A Conceptual Framework for Improving Critical Care Patient Flow and Bed Use

The hip fracture assessment tool: A scoring system to assess high risk geriatric hip fracture patients for post-operative critical care monitoring

INTRODUCTION

Intensive care unit risk stratification models have been utilized in elective joint arthroplasty; however, hip fracture patients are fundamentally different in their clinical course. Having a critical care risk calculator utilizing pre-operative risk factors can improve resourcing for hip fracture patients in the peri‑operative period.

METHODS

A cohort of geriatric hip fracture patients at a single institution were reviewed over a three-year period. Non-operative patients, peri‑implant fractures, additional procedures performed under the same anesthesia period, and patients admitted to the intensive care unit (ICU) prior to surgery were excluded. Pre-operative laboratory values, Revised Cardiac Risk Index (RCRI), and American Society of Anesthesiologists (ASA) scores were calculated. Pre-operative ambulatory status was determined. The primary outcome measure was ICU admission in the post-operative period. Outcomes were assessed with Fisher's exact test, Kruskal-Wallis test, logistic regression, and ROC curve.

RESULTS

315 patient charts were analyzed with 262 patients meeting inclusion criteria. Age ≥ 80 years, ASA ≥ 4, pre-operative hemoglobin < 10 g/dL, and a history of CVA/TIA were found to be significant factors and utilized within a "training" data set to create a 4-point scoring system after reverse stepwise elimination. The 4-point scoring system was then assessed within a separate "validation" data set to yield an ROC area under the curve (AUC) of 0.747. Score ≥ 3 was associated with 96.8 % specificity and 14.2 % sensitivity for predicting post-op ICU admission. Score ≥ 3 was associated with a 50 % positive predictive value and 83 % negative predictive value.

CONCLUSION

A hip fracture risk stratification scoring system utilizing pre-operative patient specific values to stratify geriatric hip patients to the ICU post-operatively can improve the pre-operative decision-making of surgical and critical care teams. This has important implications for triaging vital hospital resources.

LEVEL OF EVIDENCE

III (retrospective study).

Effects of a medical admission unit on in-hospital patient flow and clinical outcomes

BACKGROUND

the burden of acute complex patients, increasingly older and poli-pathological, accessing to Emergency Departments (ED) leads up hospital overcrowding and the outlying phenomenon. These issues highlight the need for new adequate patients' management strategies. The aim of this study is to analyse the effects on in-hospital patient flow and clinical outcomes of a high-technology and time-limited Medical Admission Unit (MAU) run by internists.

METHODS

all consecutive patients admitted to MAU from Dec-2017 to Nov-2019 were included in the study. The admissions number from ED and hospitalization rate, the overall in-hospital mortality rate in medical department, the total days of hospitalization and the overall outliers bed days were compared to those from the previous two years.

RESULTS

2162 patients were admitted in MAU, 2085(95.6%) from ED, 476(22.0%) were directly discharged, 88(4.1%) died and 1598(73.9%) were transferred to other wards, with a median in-MAU time of stay of 64.5 [0.2-344.2] hours. Comparing the 24 months before, despite the increase in admissions/year from ED in medical department (3842 ± 106 in Dec2015-Nov2017 vs 4062 ± 100 in Dec2017-Nov2019, p<0.001), the number of the outlier bed days has been reduced, especially in surgical department (11.46 ± 6.25% in Dec2015-Nov2017 vs 6.39 ± 3.08% in Dec2017-Nov2019, p=0.001), and mortality in medical area has dropped from 8.74 ± 0.37% to 7.29 ± 0.57%, p<0.001.

CONCLUSIONS

over two years, a patient-centred and problem-oriented approach in a medical admission buffer unit run by internists has ensured a constant flow of acute patients with positive effects on clinical risk and quality of care reducing medical outliers and in-hospital mortality.

Transition of care from post-acute services for the older adults in Quebec: a pilot impact evaluation

BACKGROUND

Early discharge of frail older adults from post-acute care service may result in individuals' reduced functional ability to carry out activities of daily living, and social, emotional, and psychological distress. To address these shortcomings, the Montreal West Island Integrated University Health and Social Services Centre in Quebec, Canada piloted a post-acute home physiotherapy program (PAHP) to facilitate the transition of older adults from the hospital to their home. This study aimed to evaluate: (1) the implementation fidelity of the PAHP program; (2) its impact on the functional independence, physical and mental health outcomes and quality of life of older adults who underwent this program (3) its potential adverse events, and (4) to identify the physical, psychological, and mental health care needs of older adults following their discharge at home.

METHODS

A quasi-experimental uncontrolled design with repeated measures was conducted between April 1st, 2021 and December 31st, 2021. Implementation fidelity was assessed using three process indicators: delay between referral to and receipt of the PAHP program, frequency of PAHP interventions per week and program duration in weeks. A battery of functional outcome measures, including the Functional Independence Measure (FIM) and the Patient-Reported Outcomes Measurement Information System (PROMIS) Global-10 scale, as well as fall incidence, emergency visits, and hospitalizations were used to assess program impact and adverse events. The Patient's Global Impression of Change (PGICS) was used to determine changes in participants' perceptions of their level of improvement/deterioration. In addition, the Camberwell Assessment of Need for the Elderly (CANE) questionnaire was administered to determine the met and unmet needs of older adults.

RESULTS

Twenty-four individuals (aged 60.8 to 94 years) participated in the PAHP program. Implementation fidelity was low in regards with delay between referral and receipt of the program, intensity of interventions, and total program duration. Repeated measures ANOVA revealed significant improvement in FIM scores between admission and discharge from the PAHP program and between admission and the 3-month follow-up. Participants also reported meaningful improvements in PGICS scores. However, no significant differences were observed on the physical or mental health T-scores of the PROMIS Global-10 scale, in adverse events related to the PAHP program, or in the overall unmet needs.

CONCLUSION

Findings from an initial sample undergoing a PAHP program suggest that despite a low implementation fidelity of the program, functional independence outcomes and patients' global impression of change have improved. Results will help develop a stakeholder-driven action plan to improve this program. A future study with a larger sample size is currently being planned to evaluate the overall impact of this program.

CLINICAL TRIAL REGISTRATION

Retrospectively registered NCT05915156 (22/06/2023).

Original Research: Breaking Through the Bottleneck: Acuity Adaptability in Noncritical Trauma Care

BACKGROUND

Achieving efficient throughput of patients is a challenge faced by many hospital systems. Factors that can impede efficient throughput include increased ED use, high surgical volumes, lack of available beds, and the complexities of coordinating multiple patient transfers in response to changing care needs. Traditionally, many hospital inpatient units operate via a fixed acuity model, relying on multiple intrahospital transfers to move patients along the care continuum. In contrast, the acuity-adaptable model allows care to occur in the same room despite fluctuations in clinical condition, removing the need for transfer. This model has been shown to be a safe and cost-effective approach to improving throughput in populations with predictable courses of hospitalization, but has been minimally evaluated in other populations, such as patients hospitalized for traumatic injury.

PURPOSE

This quality improvement project aimed to evaluate implementation of an acuity-adaptable model on a 20-bed noncritical trauma unit. Specifically, we sought to examine and compare the pre- and postimplementation metrics for throughput efficiency, resource utilization, and nursing quality indicators; and to determine the model's impact on patient transfers for changes in level of care.

METHODS

This was a retrospective, comparative analysis of 1,371 noncritical trauma patients admitted to a level 1 trauma center before and after the implementation of an acuity-adaptable model. Outcomes of interest included throughput efficiency, resource utilization, and quality of nursing care. Inferential statistics were used to compare patients pre- and postimplementation, and logistic regression analyses were performed to determine the impact of the acuity-adaptable model on patient transfers.

RESULTS

Postimplementation, the median ED boarding time was reduced by 6.2 hours, patients more often remained in their assigned room following a change in level of care, more progressive care patient days occurred, fall and hospital-acquired pressure injury index rates decreased respectively by 0.9 and 0.3 occurrences per 1,000 patient days, and patients were more often discharged to home. Logistic regression analyses revealed that under the new model, patients were more than nine times more likely to remain in the same room for care after a change in acuity and 81.6% less likely to change rooms after a change in acuity. An increase of over $11,000 in average daily bed charges occurred postimplementation as a result of increased progressive care-level bed capacity.

CONCLUSIONS

The implementation of an acuity-adaptable model on a dedicated noncritical trauma unit improved throughput efficiency and resource utilization without sacrificing quality of care. As hospitals continue to face increasing demand for services as well as numerous barriers to meeting such demand, leaders remain challenged to find innovative ways to optimize operational efficiency and resource utilization while ensuring delivery of high-quality care. The findings of this study demonstrate the value of the acuity-adaptable model in achieving these goals in a noncritical trauma care population.

Flow-Sizing Critical Care Resources

OBJECTIVES

To describe the factors affecting critical care capacity and how critical care organizations (CCOs) within academic centers in the U.S. flow-size critical care resources under normal operations, strain, and surge conditions.

DATA SOURCES

PubMed, federal agency and American Hospital Association reports, and previous CCO survey results were reviewed.

STUDY SELECTION

Studies and reports of critical care bed capacity and utilization within CCOs and in the United States were selected.

DATA EXTRACTION

The Academic Leaders in the Critical Care Medicine Task Force established regular conference calls to reach a consensus on the approach of CCOs to "flow-sizing" critical care services.

DATA SYNTHESIS

The approach of CCOs to "flow-sizing" critical care is outlined. The vertical (relation to institutional resources, e.g., space allocation, equipment, personnel redistribution) and horizontal (interdepartmental, e.g., emergency department, operating room, inpatient floors) integration of critical care delivery (ICUs, rapid response) for healthcare organizations and the methods by which CCOs flow-size critical care during normal operations, strain, and surge conditions are described. The advantages, barriers, and recommendations for the rapid and efficient scaling of critical care operations via a CCO structure are explained. Comprehensive guidance and resources for the development of "flow-sizing" capability by a CCO within a healthcare organization are provided.

CONCLUSIONS

We identified and summarized the fundamental principles affecting critical care capacity. The taskforce highlighted the advantages of the CCO governance model to achieve rapid and cost-effective "flow-sizing" of critical care services and provide recommendations and resources to facilitate this capability. The relevance of a comprehensive approach to "flow-sizing" has become particularly relevant in the wake of the latest COVID-19 pandemic. In light of the growing risks of another extreme epidemic, planning for adequate capacity to confront the next critical care crisis is urgent.

Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients

Early bed assignments of elective surgical patients can be a useful planning tool for hospital staff; they provide certainty in patient placement and allow nursing staff to prepare for patients' arrivals to the unit. However, given the variability in the surgical schedule, they can also result in timing mismatches-beds remain empty while their assigned patients are still in surgery, while other ready-to-move patients are waiting for their beds to become available. In this study, we used data from four surgical units in a large academic medical center to build a discrete-event simulation with which we show how a Just-In-Time (JIT) bed assignment, in which ready-to-move patients are assigned to ready-beds, would decrease bed idle time and increase access to general care beds for all surgical patients. Additionally, our simulation demonstrates the potential synergistic effects of combining the JIT assignment policy with a strategy that co-locates short-stay surgical patients out of inpatient beds, increasing the bed supply. The simulation results motivated hospital leadership to implement both strategies across these four surgical inpatient units in early 2017. In the several months post-implementation, the average patient wait time decreased 25.0% overall, driven by decreases of 32.9% for ED-to-floor transfers (from 3.66 to 2.45 hours on average) and 37.4% for PACU-to-floor transfers (from 2.36 to 1.48 hours), the two major sources of admissions to the surgical floors, without adding additional capacity.

Managing inpatient bed setup: an action-research approach using lean technical practices and lean social practices

PURPOSE

This article aims to introduce a guide to improving hospital bed setup by combining lean technical practices (LTPs), such as kaizen and value stream mapping (VSM) and lean social practices (LSPs), such as employee empowerment.

DESIGN/METHODOLOGY/APPROACH

Action research approach was employed to analyze the process of reconfiguration of bed setup management in a Brazilian public hospital.

FINDINGS

The study introduces three contributions: (1) presents the use of VSM focused specifically on bed setup, while the current literature presents studies mainly focused on patient flow management, (2) combines the use of LSPs and LTPs in the context of bed management, expanding current studies that are focused either on mathematical models or on social and human aspects of work, (3) introduces a practical guide based on six steps that combine LSPs and LSPs to improve bed setup management.

RESEARCH LIMITATIONS/IMPLICATIONS

The research focused on the analysis of patient beds. Surgical beds, delivery, emergency care and intensive care unit (ICU) were not considered in this study. In addition, the process indicators analyzed after the implementation of the improvements did not contemplate the moment of the COVID-19 pandemic. Finally, this research focused on the implementation of the improvement in the context of only one Brazilian public hospital.

PRACTICAL IMPLICATIONS

The combined use of LSPs and LTPs can generate considerable gains in bed setup efficiency and consequently increase the capacity of a hospital to admit new patients, without the ampliation of the physical space and workforce.

SOCIAL IMPLICATIONS

The improvement of bed setup has an important social character, whereas it can generate important social benefits such as the improvement of the admission service to patients, reducing the waiting time, reducing hospitalization costs and improving the hospital capacity without additional physical resources. All these results are crucial for populations, their countries and regions.

ORIGINALITY/VALUE

While the current literature on bed management is more focused on formal models or pure human and social perspectives, this article brings these two perspectives together in a single, holistic framework. As a result, this article points out that the complex bed management problem can be efficiently solved by combining LSPs and LTPs to present theoretical and practical contributions to the important social problem of hospital bed management.

Deterioration Index in Critically Injured Patients: A Feasibility Analysis

INTRODUCTION

Continuous prediction surveillance modeling is an emerging tool giving dynamic insight into conditions with potential mitigation of adverse events (AEs) and failure to rescue. The Epic electronic medical record contains a Deterioration Index (DI) algorithm that generates a prediction score every 15 min using objective data. Previous validation studies show rapid increases in DI score (≥14) predict a worse prognosis. The aim of this study was to demonstrate the utility of DI scores in the trauma intensive care unit (ICU) population.

METHODS

A prospective, single-center study of trauma ICU patients in a Level 1 trauma center was conducted during a 3-mo period. Charts were reviewed every 24 h for minimum and maximum DI score, largest score change (Δ), and AE. Patients were grouped as low risk (ΔDI <14) or high risk (ΔDI ≥14).

RESULTS

A total of 224 patients were evaluated. High-risk patients were more likely to experience AEs (69.0% versus 47.6%, P = 0.002). No patients with DI scores <30 were readmitted to the ICU after being stepped down to the floor. Patients that were readmitted and subsequently died all had DI scores of ≥60 when first stepped down from the ICU.

CONCLUSIONS

This study demonstrates DI scores predict decompensation risk in the surgical ICU population, which may otherwise go unnoticed in real time. This can identify patients at risk of AE when transferred to the floor. Using the DI model could alert providers to increase surveillance in high-risk patients to mitigate unplanned returns to the ICU and failure to rescue.

Prognostic value of National Early Warning Score and Modified Early Warning Score on intensive care unit readmission and mortality: A prospective observational study

Background

Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) are widely used in predicting the mortality and intensive care unit (ICU) admission of critically ill patients. This study was conducted to evaluate and compare the prognostic value of NEWS and MEWS for predicting ICU readmission, mortality, and related outcomes in critically ill patients at the time of ICU discharge.

Methods

This multicenter, prospective, observational study was conducted over a year, from April 2019 to March 2020, in the general ICUs of two university-affiliated hospitals in Northwest Iran. MEWS and NEWS were compared based on the patients’ outcomes (including mortality, ICU readmission, time to readmission, discharge type, mechanical ventilation (MV), MV duration, and multiple organ failure after readmission) using the univariable and multivariable binary logistic regression. The receiver operating characteristic (ROC) curve was used to determine the outcome predictability of MEWS and NEWS.

Results

A total of 410 ICU patients were enrolled in this study. According to multivariable logistic regression analysis, both MEWS and NEWS were predictors of ICU readmission, time to readmission, MV status after readmission, MV duration, and multiple organ failure after readmission. The area under the ROC curve (AUC) for predicting mortality was 0.91 (95% CI = 0.88–0.94, P < 0.0001) for the NEWS and 0.88 (95% CI = 0.84–0.91, P < 0.0001) for the MEWS. There was no significant difference between the AUC of the NEWS and the MEWS for predicting mortality (P = 0.082). However, for ICU readmission (0.84 vs. 0.71), time to readmission (0.82 vs. 0.67), MV after readmission (0.83 vs. 0.72), MV duration (0.81 vs. 0.67), and multiple organ failure (0.833 vs. 0.710), the AUCs of MEWS were significantly greater (P < 0.001).

Conclusion

National Early Warning Score and MEWS values of >4 demonstrated high sensitivity and specificity in identifying the risk of mortality for the patients’ discharge from ICU. However, we found that the MEWS showed superiority over the NEWS score in predicting other outcomes. Eventually, MEWS could be considered an efficient prediction score for morbidity and mortality of critically ill patients.

A retrospective cohort study of short-stay admissions to the medical intensive care unit: Defining patient characteristics and critical care resource utilization

Background

Little is known about the mortality and utilization outcomes of short-stay intensive care unit (ICU) patients who require <24 h of critical care. We aimed to define characteristics and outcomes of short-stay ICU patients whose need for ICU level-of-care is ≤24 h compared to nonshort-stay patients.

Methods

Single-center retrospective cohort study of patients admitted to the medical ICU at an academic tertiary care center in 2019. Fisher's exact test or Chi-square for descriptive categorical variables, t-test for continuous variables, and Mann-Whitney two-sample test for length of stay (LOS) outcomes.

Results

Of 819 patients, 206 (25.2%) were short-stay compared to 613 (74.8%) nonshort-stay. The severity of illness as measured by the Mortality Probability Model-III was significantly lower among short-stay compared to nonshort-stay patients (P = 0.0001). Most short-stay patients were admitted for hemodynamic monitoring not requiring vasoactive medications (77, 37.4%). Thirty-six (17.5%) of the short-stay cohort met Society of Critical Care Medicine's guidelines for ICU admission. Nonfull-ICU LOS, or time spent waiting for transfer out to a non-ICU bed, was similar between the two groups. Hospital mortality was lower among short-stay patients compared to nonshort-stay patients (P = 0.01).

Conclusions

Despite their lower illness severity and fewer ICU-level care needs, short-stay patients spend an equally substantial amount of time occupying an ICU bed while waiting for a floor bed as nonshort-stay patients. Further investigation into the factors influencing ICU triage of these subacute patients and contributors to system inefficiencies prohibiting their timely transfer may improve ICU resource allocation, hospital throughput, and patient outcomes.

Real-time prediction of COVID-19 patients health situations using Artificial Neural Networks and Fuzzy Interval Mathematical modeling

At the end of 2019, the SARS-CoV-2 virus caused an outbreak of COVID-19 disease. The spread of this once-in-a-century pathogen increases demand for appropriate medical care, which strains the capacity and resources of hospitals in a critical way. Given the limited time available to prepare for the required demand, health care administrators fear they will not be ready to face patient’s influx. To aid health managers with the Prioritization and Scheduling COVID-19 Patients problem, a tool based on Artificial Intelligence (AI) through the Artificial Neural Networks (ANN) method, and Operations Research (OR) through a Fuzzy Interval Mathematical model was developed. The results indicated that combining both models provides an effective assessment under scarce initial information to select a suitable list of patients for a set of hospitals. The proposed approach allows to achieve a key goal: minimizing death rates under each hospital constraints of available resources. Furthermore, there is a serious concern regarding the resurgence of the COVID-19 virus which could cause a more severe pandemic. Thus, the main outcome of this study is the application of the above-mentioned approaches, especially when combining them, as efficient tools serving health establishments to manage critical resources.

Boarding of Critically Ill Patients in the Emergency Department

Supplemental Digital Content is available in the text.

Objectives:

Emergency department boarding is the practice of caring for admitted patients in the emergency department after hospital admission, and boarding has been a growing problem in the United States. Boarding of the critically ill has achieved specific attention because of its association with poor clinical outcomes. Accordingly, the Society of Critical Care Medicine and the American College of Emergency Physicians convened a Task Force to understand the implications of emergency department boarding of the critically ill. The objective of this article is to review the U.S. literature on (1) the frequency of emergency department boarding among the critically ill, (2) the outcomes associated with critical care patient boarding, and (3) local strategies developed to mitigate the impact of emergency department critical care boarding on patient outcomes.

Data Sources and Study Selection:

Review article.

Data Extraction and Data Synthesis:

Emergency department–based boarding of the critically ill patient is common, but no nationally representative frequency estimates has been reported. Boarding literature is limited by variation in the definitions used for boarding and variation in the facilities studied (boarding ranges from 2% to 88% of ICU admissions). Prolonged boarding in the emergency department has been associated with longer duration of mechanical ventilation, longer ICU and hospital length of stay, and higher mortality. Health systems have developed multiple mitigation strategies to address emergency department boarding of critically ill patients, including emergency department-based interventions, hospital-based interventions, and emergency department–based resuscitation care units.

Conclusions:

Emergency department boarding of critically ill patients was common and was associated with worse clinical outcomes. Health systems have generated a number of strategies to mitigate these effects. A definition for emergency department boarding is proposed. Future work should establish formal criteria for analysis and benchmarking of emergency department–based boarding overall, with subsequent efforts focused on developing and reporting innovative strategies that improve clinical outcomes of critically ill patients boarded in the emergency department.

A hybrid analytical model for an entire hospital resource optimisation

Given the escalating healthcare costs around the world (more than 10% of the world's GDP) and increasing demand hospitals are under constant scrutiny in terms of managing services with limited resources and tighter budgets. Hospitals endeavour to find sustainable solutions for a variety of challenges ranging from productivity enhancements to resource allocation. For instance, in the UK, evidence suggests that hospitals are struggling due to increased delayed transfers of care, bed-occupancy rates well above the recommended levels of 85% and unmet A&E performance targets. In this paper, we present a hybrid forecasting-simulation-optimisation model for an NHS Foundation Trust in the UK. Using the Hospital Episode Statistics dataset for A&E, outpatient and inpatient services, we estimate the future patient demands for each speciality and model how it behaves with the forecasted activity in the future. Discrete event simulation is used to capture the entire hospital within a simulation environment, where the outputs is used as inputs into a multi-period integer linear programming (MILP) model to predict three vital resource requirements (on a monthly basis over a 1-year period), namely beds, physicians and nurses. We further carry out a sensitivity analysis to establish the robustness of solutions to changes in parameters, such as nurse-to-bed ratio. This type of modelling framework is developed for the first time to better plan the needs of hospitals now and into the future.

Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges

Supplemental Digital Content is available in the text.

Objectives:

As the demand for critical care beds rises each year, hospitals must be able to adapt. Delayed transfer of care reduces available critical care capacity and increases occupancy. The use of mathematic modeling within healthcare systems has the ability to aid planning of resources. Discrete-event simulation models can determine the optimal number of critical care beds required and simulate different what-if scenarios.

Design:

Complex discrete-event simulation model was developed using a warm-up period of 30 days and ran for 30 trials against a 2-year period with the mean calculated for the runs. A variety of different scenarios were investigated to determine the effects of increasing capacity, increasing demand, and reduction of proportion and length of delayed transfer of care out of the ICU.

Setting:

Combined data from two ICUs in United Kingdom.

Patients:

The model was developed using 1,728 patient records and was validated against an independent dataset of 2,650 patients.

Interventions:

None.

Measurements and Main Results:

During model validation, the average bed utilization and admittance rate were equal to the real-world data. In the what-if scenarios, we found that increasing bed numbers from 23 to 28 keeping the arrival rate stable reduces the average occupancy rate to 70%. We found that the projected 4% yearly increase in admissions could overwhelm even the 28-bedded unit, without change in the delayed transfer of care episodes. Reduction in the proportion of patients experiencing delayed transfer of care had the biggest effect on occupancy rates, time spent at full capacity, and average bed utilization.

Conclusions:

Using discrete-event simulation of commonly available baseline patient flow and patient care data produces reproducible models. Reducing the proportion of patients with delayed transfer of care had a greater effect in reducing occupancy levels than simply increasing bed numbers even when demand is increased.

Reserved Bed Program Reduces Neurosciences Intensive Care Unit Capacity Strain: An Implementation Study

BACKGROUND

Neurosciences intensive care units (NICUs) provide institutional centers for specialized care. Despite a demonstrable reduction in morbidity and mortality, NICUs may experience significant capacity strain with resulting supraoptimal utilization and diseconomies of scale. We present an implementation study in the recognition and management of capacity strain within a large NICU in the United States. Excessive resource demand in an NICU creates significant operational issues.

OBJECTIVE

To evaluate the efficacy of a Reserved Bed Pilot Program (RBPP), implemented to maximize economies of scale, to reduce transfer declines due to lack of capacity, and to increase transfer volume for the neurosciences service-line.

METHODS

Key performance indicators (KPIs) were created to evaluate RBPP efficacy with respect to primary (strategic) objectives. Operational KPIs were established to evaluate changes in operational throughput for the neurosciences and other service-lines. For each KPI, pilot-period data were compared to the previous fiscal year.

RESULTS

RBPP implementation resulted in a significant increase in accepted transfer volume to the neurosciences service-line (P = .02). Transfer declines due to capacity decreased significantly (P = .01). Unit utilization significantly improved across service-line units relative to theoretical optima (P < .03). Care regionalization was achieved through a significant reduction in “off-service” patient placement (P = .01). Negative externalities were minimized, with no significant negative impact in the operational KPIs of other evaluated service-lines (P = .11).

CONCLUSION

Capacity strain is a significant issue for hospital units. Reducing capacity strain can increase unit efficiency, improve resource utilization, and augment service-line throughput. RBPP implementation resulted in a significant improvement in service-line operations, regional access to care, and resource efficiency, with minimal externalities at the institutional level.

Boarding of critically Ill patients in the emergency department

OBJECTIVES

Emergency department boarding is the practice of caring for admitted patients in the emergency department after hospital admission, and boarding has been a growing problem in the United States. Boarding of the critically ill has achieved specific attention because of its association with poor clinical outcomes. Accordingly, the Society of Critical Care Medicine and the American College of Emergency Physicians convened a Task Force to understand the implications of emergency department boarding of the critically ill. The objective of this article is to review the U.S. literature on (1) the frequency of emergency department boarding among the critically ill, (2) the outcomes associated with critical care patient boarding, and (3) local strategies developed to mitigate the impact of emergency department critical care boarding on patient outcomes.

DATA SOURCES AND STUDY SELECTION

Review article.

DATA EXTRACTION AND DATA SYNTHESIS

Emergency department-based boarding of the critically ill patient is common, but no nationally representative frequency estimates has been reported. Boarding literature is limited by variation in the definitions used for boarding and variation in the facilities studied (boarding ranges from 2% to 88% of ICU admissions). Prolonged boarding in the emergency department has been associated with longer duration of mechanical ventilation, longer ICU and hospital length of stay, and higher mortality. Health systems have developed multiple mitigation strategies to address emergency department boarding of critically ill patients, including emergency department-based interventions, hospital-based interventions, and emergency department-based resuscitation care units.

CONCLUSIONS

Emergency department boarding of critically ill patients was common and was associated with worse clinical outcomes. Health systems have generated a number of strategies to mitigate these effects. A definition for emergency department boarding is proposed. Future work should establish formal criteria for analysis and benchmarking of emergency department-based boarding overall, with subsequent efforts focused on developing and reporting innovative strategies that improve clinical outcomes of critically ill patients boarded in the emergency department.

The Impact of Step-Down Unit Care on Patient Outcomes After ICU Discharge

Supplemental Digital Content is available in the text.

Objectives:

To examine whether and how step-down unit admission after ICU discharge affects patient outcomes.

Design:

Retrospective study using an instrumental variable approach to remove potential biases from unobserved differences in illness severity for patients admitted to the step-down unit after ICU discharge.

Setting:

Ten hospitals in an integrated healthcare delivery system in Northern California.

Patients:

Eleven-thousand fifty-eight episodes involving patients who were admitted via emergency departments to a medical service from July 2010 to June 2011, were admitted to the ICU at least once during their hospitalization, and were discharged from the ICU to the step-down unit or the ward.

Interventions:

None.

Measurements and Main Results:

Using congestion in the step-down unit as an instrumental variable, we quantified the impact of step-down unit care in terms of clinical and operational outcomes. On average, for ICU patients with lower illness severity, we found that availability of step-down unit care was associated with an absolute decrease in the likelihood of hospital readmission within 30 days of 3.9% (95% CI, 3.6–4.1%). We did not find statistically significant effects on other outcomes. For ICU patients with higher illness severity, we found that availability of step-down unit care was associated with an absolute decrease in in-hospital mortality of 2.5% (95% CI, 2.3–2.6%), a decrease in remaining hospital length-of-stay of 1.1 days (95% CI, 1.0–1.2 d), and a decrease in the likelihood of ICU readmission within 5 days of 3.6% (95% CI, 3.3–3.8%).

Conclusions:

This study shows that there exists a subset of patients discharged from the ICU who may benefit from care in an step-down unit relative to that in the ward. We found that step-down unit care was associated with statistically significant improvements in patient outcomes especially for high-risk patients. Our results suggest that step-down units can provide effective transitional care for ICU patients.

Management of bed availability in intensive care in the context of hospitalization by court order

Objective:

to identify, from the nurse perspective, situations that interfere with the availability of beds in the intensive care unit in the context of hospitalization by court order.

Method:

qualitative exploratory, analytical research carried out with 42 nurses working in adult intensive care. The selection took place by non-probabilistic snowball sampling. Data collected by interview and analyzed using the Discursive Textual Analysis technique.

Results:

three categories were analyzed, entitled deficiency of physical structure and human resources; Lack of clear policies and criteria for patient admission and inadequate discharge from the intensive care unit. In situations of hospitalization by court order, there is a change in the criteria for the allocation of intensive care beds, due to the credibility of professionals, threats of medico-legal processes by family members and judicial imposition on institutions and health professionals.

Conclusion:

nurses defend the needs of the patients, too, with actions that can positively impact the availability of intensive care beds and adequate care infrastructure.

Associations between hospital occupancy, intensive care unit transfer delay and hospital mortality

PURPOSE

Hospital occupancy (HospOcc) pressures often lead to longer intensive care unit (ICU) stay after physician recognition of discharge readiness. We evaluated the relationships between HospOcc, extended ICU stay, and patient outcomes.

MATERIALS AND METHODS

7-year retrospective cohort study of 8500 alive discharge encounters from 4 adult ICUs of a tertiary hospital. We estimated associations between i) HospOcc and ICU transfer delay; and ii) ICU transfer delay and hospital mortality.

RESULTS

Median (IQR) ICU transfer delay was 4.8 h (1.6-11.7), 1.4% (119) suffered in-hospital death, and 4% (341) were readmitted. HospOcc was non-linearly related with ICU transfer delay, with a spline knot at 80% (mean transfer delay 8.8 h [95% CI: 8.24, 9.38]). Higher HospOcc level above 80% was associated with longer transfer delays, (mean increase 5.4% per % HospOcc increase; 95% CI, 4.7 to 6.1; P < .001). Longer ICU transfer delay was associated with increasing odds of in-hospital death or ICU readmission (odds ratio 1.01 per hour; 95% CI 1.00 to 1.01; P = .04) but not with ICU readmission alone (OR 1.01 per hour; 95% CI 1.00 to 1.01, P = .14).

CONCLUSIONS

ICU transfer delay exponentially increased above a threshold hospital occupancy and may be associated with increased hospital mortality.

Using probabilistic patient flow modelling helps generate individualised intensive care unit operational predictions and improved understanding of current organisational behaviours

Purpose

We sought a bespoke, stochastic model for our specific, and complex ICU to understand its organisational behaviour and how best to focus our resources in order to optimise our intensive care unit's function.

Methods

Using 12 months of ICU data from 2017, we simulated different referral rates to find the threshold between occupancy and failed admissions and unsafe days. We also modelled the outcomes of four change options.

Results

Ninety-two percent bed occupancy is our threshold between practical unit function and optimal resource use. All change options reduced occupancy, and less predictably unsafe days and failed admissions. They were ranked by magnitude and direction of change.

Conclusions

This approach goes one step further from past models by examining efficiency limits first, and then allowing change options to be quantitatively compared. The model can be adapted by any intensive care unit in order to predict optimal strategies for improving ICU efficiency.

The Boarding Patient: Effects of ICU and Hospital Occupancy Surges on Patient Flow

Patients admitted to a hospital's intensive care unit (ICU) often endure prolonged boarding within the ICU following receipt of care, unnecessarily occupying a critical care bed, and thereby delaying admission for other incoming patients due to bed shortage. Using patient-level data over two years at two major academic medical centers, we estimate the impact of ICU and ward occupancy levels on ICU length of stay (LOS), and test whether simultaneous "surge occupancy" in both areas impacts overall ICU length of stay. In contrast to prior studies that only measure total LOS, we split LOS into two individual periods based on physician requests for bed transfers. We find that "service time" (when critically ill patients are stabilized and treated) is unaffected by occupancy levels. However, the less essential "boarding time" (when patients wait to exit the ICU) is accelerated during periods of high ICU occupancy and, conversely, prolonged when hospital ward occupancy levels are high. When the ICU and wards simultaneously encounter bed occupancies in the top quartile of historical levels-which occurs 5% of the time-ICU boarding increases by 22% compared to when both areas experience their lowest utilization, suggesting that ward bed availability dominates efforts to accelerate ICU discharges to free up ICU beds. We find no adverse effects of high occupancy levels on ICU bouncebacks, in-hospital deaths, or 30-day hospital readmissions, which supports our finding that the largely discretionary boarding period fluctuates with changing bed occupancy levels.

Effect of Emergency Department and ICU Occupancy on Admission Decisions and Outcomes for Critically Ill Patients

OBJECTIVES

ICU admission delays can negatively affect patient outcomes, but emergency department volume and boarding times may also affect these decisions and associated patient outcomes. We sought to investigate the effect of emergency department and ICU capacity strain on ICU admission decisions and to examine the effect of emergency department boarding time of critically ill patients on in-hospital mortality.

DESIGN

A retrospective cohort study.

SETTING

Single academic tertiary care hospital.

PATIENTS

Adult critically ill emergency department patients for whom a consult for medical ICU admission was requested, over a 21-month period.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Patient data, including severity of illness (Mortality Probability Model III on Admission), outcomes of mortality and persistent organ dysfunction, and hourly census reports for the emergency department, for all ICUs and all adult wards were compiled. A total of 854 emergency department requests for ICU admission were logged, with 455 (53.3%) as "accept" and 399 (46.7%) as "deny" cases, with median emergency department boarding times 4.2 hours (interquartile range, 2.8-6.3 hr) and 11.7 hours (3.2-20.3 hr) and similar rates of persistent organ dysfunction and/or death 41.5% and 44.6%, respectively. Those accepted were younger (mean ± SD, 61 ± 17 vs 65 ± 18 yr) and more severely ill (median Mortality Probability Model III on Admission score, 15.3% [7.0-29.5%] vs 13.4% [6.3-25.2%]) than those denied admission. In the multivariable model, a full medical ICU was the only hospital-level factor significantly associated with a lower probability of ICU acceptance (odds ratio, 0.55 [95% CI, 0.37-0.81]). Using propensity score analysis to account for imbalances in baseline characteristics between those accepted or denied for ICU admission, longer emergency department boarding time after consult was associated with higher odds of mortality and persistent organ dysfunction (odds ratio, 1.77 [1.07-2.95]/log10 hour increase).

CONCLUSIONS

ICU admission decisions for critically ill emergency department patients are affected by medical ICU bed availability, though higher emergency department volume and other ICU occupancy did not play a role. Prolonged emergency department boarding times were associated with worse patient outcomes, suggesting a need for improved throughput and targeted care for patients awaiting ICU admission.

The Impact of Opening a Medical Step-Down Unit on Medically Critically Ill Patient Outcomes and Throughput: A Difference-in-Differences Analysis

OBJECTIVE

To understand the impact of adding a medical step-down unit (SDU) on patient outcomes and throughput in a medical intensive care unit (ICU).

DESIGN

Retrospective cohort study.

SETTING

Two academic tertiary care hospitals within the same health-care system.

PATIENTS

Adults admitted to the medical ICU at either the control or intervention hospital from October 2013 to March 2014 (preintervention) and October 2014 to March 2015 (postintervention).

INTERVENTIONS

Opening a 4-bed medical SDU at the intervention hospital on April 1, 2014.

MEASUREMENTS AND MAIN RESULTS

Using standard summary statistics, we compared patients across hospitals. Using a difference-in-differences approach, we quantified the association of opening an SDU and outcomes (hospital mortality, hospital and ICU length of stay [LOS], and time to transfer to the ICU) after adjustment for secular trends in patient case-mix and patient-level covariates which might impact outcome. We analyzed 500 (245 pre- and 255 postintervention) patients in the intervention hospital and 678 (323 pre- and 355 postintervention) in the control hospital. Patients at the control hospital were younger (60.5-60.6 vs 64.0-65.4 years, P < .001) with a higher severity of acute illness at the time of evaluation for ICU admission (Sequential Organ Failure Assessment score: 4.9-4.0 vs 3.9-3.9, P < .001). Using the difference-in-differences methodology, we identified no association of hospital mortality (odds ratio [95% confidence interval]: 0.81 [0.42 to 1.55], P = .52) or hospital LOS (% change [95% confidence interval]: -8.7% [-28.6% to 11.2%], P = .39) with admission to the intervention hospital after SDU opening. The ICU LOS overall was not associated with admission to the intervention hospital in the postintervention period (-23.7% [-47.9% to 0.5%], P = .06); ICU LOS among survivors was significantly reduced (-27.5% [-50.5% to -4.6%], P = .019). Time to transfer to ICU was also significantly reduced (-26.7% [-44.7% to -8.8%], P = .004).

CONCLUSIONS

Opening our medical SDU improved medical ICU throughput but did not affect more patient-centered outcomes of hospital mortality and LOS.

Data-driven optimization methodology for admission control in critical care units

The decision of whether to admit a patient to a critical care unit is a crucial operational problem that has significant influence on both hospital performance and patient outcomes. Hospitals currently lack a methodology to selectively admit patients to these units in a way that patient health risk metrics can be incorporated while considering the congestion that will occur. The hospital is modeled as a complex loss queueing network with a stochastic model of how long risk-stratified patients spend time in particular units and how they transition between units. A Mixed Integer Programming model approximates an optimal admission control policy for the network of units. While enforcing low levels of patient blocking, we optimize a monotonic dual-threshold admission policy. A hospital network including Intermediate Care Units (IMCs) and Intensive Care Units (ICUs) was considered for validation. The optimized model indicated a reduction in the risk levels required for admission, and weekly average admissions to ICUs and IMCs increased by 37% and 12%, respectively, with minimal blocking. Our methodology captures utilization and accessibility in a network model of care pathways while supporting the personalized allocation of scarce care resources to the neediest patients. The interesting benefits of admission thresholds that vary by day of week are studied.

An Innovative Framework to Improve Efficiency of Interhospital Transfer of Children in Respiratory Failure

RATIONALE

High mortality and resource use burden are associated with hospitalization of critically ill children transferred from level II pediatric intensive care units (PICUs) to level I PICUs for escalated care. Guidelines urge transfer of the most severely ill children to level I PICUs without specification of either the criteria or the best timing of transfer to achieve good outcomes.

OBJECTIVES

To identify factors associated with transfer, develop a modeling framework that uses those factors to determine thresholds to guide transfer decisions, and test these thresholds against actual patient transfer data to determine if delay in transfer could be reduced.

METHODS

A multistep approach was adopted, with initial identification of factors associated with transfer status using data from a prior case-control study conducted with children with respiratory failure admitted to six level II PICUs between January 1, 1997, and December 31, 2007. To identify when to transfer a patient, thresholds for transfer were created using generalized estimating equations and discrete event simulation. The transfer policies were then tested against actual transfer data.

MEASUREMENTS AND MAIN RESULTS

Multivariate logistic regression revealed that the absolute difference of a patient's pediatric logistic organ dysfunction score from the admission value, high-frequency oscillatory ventilation use, antibiotic use, and blood transfusions were all significantly associated with transfer status. The resulting threshold policies led to average transfer delay reduction ranging from 0.5 to 2.3 days in the testing dataset.

CONCLUSIONS

Current transfer guidelines are devoid of criteria to identify critically ill children who might benefit from transfer and when the best time to transfer might be. In this study, we used innovative methods to create thresholds of transfer that might reduce delay in transfer.

Identifying congestion levels, sources and determinants on intensive care units: the Portuguese case

Healthcare systems are facing a resources scarcity so they must be efficiently managed. On the other hand, it is commonly accepted that the higher the consumed resources, the higher the hospital production, although this is not true in practice. Congestion on inputs is an economic concept dealing with such situation and it is defined as the decreasing of outputs due to some resources overuse. This scenario gets worse when inpatients' high severity requires a strict and effective resources management, as happens in Intensive Care Units (ICU). The present paper employs a set of nonparametric models to evaluate congestion levels, sources and determinants in Portuguese Intensive Care Units. Nonparametric models based on Data Envelopment Analysis are employed to assess both radial and non-radial (in)efficiency levels and sources. The environment adjustment models and bootstrapping are used to correct possible bias, to remove the deterministic nature of nonparametric models and to get a statistical background on results. Considerable inefficiency and congestion levels were identified, as well as the congestion determinants, including the ICU specialty and complexity, the hospital differentiation degree and population demography. Both the costs associated with staff and the length of stay are the main sources of (weak) congestion in ICUs. ICUs management shall make some efforts towards resource allocation to prevent the congestion effect. Those efforts shall, in general, be focused on costs with staff and hospital days, although these congestion sources may vary across hospitals and ICU services, once several congestion determinants were identified.

Triage decisions for ICU admission: Report from the Task Force of the World Federation of Societies of Intensive and Critical Care Medicine

Demand for intensive care unit (ICU) resources often exceeds supply, and shortages of ICU beds and staff are likely to persist. Triage requires careful weighing of the benefits and risks involved in ICU admission while striving to guarantee fair distribution of available resources. We must ensure that the patients who occupy ICU beds are those most likely to benefit from the ICU's specialized technology and professionals. Although prognosticating is not an exact science, preference should be given to patients who are more likely to survive if admitted to the ICU but unlikely to survive or likely to have more significant morbidity if not admitted. To provide general guidance for intensivists in ICU triage decisions, a task force of the World Federation of Societies of Intensive and Critical Care Medicine addressed 4 basic questions regarding this process. The team made recommendations and concluded that triage should be led by intensivists considering input from nurses, emergency medicine professionals, hospitalists, surgeons, and allied professionals. Triage algorithms and protocols can be useful but can never supplant the role of skilled intensivists basing their decisions on input from multidisciplinary teams. Infrastructures need to be organized efficiently both within individual hospitals and at the regional level. When resources are critically limited, patients may be refused ICU admission if others may benefit more on the basis of the principle of distributive justice.

The effect of physician staffing model on patient outcomes in a medical progressive care unit

PURPOSE

Although evidence supports the impact of intensivist physician staffing in improving intensive care unit (ICU) outcomes, the optimal coverage for progressive care units (PCU) is unknown. We sought to determine how physician staffing models influence outcomes for intermediate care patients.

MATERIALS AND METHODS

We conducted a retrospective observational comparison of patients admitted to the medical PCU of an academic hospital during 12-month periods of high-intensity and low-intensity staffing.

RESULTS

A total of 318 PCU patients were eligible for inclusion (143 high-intensity and 175 low-intensity). We found that low-intensity patients were more often stepped up from the emergency department and floor, whereas high-intensity patients were ICU transfers (61% vs 42%, P = .001). However, Mortality Probability Model scoring was similar between the 2 groups. In adjusted analysis, there was no association between intensity of staffing and hospital mortality (odds ratio, 0.84; 95% confidence interval, 0.36-1.99; P = .69) or PCU mortality (odds ratio, 0.96; 95% confidence interval, 0.38-2.45; P = .69). There was also no difference in subsequent ICU admission rates or in PCU length of stay.

CONCLUSIONS

We found no evidence that high-intensity intensivist physician staffing improves outcomes for intermediate care patients. In a strained critical care system, our study raises questions about the role of the intensivist in the graded care options between intensive and conventional ward care.