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This study aims to investigate the factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to seven days.

In this study, first, the important factors to influence the demand in EDs were extracted from literature then the relevant factors to the study are selected. Then, a deep neural network is applied to constructing a reliable predictor.

Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable by using the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression, autoregressive integrated moving average, support vector regression, generalized linear models, generalized estimating equations, seasonal ARIMA and combined ARIMA and linear regression.

The authors applied this study in a single ED to forecast patient visits. Applying the same method in different EDs may give a better understanding of the performance of the model to the authors. The same approach can be applied in any other demand forecasting after some minor modifications.

To the best of the knowledge, this is the first study to propose the use of long short-term memory for constructing a predictor of the number of patient visits in EDs.

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.

As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.

The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.

With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.

To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

Once upon a time, illness and disease were acts of God visited upon defenceless individuals. Now, science is able to map the interplay of genes, environment and lifestyle, allowing doctors to forecast our health and longevity with startling accuracy. But what if these resources were put in the hands of patients themselves? Might it inspire them to help, even heal themselves?

Patient satisfaction with the Emergency Department (ED) in a hospital is related to the length of stay, and especially to the amount of waiting time for medical treatments. ED overcrowding decreases quality and efficiency, therefore affecting hospitals’ profitability. This paper presents a forecasting and simulation model for resource management of the ED at Moses H. Cone Memorial Hospital. A linear regression forecasting model is proposed to predict the number of ED patient arrivals, and then a simulation model is provided to estimate the length of stay of ED patients, system throughput, and the utilization of resources such as triage nurses, patient beds, registered nurses, and medical doctors. The near future load level of each resource is presented using the proposed models.

This study tests the effects of incomplete institutionalization in outpatient healthcare delivery settings on the quality and quantity of services provided after controlling for technical and agency factors. One dimension of quality (provider-patient contact time) and one dimension of quantity (number of services provided) were examined using the National Ambulatory Medical Care Survey for the year 2000. Regression models capture 27.8% and 36.4% of the variance in these respective dimensions (p<.001). The results reaffirm that uncertainty breeds variation and that ownership differences matter. From a management perspective, the regression model associated with provider-contact time has added utility in that a priori knowledge of certain variables might be used as decision support for provider (and service) scheduling.

As the US health system moves to value-based care and aligns payment with quality, the role of the primary care provider (PCP) is becoming ever more important. The purpose of this paper is to outline a successful population health and care management strategy depending on accountable teams to standard workflow and agreed upon process and outcome measures in order to achieve the triple aim of improved health, patient experience, and value.

Two major areas of focus for primary care are ensuring that all patients receive appropriate evidence-based screening and prevention services and coordinating the care of patients with chronic conditions. The former initiative will promote the general health and well-being of patients, while the latter is a key strategy for achieving better outcomes and reducing costs for patients with chronic conditions.

To achieve these goals while managing a busy practice requires that the authors leverage the PCP by engaging clinical and non-clinical team members in the care of their patient population. It is essential that each team member’s role be clearly defined and ensures they are working at the top of their scope.

This initiative was successful because of the compelling objectives, the buy-in generated by using Lean methodology and engaging the team in the design process, use of multiple feedback mechanisms including stories, dashboards, and patient feedback, and the positive impact on providers, staff, and patients.

The efficiency and effectiveness of hospital emergency rooms depend on the effectiveness of the information and communication system as well as on the physical facility itself. Describes the role of information technology in the design of contemporary ER systems. A computerized information board is one system that can enhance the operation of an ER facility. Describes the structure of this system, as well as its integration with other computerized systems. Also describes design features that may help to reduce ER delays/frustration. Because many types of professionals are involved in the daily operations of an ER facility, their input to the design is essential. For this reason, also describes a group decision‐making process.

The primary objective of this paper is to show a systematic and methodological approach for the digitalization of critical clinical pathways (CPs) within the healthcare domain.

The methodology entails the integration of service design (SD) and action research (AR) methodologies, characterized by iterative phases that systematically alternate between action and reflective processes, fostering cycles of change and learning. Within this framework, stakeholders are engaged through semi-structured interviews, while the existing and envisioned processes are delineated and represented using BPMN 2.0. These methodological steps emphasize the development of an autonomous, patient-centric web application alongside the implementation of an adaptable and patient-oriented scheduling system. Also, business processes simulation is employed to measure key performance indicators of processes and test for potential improvements. This method is implemented in the context of the CP addressing transient loss of consciousness (TLOC), within a publicly funded hospital setting.

The methodology integrating SD and AR enables the detection of pivotal bottlenecks within diagnostic CPs and proposes optimal corrective measures to ensure uninterrupted patient care, all the while advancing the digitalization of diagnostic CP management. This study contributes to theoretical discussions by emphasizing the criticality of process optimization, the transformative potential of digitalization in healthcare and the paramount importance of user-centric design principles, and offers valuable insights into healthcare management implications.

The study’s relevance lies in its ability to enhance healthcare practices without necessitating disruptive and resource-intensive process overhauls. This pragmatic approach aligns with the imperative for healthcare organizations to improve their operations efficiently and cost-effectively, making the study’s findings relevant.

This case looks at an important business task: forecasting a new product. The case can be used to teach finance, marketing (new product introduction), and healthcare strategy. The product is one of Amgen's most important new products: denosumab. On the surface, the case is fairly easy; students simply have to do some simple mathematical calculations. However, the challenges of forecasting quickly become apparent; every forecast depends on some critical assumptions, and the answer can vary dramatically.

Highlight the importance of forecasting as a business task. Give students the opportunity to create a forecast, using logical assumptions to generate an answer. Illustrate how dramatically forecasts can vary. Demonstrate why sensitivity analysis and customer understanding are both critical when trying to forecast a new product launch.

The purpose of this paper is to apply GM (1, 1) and TFGM (1, 1) models on the healthcare sector, which is a new area, and to show TFGM (1, 1) forecasting accuracy on this sector.

GM (1, 1) and TFGM (1, 1) models are presented. A hospital’s nine months (monthly) demand data is used for forecasting. Models are applied to the data, and the results are evaluated with MAPE, MSE and MAD metrics. The results for GM (1, 1) and TFGM (1, 1) are compared to show the accuracy of forecasting models. The grey models are also compared with Holt–Winters method, which is a traditional forecasting approach and performs well.

The results of this study indicate that TFGM (1, 1) has better forecasting performance than GM (1, 1) and Holt–Winters. GM (1, 1) has 8.01 per cent and TFGM (1, 1) 7.64 per cent MAPE, which means excellent forecasting power. So, TFGM (1, 1) is also an applicable forecasting method for the healthcare sector.

Future studies may focus on developed grey models for health sector demand. To perform better results, parameter optimisation may be integrated to GM (1, 1) and TFGM (1, 1). The demand may be predicted not only for the total demand on hospital, but also for the demand of hospital departments.

This study contributes to relevant literature by proposing fuzzy grey forecasting, which is used to predict the health demand. Therefore, the new application area as the health sector is handled with the grey model.