Addressing the variation of post-surgical inpatient census with computer simulation

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.

Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus

Providing sufficient testing capacities and accurate results in a time-efficient way are essential to prevent the spread and lower the curve of a health crisis, such as the COVID-19 pandemic. In line with recent research investigating how simulation-based models and tools could contribute to mitigating the impact of COVID-19, a discrete event simulation model is developed to design optimal saliva-based COVID-19 testing stations performing sensitive, non-invasive, and rapid-result RT-qPCR tests processing. This model aims to determine the adequate number of machines and operators required, as well as their allocation at different workstations, according to the resources available and the rate of samples to be tested per day. The model has been built and experienced using actual data and processes implemented on-campus at the University of Illinois at Urbana-Champaign, where an average of around 10,000 samples needed to be processed on a daily basis, representing at the end of August 2020 more than 2% of all the COVID-19 tests performed per day in the USA. It helped identify specific bottlenecks and associated areas of improvement in the process to save human resources and time. Practically, the overall approach, including the proposed modular discrete event simulation model, can easily be reused or modified to fit other contexts where local COVID-19 testing stations have to be implemented or optimized. It could notably support on-site managers and decision-makers in dimensioning testing stations by allocating the appropriate type and quantity of resources.

Theoretical bounds and approximation of the probability mass function of future hospital bed demand

Failing to match the supply of resources to the demand for resources in a hospital can cause non-clinical transfers, diversions, safety risks, and expensive under-utilized resource capacity. Forecasting bed demand helps achieve appropriate safety standards and cost management by proactively adjusting staffing levels and patient flow protocols. This paper defines the theoretical bounds on optimal bed demand prediction accuracy and develops a flexible statistical model to approximate the probability mass function of future bed demand. A case study validates the model using blinded data from a mid-sized Massachusetts community hospital. This approach expands upon similar work by forecasting multiple days in advance instead of a single day, providing a probability mass function of demand instead of a point estimate, using the exact surgery schedule instead of assuming a cyclic schedule, and using patient-level duration-varying length-of-stay distributions instead of assuming patient homogeneity and exponential length of stay distributions. The primary results of this work are an accurate and lengthy forecast, which provides managers better information and more time to optimize short-term staffing adaptations to stochastic bed demand, and a derivation of the minimum mean absolute error of an ideal forecast.

Inpatient flow management: a systematic review

Purpose

In hospitals, several patient flows compete for access to shared resources. Failure to manage these flows result in one or more disruptions within a hospital system. To ensure continuous care delivery, solving flow problems must not be limited to one unit, but should be extended to other departments – a prerequisite for solving flow problems in the entire hospital. Since most current studies focus solely on overcrowding in emergency units, additional insights are needed on system-wide patient flow management. The purpose of this paper is to look at the information available in system-wide patient flow management studies, which were also systematically evaluated to demonstrate which interventions improve inpatient flow.

Design/methodology/approach

The authors searched PubMed and Web of Science (Core Collection) literature databases and collected full-text articles using two selection and classification stages. Stage 1 was used to screen articles relating to patient flow management for inpatient settings with typical characteristics. Stage 2 was used to classify the articles selected in Stage 1 according to the interventions and their impact on patient flow within a hospital system.

Findings

In Stage 1, 107 studies were selected. Although a growing trend was observed, there were fewer studies on patient flow management in inpatient than studies in emergency settings. In Stage 2, 61 intervention studies were classified. The authors found that most interventions were about creating and adding supply resources. Since many hospital managers these days cannot easily add capacity owing to cost and resource constraints, using existing capacity efficiently is important – unfortunately not addressed in many studies. Furthermore, arrival variability was the factor most frequently mentioned as affecting flow. Of all interventions addressed in this review, the most prominent for advancing patient access to inpatient units was employing a specialized individual or team to maintain patient flow and bed placement across hospital units.

Originality/value

This study provides the first patient flow management systematic overview within an inpatient setting context.

Simulation to Predict Effect of Citywide Events on Emergency Department Operations

Medical emergency preparedness has been an issue of medical relevance since the advent of hospital care. Studies have simulated emergency department (ED) overcrowding but not yet characterized effects of large-scale, planned events that drastically alter a city's demography, such as in Philadelphia, Pennsylvania during the 2015 World Meeting of Families. A discrete event simulation of the ED at the Children's Hospital of Philadelphia was designed and validated using past data. The model was used to predict the patient length of stay (LOS) and number of admitted patients if the arrival stream to the ED were to change by 50% from typical arrivals in either direction. We compared the model's estimations with data produced during the papal visit that had 39.65% fewer patient arrivals. For validation, the simulated mean LOS was 226.1 ± 173.3 minutes (mean ± SD) for all patients and 352.1 ± 170.3 minutes for admitted patients. Real-world mean LOSs for the fiscal year 2014 were 230.6 ± 134.8 for all patients and 345.0 ± 147.7 for admitted patients. For the estimation of the World Meeting of Families, the simulation accurately estimated the LOS of both patients overall and admitted patients within 10%. These results show that it is possible to use simulations to project the patient flow effects in EDs in case of large-scale events. Providing efficient care is essential to emergency operations, and projections of demand are crucial for targeting appropriate changes during large-scale events. Analysis of validated computer simulations allows for evidence-based decision making in a complex clinical environment.