Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial.

OBJECTIVES: Machine learning algorithms can outperform older methods in predicting clinical deterioration, but rigorous prospective data on their real-world efficacy are limited. We hypothesized that real-time machine learning generated alerts sent directly to front-line providers would reduce escalations. DESIGN: Single-center prospective pragmatic nonrandomized clustered clinical trial. SETTING: Academic tertiary care medical center. PATIENTS: Adult patients admitted to four medical-surgical units. Assignment to intervention or control arms was determined by initial unit admission. INTERVENTIONS: Real-time alerts stratified according to predicted likelihood of deterioration sent either to the primary team or directly to the rapid response team (RRT). Clinical care and interventions were at the providers' discretion. For the control units, alerts were generated but not sent, and standard RRT activation criteria were used. MEASUREMENTS AND MAIN RESULTS: The primary outcome was the rate of escalation per 1000 patient bed days. Secondary outcomes included the frequency of orders for fluids, medications, and diagnostic tests, and combined in-hospital and 30-day mortality. Propensity score modeling with stabilized inverse probability of treatment weight (IPTW) was used to account for differences between groups. Data from 2740 patients enrolled between July 2019 and March 2020 were analyzed (1488 intervention, 1252 control). Average age was 66.3 years and 1428 participants (52%) were female. The rate of escalation was 12.3 vs. 11.3 per 1000 patient bed days (difference, 1.0; 95% CI, -2.8 to 4.7) and IPTW adjusted incidence rate ratio 1.43 (95% CI, 1.16-1.78; p < 0.001). Patients in the intervention group were more likely to receive cardiovascular medication orders (16.1% vs. 11.3%; 4.7%; 95% CI, 2.1-7.4%) and IPTW adjusted relative risk (RR) (1.74; 95% CI, 1.39-2.18; p < 0.001). Combined in-hospital and 30-day-mortality was lower in the intervention group (7% vs. 9.3%; -2.4%; 95% CI, -4.5% to -0.2%) and IPTW adjusted RR (0.76; 95% CI, 0.58-0.99; p = 0.045). CONCLUSIONS: Real-time machine learning alerts do not reduce the rate of escalation but may reduce mortality.

A Quality Improvement Project to Reduce Rapid Response System Inequities for Patients with Limited English Proficiency at a Quaternary Academic Medical Center.

BACKGROUND: Recognition of clinically deteriorating hospitalized patients with activation of rapid response (RR) systems can prevent patient harm. Patients with limited English proficiency (LEP), however, experience less benefit from RR systems than do their English-speaking counterparts. OBJECTIVE: To improve outcomes among hospitalized LEP patients experiencing clinical deteriorations. DESIGN: Quasi-experimental pre-post design using quality improvement (QI) statistics. PARTICIPANTS: All adult hospitalized non-intensive care patients with LEP who were admitted to a large academic medical center from May 2021 through March 2023 and experienced RR system activation were included in the evaluation. All patients included after May 2022 were exposed to the intervention. INTERVENTIONS: Implementation of a modified RR system for LEP patients in May 2022 that included electronic dashboard monitoring of early warning scores (EWSs) based on electronic medical record data; RR nurse initiation of consults or full RR system activation; and systematic engagement of interpreters. MAIN MEASURES: Process of care measures included monthly rates of RR system activation, critical response nurse consultations, and disease severity scores prior to activation. Main outcomes included average post-RR system activation length of stay, escalation of care, and in-hospital mortality. Analyses used QI statistics to identify special cause variation in pre-post control charts based on monthly data aggregates. KEY RESULTS: In total, 222 patients experienced at least one RR system activation during the study period. We saw no special cause variation for process measures, or for length of hospitalization or escalation of care. There was, however, special cause variation in mortality rates with an overall pre-post decrease in average monthly mortality from 7.42% (n = 8/107) to 6.09% (n = 7/115). CONCLUSIONS: In this pilot study, prioritized tracking, utilization of EWS-triggered evaluations, and interpreter integration into the RR system for LEP patients were feasible to implement and showed promise for reducing post-RR system activation mortality.

Assessment of Hospital Medical Emergency Team Operations in a Tertiary Care Center in Turkey.

BACKGROUND: Rapid Response Teams, strategically devised to mitigate mortality and morbidity stemming from unforeseen deteriorations and cardiac arrests within healthcare facilities, are ubiquitously implemented on a global scale. AIM: The aim of the study is to compare emergency physicians (EPs) and non-EPs on management protocols of Hospital Medical Emergency Teams (HoMET). METHODS: This was a retrospective cross-sectional study. The hospital archive underwent a retrospective scanning process, and patient records were meticulously examined. The assessment encompassed various facets, including demographic characteristics, activation locations, and response and intervention times of HoMET teams, composed of both EPs and other healthcare professionals. Data analysis was conducted using SPSS software version 20.0. RESULTS: A total of 1056 calls were included, with 52% (n = 549) involving male patients. The average age was 67.15 ± 19.45 years. EPs served as the team leader in 53% of the calls. Cardiac arrest was considered in 93.6% of the cases. The EPs group exhibited a higher average patient age, longer intervention times, and shorter arrival times (P < 0.001, P = 0.027, P < 0.001, respectively). A significant difference was observed in the locations of the calls and the groups of calls considering cardiac arrest (P < 0.001, P < 0.001, respectively). CONCLUSION: The optimization of intervention teams is imperative given the persistently high incidence and mortality rates associated with in-hospital cardiac arrests. Leveraging the expertise of EPs in the management of arrests and critical patients can potentially enhance the effectiveness of these teams. Nonetheless, further research is warranted to comprehensively explore and validate this aspect.

Education, Implementation, and Teams: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations.

For this 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations, the Education, Implementation, and Teams Task Force applied the population, intervention, comparator, outcome, study design, time frame format and performed 15 systematic reviews, applying the Grading of Recommendations, Assessment, Development, and Evaluation guidance. Furthermore, 4 scoping reviews and 7 evidence updates assessed any new evidence to determine if a change in any existing treatment recommendation was required. The topics covered included training for the treatment of opioid overdose; basic life support, including automated external defibrillator training; measuring implementation and performance in communities, and cardiac arrest centers; advanced life support training, including team and leadership training and rapid response teams; measuring cardiopulmonary resuscitation performance, feedback devices, and debriefing; and the use of social media to improve cardiopulmonary resuscitation application.

Rapid response team integration at a quaternary care academic centre: new paradigm for critical care organistions.

BACKGROUND: Decompensating patients require expeditious and focused care at the bedside. This can be particularly challenging when there are multiple layers of providers, each with differing specialisation, experience and autonomy. We examined the impact of our intensivist-driven hospital-wide rapid response team (RRT) at our 1171-bed quaternary care centre. DESIGN: Single-centre retrospective cohort study. METHODS: RRT service was implemented to assess, manage and triage acutely ill patients outside the intensive care unit (ICU). Criteria for consultation and workflow were established. The 24/7 team was led by an intensivist and included nurse practitioners and respiratory therapists. Over 3 years, we reviewed the impact of the RRT on patient outcomes and critical care support beyond the ICU. RESULTS: Over 3 years, the RRT received 31 392 consults for 12 122 individual patients averaging 30 consults over 24 hours. 58.9% of the calls received were for sepsis alerts/risk of decompensation and 41.1% of the consults were for reasons of acute decompensation. Among patients that were seen by the RRT, over the course of their hospital stay, 14% were upgraded to a step-down unit, 18% were upgraded to the ICU and 68% completed care without requiring any escalation. The average mortality rate for patients seen by the RRT service during their hospital stay was 11.3% with an average 30-day readmission rate of 16.5% and average hospital length of stay 16 days without significant variation between the 3 years. CONCLUSIONS: Intensivist-led RRT ensured consistent high value care. Early intervention and consistent supervision enabled timely and efficient delivery of critical care services.

Dealing with the impact of the COVID-19 pandemic on a rapid response team operation in Brazil: Quality in practice.

QUALITY PROBLEM OR ISSUE: Up to 13 July 2020, >12 million laboratory-confirmed cases of coronavirus disease of 2019 (COVID-19) infection have been reported worldwide, 1 864 681 in Brazil. We aimed to assess an intervention to deal with the impact of the COVID-19 pandemic on the operations of a rapid response team (RRT). INITIAL ASSESSMENT: An observational study with medical record review was carried out at a large tertiary care hospital in Fortaleza, a 400-bed quaternary hospital, 96 of which are intensive care unit beds. All adult patients admitted to hospital wards, treated by the RRTs during the study period, were included, and a total of 15 461 RRT calls were analyzed. CHOICE OF SOLUTION: Adequacy of workforce sizing. IMPLEMENTATION: The hospital adjusted the size of its RRTs during the period, going from two to four simultaneous on-duty medical professionals. EVALUATION: After the beginning of the pandemic, the number of treated cases in general went from an average of 30.6 daily calls to 79.2, whereas the extremely critical cases went from 3.5 to 22 on average. In percentages, the extremely critical care cases went from 10.47 to 20%, with P < 0.001. Patient mortality remained unchanged. The number of critically ill cases and the number of treated patients increased 2-fold in relation to the prepandemic period, but the effectiveness of the RRT in relation to mortality was not affected. LESSONS LEARNED: The observation of these data is important for hospital managers to adjust the size of their RRTs according to the new scenario, aiming to maintain the intervention effectiveness.

Rapid Response System Should Be Enhanced at Non-general Ward Locations: a Retrospective Multicenter Cohort Study in Korea.

BACKGROUND: A rapid response system (RRS) contributes to the safety of hospitalized patients. Clinical deterioration may occur in the general ward (GW) or in non-GW locations such as radiology or dialysis units. However, there are few studies regarding RRS activation in non-GW locations. This study aimed to compare the clinical characteristics and outcomes of patients with RRS activation in non-GW locations and in the GW. METHODS: From January 2016 to December 2017, all patients requiring RRS activation in nine South Korean hospitals were retrospectively enrolled and classified according to RRS activation location: GW vs non-GW RRS activations. RESULTS: In total, 12,793 patients were enrolled; 222 (1.7%) were non-GW RRS activations. There were more instances of shock (11.6% vs. 18.5%) and cardiac arrest (2.7% vs. 22.5%) in non-GW RRS activation patients. These patients also had a lower oxygen saturation (92.6% ± 8.6% vs. 88.7% ± 14.3%, P < 0.001) and a higher National Early Warning Score 2 (7.5 ± 3.4 vs. 8.9 ± 3.8, P < 0.001) than GW RRS activation patients. Although non-GW RRS activation patients received more intubation (odds ratio [OR], 3.135; P < 0.001), advanced cardiovascular life support (OR, 3.912; P < 0.001), and intensive care unit transfer (OR, 2.502; P < 0.001), their hospital mortality (hazard ratio, 0.630; P = 0.013) was lower than GW RRS activation patients upon multivariate analysis. CONCLUSION: Considering that there were more critically ill but recoverable cases in non-GW locations, active RRS involvement should be required in such locations.

Characteristics and Prognosis of Hospitalized Patients at High Risk of Deterioration Identified by the Rapid Response System: a Multicenter Cohort Study.

We aimed to investigate the characteristics and prognosis of high risk hospitalized patients identified by the rapid response system (RRS). A multicentered retrospective cohort study was conducted from June 2019 to December 2020. The National Early Warning Score (NEWS) was used for RRS activation. The outcome was unexpected intensive care unit (ICU) admission within 24 hours after RRS activation. The 11,459 patients with RRS activations were included. We found distinct clinical characteristics in patients who underwent ICU admission. All NEWS parameters were associated with the risk of unexpected ICU admission except body temperature. Body mass index, pulmonary disease, and cancer are related to the decreased risk of unexpected ICU admission. In conclusion, there were differences in clinical characteristics among high risk patients, and those differences were associated with unexpected ICU admissions. Clinicians should consider factors relating to unexpected ICU admission in the management of high risk patients identified by RRS.

A rapid response and treatment service for care homes: a case study.

Rapid response services provide opportunities for older people living with frailty to remain in their own homes during an episode of deteriorating health. The government has announced additional funding to increase capacity and responsiveness for these services through the Ageing Well programme as part of the NHS Long Term Plan. Older people living with frailty are particularly at risk of the adverse effects of a hospital admission and evidence is emerging of the benefits of enhanced healthcare support to allow them to remain in their own home. The Hospital at Home model offers short-term, targeted interventions at acute hospital level care that can provide a truly person-centred experience within the home. This article describes a Rapid Response and Treatment service for older people living in care homes in Berkshire West and shares Sid's story to demonstrate how such a service is delivered. The COVID-19 pandemic has presented additional challenges and opportunities that highlight the ongoing need for the development of services that will support older people to prioritise what matters to them most.

Quels sont le fonctionnement, les caractéristiques, les effets et les modalités d'implantation des équipes d'intervention rapide ? Une revue de la littérature.

Introduction : Hospitalized patients are at risk of unrecognized clinical deterioration that may lead to adverse events.Context : Rapid Response Teams (RRTs) exist around the world as a strategy to improve patient safety.Objective : To explore how RRTs work, their characteristics, impacts, and methods of implementation.Design : Literature review.Method : Consultation of the databases CINAHL, MEDLINE, PUBMED, COCHRANE library, SCOPUS, and PROQUEST Dissertations and Theses. Keywords : “health care team” and “rapid response team”.Results : 121 articles were included. The collected data were divided into five categories : 1) composition and operation of RRTs, 2) benefits and limitations of RRTs, 3) perceptions of RRTs by health care teams, organizations, and patients, 4) implementation strategies, and 5) facilitators and barriers to implementation.Discussion : Although there are many articles related to RRTs, it appears that : 1) few studies analyze the difference in outcomes in hospitalized patients related to the composition of RRTs, 2) few studies describe how RRTs should work, 3) more studies are needed on the impacts of RRTs on hospitalized patients, 4) organizations’ and patients’ perceptions of RRTs are not well studied, and 5) more studies are needed on the best way to implement an RRT.Conclusion : The results show that there is a lack of studies on the difference in outcomes in hospitalized patients related to the composition of RRTs, on how RRTs should work, on the impacts of RRTs on hospitalized patients, on organizations’ and patients’ perceptions of RRTs, and on the factors that influence the success or failure of the implementation of an RRT.

Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System.

OBJECTIVES: As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning-based early warning system. The purpose of this study was to compare the performance of an artificial intelligence-based early warning system with that of conventional methods in a real hospital situation. DESIGN: Retrospective cohort study. SETTING: This study was conducted at a hospital in which deep learning-based early warning system was implemented. PATIENTS: We reviewed the records of adult patients who were admitted to the general ward of our hospital from April 2018 to March 2019. INTERVENTIONS: The study population included 8,039 adult patients. A total 83 events of deterioration occurred during the study period. The outcome was events of deterioration, defined as cardiac arrest and unexpected ICU admission. We defined a true alarm as an alarm occurring within 0.5-24 hours before a deteriorating event. MEASUREMENTS AND MAIN RESULTS: We used the area under the receiver operating characteristic curve, area under the precision-recall curve, number needed to examine, and mean alarm count per day as comparative measures. The deep learning-based early warning system (area under the receiver operating characteristic curve, 0.865; area under the precision-recall curve, 0.066) outperformed the modified early warning score (area under the receiver operating characteristic curve, 0.682; area under the precision-recall curve, 0.010) and reduced the number needed to examine and mean alarm count per day by 69.2% and 59.6%, respectively. At the same specificity, deep learning-based early warning system had up to 257% higher sensitivity than conventional methods. CONCLUSIONS: The developed artificial intelligence based on deep-learning, deep learning-based early warning system, accurately predicted deterioration of patients in a general ward and outperformed conventional methods. This study showed the potential and effectiveness of artificial intelligence in an rapid response system, which can be applied together with electronic health records. This will be a useful method to identify patients with deterioration and help with precise decision-making in daily practice.

Re-designing a rapid response system: effect on staff experiences and perceptions of rapid response team calls.

BACKGROUND: Rapid Response Team (RRT) calls are clinical crises. Clinical and time pressures can hinder effective liaison between staff who call the RRT ('users') and those responding as part of the RRT ('members'). Non-technical skills (NTS) training has been shown to improve communication and cooperation but requires time and financial resources that may not be available in acute care hospitals. Rapid Response System (RRS) re-design, aiming to promote use of NTS, may provide an alternative approach to improving interactions within RRTs and between members and users. METHODS: Re-design of an existing mature RRS was undertaken in a tertiary, metropolitan hospital incorporating the addition of: 1) regular RRT meetings 2) RRT role badges and 3) a structured member-to-user patient care responsibility "hand-off" process. To compare experiences and perceptions of calls, users and members were surveyed pre and post re-design. RESULTS: Post re-design there were improvements in members' understanding of RRT roles (P = 0.03) and responsibilities (P < 0.01), and recollection of introducing themselves to users (P = 0.02). For users, after the re-design, there were improvements in identification of the RRT leader (P < 0.01), and in the development of clinical plans for patients remaining on the ward at the end of an RRT call (P < 0.01). However, post-re-design, fewer users agreed that the structured hand-off was useful or that they should be involved in the process. Both members and users reported fewer experiences of conflict at RRT calls post-re-design (both P < 0.01). CONCLUSION: The RRS re-design yielded improvements in interactions between members in RRTs and between RRT members and users. However, some unintended consequences arose, particularly around user satisfaction with the structured hand-off. These findings suggest that refinement and improvement of the RRS is possible, but should be an ongoing iterative effort, ideally supported by staff training. TRIAL REGISTRATION: NCT01551160. Registered: 12th March 2012.

The prevalence and characteristics of rapid response systems in hospitals with pediatric intensive care units in Japan and barriers to their use.

OBJECTIVE: The use of pediatric rapid response systems (RRSs) to improve the safety of hospitalized children has spread in various western countries including the United States and the United Kingdom. We aimed to determine the prevalence and characteristics of pediatric RRSs and barriers to use in Japan, where epidemiological information is limited. DESIGN: A cross-sectional online survey. SETTING: All 34 hospitals in Japan with pediatric intensive care units (PICUs) in 2019. PARTICIPANTS: One PICU physician per hospital responded to the questionnaire as a delegate. MAIN OUTCOME MEASURES: Prevalence of pediatric RRSs in Japan and barriers to their use. RESULTS: The survey response rate was 100%. Pediatric RRSs had been introduced in 14 (41.2%) institutions, and response teams comprised a median of 6 core members. Most response teams employed no full-time members and largely comprised members from multiple disciplines and departments who served in addition to their main duties. Of 20 institutions without pediatric RRSs, 11 (55%) hoped to introduce them, 14 (70%) had insufficient knowledge concerning them and 11 (55%) considered that their introduction might be difficult. The main barrier to adopting RRSs was a perceived personnel and/or funding shortage. There was no significant difference in hospital beds (mean, 472 vs. 524, P = 0.86) and PICU beds (mean, 10 vs. 8, P = 0.34) between institutions with/without pediatric RRSs. CONCLUSIONS: Fewer than half of Japanese institutions with PICUs had pediatric RRSs. Operating methods for and obstructions to RRSs were diverse. Our findings may help to popularize pediatric RRSs.

Predictors of mortality and cost among surgical patients requiring rapid response team activation.

Background: Prior studies of rapid response team (RRT) implementation for surgical patients have demonstrated mixed results with respect to reductions in poor outcomes. The aim of this study was to identify predictors of in-hospital mortality and hospital costs among surgical inpatients requiring RRT activation. Methods: We analyzed data prospectively collected from May 2012 to May 2016 at The Ottawa Hospital. We included patients who were at least 18 years of age, who were admitted to hospital, who received either preoperative or postoperative care, and and who required RRT activation. We created a multivariable logistic regression model to describe mortality predictors and a multivariable generalized linear model to describe cost predictors. Results: We included 1507 patients. The in-hospital mortality rate was 15.9%. The patient-related factors most strongly associated with mortality included an Elixhauser Comorbidity Index score of 20 or higher (odds ratio [OR] 3.60, 95% confidence interval [CI] 1.96-6.60) and care designations excluding admission to the intensive care unit and cardiopulmonary resuscitation (OR 3.52, 95% CI 2.25-5.52). The strongest surgical predictors included neurosurgical admission (OR 2.09, 95% CI 1.17-3.75), emergent surgery (OR 2.04, 95% CI 1.37-3.03) and occurrence of 2 or more operations (OR 1.73, 95% CI 1.21-2.46). Among RRT factors, occurrence of 2 or more RRT assessments (OR 2.01, 95% CI 1.44-2.80) conferred the highest mortality. Increased cost was strongly associated with admitting service, multiple surgeries, multiple RRT assessments and medical comorbidity. Conclusion: RRT activation among surgical inpatients identifies a population at high risk of death. We identified several predictors of mortality and cost, which represent opportunities for future quality improvement and patient safety initiatives.

Contexte: Les études sur la mobilisation d'équipes d'intervention rapide (EIR) auprès de patients en chirurgie ont donné des résultats mitigés quant à la réduction des issues négatives. La présente étude visait à déterminer les facteurs prédictifs de coûts pour les hôpitaux et de mortalité chez les patients en chirurgie nécessitant la mobilisation d'une EIR. Méthodes: Nous avons analysé des données recueillies de manière prospective de mai 2012 à mai 2016 à l'Hôpital d'Ottawa. Nous avons inclus les patients hospitalisés de 18 ans et plus qui ont reçu des soins préopératoires ou postopératoires et qui ont nécessité l'intervention d'une EIR. Nous avons ensuite créé un modèle de régression logistique multivariée pour décrire les facteurs prédictifs de mortalité et un modèle linéaire généralisé multivarié pour décrire les facteurs prédictifs de coûts. Résultats: Nous avons retenus 1507 patients. Le taux global de mortalité à l'hôpital était de 15,9 %. Les principaux facteurs de mortalité liés au patient étaient un indice de comorbidité d'Elixhauser supérieur ou égal à 20 (rapport de cotes [RC] 3,60, intervalle de confiance [IC] à 95 % 1,96­6,60) et des objectifs de soins excluant l'admission à l'unité des soins intensifs et la réanimation cardiorespiratoire (RC 3,52, IC à 95 % 2,25­5,52). Les principaux facteurs prédictifs liés aux interventions sont l'admission en neurochirurgie (RC 2,09, IC à 95 % 1,17­3,75), l'intervention chirurgicale d'urgence (RC 2,04, IC à 95 % 1,37­3,03) et le fait d'avoir subi au moins 2 opérations (RC 1,73, IC à 95 % 1,21­2,46). Parmi les facteurs liés aux EIR, la tenue d'au moins 2 évaluations par l'EIR s'accompagnait du mortalité le plus élevé (RC 2,01, IC à 95 % 1,44­2,80). L'augmentation des coûts était étroitement associée au service d'admission, aux interventions chirurgicales multiples, aux évaluations multiples par l'EIR et à la comorbidité médicale. Conclusion: La mobilisation d'EIR auprès de patients en chirurgie permet de mettre en évidence une population à risque élevé de décès. Nous avons découvert plusieurs facteurs prédictifs de mortalité et de coûts, dont on pourra se servir pour améliorer la qualité des soins et la sécurité des patients.

Evaluation of a patient and family activated escalation system: Ryan's Rule.

INTRODUCTION: Patients experience physiological changes in the hours preceding adverse medical events, and patients or their family can be the first to identify ominous signs of clinical deterioration that have gone undetected by health professionals. Patient and family activated escalation systems provide consumers access to a referral system that can address their concerns. In Queensland, this escalation system is called Ryan's Rule and once activated, triggers an independent clinical review. This study aimed to access clinicians' and activators' experiences to develop an understanding of the incidence, contributing factors, and outcomes surrounding Ryan's Rule activations. METHOD: The study involved a retrospective chart review of Ryan's Rule (n = 57) activations in a regional hospital, over a 24-month period. RESULTS: On average, there were 2.4 activations a month. There are three major findings: first, communication issues were central to more than half the activations, 35% of cases required no clinical intervention, with communication alone sufficient to achieve resolution. Second, this initiative was valued with 65% of activators stating that they would be comfortable calling again and having access to the escalation process was reassuring and improved communication between clinicians and patients. While clinicians doubted the appropriateness of activators use of the escalation tool, 15% of patients were transferred to receive a higher level of care. Lastly, clinicians labelled activations as a 'complaints' as opposed to a 'concern' and reasoned that a 'complaint' did not justify a full review of the consumer's perspective for the activation. CONCLUSION: Consumers who activated a Ryan's Rule were satisfied and valued the process. It provides a reassuring safety net, empowering them to speak up and initiate a clinical review. Clear communication among clinicians and between clinicians and consumers is essential. Clinicians are hesitant to fully embrace Ryan's Rule, and this discordance contributes to the failure to fully evaluate reasons for call activation.

[Early Warning Systems: Introduction and Application].

Related studies in the literature indicate that over half (50-84%) of patients exhibit physiological variations 6 hours before experiencing cardiac arrest. Early warning systems improve the ability of medical teams to detect patient deterioration and then immediately treat sudden cardiac arrest during patient hospitalization. This article aims to strengthen general understanding among clinical medical staffs of the early warning system. Understanding the reasons and motivations for establishing this system is expected to help readers better distinguish the physiological monitoring indicators of this system and its importance in terms of improving patient safety. In particular, using the system to identify patients at risk levels of medium or higher will help facilitate their timely transfer to an intensive care unit for appropriate monitoring and care. This article further explores the application of early warning systems in nursing to help nurses understand their professional roles and responsibilities as members of the rapid-response team. Finally, information in this article teaches medical staffs how to avoid unanticipated cardiac arrest events, create a comprehensive patient safety environment, and improve the quality of medical care.

Efficacy and Safety of Pediatric Critical Care Physician Telemedicine Involvement in Rapid Response Team and Code Response in a Satellite Facility.

OBJECTIVES: Satellite inpatient facilities of larger children's hospitals often do not have on-site intensivist support. In-house rapid response teams and code teams may be difficult to operationalize in such facilities. We developed a system using telemedicine to provide pediatric intensivist involvement in rapid response team and code teams at the satellite facility of our children's hospital. Herein, we compare this model with our in-person model at our main campus. DESIGN: Cross-sectional. SETTING: A tertiary pediatric center and its satellite facility. PATIENTS: Patients admitted to the satellite facility. INTERVENTIONS: Implementation of a rapid response team and code team model at a satellite facility using telemedicine to provide intensivist support. MEASUREMENTS AND MAIN RESULTS: We evaluated the success of the telemedicine model through three a priori outcomes: 1) reliability: involvement of intensivist on telemedicine rapid response teams and codes, 2) efficiency: time from rapid response team and code call until intensivist response, and 3) outcomes: disposition of telemedicine rapid response team or code calls. We compared each metric from our telemedicine model with our established main campus model. MAIN RESULTS: Critical care was involved in satellite campus rapid response team activations reliably (94.6% of the time). The process was efficient (median response time 7 min; mean 8.44 min) and effective (54.5 % patients transferred to PICU, similar to the 45-55% monthly rate at main campus). For code activations, the critical care telemedicine response rate was 100% (6/6), with a fast response time (median 1.5 min). We found no additional risk to patients, with no patients transferred from the satellite campus requiring a rapid escalation of care defined as initiation of vasoactive support, greater than 60 mL/kg in fluid resuscitation, or endotracheal intubation. CONCLUSIONS: Telemedicine can provide reliable, timely, and effective critical care involvement in rapid response team and Code Teams at satellite facilities.

Parent escalation of care for the deteriorating child in hospital: A health-care improvement study.

OBJECTIVE: To evaluate the implementation of an intervention for parents to escalate care if concerned about their child's clinical condition. DESIGN: Mixed-methods health-care improvement approach guided by the Theoretical Domains Framework. METHODS: Implementation of the 'Calling for Help' (C4H) intervention was informed by previously identified barriers and facilitators. Evaluation involved audit, review of clinical deterioration incidents, interviews and focus groups. SETTING: Australian specialist paediatric hospital. PARTICIPANTS: Convenience sample of 75 parents from inpatient areas during the audit, interviews with ten parents who had expressed concern about their child's clinical condition; five focus groups with 35 ward nurses. MAIN OUTCOME MEASURES: Parent awareness and utilization of C4H, parent and nurse views of factors influencing implementation. RESULTS: Parent awareness of C4H improved to 35% (25/75). Parent concern was documented prior to 21/174 (12%) clinical deterioration events. All interviewed parents and nurses who participated in focus groups were positive about C4H. Parents preferred to be informed about C4H by nurses, but nurses described this as time-consuming and selectively chose parents who they believed would benefit most. Parents and nurses described frustrations with and trepidation in escalating care. Nurses had used C4H to expedite urgent medical review. CONCLUSIONS: There was an improvement in the level of parent awareness of C4H, which was viewed positively by parents and nurses alike. To achieve a high level of parent awareness in a sustainable way, a multifaceted approach is required. Further strategies will be required for parents to feel confident enough to use C4H and to address interprofessional communication barriers.

An Integrative Literature Review of Psychiatric Rapid Response Teams and Their Implementation for De-escalating Behavioral Crises in Nonpsychiatric Hospital Settings.

OBJECTIVE: To synthesize articles exploring the implementation of psychiatric rapid response teams (RRTs) for behavioral crises in hospital settings. BACKGROUND: Psychiatric/behavioral crises in nonpsychiatric hospital settings can lead to restraint use, staff injuries, and poor patient outcomes. Psychiatric RRTs may provide a solution, but they are a new, understudied intervention, and their implementation varies across institutions. METHODS: A systematic, integrative literature review of nursing and biomedical literature yielded 7 articles about psychiatric RRTs. Data were extracted on RRT structure, processes, outcomes, and implementation. RESULTS: Psychiatric RRTs were structured as a nurse-led, interdisciplinary intervention. When implemented using evidence-based models, they reduced security calls, restraint use, and staff injuries while moderately improving staff knowledge and self-efficacy. RRTs that included education, debriefing, and role modeling appeared to increase staff behavioral management skills and eventually reduced the need for RRTs. CONCLUSIONS: Psychiatric RRTs have demonstrated promise in quality improvement projects for reducing adverse outcomes related to behavioral health in hospitals.

Patient Survival and Length of Stay Associated With Delayed Rapid Response System Activation.

The objective of this study was to investigate the difference in mortality and length of stay between patients who experienced a delay in rapid response system (RRS) activation and those who did not. A retrospective comparative cohort study investigated all adult inpatient cases that experienced an RRS activation from January 1, 2017, through January 1, 2018. Cases experiencing a delay in RRS activation were compared with cases without delay. During the study period a total of 3580 RRS activations that took place and 1086 RRS activations met inclusion criteria for analysis. Delayed RRS activations occurred in 325 cases (29.8%) and nondelayed RRS activations occurred in 766 cases (70.2%). The mean age was roughly the same for both groups (60 years old) and both groups consisted of approximately 60% males. Delay in activation was significantly associated with an increase in length of hospitalization (19.9 days vs 32.4 days; P < .001) and also a higher likelihood of not surviving hospitalization (hazard ratio = 2.70; 95% confidence interval, 1.96-3.71; P < .001). This study demonstrates that delayed RRS activation occurs frequently and exposes patients to higher mortality and longer length of hospitalization.