Dynamic patient grouping and prioritization: a new approach to emergency department flow improvement

q-Rung orthopair fuzzy 2-tuple linguistic WASPAS algorithm for patients' prioritization based on prioritized Maclaurin symmetric mean aggregation operators

Due to the fuzziness of the medical field, q-rung orthopair fuzzy 2-tuple linguistic (q-RF2L) set is the privileged way to aid medical professionals in conveying their assessments in the patient prioritization problem. The theme of the present study is to put forward a novel approach centered around the merging of prioritized averaging (PA) and the Maclaurin symmetric mean (MSM) operator within q-RF2L context. According to the prioritization of the professionals and the correlation among the defined criteria, we apply both PA and MSM to assess priority degrees and relationships, respectively. Keeping the pluses of the PA and MSM operators in mind, we introduce two aggregation operators (AOs), namely q-RF2L prioritized Maclaurin symmetric mean and q-RF2L prioritized dual Maclaurin symmetric mean operators. Meanwhile, some essential features and remarks of the proposed AOs are discussed at length. Based on the formulated AOs, we extend the weighted aggregated sum product assessment methodology to cope with q-RF2L decision-making problems. Ultimately, to illustrate the practicality and effectiveness of the stated methodology, a real-world example of patients' prioritization problem is addressed, and an in-depth analysis with prevailing methods is performed.

Reducing the length of stay of cardiac patients in the Adult Emergency department by using a Quality improvement framework

BACKGROUND

Crowding is now a familiar challenge in the Emergency Department that can lead to significant adverse outcomes, including reduced quality of care and increased length of stay.

OBJECTIVE

Reduce cardiac patient length of stay from 72 h to 48 h in the Tikur Anbessa Specialized Hospital Adult Emergency department from August 16, 2021, to January 29, 2022, in Addis Ababa, Ethiopia.

METHODS

Process mapping was done by members of the team working in the emergency department on patient treatment and divided into three categories. Problems were then identified in each category that contributed to prolonged stay length. The run chart was then used to display the data for analysis, and nonrandom variation was looked for using the four-run chart rules.

RESULTS

Improved patient throughput in the Emergency Department (ED) was achieved, reducing the average length of stay from 78 h to 25 h by implementing the following change strategies: Streamlining morning discharge times by adjusting from the baseline of 9:10 AM to 9:00 AM; Enhancing patient handover processes to ensure 100% of patients are included on the handover form during transition hours; and Consistently assigning residents to the waiting area, resulting in a 100% presence rate. These changes effectively decreased the prolonged patient length of stay in the ED.

CONCLUSION

Poor management support and staff integration were identified as the leading cause of prolonged patient stay. In conclusion, a bundle of interventions is necessary to reduce the prolonged patient length of stay.

Main Features and Control Strategies to Reduce Overcrowding in Emergency Departments: A Systematic Review of the Literature

Purpose

Overcrowding is a problem that affects emergency departments (ED) all over the world; it occurs due to a disproportion between user demand and the physical, human and structural resources available. Essential prerequisites to assessing and managing the phenomenon are its accurate measurement and an understanding of its impact. The objective of this systematic review is to identify the characteristics of the problem, analyzing the proposed strategies aimed at improving patient flow, delay in services provided and overcrowding of emergency departments.

Methods

To achieve our objectives, a manual computerized search was performed in the bibliographic databases using as keywords "Emergency Department", "Overcrowding", "Emergency Room", "Emergency Service", "Emergency Unit"",Emergency Ward", "Emergency Outpatient Unit", "Emergency Hospital", "Crowding", "Mass Gathering", "Management" and "Comprehensive Health Care". Two independent reviewers analyzed abstracts, titles and full text articles for admissibility, according to the selected inclusion and exclusion criteria.

Results

The process lead to include 19 articles. It was possible to group the solutions proposed in five categories: work organization, investment in primary care, creation of new dedicated professional figures, work and structural modifications and implementation of predictive simulation models using mathematical algorithms.

Conclusion

The most effective measures to guarantee an improvement in the flow of patients are represented by both improving the efficiency of human resources and by developing predictive mathematical models, regardless of the type of hospital and its location. Considering the complexity of EDs and the multiple characteristics of overcrowding and that the causes of crowding are different and site-specific, a careful examination of the specifics of each ED is necessary to identify improving fields.

Queueing Problems in Emergency Departments: A Review of Practical Approaches and Research Methodologies

Problems related to patient scheduling and queueing in emergency departments are gaining increasing attention in theory, in the fields of operations research and emergency and healthcare services, and in practice. This paper aims to provide an extensive review of studies addressing queueing-related problems explicitly related to emergency departments. We have reviewed 229 articles and books spanning seven decades and have sought to organize the information they contain in a manner that is accessible and useful to researchers seeking to gain knowledge on specific aspects of such problems. We begin by presenting a historical overview of applications of queueing theory to healthcare-related problems. We subsequently elaborate on managerial approaches used to enhance efficiency in emergency departments. These approaches include bed management, fast-track, dynamic resource allocation, grouping/prioritization of patients, and triage approaches. Finally, we discuss scientific methodologies used to analyze and optimize these approaches: algorithms, priority models, queueing models, simulation, and statistical approaches.

Evaluation of hand injury management at the emergency department - are we getting better?

Background/Aim. Hand injuries are one of the most common injuries seen in emergency departments. Inadequate treatment can lead to prolonged healing, complications, significant morbidity, and serious disability. The aim of this study was to evaluate the epidemiology, risk factors, and treatment of hand injuries in one tertiary care level clinical center. Methods. This study was designed as a descriptive retrospective epidemiological study that involved all patients with hand injuries treated at the University Clinical Center of Vojvodina, Novi Sad, Serbia for seven years. The authors collected sociodemographic and clinical data such as age, gender, mechanism of injury, type of injury, days of hospitalization, type of defect reconstruction, the time of in-jury, the timing of surgery, and reasons for operative treatment delay. For every hospitalized patient the Modified Hand Injury Severity Score (MHISS) was calculated. All data were analyzed using SPSS IBM 21.0 software. Results. From 2012 to 2018, 34,796 patients were treated for hand injury at the University Clinical Center of Vojvodina, with 554 (1.6%) hospitalized patients. The mean age of patients was 43.2 years; the majority of them (87.55%) were men, and most (47.2%) were injured at home. Most injuries occurred during knife handling. The average length of stay for hospitalized patients was 4 days. MHISS score for most patients was over 50 and was classified a s severe. It was noticed that the waiting time for operation became shorter throughout the selected years. Conclusion. Hand injuries present a complex problem that can sometimes be underestimated by patients. The requirement of highly specialized hand surgeons, sometimes special equipment (e.g., microscope), multiple operations, prolonged rehabilitation, possible invalidity, and high cost of treatment calls for careful evaluation of the problem and the development of proper strategies in order to be able to lower the costs and obtain better medical care for all people with higher injury risk.

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.

Optimization of Patient Flow in Urgent Care Centers Using a Digital Tool for Recording Patient Symptoms and History: Simulation Study

Background

Crowding can negatively affect patient and staff experience, and consequently the performance of health care facilities. Crowding can potentially be eased through streamlining and the reduction of duplication in patient history-taking through the use of a digital symptom-taking app.

Objective

We simulated the introduction of a digital symptom-taking app on patient flow. We hypothesized that waiting times and crowding in an urgent care center (UCC) could be reduced, and that this would be more efficient than simply adding more staff.

Methods

A discrete-event approach was used to simulate patient flow in a UCC during a 4-hour time frame. The baseline scenario was a small UCC with 2 triage nurses, 2 doctors, 1 treatment/examination nurse, and 1 discharge administrator in service. We simulated 33 scenarios with different staff numbers or different potential time savings through the app. We explored average queue length, waiting time, idle time, and staff utilization for each scenario.

Results

Discrete-event simulation showed that even a few minutes saved through patient app-based self-history recording during triage could result in significantly increased efficiency. A modest estimated time saving per patient of 2.5 minutes decreased the average patient wait time for triage by 26.17%, whereas a time saving of 5 minutes led to a 54.88% reduction in patient wait times. Alternatively, adding an additional triage nurse was less efficient, as the additional staff were only required at the busiest times.

Conclusions

Small time savings in the history-taking process have potential to result in substantial reductions in total patient waiting time for triage nurses, with likely effects of reduced patient anxiety, staff anxiety, and improved patient care. Patient self-history recording could be carried out at home or in the waiting room via a check-in kiosk or a portable tablet computer. This formative simulation study has potential to impact service provision and approaches to digitalization at scale.

Process improvement approaches in emergency departments: a review of the current knowledge

Purpose

The healthcare system has been under pressure to provide timely and quality healthcare. The influx of patients in the emergency departments (EDs) is testing the capacity of the system to its limit. In order to increase EDs' capacity and performance, healthcare managers and practitioners are adopting process improvement (PI) approaches in their operations. Thus, this study aims to identify the main PI approaches implemented in EDs, as well as the benefits and barriers to implement these approaches.

Design/methodology/approach

The study is based on a rigorous systematic literature review of 115 papers. Furthermore, under the lens of thematic analysis, the authors present the descriptive and prescriptive findings.

Findings

The descriptive analysis found copious information related to PI approaches implemented in EDs, such as main PIs used in EDs, type of methodological procedures applied, as well as a set of barriers and benefits. Aiming to provide an in-depth analysis and prescriptive results, the authors carried out a thematic analysis that found underlying barriers (e.g. organisational, technical and behavioural) and benefits (e.g. for patients, the organisation and processes) of PI implementation in EDs.

Originality/value

The authors contribute to knowledge by providing a comprehensive review of the main PI methodologies applied in EDs, underscoring the most prominent ones. This study goes beyond descriptive studies that identify lists of barriers and benefits, and instead the authors categorize prescriptive elements that influence these barriers and benefits. Finally, this study raises discussions about the behavioural influence of patients and medical staff on the implementation of PI approaches.

E-Triage Systems for COVID-19 Outbreak: Review and Recommendations

With population growth and aging, the emergence of new diseases and immunodeficiency, the demand for emergency departments (EDs) increases, making overcrowding in these departments a global problem. Due to the disease severity and transmission rate of COVID-19, it is necessary to provide an accurate and automated triage system to classify and isolate the suspected cases. Different triage methods for COVID-19 patients have been proposed as disease symptoms vary by country. Still, several problems with triage systems remain unresolved, most notably overcrowding in EDs, lengthy waiting times and difficulty adjusting static triage systems when the nature and symptoms of a disease changes. In this paper, we conduct a comprehensive review of general ED triage systems as well as COVID-19 triage systems. We identified important parameters that we recommend considering when designing an e-Triage (electronic triage) system for EDs, namely waiting time, simplicity, reliability, validity, scalability, and adaptability. Moreover, the study proposes a scoring-based e-Triage system for COVID-19 along with several recommended solutions to enhance the overall outcome of e-Triage systems during the outbreak. The recommended solutions aim to reduce overcrowding and overheads in EDs by remotely assessing patients' conditions and identifying their severity levels.

Coupling Wearable Devices and Decision Theory in the United States Emergency Department Triage Process: A Narrative Review

This research was motivated by the nurses' decision-making process in the current emergency department (ED) triage process in the United States. It explores how continuous vital signs monitoring can be integrated into the ED. The article presents four shortcomings on current ED triage systems and proposes a new conceptual clinical decision support model that exploits the benefits of combining wireless wearable devices with Multi-Attribute Utility Theory to address those shortcomings. A literature review was conducted using various engineering and medical research databases, analyzing current practices and identifying potential improvement opportunities. The results from the literature review show that advancements in wireless wearable devices provide opportunities to enhance current ED processes by monitoring patients while they wait after triage and, therefore, reduce the risk of an adverse event. A dynamic mathematical decision support model to prioritize patients is presented, creating a feedback loop in the ED. The coupling of wearable devices (to collect data) with decision theory (to synthesize and organize the information) can assist in reducing sources of uncertainty inherent to ED systems. The authors also address the feasibility of the proposed conceptual model.

Reducing waiting time for remote patients in telemedicine with considering treated patients in emergency department based on body sensors technologies and hybrid computational algorithms: Toward scalable and efficient real time healthcare monitoring system

BACKGROUND

Scalability challenge in real time healthcare monitoring system relates to several issues. One of the insistent issues is the increasing in the number of patients. Increasing in the patients' number causes long queue and increase the waiting time for the patients in their seeking for healthcare services. Thus, an ethical issue raises as the healthcare providers should provide fast services for all patients. Recent studies have proposed scalable models that are limited to (1) triaging remote patients for the optimal emergency level and (2) prioritizing remote patients with the highest triage level to receive immediate healthcare services. However, these studies have shown limitations, that is, (1) they have not addressed the waiting time for all patients with different triage levels in the same waiting queue; and (2) they have not considered Emergency Department EDs patients. Therefore, considering the remote patients with the treated patients in EDs in one healthcare system is a demand, to efficiently handle all the patients' requests and productively manage the medical resources.

OBJECTIVE

This study aims to reduce the waiting time for the remote patients in telemedicine with considering treated patients in EDs. The study presents a scalable telemedicine model to improve the ability of real time healthcare monitoring system in accommodating the increasing number of patients with chronic heart disease by reducing their waiting time for healthcare services, prioritizing the patients who have the most emergency cases and provide all the patients by fast healthcare services. The proposed model called Triaging and Prioritizing Model "TPM".

METHOD

The proposed model "TPM" considers triaging and prioritizing all patients (remote and EDs patients) as two sequential processes. The TPM was formulated to triage the patients based on hybrid algorithms which combine Evidence-Theory with Fuzzy Cluster Means (FCM) and then prioritize the patients based on dedicated computational algorithm. A simulation, on 580 chronic heart diseases patients, was implemented. The patients considered as they have different emergency levels based on four vital data acquisition tools: electrocardiogram sensor, blood pressure sensor, oxygen saturation sensor and a text input as non-sensory based acquisition tool.

RESULTS

Computational results show the superiority of the proposed model (TPM) in accommodating large numbers of patients and reducing their waiting time for services compared with relevant benchmark studies. In 1,185 min, TPM managed the (580) patients' requests. By contrast, the benchmark managed only 256 patients at the same amount of time. In addition to that, TPM shows improvements in terms of waiting time and services provisioning rates compared with benchmark methods.

CONCLUSION

All patients with the different emergency levels receive services with less waiting time compared with the relevant studies. The proposed model (TPM) model considers both of remote patients and treated patients in EDs efficiently. TPM improves response time for the medical services, reduces waiting time for all patients and consequently, saves more lives.

Addressing overcrowding in an emergency department: an approach for identifying and treating influential factors and a real-life application

Background

Overcrowding in hospital emergency departments that arises from long length-of-stay is an unfortunate common occurrence. While some factors affecting length-of-stay are well known, there may be additional factors that have not yet been properly addressed. This research offers a method for emergency department managers to use available data from their departments to identify new factors that significantly influence emergency departments crowding and patient length-of-stay.

Methods

We propose an algorithm that can assist emergency department managers in determining which of these factors to address, given budgetary constraints. We implemented it in a case study which takes into account factors that are known to be influential, e.g., reason for arrival, occupancy in the emergency department, and arrival time, as well as factors that are explored for the first time in this paper, such as patient heart rate, the number of accompanying escorts, and the number of tests assigned to patients (e.g., blood tests and urinalysis).

Results

All the implemented and new factors are shown to have a significant influence on the length-of-stay and crowding. We also obtained additional support for our results by interviewing emergency departments physicians and nurses from various hospitals.

Conclusions

It is expected that, by taking all the above factors into consideration, emergency departments efficiency can be improved. The algorithm constructed here allows the choice of the most cost-effective factors to be improved, subject to a given budget. We have been able to derive practical recommendations that emergency departments managers might use to limit crowding and patient length-of-stay.

Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review

The most commonly used techniques for addressing each Emergency Department (ED) problem (overcrowding, prolonged waiting time, extended length of stay, excessive patient flow time, and high left-without-being-seen (LWBS) rates) were specified to provide healthcare managers and researchers with a useful framework for effectively solving these operational deficiencies. Finally, we identified the existing research tendencies and highlighted opportunities for future work. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to undertake a review including scholarly articles published between April 1993 and October 2019. The selected papers were categorized considering the leading ED problems and publication year. Two hundred and three (203) papers distributed in 120 journals were found to meet the inclusion criteria. Furthermore, computer simulation and lean manufacturing were concluded to be the most prominent approaches for addressing the leading operational problems in EDs. In future interventions, ED administrators and researchers are widely advised to combine Operations Research (OR) methods, quality-based techniques, and data-driven approaches for upgrading the performance of EDs. On a different tack, more interventions are required for tackling overcrowding and high left-without-being-seen rates.

An Intuitionistic Multiplicative ORESTE Method for Patients' Prioritization of Hospitalization

The tension brought about by sickbeds is a common and intractable issue in public hospitals in China due to the large population. Assigning the order of hospitalization of patients is difficult because of complex patient information such as disease type, emergency degree, and severity. It is critical to rank the patients taking full account of various factors. However, most of the evaluation criteria for hospitalization are qualitative, and the classical ranking method cannot derive the detailed relations between patients based on these criteria. Motivated by this, a comprehensive multiple criteria decision making method named the intuitionistic multiplicative ORESTE (organísation, rangement et Synthèse dedonnées relarionnelles, in French) was proposed to handle the problem. The subjective and objective weights of criteria were considered in the proposed method. To do so, first, considering the vagueness of human perceptions towards the alternatives, an intuitionistic multiplicative preference relation model is applied to represent the experts’ preferences over the pairwise alternatives with respect to the predetermined criteria. Then, a correlation coefficient-based weight determining method is developed to derive the objective weights of criteria. This method can overcome the biased results caused by highly-related criteria. Afterwards, we improved the general ranking method, ORESTE, by introducing a new score function which considers both the subjective and objective weights of criteria. An intuitionistic multiplicative ORESTE method was then developed and further highlighted by a case study concerning the patients’ prioritization.

Based on Real Time Remote Health Monitoring Systems: A New Approach for Prioritization "Large Scales Data" Patients with Chronic Heart Diseases Using Body Sensors and Communication Technology

This paper presents a new approach to prioritize "Large-scale Data" of patients with chronic heart diseases by using body sensors and communication technology during disasters and peak seasons. An evaluation matrix is used for emergency evaluation and large-scale data scoring of patients with chronic heart diseases in telemedicine environment. However, one major problem in the emergency evaluation of these patients is establishing a reasonable threshold for patients with the most and least critical conditions. This threshold can be used to detect the highest and lowest priority levels when all the scores of patients are identical during disasters and peak seasons. A practical study was performed on 500 patients with chronic heart diseases and different symptoms, and their emergency levels were evaluated based on four main measurements: electrocardiogram, oxygen saturation sensor, blood pressure monitoring, and non-sensory measurement tool, namely, text frame. Data alignment was conducted for the raw data and decision-making matrix by converting each extracted feature into an integer. This integer represents their state in the triage level based on medical guidelines to determine the features from different sources in a platform. The patients were then scored based on a decision matrix by using multi-criteria decision-making techniques, namely, integrated multi-layer for analytic hierarchy process (MLAHP) and technique for order performance by similarity to ideal solution (TOPSIS). For subjective validation, cardiologists were consulted to confirm the ranking results. For objective validation, mean ± standard deviation was computed to check the accuracy of the systematic ranking. This study provides scenarios and checklist benchmarking to evaluate the proposed and existing prioritization methods. Experimental results revealed the following. (1) The integration of TOPSIS and MLAHP effectively and systematically solved the patient settings on triage and prioritization problems. (2) In subjective validation, the first five patients assigned to the doctors were the most urgent cases that required the highest priority, whereas the last five patients were the least urgent cases and were given the lowest priority. In objective validation, scores significantly differed between the groups, indicating that the ranking results were identical. (3) For the first, second, and third scenarios, the proposed method exhibited an advantage over the benchmark method with percentages of 40%, 60%, and 100%, respectively. In conclusion, patients with the most and least urgent cases received the highest and lowest priority levels, respectively.

Based Real Time Remote Health Monitoring Systems: A Review on Patients Prioritization and Related "Big Data" Using Body Sensors information and Communication Technology

The growing worldwide population has increased the need for technologies, computerised software algorithms and smart devices that can monitor and assist patients anytime and anywhere and thus enable them to lead independent lives. The real-time remote monitoring of patients is an important issue in telemedicine. In the provision of healthcare services, patient prioritisation poses a significant challenge because of the complex decision-making process it involves when patients are considered 'big data'. To our knowledge, no study has highlighted the link between 'big data' characteristics and real-time remote healthcare monitoring in the patient prioritisation process, as well as the inherent challenges involved. Thus, we present comprehensive insights into the elements of big data characteristics according to the six 'Vs': volume, velocity, variety, veracity, value and variability. Each of these elements is presented and connected to a related part in the study of the connection between patient prioritisation and real-time remote healthcare monitoring systems. Then, we determine the weak points and recommend solutions as potential future work. This study makes the following contributions. (1) The link between big data characteristics and real-time remote healthcare monitoring in the patient prioritisation process is described. (2) The open issues and challenges for big data used in the patient prioritisation process are emphasised. (3) As a recommended solution, decision making using multiple criteria, such as vital signs and chief complaints, is utilised to prioritise the big data of patients with chronic diseases on the basis of the most urgent cases.