Process Modeling of ICU Patient Flow: Effect of Daily Load Leveling of Elective Surgeries on ICU Diversion

Arizona Surge Line: An emergent statewide COVID-19 transfer service with equity as an outcome

Introduction

The Arizona Surge Line was an emergent initiative during the COVID-19 pandemic to facilitate COVID-19 patient transfers and load-level hospitals on a statewide level. It was designed and implemented by the Arizona Department of Health Services in preparation for the first hospital surge due to COVID-19, recognizing the disproportionate impact that hospital surge would have on rural and tribal populations.

Methods

We analyzed the Arizona Surge Line transfer data for the state's first two COVID-19 surges (4/16/2020-3/6/2021). Transfer data included transfer request characteristics, patient demographics and participating hospital characteristics. When applicable, we compared this data with Arizona census data, COVID-19 case data, and the CDC/ATSDR Social Vulnerability Index. The primary outcomes studied were the proportion of COVID-19 patient requests being successfully transferred, the median transfer time, and the proportion of vulnerable populations impacted.

Results

During the period of study, 160 hospitals in Arizona made 6,732 requests for transfer of COVID-19 patients. The majority of these patients (84%, 95% CI: 83-85%) were placed successfully with a median transfer time of 59 min (inter-quartile range 33-116). Of all transfer requests, 58% originated from rural hospitals, 53% were for patients of American Indian/Alaska Native ethnicity, and 73% of patients originated from highly vulnerable areas. The majority (98%) of receiving facilities were in urban areas. The Arizona Surge Line matched the number of transfers with licensed market shares during the period of study.

Conclusions

The Arizona Surge Line is an equity-enhancing initiative that disproportionately benefited vulnerable populations. This statewide transfer infrastructure could become a standard public health mechanism to manage hospital surges and enhance access to care during a health emergency.

Impact of hospital strain on excess deaths during the COVID-19 pandemic-United States, july 2020-july 2021

This article describes the excess deaths observed when hospitals are strained by COVID-19 admissions, as defined by intensive care unit bed occupancy. Specifically, when intensive care unit bed occupancy reaches 75% of capacity, there are an estimated 12,000 additional excess deaths; when hospitals exceed 100% of their intensive care unit bed capacity, approximately 80,000 excess deaths are expected in the following 2 weeks nationally. This report suggests that all patient populations, including transplant candidates and recipients, will experience additional excess deaths when there is increased strain on hospitals due to COVID-19 surges.

Hospital preparedness during epidemics using simulation: the case of COVID-19

This paper presents a discrete event simulation model to support decision-making for the short-term planning of hospital resource needs, especially Intensive Care Unit (ICU) beds, to cope with outbreaks, such as the COVID-19 pandemic. Given its purpose as a short-term forecasting tool, the simulation model requires an accurate representation of the current system state and high fidelity in mimicking the system dynamics from that state. The two main components of the simulation model are the stochastic modeling of patient admission and patient flow processes. The patient arrival process is modelled using a Gompertz growth model, which enables the representation of the exponential growth caused by the initial spread of the virus, followed by a period of maximum arrival rate and then a decreasing phase until the wave subsides. We conducted an empirical study concluding that the Gompertz model provides a better fit to pandemic-related data (positive cases and hospitalization numbers) and has superior prediction capacity than other sigmoid models based on Richards, Logistic, and Stannard functions. Patient flow modelling considers different pathways and dynamic length of stay estimation in several healthcare stages using patient-level data. We report on the application of the simulation model in two Autonomous Regions of Spain (Navarre and La Rioja) during the two COVID-19 waves experienced in 2020. The simulation model was employed on a daily basis to inform the regional logistic health care planning team, who programmed the ward and ICU beds based on the resulting predictions.

Design and comparison of scheduling strategy for teleconsultation

BACKGROUND

Telemedicine is playing an increasingly more important role in disease diagnosis and treatment. The market of telemedicine application is continuously promoted, thus bringing some issues on telemedicine operations management.

OBJECTIVE

We aimed to compare the teleconsultation scheduling performance of newly designed proactive strategy and existing static strategy and explore the decision-making under different conditions.

METHODS

We developed a discrete-event simulation model based on practical investigation to describe the existing static scheduling strategy of teleconsultation. The static strategy model was verified by comparing it with the historical data. Then a new proactive strategy was proposed, whose average waiting time, variance of waiting time and completed numbers were compared with the static strategy.

RESULTS

The analysis indicated that the proactive strategy performed better than static under the current resource allocation. Furthermore, we explored the impact on the system of both strategies varying arrival rate and experts' shift time.

CONCLUSIONS

Under different shift times and arrival rates, the managers of telemedicine center should select different strategy. The experts' shift time had a significant impact on all system performance indicators. Therefore, if managers wanted to improve the system performance to a greater extent, they needed to reduce the shift time as much as possible.

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.

Cancellation of Surgeries: Integrative Review

PURPOSE

To analyze cancellations of surgeries and identify evidence-based interventions to address this issue.

DESIGN

Integrative literature review.

METHODS

An integrative literature search was conducted in four databases: CINAHL, PubMed, Embase, and Cochrane and included literature sources dated January 2011 to January 2016. The complete list of search terms consisted of the following: ambulatory surgery, day surgery center, elective surgical procedure, elective operation, elective surgery, schedule, access to care, surgery cancellation, operation cancellation, and surgery delay.

FINDINGS

Twenty-three literature sources were identified. Evidence included one randomized controlled trial and multiple studies. Causes of cancellations were classified into three categories: hospital-related reasons, patient-related reasons, and surgeon-related reasons. Evidence confirmed most cancellations were avoidable.

CONCLUSIONS

Cancellation of scheduled surgeries has a significant impact on patients' health, resources, cost, and quality of care. It is difficult to devise a solution without understanding the cause of cancellations.

A proactive transfer policy for critical patient flow management

Hospital emergency departments are often overcrowded, resulting in long wait times and a public perception of poor attention. Delays in transferring patients needing further treatment increases emergency department congestion, has negative impacts on their health and may increase their mortality rates. A model built around a Markov decision process is proposed to improve the efficiency of patient flows between the emergency department and other hospital units. With each day divided into time periods, the formulation estimates bed demand for the next period as the basis for determining a proactive rather than reactive transfer decision policy. Due to the high dimensionality of the optimization problem involved, an approximate dynamic programming approach is used to derive an approximation of the optimal decision policy, which indicates that a certain number of beds should be kept free in the different units as a function of the next period demand estimate. Testing the model on two instances of different sizes demonstrates that the optimal number of patient transfers between units changes when the emergency patient arrival rate for transfer to other units changes at a single unit, but remains stable if the change is proportionally the same for all units. In a simulation using real data for a hospital in Chile, significant improvements are achieved by the model in key emergency department performance indicators such as patient wait times (reduction higher than 50%), patient capacity (21% increase) and queue abandonment (from 7% down to less than 1%).

A Discrete Event Simulation Model of Patient Flow in a General Hospital Incorporating Infection Control Policy for Methicillin-Resistant Staphylococcus Aureus (MRSA) and Vancomycin-Resistant Enterococcus (VRE)

BACKGROUND

Hospitalized patients are assigned to available staffed beds based on patient acuity and services required. In hospitals with double-occupancy rooms, patients must be additionally matched by gender. Patients with methicillin-resistant Staphylococcus aureus (MRSA) or vancomycin-resistant Enterococcus (VRE) must be bedded in single-occupancy rooms or cohorted with other patients with similar MRSA/VRE flags.

METHODS

We developed a discrete event simulation (DES) model of patient flow through an acute care hospital. Patients are matched to beds based on acuity, service, gender, and known MRSA/VRE colonization. Outcomes included time to bed arrival, length of stay, patient-bed acuity mismatches, occupancy, idle beds, acuity-related transfers, rooms with discordant MRSA/VRE colonization, and transmission due to discordant colonization.

RESULTS

Observed outcomes were well-approximated by model-generated outcomes for time-to-bed arrival (6.7 v. 6.2 to 6.5 h) and length of stay (3.3 v. 2.9 to 3.0 days), with overlapping 90% coverage intervals. Patient-bed acuity mismatches, where patient acuity exceeded bed acuity and where patient acuity was lower than bed acuity, ranged from 0.6 to 0.9 and 8.6 to 11.1 mismatches per h, respectively. Values for observed occupancy, total idle beds, and acuity-related transfers compared favorably to model-predicted values (91% v. 86% to 87% occupancy, 15.1 v. 14.3 to 15.7 total idle beds, and 27.2 v. 22.6 to 23.7 transfers). Rooms with discordant colonization status and transmission due to discordance were modeled without an observed value for comparison. One-way and multi-way sensitivity analyses were performed for idle beds and rooms with discordant colonization.

CONCLUSIONS

We developed and validated a DES model of patient flow incorporating MRSA/VRE flags. The model allowed for quantification of the substantial impact of MRSA/VRE flags on hospital efficiency and potentially avoidable nosocomial transmission.

Simulation-Based Design of ED Operations with Care Streams to Optimize Care Delivery and Reduce Length of Stay in the Emergency Department

Faced with the opportunity to significantly deviate from classic operations, a new emergency department (ED) and novel strategy for patient care delivery were simultaneously initiated with the aid of model-based simulation. To answer the design and implementation questions, a traditional strategy for construction of discrete-eventmodel simulation was employed to define ED operations for a newly constructed facility in terms of workflow, variables, resources, structure, process logic and associated assumptions. Benefits were achieved before, during and after implementation of an unprecedented operations strategy-i.e., the organization of the ED care delivery around four care streams: Critical, Diagnostic, Therapeutic and Fast Track. Prior to opening, it shed light on the range of context variables where benefits might be anticipated, and it facilitated staff understanding and judgements of performance. Two years after opening, the operations data is compared to the simulation with encouraging results that shed light on where to continue pursuit of improvement.

Hospital-Level Changes in Adult ICU Bed Supply in the United States

OBJECTIVES

Although the number of intensive care beds in the United States is increasing, little is known about the hospitals responsible for this growth. We sought to better characterize national growth in intensive care beds by identifying hospital-level factors associated with increasing numbers of intensive care beds over time.

DESIGN

We performed a repeated-measures time series analysis of hospital-level intensive care bed supply using data from Centers for Medicare and Medicaid Services.

SETTING

All United States acute care hospitals with adult intensive care beds over the years 1996-2011.

PATIENTS

None.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We described the number of beds, teaching status, ownership, intensive care occupancy, and urbanicity for each hospital in each year of the study. We then examined the relationship between increasing intensive care beds and these characteristics, controlling for other factors. The study included 4,457 hospitals and 55,865 hospital-years. Overall, the majority of intensive care bed growth occurred in teaching hospitals (net, +13,471 beds; 72.1% of total growth), hospitals with 250 or more beds (net, +18,327 beds; 91.8% of total growth), and hospitals in the highest quartile of occupancy (net, +10,157 beds; 54.0% of total growth). In a longitudinal multivariable model, larger hospital size, teaching status, and high intensive care occupancy were associated with subsequent-year growth. Furthermore, the effects of hospital size and teaching status were modified by occupancy: the greatest odds of increasing ICU beds were in hospitals with 500 or more beds in the highest quartile of occupancy (adjusted odds ratio, 18.9; 95% CI, 14.0-25.5; p < 0.01) and large teaching hospitals in the highest quartile of occupancy (adjusted odds ratio, 7.3; 95% CI, 5.3-9.9; p < 0.01).

CONCLUSIONS

Increasingly, intensive care bed expansion in the United States is occurring in larger hospitals and teaching centers, particularly following a year with high ICU occupancy.

Operations research in intensive care unit management: a literature review

The intensive care unit (ICU) is a crucial and expensive resource largely affected by uncertainty and variability. Insufficient ICU capacity causes many negative effects not only in the ICU itself, but also in other connected departments along the patient care path. Operations research/management science (OR/MS) plays an important role in identifying ways to manage ICU capacities efficiently and in ensuring desired levels of service quality. As a consequence, numerous papers on the topic exist. The goal of this paper is to provide the first structured literature review on how OR/MS may support ICU management. We start our review by illustrating the important role the ICU plays in the hospital patient flow. Then we focus on the ICU management problem (single department management problem) and classify the literature from multiple angles, including decision horizons, problem settings, and modeling and solution techniques. Based on the classification logic, research gaps and opportunities are highlighted, e.g., combining bed capacity planning and personnel scheduling, modeling uncertainty with non-homogenous distribution functions, and exploring more efficient solution approaches.

Sustaining critical care: using evidence-based simulation to evaluate ICU management policies

Intensive Care Units (ICU) are costly yet critical hospital departments that should be available to care for patients needing highly specialized critical care. Shortage of ICU beds in many regions of the world and the constant fire-fighting to make these beds available through various ICU management policies motivated this study. The paper discusses the application of a generic system dynamics model of emergency patient flow in a typical hospital, populated with empirical evidence found in the medical and hospital administration literature, to explore the dynamics of intended and unintended consequences of such ICU management policies under a natural disaster crisis scenario. ICU management policies that can be implemented by a single hospital on short notice, namely premature transfer from ICU, boarding in ward, and general ward admission control, along with their possible combinations, are modeled and their impact on managerial and health outcome measures are investigated. The main insight out of the study is that the general ward admission control policy outperforms the rest of ICU management policies under such crisis scenarios with regards to reducing total mortality, which is counter intuitive for hospital administrators as this policy is not very effective at alleviating the symptoms of the problem, namely high ED and ICU occupancy rates that are closely monitored by hospital management particularly in times of crisis. A multivariate sensitivity analysis on parameters with diverse range of values in the literature found the superiority of the general ward admission control to hold true in every scenario.

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

RATIONALE

High demand for intensive care unit (ICU) services and limited bed availability have prompted hospitals to address capacity planning challenges. Simulation modeling can examine ICU bed assignment policies, accounting for patient acuity, to reduce ICU admission delays.

OBJECTIVES

To provide a framework for data-driven modeling of ICU patient flow, identify key measurable outcomes, and present illustrative analysis demonstrating the impact of various bed allocation scenarios on outcomes.

METHODS

A description of key inputs for constructing a queuing model was outlined, and an illustrative simulation model was developed to reflect current triage protocol within the medical ICU and step-down unit (SDU) at a single tertiary-care hospital. Patient acuity, arrival rate, and unit length of stay, consisting of a "service time" and "time to transfer," were estimated from 12 months of retrospective data (n = 2,710 adult patients) for 36 ICU and 15 SDU staffed beds. Patient priority was based on acuity and whether the patient originated in the emergency department. The model simulated the following hypothetical scenarios: (1) varied ICU/SDU sizes, (2) reserved ICU beds as a triage strategy, (3) lower targets for time to transfer out of the ICU, and (4) ICU expansion by up to four beds. Outcomes included ICU admission wait times and unit occupancy.

MEASUREMENTS AND MAIN RESULTS

With current bed allocation, simulated wait time averaged 1.13 (SD, 1.39) hours. Reallocating all SDU beds as ICU decreased overall wait times by 7.2% to 1.06 (SD, 1.39) hours and increased bed occupancy from 80 to 84%. Reserving the last available bed for acute patients reduced wait times for acute patients from 0.84 (SD, 1.12) to 0.31 (SD, 0.30) hours, but tripled subacute patients' wait times from 1.39 (SD, 1.81) to 4.27 (SD, 5.44) hours. Setting transfer times to wards for all ICU/SDU patients to 1 hour decreased wait times for incoming ICU patients, comparable to building one to two additional ICU beds.

CONCLUSIONS

Hospital queuing and simulation modeling with empiric data inputs can evaluate how changes in ICU bed assignment could impact unit occupancy levels and patient wait times. Trade-offs associated with dedicating resources for acute patients versus expanding capacity for all patients can be examined.

StratBAM: A Discrete-Event Simulation Model to Support Strategic Hospital Bed Capacity Decisions

The ability to accurately measure and assess current and potential health care system capacities is an issue of local and national significance. Recent joint statements by the Institute of Medicine and the Agency for Healthcare Research and Quality have emphasized the need to apply industrial and systems engineering principles to improving health care quality and patient safety outcomes. To address this need, a decision support tool was developed for planning and budgeting of current and future bed capacity, and evaluating potential process improvement efforts. The Strategic Bed Analysis Model (StratBAM) is a discrete-event simulation model created after a thorough analysis of patient flow and data from Geisinger Health System's (GHS) electronic health records. Key inputs include: timing, quantity and category of patient arrivals and discharges; unit-level length of care; patient paths; and projected patient volume and length of stay. Key outputs include: admission wait time by arrival source and receiving unit, and occupancy rates. Electronic health records were used to estimate parameters for probability distributions and to build empirical distributions for unit-level length of care and for patient paths. Validation of the simulation model against GHS operational data confirmed its ability to model real-world data consistently and accurately. StratBAM was successfully used to evaluate the system impact of forecasted patient volumes and length of stay in terms of patient wait times, occupancy rates, and cost. The model is generalizable and can be appropriately scaled for larger and smaller health care settings.

Planning intensive care unit design using computer simulation modeling: optimizing integration of clinical, operational, and architectural requirements

Nurses have increasingly been regarded as critical members of the planning team as architects recognize their knowledge and value. But the nurses' role as knowledge experts can be expanded to leading efforts to integrate the clinical, operational, and architectural expertise through simulation modeling. Simulation modeling allows for the optimal merge of multifactorial data to understand the current state of the intensive care unit and predict future states. Nurses can champion the simulation modeling process and reap the benefits of a cost-effective way to test new designs, processes, staffing models, and future programming trends prior to implementation. Simulation modeling is an evidence-based planning approach, a standard, for integrating the sciences with real client data, to offer solutions for improving patient care.

Reducing ambulance diversion at hospital and regional levels: systemic review of insights from simulation models

Introduction:

Optimal solutions for reducing diversion without worsening emergency department (ED) crowding are unclear. We performed a systematic review of published simulation studies to identify: 1) the tradeoff between ambulance diversion and ED wait times; 2) the predicted impact of patient flow interventions on reducing diversion; and 3) the optimal regional strategy for reducing diversion.

Methods:

Data Sources: Systematic review of articles using MEDLINE, Inspec, Scopus. Additional studies identified through bibliography review, Google Scholar, and scientific conference proceedings. Study Selection: Only simulations modeling ambulance diversion as a result of ED crowding or inpatient capacity problems were included. Data extraction: Independent extraction by two authors using predefined data fields.

Results:

We identified 5,116 potentially relevant records; 10 studies met inclusion criteria. In models that quantified the relationship between ED throughput times and diversion, diversion was found to only minimally improve ED waiting room times. Adding holding units for inpatient boarders and ED-based fast tracks, improving lab turnaround times, and smoothing elective surgery caseloads were found to reduce diversion considerably. While two models found a cooperative agreement between hospitals is necessary to prevent defensive diversion behavior by a hospital when a nearby hospital goes on diversion, one model found there may be more optimal solutions for reducing region wide wait times than a regional ban on diversion.

Conclusion:

Smoothing elective surgery caseloads, adding ED fast tracks as well as holding units for inpatient boarders, improving ED lab turnaround times, and implementing regional cooperative agreements among hospitals are promising avenues for reducing diversion.

Health systems engineering fellowship: curriculum and program development

Industrial engineering and related disciplines have been used widely in improvement efforts in many industries. These approaches have been less commonly attempted in health care. One factor limiting application is the limited workforce resulting from a lack of specific education and professional development in health systems engineering (HSE). The authors describe the development of an HSE fellowship within the United States Department of Veterans Affairs, Veterans Health Administration (VA). This fellowship includes a novel curriculum based on specifically established competencies for HSE. A 1-year HSE curriculum was developed and delivered to fellows at several VA engineering resource centers over several years. On graduation, a majority of the fellows accepted positions in the health care field. Challenges faced in developing the fellowship are discussed. Advanced educational opportunities in applied HSE have the potential to develop the workforce capacity needed to improve the quality of health care.

Analyzing Discharge Strategies during Acute Care

Background. When to discharge acute care patients is a complex decision that depends on both patient- and system-level factors. Such a decision for one patient affects other patients and operations in a hospital. The key tradeoff that we analyzed was the effect of discharge timing on several emergency department (ED)-related measures and the number of readmissions. Methods. We developed a discrete-event simulation model of patient pathway through an acute care hospital that comprises an ED and several inpatient units. The effects of discharge timing on ED waiting and boarding times, ambulance diversions, leave without treatment, and readmissions were explicitly modeled. We then analyzed the impact of 1 static and 2 proactive discharge strategies on these system outcomes. Results. Our analysis indicated that although the 2 proactive discharge strategies significantly reduced ED waiting and boarding times, and several other measures, compared with the static strategy ( P < 0.01), the number of readmissions increased substantially. Further analysis indicated that these findings are sensitive to changes in patient arrival rate and conditions for ambulance diversion. Conclusions. Determining the appropriate time to discharge patients not only can affect individual patients’ health outcomes, but also can affect various aspects of the hospital. The study improves our understanding of how individual inpatient discharge decisions can be objectively viewed in terms of their impact on other operations, such as ED crowding and readmission, in an acute care hospital.

Modeling the patient journey from injury to community reintegration for persons with acute traumatic spinal cord injury in a Canadian centre

Background

A patient’s journey through the health care system is influenced by clinical and system processes across the continuum of care.

Methods

To inform optimized access to care and patient flow for individuals with traumatic spinal cord injury (tSCI), we developed a simulation model that can examine the full impact of therapeutic or systems interventions across the care continuum for patients with traumatic spinal cord injuries. The objective of this paper is to describe the detailed development of this simulation model for a major trauma and a rehabilitation centre in British Columbia (BC), Canada, as part of the Access to Care and Timing (ACT) project and is referred to as the BC ACT Model V1.0.

Findings

To demonstrate the utility of the simulation model in clinical and administrative decision-making we present three typical scenarios that illustrate how an investigator can track the indirect impact(s) of medical and administrative interventions, both upstream and downstream along the continuum of care. For example, the model was used to estimate the theoretical impact of a practice that reduced the incidence of pressure ulcers by 70%. This led to a decrease in acute and rehabilitation length of stay of 4 and 2 days, respectively and a decrease in bed utilization of 9% and 3% in acute and rehabilitation.

Conclusion

The scenario analysis using the BC ACT Model V1.0 demonstrates the flexibility and value of the simulation model as a decision-making tool by providing estimates of the effects of different interventions and allowing them to be objectively compared. Future work will involve developing a generalizable national Canadian ACT Model to examine differences in care delivery and identify the ideal attributes of SCI care delivery.

The application of operations research methodologies to the delivery of care model for traumatic spinal cord injury: the access to care and timing project

The long-term impact of spinal cord injury (SCI) on the health care system imposes a need for greater efficiency in the use of resources and the management of care. The Access to Care and Timing (ACT) project was developed to model the health care delivery system in Canada for patients with traumatic SCI. Techniques from Operations Research, such as simulation modeling, were used to predict the impact of best practices and policy initiatives on outcomes related to both the system and patients. These methods have been used to solve similar problems in business and engineering and may offer a unique solution to the complexities encountered in SCI care delivery. Findings from various simulated scenarios, from the patients' point of injury to community re-integration, can be used to inform decisions on optimizing practice across the care continuum. This article describes specifically the methodology and implications of producing such simulations for the care of traumatic SCI in Canada. Future publications will report on specific practices pertaining to the access to specialized services and the timing of interventions evaluated using the ACT model. Results from this type of research will provide the evidence required to support clinical decision making, inform standards of care, and provide an opportunity to engage policymakers.

Balancing operating theatre and bed capacity in a cardiothoracic centre

Cardiothoracic surgery requires many expensive resources. This paper examines the balance between operating theatres and beds in a specialist facility providing elective heart and lung surgery. Without both operating theatre time and an Intensive Care bed a patient's surgery has to be postponed. While admissions can be managed, there are significant stochastic features, notably the cancellation of theatre procedures and patients' length of stay on the Intensive Care Unit. A simulation was developed, with clinical and management staff, to explore the interdependencies of resource availabilities and the daily demand. The model was used to examine options for expanding the capacity of the whole facility. Ideally the bed and theatre capacity should be well balanced but unmatched increases in either resource can still be beneficial. The study provides an example of a capacity planning problem in which there is uncertainty in the demand for two symbiotic resources.

Managing ICU throughput and understanding ICU census

PURPOSE OF REVIEW

Traditionally, hospitals have coped with chronically high ICU census by building more ICU beds, but this strategy is unlikely to be tenable under future financial models. Therefore, ICUs need additional tools to manage census, inflow, and throughput.

RECENT FINDINGS

Higher ICU census, without compensatory surges in nursing capacity, is associated with several adverse effects on patients and providers, but its relationship to mortality is uncertain. Providers also discharge patients more aggressively during times of high census. Little's Law (L = λ W), a cornerstone of queuing theory, provides an eminently practical basis for managing ICU census and throughput. One target for improving throughput is minimizing process steps that are without value to the patient, e.g., waiting for a bed at ICU discharge. Larger gains in ICU throughput can be found in ICU quality improvement. For example, spontaneous breathing trials, daily wake-ups, and early physical/occupational therapy programmes are all likely to improve throughput by reducing ICU length of stay. The magnitude of these interventions' effects on ICU census can be startling.

SUMMARY

ICUs should actively manage throughput and census. Operations management tools such as Little's Law can provide practical guidance about the relationship between census, throughput, and patient demand. Standard ICU quality improvement techniques can meaningfully affect both ICU census and throughput.

ICU capacity strain and the quality and allocation of critical care

PURPOSE OF REVIEW

Increasing demand for critical care, with limited potential for comparable expansion of supply, may strain the abilities of ICUs to provide high-quality care in an equitable fashion. Efforts to counter the untoward consequences for the quality and ethics of critical care delivery are limited by the absence of a specific and validated metric of ICU capacity strain.

RECENT FINDINGS

This manuscript presents a conceptual framework for ICU capacity strain, considers what data elements may contribute to it, and suggests methods for determining the optimal metric. Next, it outlines the range of potential consequences of increased capacity strain, in terms of both the quality and ethics of care delivered. Finally, consideration is given to how untoward consequences of ICU capacity strain might be mitigated through better understanding of what makes some ICUs better able than others to withstand temporal fluctuations in the demand for their services.

SUMMARY

Development of an appropriately accurate and parsimonious measure of ICU capacity strain may augment the precision of future critical care outcomes research by reducing unexplained variance attributable to temporal fluctuations in ICU-level factors; elucidate organizational characteristics that make some ICUs better able to withstand high-capacity strain without substantive degradations in quality; and enhance the transparency of critical care rationing while helping to improve its equity and efficiency, thereby promoting the ethics of this inevitable practice.

An open source software project for obstetrical procedure scheduling and occupancy analysis

Increases in the rate of births via cesarean section and induced labor have led to challenging scheduling and capacity planning problems for hospital inpatient obstetrical units. We present occupancy and patient scheduling models to help address these challenges. These patient flow models can be used to explore the relationship between procedure scheduling practices and the resulting occupancy on inpatient obstetrical units such as labor and delivery and postpartum. The models capture numerous important characteristics of inpatient obstetrical patient flow such as time of day and day of week dependent arrivals and length of stay, multiple patient types and clinical interventions, and multiple patient care units with inter-unit patient transfers. We have used these models in several projects at different hospitals involving design of procedure scheduling templates and analysis of inpatient obstetrical capacity. In the development of these models, we made heavy use of open source software tools and have released the entire project as a free and open source model and software toolkit.

Redesigning intensive care unit flow using variability management to improve access and safety

BACKGROUND

Poor flow of patients into and out of the ICU can result in gridlock and bottlenecks that disrupt care and have a detrimental effect on patient safety and satisfaction, hospital efficiency, staff stress and morale, and revenue. Beginning in 2006, Cincinnati Children's Hospital Medical Center implemented a series of interventions to "smooth" patient flow through the system.

METHODS

Key activities included patient flow models based on surgical providers' predicted need for intensive care and predicted length of stay; scheduling the case and an ICU bed at the same time; capping and simulation models to identify the appropriate number of elective surgical cases to maximize occupancy without cancelling elective cases; and a morning huddle by the chief of staff, manager of patient services, and representatives from the operating room, pediatric ICUS, and anesthesia to confirm that day's plan and anticipate the next day's needs.

RESULTS

New elective surgical admissions to the pediatric ICU were restricted to a maximum of five cases per day. Diversion of patients to the cardiac ICU, keeping patients in the postanesthesia care unit longer than expected, and delaying or canceling cases are now rare events. Since implementation of the operations management interventions, there have been no cases when beds in the pediatric ICU were not available when needed for urgent medical or surgical use.

DISCUSSION

A system for smoothing flow, based on an advanced predictive model for need, occupancy, and length of stay, coupled with an active daily strategy for demand/capacity matching of resources and needs, allowed much better early planning, predictions, and capacity management, thereby ensuring that all patients are in suitable ICU environments.