scholarly journals Implementation of an Interdisciplinary AACN Early Mobility Protocol

2020 ◽  
Vol 40 (4) ◽  
pp. e7-e17 ◽  
Author(s):  
Marilyn Schallom ◽  
Heidi Tymkew ◽  
Kara Vyers ◽  
Donna Prentice ◽  
Carrie Sona ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Critical Care ◽  
Intensive Care ◽  
Length Of Stay ◽  
Intensive Care Units ◽  
American Association ◽  
Phase 1 ◽  
Phase 2 ◽  
Early Mobility ◽  
Protocol Implementation

Background Increasing mobility in the intensive care unit is an important part of the ABCDEF bundle. Objective To examine the impact of an interdisciplinary mobility protocol in 7 specialty intensive care units that previously implemented other bundle components. Methods A staggered quality improvement project using the American Association of Critical-Care Nurses mobility protocol was conducted. In phase 1, data were collected on patients with intensive care unit stays of 24 hours or more for 2 months before and 2 months after protocol implementation. In phase 2, data were collected on a random sample of 20% of patients with an intensive care unit stay of 3 days or more for 2 months before and 12 months after protocol implementation. Results The study population consisted of 1266 patients before and 1420 patients after implementation in phase 1 and 258 patients before and 1681 patients after implementation in phase 2. In phase 1, the mean (SD) mobility level increased in all intensive care units, from 1.45 (1.03) before to 1.64 (1.03) after implementation (P < .001). Mean (SD) ICU Mobility Scale scores increased on initial evaluation from 4.4 (2.8) to 5.0 (2.8) (P = .01) and at intensive care unit discharge from 6.4 (2.5) to 6.8 (2.3) (P = .04). Complications occurred in 0.2% of patients mobilized. In phase 2, 84% of patients had out-of-bed activity after implementation. The time to achieve mobility levels 2 to 4 decreased (P = .05). Intensive care unit length of stay decreased significantly in both phases. Conclusions Implementing the American Association of Critical-Care early mobility protocol in intensive care units with ABCDEF components in place can increase mobility levels, decrease length of stay, and decrease delirium with minimal complications.

Differential impact of on-site or telepharmacy in the intensive care unit: a controlled before–after study

2021 ◽  
Vol 33 (1) ◽  
Author(s):  
Joao Gabriel Rosa Ramos ◽  
Sandra Cristina Hernandes ◽  
Talita Teles Teixeira Pereira ◽  
Shana Oliveira ◽  
Denis de Melo Soares ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Phase 1 ◽  
Phase 2 ◽  
Differential Impact ◽  
Scarce Resources ◽  
Clinical Pharmacists ◽  
Versus Phase ◽  
Quasi Experimental ◽  
The Impact

Abstract Background Clinical pharmacists have an important role in the intensive care unit (ICU) team but are scarce resources. Our aim was to evaluate the impact of on-site pharmacists on medical prescriptions in the ICU. Methods This is a retrospective, quasi-experimental, controlled before-after study in two ICUs. Interventions by pharmacists were evaluated in phase 1 (February to November 2016) and phase 2 (February to May 2017) in ICU A (intervention) and ICU B (control). In phase 1, both ICUs had a telepharmacy service in which medical prescriptions were evaluated and interventions were made remotely. In phase 2, an on-site pharmacist was implemented in ICU A, but not in ICU B. We compared the number of interventions that were accepted in phase 1 versus phase 2. Results During the study period, 8797/9603 (91.6%) prescriptions were evaluated, and 935 (10.6%) needed intervention. In phase 2, there was an increase in the proportion of interventions that were accepted by the physician in comparison to phase 1 (93.9% versus 76.8%, P < 0.001) in ICU A, but there was no change in ICU B (75.2% versus 73.9%, P = 0.845). Conclusion An on-site pharmacist in the ICU was associated with an increase in the proportion of interventions that were accepted by physicians.

scholarly journals Patient length of stay and mortality prediction: A survey

2017 ◽  
Vol 30 (2) ◽  
pp. 105-120 ◽  
Author(s):  
Aya Awad ◽  
Mohamed Bader–El–Den ◽  
James McNicholas
Keyword(s):  
Intensive Care Unit ◽  
Critical Care ◽  
Intensive Care ◽  
Length Of Stay ◽  
Length Of Hospital Stay ◽  
Hospital Performance ◽  
Mortality Prediction ◽  
Cost Of Care ◽  
Resource Utilisation ◽  
Number Of Patients

Over the past few years, there has been increased interest in data mining and machine learning methods to improve hospital performance, in particular hospitals want to improve their intensive care unit statistics by reducing the number of patients dying inside the intensive care unit. Research has focused on prediction of measurable outcomes, including risk of complications, mortality and length of hospital stay. The length of stay is an important metric both for healthcare providers and patients, influenced by numerous factors. In particular, the length of stay in critical care is of great significance, both to patient experience and the cost of care, and is influenced by factors specific to the highly complex environment of the intensive care unit. The length of stay is often used as a surrogate for other outcomes, where those outcomes cannot be measured; for example as a surrogate for hospital or intensive care unit mortality. The length of stay is also a parameter, which has been used to identify the severity of illnesses and healthcare resource utilisation. This paper examines a range of length of stay and mortality prediction applications in acute medicine and the critical care unit. It also focuses on the methods of analysing length of stay and mortality prediction. Moreover, the paper provides a classification and evaluation for the analytical methods of the length of stay and mortality prediction associated with a grouping of relevant research papers published in the years 1984 to 2016 related to the domain of survival analysis. In addition, the paper highlights some of the gaps and challenges of the domain.

scholarly journals Probabilistic analysis of COVID-19 patients’ individual length of stay in Swiss intensive care units

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247265
Author(s):  
Alexander Henzi ◽  
Gian-Reto Kleger ◽  
Matthias P. Hilty ◽  
Pedro D. Wendel Garcia ◽  
Johanna F. Ziegel ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Acute Respiratory Distress Syndrome ◽  
Intensive Care ◽  
Length Of Stay ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Intensive Care Units ◽  
Distress Syndrome ◽  
Unit Length ◽  
Training Data

Rationale The COVID-19 pandemic induces considerable strain on intensive care unit resources. Objectives We aim to provide early predictions of individual patients’ intensive care unit length of stay, which might improve resource allocation and patient care during the on-going pandemic. Methods We developed a new semiparametric distributional index model depending on covariates which are available within 24h after intensive care unit admission. The model was trained on a large cohort of acute respiratory distress syndrome patients out of the Minimal Dataset of the Swiss Society of Intensive Care Medicine. Then, we predict individual length of stay of patients in the RISC-19-ICU registry. Measurements The RISC-19-ICU Investigators for Switzerland collected data of 557 critically ill patients with COVID-19. Main results The model gives probabilistically and marginally calibrated predictions which are more informative than the empirical length of stay distribution of the training data. However, marginal calibration was worse after approximately 20 days in the whole cohort and in different subgroups. Long staying COVID-19 patients have shorter length of stay than regular acute respiratory distress syndrome patients. We found differences in LoS with respect to age categories and gender but not in regions of Switzerland with different stress of intensive care unit resources. Conclusion A new probabilistic model permits calibrated and informative probabilistic prediction of LoS of individual patients with COVID-19. Long staying patients could be discovered early. The model may be the basis to simulate stochastic models for bed occupation in intensive care units under different casemix scenarios.

scholarly journals A California Hospital’s Response to COVID-19: From a Ripple to a Tsunami Warning

2020 ◽  
Vol 40 (6) ◽  
pp. e1-e16
Author(s):  
Mary Kay Bader ◽  
Annabelle Braun ◽  
Cherie Fox ◽  
Lauren Dwinell ◽  
Jennifer Cord ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Critical Care ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Task Force ◽  
The United States ◽  
Tsunami Warning ◽  
Care Services ◽  
Evidence Review ◽  
Excellent Care

Background The outbreak of coronavirus disease 2019 (COVID-19) rippled across the world from Wuhan, China, to the shores of the United States within a few months. Hospitals and intensive care units were suddenly faced with a “tsunami” warning requiring instantaneous implementation and escalation of disaster plans. Evidence Review An evidence-based question was developed and an extensive review of the literature was completed, resulting in a structured plan for the intensive care units to manage a surge of patients critically ill with COVID-19 in March 2020. Twenty-five sources of evidence focusing on pandemic intensive care unit and COVID-19 management laid the foundation for the team to navigate the crisis. Implementation The Critical Care Services task force adopted recommendations from the CHEST consensus statement on surge capacity principles and other sources, which served as the framework for the organized response. The 4 S’s became the focus: space, staff, supplies, and systems. Development of algorithms, workflows, and new processes related to treating patients, staffing shortages, and limited supplies. New intensive care unit staffing solutions were adopted. Evaluation Using a framework based on the literature reviewed, the Critical Care Services task force controlled the surge of patients with COVID-19 in March through May 2020. Patients received excellent care, and the mortality rate was 0.008%. The intensive care unit team had the needed respiratory and general supplies but had to continually adapt to shortages of personal protective equipment, cleaning products, and some medications. Sustainability The intensive care unit pandemic response plan has been established and the team is prepared for the next wave of COVID-19.

Answering the Call: Impact of Tele-ICU Nurses During the COVID-19 Pandemic

2020 ◽  
Vol 40 (4) ◽  
pp. 25-31 ◽  
Author(s):  
Sandy L. Arneson ◽  
Sara J. Tucker ◽  
Marie Mercier ◽  
Jaspal Singh
Keyword(s):  
Intensive Care Unit ◽  
Critical Care ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Holistic Approach ◽  
Critical Care Units ◽  
Work From Home ◽  
New Strategy ◽  
High Level ◽  
Intensive Care Unit Nurses

Background The coronavirus disease 2019 pandemic has exacerbated staffing challenges already facing critical care nurses in intensive care units. Many intensive care units have been understaffed and the majority of nurses working in these units have little experience. Objective To describe how the skilled tele–intensive care unit nurses in our health system quickly changed from a patient-focused strategy to a clinician-focused approach during the coronavirus disease 2019 crisis. Methods We modified workflows, deployed home workstations, and changed staffing models with the goal of providing additional clinical support to bedside colleagues while reducing exposure time and conserving personal protective equipment for those caring for this highly contagious patient population. The unit changed focus and granted more than 300 clinicians access to technology that enabled them to care for patients remotely, added nearly 200 mobile carts, and allowed more than 20 tele–intensive care unit nurses to work from home. Results Tele–intensive care unit nursing provided clinical knowledge to the nurses covering current and expanded critical care units. Using technology, virtual rounding, and increased collaboration with nurses, tele–intensive care unit nursing minimized the risk to bedside nurses while maintaining a high level of care for patients. Conclusion Tele–intensive care unit nurses provided a proactive, holistic approach to caring for critically ill patients via camera as part of their routine workflow. In addition, during the coronavirus disease 2019 pandemic, these nurses created a new strategy in virtual health care to be implemented during a crisis.

Early Mobility in the Intensive Care Unit: Standard Equipment vs a Mobility Platform

2014 ◽  
Vol 23 (6) ◽  
pp. 451-457 ◽  
Author(s):  
Melanie Roberts ◽  
Laura Adele Johnson ◽  
Trent L. Lalonde
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Data Collection ◽  
Intensive Care Units ◽  
Research Study ◽  
Medical Decision ◽  
General Belief ◽  
Standard Equipment ◽  
Early Mobility ◽  
Intensive Care Patients

Background Despite the general belief that mobility and exercise play an important role in the recovery of functional status, mobility is difficult to implement in patients in intensive care units. Objectives To compare a mobility platform with standard equipment, assessing efficiency (decreased time and staff required to prepare patient), effectiveness (increased activity time), and safety (no falls, unplanned tube removals, or emergency situations) for intensive care patients. Methods This observational study was approved by the institutional review board, and informed consent was obtained from the patient or the medical decision maker. Intensive care patients were assigned to a room in the usual manner, with platforms in odd-numbered rooms and standard equipment in even-numbered rooms. Standardized data collection tools were designed to collect data for 24 hours for each patient. The nurses caring for the patients completed the data collection tools in real time during the activity. The stages of activity and the physiological states that would preclude mobility were very specifically defined for the research study. Results Data were collected for a total of 71 patients and 238 activities. Important (although not significant) descriptive statistics regarding early mobility in the intensive care unit were discovered. The unintended result of the research study was a change in the culture and practice regarding early mobility in the intensive care unit. Conclusions Early mobility can be implemented in intensive care units. Standard equipment can be used to mobilize such patients safely; however, for patients who ambulate, a platform may increase efficiency and effectiveness.

scholarly journals Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units

2021 ◽  
Author(s):  
Stef Baas ◽  
Sander Dijkstra ◽  
Aleida Braaksma ◽  
Plom van Rooij ◽  
Fieke J. Snijders ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Length Of Stay ◽  
The Netherlands ◽  
Real Time ◽  
Data Warehouse ◽  
Intensive Care Units ◽  
Bed Occupancy ◽  
Control Centre

AbstractThis paper presents a mathematical model that provides a real-time forecast of the number of COVID-19 patients admitted to the ward and the Intensive Care Unit (ICU) of a hospital based on the predicted inflow of patients, their Length of Stay (LoS) in both the ward and the ICU as well as transfer of patients between the ward and the ICU. The data required for this forecast is obtained directly from the hospital’s data warehouse. The resulting algorithm is tested on data from the first COVID-19 peak in the Netherlands, showing that the forecast is very accurate. The forecast may be visualised in real-time in the hospital’s control centre and is used in several Dutch hospitals during the second COVID-19 peak.

scholarly journals Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units

2020 ◽  
Author(s):  
Alexander Henzi ◽  
Gian-Reto Kleger ◽  
Matthias P. Hilty ◽  
Pedro D. Wendel Garcia ◽  
Johanna F. Ziegel
Keyword(s):  
Intensive Care Unit ◽  
Acute Respiratory Distress Syndrome ◽  
Intensive Care ◽  
Length Of Stay ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Intensive Care Units ◽  
Distress Syndrome ◽  
Unit Length ◽  
Training Data

Rationale: The COVID-19 pandemic induces considerable strain on intensive care unit resources. Objectives: We aim to provide early predictions of individual patients' intensive care unit length of stay, which might improve resource allocation and patient care during the on-going pandemic. Methods: We developed a new semiparametric distributional index model depending on covariates which are available within 24h after intensive care unit admission. The model was trained on a large cohort of acute respiratory distress syndrome patients out of the Minimal Dataset of the Swiss Society of Intensive Care Medicine. Then, we predict individual length of stay of patients in the RISC-19-ICU registry. Measurements: The RISC-19-ICU Investigators for Switzerland collected data of 557 critically ill patients with COVID-19. Main Results: The model gives probabilistically and marginally calibrated predictions which are more informative than the empirical length of stay distribution of the training data. However, marginal calibration was worse after approximately 20 days in the whole cohort and in different subgroups. Long staying COVID-19 patients have shorter length of stay than regular acute respiratory distress syndrome patients. We found differences in LoS with respect to age categories and gender but not in regions of Switzerland with different stress of intensive care unit resources. Conclusion: A new probabilistic model permits calibrated and informative probabilistic prediction of LoS of individual patients with COVID-19. Long staying patients could be discovered early. The model may be the basis to simulate stochastic models for bed occupation in intensive care units under different casemix scenarios.

scholarly journals Equity of access to critical care services in Scotland: A Bayesian spatial analysis

2020 ◽  
pp. 175114372091446
Author(s):  
Philip Emerson ◽  
David R Green ◽  
Steve Stott ◽  
Graeme Maclennan ◽  
Marion K Campbell ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Critical Care ◽  
Intensive Care ◽  
Spatial Analysis ◽  
Intensive Care Units ◽  
Geographical Variation ◽  
Admission Rates ◽  
Care Services ◽  
Co Morbidity ◽  
Bayesian Spatial Analysis

Background There is increasing evidence that access to critical care services is not equitable. We aimed to investigate whether location of residence in Scotland impacts on the risk of admission to an Intensive Care Unit and on outcomes. Methods This was a population-based Bayesian spatial analysis of adult patients admitted to Intensive Care Units in Scotland between January 2011 and December 2015. We used a Besag–York–Mollié model that allows us to make direct probabilistic comparisons between areas regarding risk of admission to Intensive Care Units and on outcomes. Results A total of 17,596 patients were included. The five-year age- and sex-standardised admission rate was 352 per 100,000 residents. There was a cluster of Council Areas in the North-East of the country which had lower adjusted admission rates than the Scottish average. Midlothian, in South East Scotland had higher spatially adjusted admission rates than the Scottish average. There was no evidence of geographical variation in mortality. Conclusion Access to critical care services in Scotland varies with location of residence. Possible reasons include differential co-morbidity burden, service provision and access to critical care services. In contrast, the probability of surviving an Intensive Care Unit admission, if admitted, does not show geographical variation.

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