Scheduling prioritized patients in emergency department laboratories

Optimization of Radiology Workflow with Artificial Intelligence

The potential of artificial intelligence (AI) in radiology goes far beyond image analysis. AI can be used to optimize all steps of the radiology workflow by supporting a variety of nondiagnostic tasks, including order entry support, patient scheduling, resource allocation, and improving the radiologist's workflow. This article discusses several principal directions of using AI algorithms to improve radiological operations and workflow management, with the intention of providing a broader understanding of the value of applying AI in the radiology department.

How to Manage Red Alert in Emergency and Disaster Unit in the Hospital? Evidence From London

This research gave an overview of coordinated hospital planning issues. In these issues, patients desire an arrangement for different source types, ideally as quickly as time permits. This field of context has just picked up academic interest, despite its reality since 1995. The way may discover a clarification for the above aspect that managing the hospital sources is regularly performed separately without taking a bigger picture. Therefore, it is particularly valid if the sources are situated in different departments. Another subsequent clarification may be related to the notoriety of the patient flow context. Hence, patients shouldn't be planned in these issues to be queued for another source or leave the system in case of their satisfaction of solicitation for the services at a particular source. The primary contribution of the present research is assisting present and new scholars via enumeration for every progression of the study of accessible decisions in the present context. Such means could be represented by major references for scientists to discover such studies endeavors tailored to their respective requirements. This principle removes the message: scientists ought to consistently coordinate their decisions concerning the setting, the capacity, and the approaches, as not all blends are conceivable.

Patient Mix Optimization in Admission Planning under Multitype Patients and Priority Constraints

Hospital beds are one of the most critical medical resources. Large hospitals in China have caused bed utilization rates to exceed 100% due to long-term extra beds. To alleviate the contradiction between the supply of high-quality medical resources and the demand for hospitalization, in this paper, we address the decision of choosing a case mix for a respiratory medicine department. We aim to generate an optimal admission plan of elective patients with the stochastic length of stay and different resource consumption. We assume that we can classify elective patients according to their registration information before admission. We formulated a general integer programming model considering heterogeneous patients and introducing patient priority constraints. The mathematical model is used to generate a scientific and reasonable admission planning, determining the best admission mix for multitype patients in a period. Compared with model II that does not consider priority constraints, model I proposed in this paper is better in terms of admissions and revenue. The proposed model I can adjust the priority parameters to meet the optimal output under different goals and scenarios. The daily admission planning for each type of patient obtained by model I can be used to assist the patient admission management in large general hospitals.

An Algorithm for Pairing Interventionalists and Surgeons for the TAVR Procedure

The Transcatheter Aortic Valve Replacement (TAVR) procedure requires an initial consultation and a subsequent procedure by an interventionalist (IC) and surgeon. The IC-surgeon pair coordination is extremely challenging, especially at Mayo Clinic due to provider time commitments distributed across practice, research, and education activities. Current practice aims to establish the coordination manually, resulting in a scheduling process that is cumbersome and time consuming for the schedulers. We develop an algorithm for pairing ICs and surgeons that minimizes the lead time (days elapsed between the clinic consult and procedure). As compared to current practice, this algorithm is able to reduce average lead time by 59% and increase possible IC-surgeon pairs by 7%. The proposed algorithm is shown to be flexible enough to incorporate practice variations such as lead time upper bound and two procedure days for a single consult day. Algorithm alternatives are also presented for practices who may find the proposed algorithm infeasible for their practice.

Applying a Human-Centered Design to Develop a Patient Prioritization Tool for a Pediatric Emergency Department: Detailed Case Study of First Iterations

BACKGROUND

Overcrowding in the emergency departments has become an increasingly significant problem. Patient triage strategies are acknowledged to help clinicians manage patient flow and reduce patients' waiting time. However, electronic patient triage systems are not developed so that they comply with clinicians' workflow.

OBJECTIVE

This case study presents the development of a patient prioritization tool (PPT) and of the related patient prioritization algorithm (PPA) for a pediatric emergency department (PED), relying on a human-centered design process.

METHODS

We followed a human-centered design process, wherein we (1) performed a work system analysis through observations and interviews in an academic hospital's PED; (2) deduced design specifications; (3) designed a mock PPT and the related PPA; and (4) performed user testing to assess the intuitiveness of the icons, the effectiveness in communicating patient priority, the fit between the prioritization model implemented and the participants' prioritization rules, and the participants' satisfaction.

RESULTS

The workflow analysis identified that the PPT interface should meet the needs of physicians and nurses, represent the stages of patient care, and contain patient information such as waiting time, test status (eg, prescribed, in progress), age, and a suggestion for prioritization. The mock-up developed gives the status of patients progressing through the PED; a strip represents the patient and the patient's characteristics, including a delay indicator that compares the patient's waiting time to the average waiting time of patients with a comparable reason for emergency. User tests revealed issues with icon intuitiveness, information gaps, and possible refinements in the prioritization algorithm.

CONCLUSIONS

The results of the user tests have led to modifications to improve the usability and usefulness of the PPT and its PPA. We discuss the value of integrating human factors into the design process for a PPT for PED. The PPT/PPA has been developed and installed in Lille University Hospital's PED. Studies are carried out to evaluate the use and impact of this tool on clinicians' situation awareness and prioritization-related cognitive load, prioritization of patients, waiting time, and patients' experience.

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.

Improving Emergency Department Efficiency by Patient Scheduling Using Deep Reinforcement Learning

Emergency departments (ED) in hospitals usually suffer from crowdedness and long waiting times for treatment. The complexity of the patient’s path flows and their controls come from the patient’s diverse acute level, personalized treatment process, and interconnected medical staff and resources. One of the factors, which has been controlled, is the dynamic situation change such as the patient’s composition and resources’ availability. The patient’s scheduling is thus complicated in consideration of various factors to achieve ED efficiency. To address this issue, a deep reinforcement learning (RL) is designed and applied in an ED patients’ scheduling process. Before applying the deep RL, the mathematical model and the Markov decision process (MDP) for the ED is presented and formulated. Then, the algorithm of the RL based on deep Q-networks (DQN) is designed to determine the optimal policy for scheduling patients. To evaluate the performance of the deep RL, it is compared with the dispatching rules presented in the study. The deep RL is shown to outperform the dispatching rules in terms of minimizing the weighted waiting time of the patients and the penalty of emergent patients in the suggested scenarios. This study demonstrates the successful implementation of the deep RL for ED applications, particularly in assisting decision-makers under the dynamic environment of an ED.

Genetic Algorithm for Scheduling Optimization Considering Heterogeneous Containers: A Real-World Case Study

In this paper, we develop and apply a genetic algorithm to solve surgery scheduling cases in a Mexican Public Hospital. Here, one of the most challenging issues is to process containers with heterogeneous capacity. Many scheduling problems do not share this restriction; because of this reason, we developed and implemented a strategy for the processing of heterogeneous containers in the genetic algorithm. The final product was named “genetic algorithm for scheduling optimization” (GAfSO). The results of GAfSO were tested with real data of a local hospital. Said hospital assigns different operational time to the operating rooms throughout the week. Also, the computational complexity of GAfSO is analyzed. Results show that GAfSO can assign the corresponding capacity to the operating rooms while optimizing their use.

Multi-criteria analysis in the health area: selection of the most appropriate triage system for the emergency care units in natal

Background

Multiobjective decision-making processes present a high degree of complexity in their solution, and tools such as multicriteria decision analysis appear as a way to facilitate the decision-makers’ solution and ensure that the decision is made cohesively and efficiently. In the public health sector, decisions are even more delicate because they work not only with the direct influence of human needs, but also with limited financial resources. An important point for the emergency care units is the triage system, which consists of a pre-evaluation of the patients, classifying them according to the degree of life risk. Through triage, the patient can be attended more quickly and efficiently, streamlining the whole process. Thus, the present research endeavored to determine the most appropriate triage protocol for emergency healthcare units in Natal-RN city in Brazil and may help others less advanced countries to determine the most appropriate triage protocol for emergency healthcare.

Methods

In this study, we used the multicriteria analysis method known as FITradeoff. In addition, interviews and structured questionnaires applied with nurses, specialists and directors.

Results

Based on the questionnaires and preferences presented by the decision-makers, the Spanish Triage System was the most suitable protocol for the emergency care units, which presented with high ease of use and implementation.

Conclusions

This study reached its main objective, which was to determine the most appropriate triage protocol. In addition, it was observed the possibility of new research, such as the development of a specific protocol for this emergency care units and the creation of an application software for this new protocol.

Multi-disciplinary planning in health care: a review

Multi-disciplinary planning in health care is an emerging research field that applies to many health care areas with similar underlying planning characteristics. We provide a review of the literature and describe cross-relations between different applications. We identify multiple fields to classify the literature upon. These fields relate to the system characteristics, decision characteristics, and applicability. The relevant papers for each of these fields are discussed, which provides a broad and thorough overview of the present research, and guides readers towards identifying the applicable literature for their research based on the characteristics of their problem. Furthermore, we disclose research gaps and present open challenges for further research.

A universal deep learning approach for modeling the flow of patients under different severities

BACKGROUND AND OBJECTIVE

The Accident and Emergency Department (A&ED) is the frontline for providing emergency care in hospitals. Unfortunately, relative A&ED resources have failed to keep up with continuously increasing demand in recent years, which leads to overcrowding in A&ED. Knowing the fluctuation of patient arrival volume in advance is a significant premise to relieve this pressure. Based on this motivation, the objective of this study is to explore an integrated framework with high accuracy for predicting A&ED patient flow under different triage levels, by combining a novel feature selection process with deep neural networks.

METHODS

Administrative data is collected from an actual A&ED and categorized into five groups based on different triage levels. A genetic algorithm (GA)-based feature selection algorithm is improved and implemented as a pre-processing step for this time-series prediction problem, in order to explore key features affecting patient flow. In our improved GA, a fitness-based crossover is proposed to maintain the joint information of multiple features during iterative process, instead of traditional point-based crossover. Deep neural networks (DNN) is employed as the prediction model to utilize their universal adaptability and high flexibility. In the model-training process, the learning algorithm is well-configured based on a parallel stochastic gradient descent algorithm. Two effective regularization strategies are integrated in one DNN framework to avoid overfitting. All introduced hyper-parameters are optimized efficiently by grid-search in one pass.

RESULTS

As for feature selection, our improved GA-based feature selection algorithm has outperformed a typical GA and four state-of-the-art feature selection algorithms (mRMR, SAFS, VIFR, and CFR). As for the prediction accuracy of proposed integrated framework, compared with other frequently used statistical models (GLM, seasonal-ARIMA, ARIMAX, and ANN) and modern machine models (SVM-RBF, SVM-linear, RF, and R-LASSO), the proposed integrated "DNN-I-GA" framework achieves higher prediction accuracy on both MAPE and RMSE metrics in pairwise comparisons.

CONCLUSIONS

The contribution of our study is two-fold. Theoretically, the traditional GA-based feature selection process is improved to have less hyper-parameters and higher efficiency, and the joint information of multiple features is maintained by fitness-based crossover operator. The universal property of DNN is further enhanced by merging different regularization strategies. Practically, features selected by our improved GA can be used to acquire an underlying relationship between patient flows and input features. Predictive values are significant indicators of patients' demand and can be used by A&ED managers to make resource planning and allocation. High accuracy achieved by the present framework in different cases enhances the reliability of downstream decision makings.