, large academic tertiary hospital in the Northeastern United States. Methods A two‐member multidisciplinary team applied a directed content analysis approach to data collected from semi‐structured interviews. Results Three recurrent themes were generated: (a) patient flow policies created imbalances of power; (b) relationships were helpful to facilitate safe transfers; and (c) method of admission order communication was a source of disagreement. Hospital quality improvement efforts could benefit from a teaming approach to minimize unintentional power imbalances and optimize communicative relationships between units.

Aim To identify the factors affecting Emergency Department Length of Stay for transferred critically ill patients. Background The Length of Stay of the transferred patients is an important indicator of Emergency Department service quality; thus, understanding the factors affecting the Emergency Department Length of Stay of transferred critically ill patients is essential. Methods Using the electronic medical records of 968 transferred critically ill Emergency Department patients of a tertiary hospital in Korea, prediction models for Emergency Department Length of Stay were built using various machine learning algorithms. Results The logistic regression (AUROC 0.85) models showed the best performance, followed by random forest (AUROC 0.83) and Naive Bayes (AUROC 0.83). The logistic regression model indicated that fewer consultations, the highest acuity level, need for an emergency operation or angiography, need for ICU admission, severe emergency disease and fewer diagnoses were the statistically significant predictors for Emergency Department Length of Stay of 6 h or less. Conclusions The transferred critically ill patients analysed in this study who required immediate or specialized care tended to receive needed care on time at the study site. Implications for Nursing Management Understanding the factors affecting the Emergency Department Length of Stay of transferred critically ill patients is crucial for developing strategies to manage the nursing resource of Emergency Department successfully.

Background Development of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach—the Emergency Severity Index (ESI). Methods Using National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ≥ 18 years). In the randomly sampled training set (70%), using routinely available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and deep neural network. As the reference model, we constructed a logistic regression model using the five-level ESI data. The clinical outcomes were critical care (admission to intensive care unit or in-hospital death) and hospitalization (direct hospital admission or transfer). In the test set (the remaining 30%), we measured the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and net benefit (decision curves) for each model. Results Of 135,470 eligible ED visits, 2.1% had critical care outcome and 16.2% had hospitalization outcome. In the critical care outcome prediction, all four machine learning models outperformed the reference model (e.g., AUC, 0.86 [95%CI 0.85–0.87] in the deep neural network vs 0.74 [95%CI 0.72–0.75] in the reference model), with less under-triaged patients in ESI triage levels 3 to 5 (urgent to non-urgent). Likewise, in the hospitalization outcome prediction, all machine learning models outperformed the reference model (e.g., AUC, 0.82 [95%CI 0.82–0.83] in the deep neural network vs 0.69 [95%CI 0.68–0.69] in the reference model) with less over-triages in ESI triage levels 1 to 3 (immediate to urgent). In the decision curve analysis, all machine learning models consistently achieved a greater net benefit—a larger number of appropriate triages considering a trade-off with over-triages—across the range of clinical thresholds. Conclusions Compared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians’ triage decision making, thereby achieving better clinical care and optimal resource utilization.

BackgroundThe transfer of patients between hospitals (inter-hospital transfer, or IHT) is a common occurrence for patients, but guidelines to ensure safe and effective IHTs are lacking. Poor IHTs result in higher rates of mortality, longer lengths of stay, and higher hospitalization costs compared to admissions from the emergency department. Nurses are often the first point of contact for IHT patients and can provide valuable insights on key challenges to IHT processes.ObjectiveTo characterize the experiences of inpatient floor-level bedside nurses caring for IHT patients and identify care coordination challenges and solutions.Design/Participants/ApproachQualitative study using semi-structured focus groups and interviews conducted from October 2019 to July 2020 with 21 inpatient floor-level nurses caring for adult medicine patients at an academic hospital. Nurses were recruited using a purposive convenience sampling approach. A combined inductive and deductive coding approach guided by thematic analysis was used for data analysis.Key ResultsResults from this study are mapped to the Agency for Healthcare Research and Quality Care Coordination Measurement Framework domains of communication, assessing needs and goals, and negotiating accountability. The following key themes characterize nurses’ experiences with IHT related to these domains: (1) challenges with information exchange and team communication during IHT, (2) environmental and information preparation needed to anticipate transfers, and (3) determining responsibility and care plans after the IHT patient has arrived at the accepting facility.ConclusionsNurses described the absence of standardized processes to coordinate care before or at the time of patient arrival. Challenges to communication and coordination during IHTs negatively impacted patient care and nursing professional satisfaction. To streamline care for IHT patients and reduce nursing stress, future IHT interventions should include standardized handoff reports, timely identification and easy access to admitting clinicians, and timely clinician evaluation and orders.

Background Inter-hospital patient transfers to hospitals with greater resource availability and expertise may improve clinical outcomes. However, there is little guidance regarding how patient transfer requests should be prioritized when hospital resources become scarce. Objective To understand the experiences of healthcare workers involved in the process of accepting inter-hospital patient transfers during a pandemic surge and determine factors impacting inter-hospital patient transfer decision-making. Design We conducted a qualitative study consisting of semi-structured interviews between October 2021 and February 2022. Participants Eligible participants were physicians, nurses, and non-clinician administrators involved in the process of accepting inter-hospital patient transfers. Participants were recruited using maximum variation sampling. Approach Semi-structured interviews were conducted with healthcare workers across Michigan. Key Results Twenty-one participants from 15 hospitals were interviewed (45.5% of eligible hospitals). About half (52.4%) of participants were physicians, 38.1% were nurses, and 9.5% were non-clinician administrators. Three domains of themes impacting patient transfer decision-making emerged: decision-maker, patient, and environmental factors. Decision-makers described a lack of guidance for transfer decision-making. Patient factors included severity of illness, predicted chance of survival, need for specialized care, and patient preferences for medical care. Decision-making occurred within the context of environmental factors including scarce resources at accepting and requesting hospitals, organizational changes to transfer processes, and alternatives to patient transfer including use of virtual care. Participants described substantial moral distress related to transfer triaging. Conclusions A lack of guidance in transfer processes may result in considerable variation in the patients who are accepted for inter-hospital transfer and in substantial moral distress among decision-makers involved in the transfer process. Our findings identify potential organizational changes to improve the inter-hospital transfer process and alleviate the moral distress experienced by decision-makers.

BackgroundPrimary PCI is the treatment of choice in patients presenting with ST elevation myocardial infarction. . The delay in reperfusion leads to increased morbidity and mortality. The secular trends in the impact of inter-hospital transfers (IT) on mortality in the United Kingdom (UK) have not been recently investigated.AimTo investigate the impact of Inter-hospital transfers on in-hospital major adverse cardiovascular events and 5 years mortality among patients admitted to a tertiary care Centre in the UKMethodsThe patient level data on STEMI patients admitted between 2011 to 2021 were retrospectively collected in line with (NICOR) data fields. We compared baseline characteristics and mortality data of IT group to age and gender matched DA group.Primary end point was in hospital major cardiovascular complications and secondary end point was mortality at five years for patients admitted from 2011 to 2016. Of 4269 patients, 3044 (71%) were directly admitted and 1225 (29%) presented by inter hospital transfer. Inter hospital transfer group were more likely to present with cardiogenic shock and their median call to balloon time was 108 minutes longer than direct admission group. There was no significant difference in MACE during hospital admission between the two groups except for in-hospital mortality which was 5.26% higher in inter hospital transfer group. Kaplan Meier Survival analysis showed that the likelihood of survival at five years was 83% for direct admission vs 77.3% for inter hospital transfer with P value < 0.01. Additionally, one month mortality was significantly higher among inter hospital transfer group.Abstract 45 Figure 1ConclusionIn all the cohort of patients, in-hospital mortality, and five years mortality was significantly higher in inter hospital transfer group compared to direct admission group.Conflict of Interestnon

BackgroundUnplanned emergency department (ED) visits of nursing home residents (NHR) are common, with many transfers not leading to hospitalization. However, there is little research on what diagnostic and therapeutic measures are performed during visits.AimsWe analyzed underlying diagnoses, characteristics and performed medical procedures of unplanned outpatient ED visits by NHR.MethodsWe conducted a multi-center study of 14 nursing homes (NHs) in northwestern Germany in 03/2018–07/2019. Hospital transfers were documented by nursing staff using a standardized questionnaire for 12 months. In addition, discharge letters were used to collect information about the respective transfer, its reasons and the extend of the medical services performed in the ED.ResultsA total of 161 unplanned ED visits were included (mean age: 84.2 years; 68.3% females). The main transfer reasons were trauma (59.0%), urinary catheter and nutritional probe problems (overall 10.6%; male NHR 25.5%) and altered mental state (9.9%). 32.9% where discharged without imaging or blood test prior. 67.4% of injured NHR (n = 95) required no or only basic wound care. Catheter-related problems (n = 17) were mainly treated by changing an existing suprapubic catheter (35.3%) and by flushing the pre-existing catheter (29.4%).DiscussionOur data suggest that the diagnostic and therapeutic interventions performed in ED, often do not exceed general practitioner (GP) care and many ED visits seem to be unnecessary.ConclusionBetter coordination and consultation with GPs as well as better training of nursing staff in handling catheter problems could help to reduce the number of ED visits.

Background Up to 75% of residents from residential aged care facilities (RACF) are transferred to emergency departments (ED) annually to access assessment and care for unplanned or acute health events. Emergency department presentations of RACF residents can be both expensive and risky, and many are unnecessary and preventable. Processes or triage systems to assess residents with a health event, prior to transfer, may reduce unnecessary ED transfer. The Aged Care Emergency (ACE) service is a nurse-led ED outreach service that provides telephone support to RACF nurses regarding residents’ health events. This service is available Monday to Friday, 8am to 4 pm (ED ACE hours). The primary objective of this study was to assess whether the augmentation of the phone-based ED ACE service with the addition of a visual telehealth consultation (VTC) would reduce RACF rate of ED presentations compared to usual care. The secondary objectives were to 1) monitor presentations to ED within 48 h post VTC to detect any adverse events and 2) measure RACF staff perceptions of VTC useability and acceptability. Methods This implementation study used a stepped wedge cluster randomised controlled trial design. Study settings were four public hospital EDs and 16 RACFs in two Local Health Districts. Each ED was linked to 4 RACFs with approximately 350 RACF beds, totalling 1435 beds across 16 participating RACFs. Facilities were randomised into eight clusters with each cluster comprising one ED and two RACFs. Results A negative binomial regression demonstrated a 29% post-implementation reduction in the rate of ED presentations (per 100 RACF beds), within ED ACE hours (IRR [95% CI]: 0.71 [0.46, 1. 09]; p  = 0.122). A 29% reduction, whilst not statistically significant, is still clinically important and impactful for residents and EDs. A post-hoc logistic regression demonstrated a statistically significant 69% reduction in the probability that an episode of care resulted in an ED presentation within ED ACE hours post-implementation compared to pre-implementation (OR [95% CI]: 0.31 [0.11, 0.87]; p  = 0.025). Conclusion Findings have shown the positive impact of augmenting ACE with a VTC. Any reduction of resident presentations to a busy ED is beneficial to healthcare overall, but more so to the individual older person who can recover safely and comfortably in their own RACF. Trial registration Australian New Zealand Clinical Trials Registry (ID ACTR N12619001692123) (02/12/2019) https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378629andisReview=true