Improving patient timeliness of care through efficient outpatient clinic layout design using data-driven simulation and optimisation

Multi-objective layout optimization of hospital outpatient clinics based on NSGA II

This study utilizes an improved NSGA-II algorithm to conduct a multi-objective optimization of the hospital outpatient department layout. By simultaneously incorporating patient walking distance, hospital operating costs, patient waiting time, and medical staff work efficiency as optimization objectives, and adopting an adaptive population size adjustment strategy, this paper optimizes the existing outpatient layout in a case study of a three-story outpatient building at Panzhihua Central Hospital. The results show that the new plan reduces patient walking distance by 57.2%, shortens waiting time by 59%, and enhances medical staff collaboration efficiency, while only increasing costs by 5.6%. This demonstrates the effectiveness and feasibility of the improved NSGA-II method in handling complex multi-objective optimization problems for outpatient layouts. The research findings provide a reference for the rational allocation of hospital outpatient resources and the improvement of service quality. Additionally, this paper discusses the applicability and limitations of the study and proposes future research directions, including validating the method's effectiveness in hospitals of various types and sizes, incorporating dynamic optimization and real-time data, and deeply integrating with hospital information systems.

Blueprints for Better Care: Unveiling the Role of Clinic Design in Enhancing Patient Experience and Efficiency

Background: The rising demand for outpatient care, fueled by improved efficiency, technology, and patient-centered models, necessitates reevaluating clinic design and healthcare delivery. This reassessment should align with evolving patient expectations to enhance care continuity and outcomes. Objective: This research explores how targeted design features affect patient behaviors, movement dynamics, and staff perceptions in outpatient clinics. The goal is to enhance patient experience, improve operational efficiency, and elevate care quality by identifying strategies to boost clinic performance and patient outcomes. Methods: This study utilizes behavior mapping, shadowing observations, surveys, and Gemba walk-throughs across four outpatient clinics to examine how design affects patient experiences and operational workflows. Behavior mapping collected 1179 data points, revealing that waiting for appointments and making phone calls were the most common activities. Shadowing 13 patients identified navigation inefficiencies, particularly the lengthy route from exam rooms to exits and the frequent movement between the scale and waiting area. Surveys and Gemba walk-throughs with 95 staff members highlighted critical design elements that enhance experiences, including aesthetics, acoustics, and room sizes. Feedback indicated a demand for improvements in accessibility, privacy, wayfinding, reduced staff travel distances, and child-friendly features to boost clinic efficiency and patient satisfaction. Conclusion: This study highlights the importance of patient-centered design in outpatient clinics. It shows that improving waiting areas, clinic navigation, privacy measures, and technology integration can enhance patient experience and operational efficiency.

Enhancing healthcare operations: a systematic literature review on approaches for hospital facility layout planning

PURPOSE

The appropriate physical layout of hospital services can help resolve management problems by streamlining the work of medical teams, improving the flow of patients between specific areas and the medical support environment. Nevertheless, the academic literature lacks structured research into how the physical layout of hospitals might be improved. Our study aims to fill this research gap, providing information for researchers and professionals who intend to guide the hospital facility layout planning (HFLP) from the steps and prescribed approaches found in the literature.

DESIGN/METHODOLOGY/APPROACH

This study analyzes the current literature status and concerning approaches that support HFLP and identifies their strengths and weaknesses. The literature was classified using the following criteria: approaches for layout generation, approaches for layout evaluation and healthcare facility layout outcomes.

FINDINGS

The hospital facility layout outcomes achieved for each phase served as a basis for identifying a list of strengths and weaknesses for the hospital layout facility generation and evaluation approaches. Readers can refer to this paper to identify the approach that best fits the desired goal and the HFLP step.

PRACTICAL IMPLICATIONS

This is a contribution to current studies into HFLP, and it provides guidelines for selecting the approach to be utilized based on the desired outcome.

ORIGINALITY/VALUE

The paper describes how to conduct an HFLP and lists the strengths and weaknesses of each approach. The research may be used as a strategy for determining which tool is most suited based on the practitioner's target purpose.

Innovate and Validate: Design-Led Simulation Optimization to Test Centralized Registration Feasibility in a Multispecialty Clinic

OBJECTIVE

This study utilizes a design-led simulation-optimization process (DLSO) to refine a hybrid registration model for a free-standing outpatient clinic. The goal is to assess the viability of employing DLSO for innovation support and highlight key factors influencing resource requirements.

BACKGROUND

Manual registration in healthcare causes delays, impacting patient services and resource allocation. This study addresses these challenges by optimizing a hybrid centralized registration and adopting technology for efficiency.

METHOD

An iterative methodology with simulation optimization was designed to test a proof of concept. Configurations of four and five registration options within a hybrid centralized system were explored under preregistration adoption rates of 30% and 50%. Three self-service kiosks served as a baseline during concept design and test fits.

RESULTS

Centralized registration accommodated a daily throughput of 2,000 people with a 30% baseline preregistration rate. Assessing preregistration impact on seating capacity showed significant reductions in demand and floor census. For four check-in stations, a 30%-50% preregistration increase led to a 32% seating demand reduction and a 26% decrease in maximum floor census. With five stations, a 50% preregistration reduced seating demand by 23% and maximum floor census by 20%.

CONCLUSION

Innovating introduces complexity and uncertainties requiring buy-in from diverse stakeholders. DLSO experimentation proves beneficial for validating novel concepts during design.

Fractured systems: a literature review of OR/MS methods applied to orthopaedic care settings and treatments

Orthopaedic systems are facing an impending wave of increased pressures as a result of global ageing populations. This is compounded by the current stresses these services face, as a result of the COVID-19 pandemic, and increasing burden of musculoskeletal conditions. It is vital that measures are taken to alleviate the pressures on these systems, to ensure timely and quality access to care for patients. This literature review presents a taxonomic classification of the applications of Operational Research and Management Science (OR/MS) methodologies to orthopaedic care settings and treatments, covering the general, medical, and methodological context of each paper. Our structured search identified 492 relevant publications that have been included in our analysis. The results found a literature largely dominated by cost analysis applications, typically utilising Markov models or decision trees. Key gaps identified in this review include the lack of holistic modelling of orthopaedic systems and pathways, and limited applications to resource and capacity planning. The implications of our review are that researchers, healthcare professionals and managers can develop a research agenda to address these gaps, and enhance decision support in orthopaedics.

Hospital reconversion in response to the COVID-19 pandemic using simulation and multi-objective genetic algorithms

With the outbreak of the novel coronavirus SARS-CoV2, many countries have faced problems because of their available hospital capacity. Health systems must be prepared to restructure their facilities and meet the requirements of the pandemic while keeping their services and specialties active. This process, known as hospital reconversion, contributes to minimizing the risk of contagion between hospital staff and patients and optimizing the efficient treatment and disposal of healthcare wastes that represent a risk of nosocomial infection contagion. A methodology based upon simulation and mathematical optimization with genetic algorithms is proposed to address the hospital reconversion problem. Firstly, a discrete event simulation model is developed to study the flow of patients within the hospital system. Subsequently, the hospital reconversion problem is formulated through a mathematical model seeking to maximize the proximity relationships between departments and minimize the costs due to the flow of agents within the system. Finally, the results obtained from the optimization process are evaluated through the simulation model. The proposed framework is validated by assessing the hospital reconversion process in a COVID-19 Hospital in Mexico. The results show the mathematical model's effectiveness by incorporating the medical personnel's expertise in decisions regarding the use of elevators, departments' location, structural dimensions, use of corridors, and the floors to which the departments are assigned when facing a pandemic. The contribution of this approach can be replicated during the hospital reconversion process in other hospitals with different characteristics.

Optimal Planning of Health Services through Genetic Algorithm and Discrete Event Simulation: A Proposed Model and Its Application to Stroke Rehabilitation Care

Background. Increasing demand for provision of care to stroke survivors creates challenges for health care planners. A key concern is the optimal alignment of health care resources between provision of acute care, rehabilitation, and among different segments of rehabilitation, including inpatient rehabilitation, early supported discharge (ESD), and outpatient rehabilitation (OPR). We propose a novel application of discrete event simulation (DES) combined with a genetic algorithm (GA) to identify the optimal configuration of rehabilitation that maximizes patient benefits subject to finite health care resources. Design. Our stroke rehabilitation optimal model (sROM) combines DES and GA to identify an optimal solution that minimizes wait time for each segment of rehabilitation by changing care capacity across different segments. sROM is initiated by generating parameters for DES. GA is used to evaluate wait time from DES. If wait time meets specified stopping criteria, the search process stops at a point at which optimal capacity is reached. If not, capacity estimates are updated, and an additional iteration of the DES is run. To parameterize the model, we standardized real-world data from medical records by fitting them into probability distributions. A meta-analysis was conducted to determine the likelihood of stroke survivors flowing across rehabilitation segments. Results. We predict that rehabilitation planners in Alberta, Canada, have the potential to improve services by increasing capacity from 75 to 113 patients per day for ESD and from 101 to 143 patients per day for OPR. Compared with the status quo, optimal capacity would provide ESD to 138 (s = 29.5) more survivors and OPR to 262 (s = 45.5) more annually while having an estimated net annual cost savings of $25.45 (s = 15.02) million. Conclusions. The combination of DES and GA can be used to estimate optimal service capacity.

HIGHLIGHTS

We created a hybrid model combining a genetic algorithm and discrete event simulation to search for the optimal configuration of health care service capacity that maximizes patient outcomes subject to finite health system resources.We applied a probability distribution fitting process to standardize real-world data to probability distributions. The process consists of choosing the distribution type and estimating the parameters of that distribution that best reflects the data. Standardizing real-word data to a best-fitted distribution can increase model generalizability.In an illustrative study of stroke rehabilitation care, resource allocation to stroke rehabilitation services under an optimal configuration allows provision of care to more stroke survivors who need services while reducing wait time.Resources needed to expand rehabilitation services could be reallocated from the savings due to reduced wait time in acute care units. In general, the predicted optimal configuration of stroke rehabilitation services is associated with a net cost savings to the health care system.

A critical review of implementing lean and simulation to improve resource utilization and patient experience in outpatient clinics

Purpose

The purpose of this paper is to explore and critically review the existing literature on applications of Lean Methodology (LM) and Discrete-Event Simulation (DES) to improve resource utilization and patient experience in outpatient clinics. In doing, it is aimed to identify how to implement LM in outpatient clinics and discuss the advantages of integrating both lean and simulation tools towards achieving the desired outpatient clinics outcomes.

Design/methodology/approach

A theoretical background of LM and DES to define a proper implementation approach is developed. The search strategy of available literature on LM and DES used to improve outpatient clinic operations is discussed. Bibliometric analysis to identify patterns in the literature including trends, associated frameworks, DES software used, and objective and solutions implemented are presented. Next, an analysis of the identified work offering critical insights to improve the implementation of LM and DES in outpatient clinics is presented.

Findings

Critical analysis of the literature on LM and DES reveals three main obstacles hindering the successful implementation of LM and DES. To address the obstacles, a framework that integrates DES with LM has been recommended and proposed. The paper provides an example of such a framework and identifies the role of LM and DES towards improving the performance of their implementation in outpatient clinics.

Originality/value

This study provides a critical review and analysis of the existing implementation of LM and DES. The current roadblocks hindering LM and DES from achieving their expected potential has been identified. In addition, this study demonstrates how LM with DES combined to achieve the desired outpatient clinic objectives.

A configurable computer simulation model for reducing patient waiting time in oncology departments

Nowadays, the increase in patient demand and the decline in resources are lengthening patient waiting times in many chemotherapy oncology departments. Therefore, enhancing healthcare services is necessary to reduce patient complaints. Reducing the patient waiting times in the oncology departments represents one of the main goals of healthcare managers. Simulation models are considered an effective tool for identifying potential ways to improve patient flow in oncology departments. This paper presents a new agent-based simulation model designed to be configurable and adaptable to the needs of oncology departments which have to interact with an external pharmacy. When external pharmacies are utilised, a courier service is needed to deliver the individual therapies from the pharmacy to the oncology department. An oncology department located in southern Italy was studied through the simulation model and different scenarios were compared with the aim of selecting the department configuration capable of reducing the patient waiting times.

Preliminary evaluation of a new initiative to centralize colorectal cancer care during the COVID-19 epidemic in Shanghai, China: a retrospective study

Background

A novel colorectal cancer center (CCC) was developed in the Shanghai Tenth People’s hospital of Tongji University during the COVID-19 epidemic. In this study, we aimed to evaluate the CCC model in terms of three aspects.

Methods

This retrospective study used data from the Shanghai Tenth People’s hospital patient databases. The research hypothesis was that the CCC reduces preoperative waiting time (PWT), length of hospital stay (LOS), and costs of hospitalization, without reducing the quality of surgery. Thus, we compared the time, cost, and quality between March 1 to December 31, 2019, and March 1 to December 31, 2020. Descriptive and inferential analyses of patient demographic characteristics, time, postoperative outcomes, and inpatient costs were conducted.

Results

A total of 965 hospitalizations for colorectal cancer (CRC) were identified—415 in 2019 and 550 in 2020. In the CCC, PWT declined by 26.2 hours (P<0.01). Patients in the CCC express group only needed to wait for 24.5 hours before undergoing surgery, with a shorter LOS than the normal group (P<0.01). None of the patients had any symptoms of COVID-19 or were high-risk COVID-19 contacts, and the incidence of immediate postoperative complications was low. The mean total inpatient cost (TIC) for all patients with CRC was 78,309.824 Chinese Yuan in 2020, which was slightly lower than that in 2019.

Conclusions

This study found that the centralized management model for CRC care could help patients save the PWT, LOS and costs of hospitalization during the COVID-19 epidemic.