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Objective: Elective‐emergency admission control referred to allocating available inpatient bed capacity between elective and emergency hospitalization demand. Existing approaches for admission control often excluded several complex factors when making decisions, such as uncertain bed capacity and unknown true probability distributions of patient arrivals and departures. We aimed to create a data‐driven newsvendor framework to study the elective‐emergency admission control problem to achieve bed operational efficiency and effectiveness. Methods: We developed a data‐driven approach that utilized the newsvendor framework to formulate the admission control problem. We also created approximation algorithms to generate a pool of candidate admission control solutions. Past observations and relevant emergency demand and bed capacity features were modeled in a newsvendor framework. Using approximation algorithmic approaches (sample average approximation, separated estimation and optimization, linear programing‐LP, and distribution‐free model) allowed us to derive computationally efficient data‐driven solutions with tight bounds on the expected in‐sample and out‐of‐sample cost guaranteed. Results: Tight generalization bounds on the expected out‐of‐sample cost of the feature‐based model were derived with respect to the LP and quadratic programing (QP) algorithms, respectively. Results showed that the optimal feature‐based model outperformed the optimal observation‐based model with respect to the expected cost. In a setting where the unit overscheduled cost was higher than the unit under‐scheduled cost, scheduling fewer elective patients would replace the benefit of incorporating related features in the model. The tighter the available bed capacity for elective patients, the bigger the difference of the schedule cost between the feature‐based model and the observation‐based model. Conclusions: The study provides a reference for the theoretical study on bed capacity allocation between elective and emergency patients under the condition of the unknown true probability distribution of bed capacity and emergency demand, and it also proves that the approximate optimal policy has good performance. [ABSTRACT FROM AUTHOR]

This paper studies the admission scheduling problem with considering the capacity usage of two inter-related resources (beds and operating rooms) between three consecutive stages of care during surgical patients' admissions to Chinese public hospitals, namely (1) pre-surgical inpatient bed, (2) surgery, (3) post-surgical inpatient bed. Demand comes from two types of patients: (1) emergency patients, who arise randomly and have to be admitted immediately, and (2) elective patients, whose admissions can be scheduled. The authors develop a Markov Decision Process (MDP) model that decides how many elective patients should be admitted each day, with the objective of optimally using both operating room and inpatient bed capacity. The authors demonstrate that the number of elective admissions scheduled each day is monotonically increasing in the state of the system and in the bed capacity, indicating that a higher level of waiting elective patients and available (or total) bed capacity pulls more elective admissions through the system. The total discounted expected cost of the system exhibits decreasing marginal returns as the capacity in each stage increases independently of other stages. Through numerical experiments, there is substantial value in making admission scheduling decisions by jointly considering inpatient beds and operating rooms. [ABSTRACT FROM AUTHOR]

The length of waiting time has become an important indicator of the efficiency of medical services and the quality of medical care. Lengthy waiting times for patients will inevitably affect their mood and reduce satisfaction. For patients who are in urgent need of hospitalization, delayed admission often leads to exacerbation of the patient's condition and may threaten the patient's life. We gathered patients' information about outpatient visits and hospital admissions in the Nephrology Department of a large tertiary hospital in western China from January 1st, 2014, to December 31st, 2016, and we used big data-enabled analysis methods, including univariate analysis and multivariate linear regression models, to explore the factors affecting waiting time. We found that gender (P = 0.048), the day of issuing the admission card (Saturday, P = 0.028), the applied period for admission (P < 0.001), and the registration interval (P < 0.001) were positive influencing factors of patients' waiting time. Disease type (after kidney transplantation, P < 0.001), number of diagnoses (P = 0.037), and the day of issuing the admission card (Sunday, P = 0.001) were negative factors. A linear regression model built using these data performed well in the identification of factors affecting the waiting time of patients in the Nephrology Department. These results can be extended to other departments and could be valuable for improving patient satisfaction and hospital service quality by identifying the factors affecting waiting time. [ABSTRACT FROM AUTHOR]

For hospitals where decisions regarding acceptable rates of elective admissions are made in advance based on expected available bed capacity and emergency requests, accurate predictions of inpatient bed capacity are especially useful for capacity reservation purposes. As given, the remaining unoccupied beds at the end of each day, bed capacity of the next day can be obtained by examining the forecasts of the number of discharged patients during the next day. The features of fluctuations in daily discharges like trend, seasonal cycles, special-day effects, and autocorrelation complicate decision optimizing, while time-series models can capture these features well. This research compares three models: a model combining seasonal regression and ARIMA, a multiplicative seasonal ARIMA (MSARIMA) model, and a combinatorial model based on MSARIMA and weighted Markov Chain models in generating forecasts of daily discharges. The models are applied to three years of discharge data of an entire hospital. Several performance measures like the direction of the symmetry value, normalized mean squared error, and mean absolute percentage error are utilized to capture the under- and overprediction in model selection. The findings indicate that daily discharges can be forecast by using the proposed models. A number of important practical implications are discussed, such as the use of accurate forecasts in discharge planning, admission scheduling, and capacity reservation. [ABSTRACT FROM AUTHOR]

Abstract In healthcare service systems, patients are not always served in the order they arrive, but are ranked with respect to their relative "importance" and "urgency" to the service system. We consider such a system where elective admission requests backlogged on a list wait to be assigned inpatient beds. To consolidate the performance of Classified Diagnose and Treatment in China, determining an optimal admission priority assignment policy for all waiting patients is vital. It is a complicated multi-criteria decision making (MCDM) problem involving both qualitative and quantitative criteria. Evaluating the admission priority of each patient is based on vague information or uncertain data in which significant dependence and feedback between the evaluation dimensions and criteria may exist. This paper applies a hybrid MCDM model that integrates the 2-tuple DEMATEL technique and the fuzzy VIKOR method to the elective admission control problem. It makes use of the modified 2-tuple DEMATEL to determine the relative weights of the evaluation criteria and the fuzzy VIKOR method to assess the alternatives (waiting patients) over the criteria. An empirical case in West China Hospital is presented to demonstrate the applicability of the proposed approach. Sensitivity analysis of the results by the proposed hybrid MCDM model and comparative analysis with other different approaches are presented. The results show that the proposed model is effective and provides insightful implications for hospital managers to refer. [ABSTRACT FROM AUTHOR]

Beds are key, scarce medical resources in hospitals. The bed occupancy rate (BOR) amongst different departments within large tertiary hospitals is very imbalanced, a situation which has led to problems between the supply of and the demand for bed resources. This study aims to balance the utilization of existing beds in a large tertiary hospital in China. We developed a data-driven hybrid three-stage framework incorporating data analysis, simulation, and mixed integer programming to minimize the gaps in BOR among different departments. The first stage is to calculate the length of stay (LOS) and BOR of each department and identify the departments that need to be allocated beds. In the second stage, we used a fitted arrival distribution and median LOS as the input to a generic simulation model. In the third stage, we built a mixed integer programming model using the results obtained in the first two stages to generate the optimal bed allocation strategy for different departments. The value of the objective function, Z, represents the severity of the imbalance in BOR. Our case study demonstrated the effectiveness of the proposed data-driven hybrid three-stage framework. The results show that Z decreases from 0.7344 to 0.0409 after re-allocation, which means that the internal imbalance has eased. Our framework provides hospital bed policy makers with a feasible solution for bed allocation. [ABSTRACT FROM AUTHOR]

Background: Exposure to air pollutants has been linked to the exacerbation of asthma, and it has become a risk to human welfare and health throughout the globe. Previous works did not achieve a systematic study by considering interactive effects of age, gender, and meteorological factors on the exposure-effect relationship between air pollutants and asthma. We aimed to quantitatively investigate the effects of air pollutants on hospitalizations for asthma in Chengdu, China., Methods: This is a retrospective and population-based study. Data of asthma hospitalization records for residents, the average daily concentrations of air pollutants including SO 2 , CO, NO 2 , O 3 , PM10, and PM2.5, and meteorological variables from 1 January 2014 to 31 December 2014 in Chengdu, China, were obtained from several government departments. A generalized additive model (GAM) was used to analyze the exposure-effect relationship between air pollutants and daily asthma hospitalizations after controlling the long-time seasonal trend, "day-of-week (DOW)" effect, holiday effect, and confounding meteorological factors., Results: A total of 7,503 hospitalizations were assessed. Significant associations between hospitalizations and air pollutants were found. The relative risk (RR) for hospitalizations for every 10 µg/m 3 increase in PM2.5 and PM10 for the male group were 1.0121 [95% confidence interval (CI): 1.0012-1.0232] and 1.0075 (95% CI: 1.0001-1.015), respectively. The elderly (≥65 years old) tended to have a higher RR (1.0022; 95% CI: 1.0001-1.0043) for each 10 mg/m 3 increase in CO than the other age groups. All pollutants had slightly protective effects on the younger age group (≤14 years old). O 3 had more significant effects in cold season, whereas SO 2 impacted more significantly in warm seasons, particularly for females and adults (14-65 years old)., Conclusions: Adverse effects of ambient concentrations of air pollutants on hospitalizations for asthma are evident, especially in specific population groups. Male patients were more susceptible to PM2.5 and PM10, and the elderly were more sensitive to CO. The effects of O 3 in China were significant in the cold season, whereas SO 2 impacted more significantly in the warm season particularly on females and adults. The study would be meaningful for asthma intervention and corresponding healthcare resource management., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-6265/coif). The authors have no conflicts of interest to declare., (2023 Annals of Translational Medicine. All rights reserved.)

Variability and unpredictability are typical features of emergency departments (EDs) where patients randomly arrive with diverse conditions. Patient length of stay (LOS) represents the consumption level of hospital resources, and it is positively skewed and heterogeneous. Both accurate modeling of patient ED LOS and analysis of potential blocking causes are especially useful for patient scheduling and resource management. To tackle the uncertainty of ED LOS, this paper introduces two methods: statistical modeling and distribution fitting. The models are applied to 894 respiratory diseases patients data in the year 2014 from ED of a Chinese public tertiary hospital. Covariates recorded include patient region, gender, age, arrival time, arrival mode, triage category, and treatment area. A Coxian phase-type (PH) distribution model with covariates is proposed as an alternative method for modeling ED LOS. The expectation-maximization (EM) algorithm is used to implement parameter estimation. The results show that ED LOS data can be modeled well by the proposed models. Distributions of ED LOS differ significantly with respect to patients' gender, arrival mode, and treatment area. Using the fitted Coxian PH model will assist ED managers in identifying patients who are most likely to have an extreme ED LOS and in predicting the forthcoming workload for resources.