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Open AccessArticle

Inpatient Flow Distribution Patterns at Shanghai Hospitals

Collaborative Innovation Center of Health Risks Governance, School of Public Health, Fudan University, Shanghai 200433, China
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(7), 2183; (registering DOI)
Received: 17 February 2020 / Revised: 15 March 2020 / Accepted: 23 March 2020 / Published: 25 March 2020
Empirical studies based on patient flow data are needed to provide more materials to summarize the general pattern of patient distribution models. This study takes Shanghai as an example and tries to demonstrate the inpatient flow distribution model for different levels and specialties of medical institutions. Power, negative exponential, Gaussian, and log-logistic models were used to fit the distributions of inpatients, and a model of inpatient distribution patterns in Shanghai was derived, based on these four models. Then, the adjusted coefficient of determination (R2) and Akaike information criterion (AIC) values were used to assess the model fitting effect. The log-logistic function model has a good simulation effect and the strongest applicability in most hospitals. The estimated value of the distance-decay parameter β in the log-logistic function model is 1.67 for all patients, 1.89 for regional hospital inpatients, 1.40 for tertiary hospital inpatients, 1.64 for traditional Chinese medicine hospital inpatients, and 0.85 for mental hospital inpatients. However, the simulations at the tumor, children’s and maternity hospitals, were not satisfactory. Based on the results of empirical analysis, the four attenuation coefficient models are valid in Shanghai, and the log-logistic model of the inpatient distributions at most hospitals have good simulation effects. However, further in-depth analysis combined with the characteristics of specific specialties is needed to obtain the inpatient model in line with the characteristics of these specialties. View Full-Text
Keywords: inpatient flow; distribution patterns; empirical study; Shanghai inpatient flow; distribution patterns; empirical study; Shanghai
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Xiong, X.; Luo, L. Inpatient Flow Distribution Patterns at Shanghai Hospitals. Int. J. Environ. Res. Public Health 2020, 17, 2183.

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