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Original research
Impact of an ICU bed capacity optimisation method on the average length of stay and average cost of hospitalisation following implementation of China’s open policy with respect to COVID-19: a difference-in-differences analysis based on information management system data from a tertiary hospital in southwest China
  1. Qingyan Zheng1,2,
  2. Zhongyi Zeng3,
  3. Xiumei Tang1,4,5,6,
  4. Li Ma1,3,5,6
  1. 1 School of Business, Sichuan Unversity, Chengdu, China
  2. 2 The Hong Kong Polytechnic University, Hong Kong, China
  3. 3 West China School of Nursing, Sichuan University, Chengdu, China
  4. 4 Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
  5. 5 Health Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China
  6. 6 Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China
  1. Correspondence to Li Ma; 1004500237{at}qq.com

Abstract

Objectives Following the implementation of China’s open policy with respect to COVID-19 on 7 December 2022, the influx of patients with infectious diseases has surged rapidly, necessitating hospitals to adopt temporary requisition and modification of ward beds to optimise hospital bed capacity and alleviate the burden of overcrowded patients. This study aims to investigate the effect of an intensive care unit (ICU) bed capacity optimisation method on the average length of stay (ALS) and average cost of hospitalisation (ACH) after the open policy of COVID-19 in China.

Design and setting A difference-in-differences (DID) approach is employed to analyse and compare the ALS and ACH of patients in four modified ICUs and eight non-modified ICUs within a tertiary hospital located in southwest China. The analysis spans 2 months before and after the open policy, specifically from 5 October 2022 to 6 December 2022, and 7 December 2022 to 6 February 2023.

Participants We used the daily data extracted from the hospital’s information management system for a total of 5944 patients admitted by the outpatient and emergency access during the 2-month periods before and after the release of the open policy in China.

Results The findings indicate that the ICU bed optimisation method implemented by the tertiary hospital led to a significant reduction in ALS (HR −0.6764, 95% CI −1.0328 to −0.3201, p=0.000) and ACH (HR −0.2336, 95% CI −0.4741 to −0.0068, p=0.057) among ICU patients after implementation of the open policy. These results were robust across various sensitivity analyses. However, the effect of the optimisation method exhibits heterogeneity among patients admitted through the outpatient and emergency channels.

Conclusions This study corroborates a significant positive impact of ICU bed optimisation in mitigating the shortage of medical resources following an epidemic outbreak. The findings hold theoretical and practical implications for identifying effective emergency coordination strategies in managing hospital bed resources during sudden public health emergency events. These insights contribute to the advancement of resource management practices and the promotion of experiences in dealing with public health emergencies.

  • China
  • COVID-19
  • decision making
  • health economics
  • health policy

Data availability statement

No data are available.

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STRENGTHS AND LIMITATIONS OF THIS STUDY

  • Data collected from the hospital information system were used, and an econometric model was constructed to validate the practical impact of the intensive care unit (ICU) bed optimisation method, thereby contributing valuable insights to the analysis and evaluation of the approach.

  • To examine the specific effects of the ICU bed optimisation method used following implementation of the open COVID-19 policy, difference-in-differences models were employed; this quasi-natural experimental approach effectively addresses endogeneity concerns and yields more robust findings.

  • The daily data from the information management system of a tertiary hospital in southwest China were exclusively used, encompassing both modified and non-modified ICUs; further verification is required to ascertain the generalisability of the results for other care units or regions.

  • It is important to acknowledge that, apart from the factors investigated in this study, there may be other broader factors that could also exert influence on the study’s outcomes.

Introduction

The COVID-19 pandemic has exerted unprecedented pressure on the global healthcare sector, leading to a critical shortage of bed capacity and resources in many hospitals. Ensuring adequate bed availability for patient treatment has become a formidable challenge.1–3 As of 5 February 2023, the global tally of confirmed COVID-19 cases has exceeded 754 million, with a death toll surpassing 6.8 million (data resource: weekly epidemiological update on COVID-19—8 February 2023; https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19---8-february-2023.). The surge in cases has been a major contributor to COVID-19-related fatalities, placing a significant strain on hospital resources.4 The rapid escalation of COVID-19 cases overwhelms existing medical resources, jeopardising the provision of consistently high-quality healthcare.5–7 This presents a devastating challenge for patient treatment, particularly for critically ill individuals, unless timely and effective solutions are implemented. As the epidemic persists, numerous developed countries, such as the USA, the UK, Japan and France, are grappling with intensive care unit (ICU) bed shortages, not to mention that low-income and middle-income countries are facing immense strain on their healthcare systems.4 8–12

The treatment of critically ill COVID-19 patients necessitates comprehensive medical resources, including fully equipped ICU beds with ventilators and an adequate healthcare workforce.13 14 To address the shortage of bed capacity and resources, various optimisation methods and improvement strategies have been implemented worldwide.4 15–18 For instance, China established Fangcang shelter hospitals as a measure to manage the outbreak.19–21 However, this optimisation method places significant demands on manpower and material resources, making it challenging to replicate in general areas.19 22 Consequently, many countries’ healthcare systems primarily adopt more convenient and expedient mitigation strategies, such as the transfer of patients, utilisation of idle facilities, establishment of specialised wards and conversion of regular beds.18 23–28 These measures provide a certain degree of relief to the bed shortage problem. While studies have analysed the implementation of these approaches to healthcare resource management and expansion, discussing their successes and challenges,3 29 30 empirical investigations confirming the differential impact of these bed optimisation approaches on patient treatment outcomes and hospital performance are lacking. Particularly, it remains unknown whether specific bed optimisation methods influence the treatment of critically ill patients in ICUs, which is crucial for reducing COVID-19 mortality.31 Therefore, the research question addressed in this study is: What is the impact of the ICU bed optimisation method on the average length of stay (ALS) and the average cost of hospitalisation (ACH) after implementation of the open policy with respect to COVID-19?

To address this research question, a tertiary hospital in southwest China was selected as the study site. The analysis employed a difference-in-differences (DID) model based on data obtained from the hospital’s information management system. Following the issuance of the Chinese government’s Notice on Further Optimizing and Implementing the Prevention and Control Measures of the Novel Coronavirus (COVID-19) on 7 December 2022 (referred to as the open policy of COVID-19) (official website of Central People’s Government of the People’s Republic of China: https://www.gov.cn/xinwen/2022-12/07/content_5730475.htm), the hospital swiftly requisitioned and modified some non-ICU wards to cater to severe patients. These modifications ensured that critically ill patients had access to respiratory equipment, including invasive or non-invasive ventilators, in these modified wards. The comparison between the modified ICUs and non-modified ICUs provides an excellent opportunity for a quasi-natural experimental investigation to assess the implementation effectiveness of the hospital’s bed capacity optimisation method under the influence of this open policy. Therefore, this study aims to empirically analyse the differences in ALS and ACH between the modified ICUs and non-modified ICUs before and after the open policy.

This study makes several significant contributions to research and practice in related fields. Theoretically, it confirms the effectiveness of the bed optimisation method in reducing both the ALS and the ACH for ICU patients. Previous studies on COVID-19 have rarely addressed the specific impact of this medical resource optimisation method.29 32 Moreover, the examination of the dynamic changes in medical resources before and after China’s open policy offers an appropriate research context to explore the implementation effectiveness of this intervention using DID models. Methodologically, the utilisation of the quasi-natural experimental estimation approach through DID models effectively addresses the issue of endogeneity, overcoming the limitations of relying solely on subjective qualitative analysis and lacking causal reasoning.33 34 Practically, this study provides a more reliable and practical reference as well as a scientifically sound method for identifying the effects of emergency coordination and bed resource management during public health pandemics in the future.

Methods

Context

On 7 December 2022, in response to the prevailing epidemic situation and the evolving nature of the virus at that time, the Chinese government introduced an open policy with respect to COVID-19. This policy aimed to address the prevailing challenges in the prevention and control efforts by relaxing personnel control and nucleic acid testing requirements. Local governments and institutions promptly disseminated the policy announcement and issued consistent management directives through various channels on the first day of its release. They actively responded to and implemented the relevant provisions as outlined in the policy. Consequently, the entire society witnessed a sharp surge in COVID-19 cases, with the number of confirmed SARS-CoV-2 infection cases (determined through PCR testing) exponentially escalating. As a result, many patients encountered difficulties in accessing medical care and securing ward beds, leading to a severe shortage of medical resources in local hospitals. This shortage imposed immense pressure on the rational allocation and management of hospital beds, which had critical and urgent implications for the treatment of critically ill patients. The focus of this study is a prominent tertiary hospital located in the capital city of Sichuan Province in southwest China. Being one of the largest general hospitals in the region, the chosen hospital assumed the primary responsibility for diagnosis and treatment during this period. Specifically, the hospital experienced a significant increase of 15.91% in ICU patients within the span of 2 months after the open policy.

Within this context, the tertiary hospital in southwest China, which is the focal point of this study, also confronted similar challenges. The rapid spread of the COVID-19 epidemic resulted in a surge of activities and disorder in the hospital’s emergency, outpatient and patient departments, leading to noticeable shortages of medical resources, including general ward beds and ICU beds. To address this scarcity, the hospital implemented various emergency bed resource scheduling strategies and optimisation methods, such as the temporary requisition and modification of ward beds to accommodate patients (refer to table 1). Specifically, the hospital employed the following approaches:

  1. Care unit selection. When selecting the care units for modification, the hospital adhered to specific criteria, including similarity in medical specialties, close geographical proximity and minimal impact on existing operations. These selected care units had fewer critically ill patients, ensuring that the optimisation method would not hinder or delay the treatment of original critically ill patients in those units. This facilitated the training of relevant medical staff and the management of critical patients.

  2. Ward equipment transformation. Recognising that ICU patients have higher requirements for respiratory support equipment, which was previously lacking in non-ICU care units, the hospital added a certain number of invasive or non-invasive ventilators to the beds in the selected modified care units. The layout of the ward was optimised accordingly, considering the placement of oxygen supply equipment.

  3. Medical staff training. In order to ensure that the modified ICU patients receive appropriate treatment and care, the hospital conducted targeted training for the medical staff working in the modified care units. This training aimed to ensure that the staff members understood the nursing requirements and guidelines for ICU patients, could operate the relevant support and breathing equipment, and provided material and financial support and guarantees for the involved medical staff.

Table 1

ICU bed capacity optimisation method

There is a lack of empirical studies that have examined the precise effects of the ICU bed capacity optimisation method within the context of the open policy of COVID-19. Consequently, the objective of this study was to compare the effectiveness of these modified ICU beds with the non-modified ICU beds within the hospital for treating patients following the implementation of the epidemic open policy. In order to assess the impact of these modified beds on the ALS and ACH, this study employed DID models to evaluate the implementation effectiveness of the ICU bed capacity optimisation method. The findings of this study can offer valuable insights and management strategies for addressing similar public health emergencies.

Data source

The daily data used in this study were obtained from the information management system of a tertiary hospital in southwest China. The data encompassed patients admitted through the outpatient and emergency departments during two distinct periods: 5 October–6 December 2022 and 7 December 2022–6 February 2023 (Around 6 February 2023, the positive detection rate of COVID-19 tests in China had reached a relatively stable level. Consequently, the focused hospital began gradually discontinuing the implementation of the ICU bed optimisation method and progressively restoring the original status quo. In light of these developments, the period from 7 December 2022 to 6 February 2023 was selected as the primary observation period for this study. To minimise potential bias and ensure consistency in the duration of the two observation periods, the control group consisted of the 2 months preceding the implementation date of the open policy, specifically from 5 October 2022 to 6 December 2022.). These periods spanned approximately 2 months before and after the release of the open policy.

To maintain focus on the treatment of critically ill patients, only data pertaining to the four modified ICUs and eight non-modified ICUs were included, while patients from other care units were excluded from the analysis. Among the admitted patients, a total of 5944 cases were considered, with 4135 patients (69.57%) admitted through the outpatient channel and 1809 patients (30.43%) admitted via the emergency department. Of these patients, 3521 were male, accounting for 59.24% of the total cases, while women comprised 40.76% (2423 patients). The age of the patients ranged from 0 to 98 years, with a morbidity index of 6.948%. The statistical analysis was conducted using Stata V.17.

Model specification

This study focused on ALS and ACH as the dependent variables for empirical estimation, considering patients across different care units to investigate the impact of the optimisation method. To achieve this, a DID model was constructed using patient records from both the modified and non-modified ICUs. Specifically, data from 2 months before and after 7 December 2022 were used in the model construction:

Embedded Image (1)

Embedded Image (2)

Where i denotes the care unit; t denotes the date; the dependent variables Embedded Image and Embedded Image represent the average length of stay and the average cost of hospitalisation of the care unit i on the day of t . Embedded Image is a variable reflecting whether to be included in the list of modified ICUs, with 1 for modified ICUs and 0 for non-modified ICUs. Embedded Image is a variable used to identify the time when the open policy was released. If the care unit i has been included in the modification list on the day of t , the value is 1; if the care unit i is not included in the list, the value is 0. Additionally, Embedded Image and Embedded Image are care unit fixed effect and date fixed effect, and Embedded Image and Embedded Image are random disturbance terms.

The core concern of this paper is whether the coefficients Embedded Image and Embedded Image are significant, that is, whether the ALS and ACH of the modified care units (treatment group) have increased (or decreased) more than that of the non-modified care units (control group) after the open policy. In addition, to get the unbiased estimators Embedded Image and Embedded Image of the core independent variable Embedded Image coefficients in models (1) and (2), the care units’ eigenvector variable Embedded Image as possible control variable is added, making Embedded Image independent of Embedded Image and Embedded Image . Since the variables do not follow a normal distribution perfectly, all variables are in logarithms (see table 2 for the descriptive statistics). The model SE of care unit-level clustering is used to alleviate possible intergroup-related problems.33

Table 2

Descriptive statistics for modified ICUs and non-modified ICUs

Accordingly, we extracted the previously determined variables that may affect the selection of modified care units (from now on referred to as ‘predetermined variable’): the case number of hospitalised patients (Embedded Image ), the average age of hospitalised patients (Embedded Image ), the number of COVID-19-positive patients (Embedded Image ), the number of patients using invasive ventilators (Embedded Image ), the number of patients using non-invasive ventilators (Embedded Image ), the number of critically ill patients (Embedded Image ), the number of seriously ill patients (Embedded Image ), the number of pressurised patients (Embedded Image ) and the number of patients on oxygen (Embedded Image ). Then, the two-valued variable Embedded Image , whether it is a modified care unit, was used to regress the above-predetermined variables. If the care unit i is a modified ICU, Embedded Image takes 1; if the care unit i is a non-modified ICU, Embedded Image takes 0.

According to the estimated results after the successive addition of control variables (see online supplemental table 1), the eigenvector Embedded Image of models (1) and (2) is equal to the time-varying vector of the selected predetermined variables: the case number of hospitalised patients (Embedded Image ), the average age of hospitalised patients (Embedded Image ), the number of COVID-19-positive patients (Embedded Image ), the number of patients using invasive ventilators (Embedded Image ), the number of pressurised patients (Embedded Image ) and the number of patients on oxygen (Embedded Image ).

Supplemental material

In order to ensure the robustness of our findings, we conducted additional checks using the DID model after benchmark results. These checks involved estimating and verifying the model using the selected predetermined variables, while manipulating the representation of modification differences in the treatment group. Moreover, we explored the effects of changing the measurement indicators of dependent variables, excluding patients from different hospital branches and adjusting the analysis period. By subjecting our analysis to these robustness checks, we aimed to strengthen the reliability and validity of our results.

Patient and public involvement

None.

Results

Benchmark results

The DID model was employed for benchmark regression analysis using Stata V.17 to estimate the statistical results. The estimated outcomes of model (1) and model (2) are presented in table 3. Columns (1) and (3) display the estimated results after incorporating fixed effects for care units and dates. Columns (2) and (4) present the results obtained by adding the eigenvector Embedded Image to the aforementioned columns. The regression findings demonstrate that the implementation of the optimisation method of ICU bed capacity involving the temporary requisition and modification of wards effectively reduces the ALS and ACH for ICU patients after the open policy.

Table 3

Regression results for benchmark models

Specifically, based on the estimated coefficients in columns (2) and (4), it is observed that the optimisation method leads to a decrease in ALS by 0.6764 (95% CI −1.0328 to −0.3201) and a decrease in ACH by 0.2336 (95% CI −0.4741 to −0.0068). Consequently, it can be inferred that patients in the modified care units experience lower ALS and lower ACH compared with those in the non-modified care units. Thus, the modification optimisation method significantly enhances the coordination and management of bed resources during unexpected situations in the tertiary hospital.

To present the regression results accurately, figures 1 and 2 display the parallel trend test results of the estimated coefficients. The horizontal axis represents the number of periods before and after the implementation of the open policy, while the vertical axis represents the estimated coefficient’s magnitude. The dashed line represents the 95% CI. As depicted in these figures, there was no significant disparity in ALS and ACH between the treatment group and the control group prior to the implementation of ICU bed capacity optimisation. Hence, the hypothesis of parallel trends cannot be rejected. However, following the intervention of the optimisation methods, the coefficients of ALS and ACH demonstrated a significant downward trend before the open policy, although this effect was not sustained in the long term for ACH. These findings indicate that the ICU bed capacity optimisation method can effectively facilitate the reduction of ALS and ACH among ICU patients in the short term.

Figure 1

Parallel trend test for average length of stay (ALS).

Figure 2

Parallel trend test for average cost of hospitalisation (ACH).

Robustness checks

In order to ascertain the impact of the bed optimisation method on the reduction of hospitalisation length and cost following the implementation of China’s open policy for COVID-19, we also employed DID models for robustness checks. We conducted various tests by manipulating the representation of modification differences in the treatment group, modifying the measurement indicators of dependent variables, excluding patients from different hospital branches and adjusting the analysis period. These tests were conducted to validate and strengthen the reliability of the aforementioned results.

Changing the representation of modification differences in the treatment group

Given the presence of modification differences among the modified care units, such as some units being fully modified while others are only partially modified, we assigned values to the variable Embedded Image accordingly. Specifically, we set Embedded Image for non-modified ICUs, Embedded Image for partially modified ICUs and Embedded Image for fully modified ICUs. On examining the estimation results presented in columns (1) and (2) of table 4, it is evident that the coefficients of Embedded Image are not significantly different from the coefficients obtained in the basic regression estimation. This observation suggests that the fundamental estimation results remain robust and unaffected by the variations introduced through the modification differences among the care units.

Table 4

Results of robustness checks

Changing the measurement indicators of dependent variables

We also selected the total length of stay (Embedded Image ) and total cost of hospitalisation (Embedded Image ) of patients in the two groups of care units as measures of the dependent variable. Specifically, Embedded Image and Embedded Image represent the total length of stay and the total cost of hospitalisation, respectively, for patients in the care unit i on the day of t . The regression results, presented in columns (3–4) of table 4, revealed that the two new coefficients associated with the main independent variable were significantly negative. This indicates that the optimisation method employed could effectively reduce both the total length of hospital stay and the total hospital cost for ICU patients following the open policy. The findings also validate the robustness of the initial regression results.

Removing patients from different hospital branches

Given that the hospital comprised various branches, the control group included patient records from these branches. To address this, we excluded patients from other branches and focused solely on analysing patients within the main hospital area. The regression results presented in columns (5) and (6) of table 4 demonstrated that the coefficients associated with the primary independent variable remained significantly negative. The finding further affirms the robustness of the estimation results, even after adjusting for the exclusion of patients from other branches.

Adjusting analysis period

In order to broaden the duration scope of our analysis, we extended the analysis period of the model to encompass records retrieved from 1 October 2022 to 6 February 2023, as captured in the hospital information management system. The results presented in columns (7) and (8) of table 4 align with the baseline findings mentioned earlier, further affirming the robustness of the estimation results.

Heterogeneous effects

Considering that patients in the study were admitted through various channels, including outpatient and emergency, it is plausible that these admission channels may influence both the length and cost of a patient’s stay.35–37 Consequently, we conducted separate analyses for patients admitted through outpatient and emergency channels, incorporating them into model 1 and model 2 to explore potential heterogeneity. The regression results revealed notable variations in the ALS and ACH among patients from different admission channels. Table 5 presents these findings, indicating that the coefficients of the core independent variables are generally insignificant, except for a significantly positive coefficient for ACH in the emergency channel. This suggests that the average expenditure for emergency patients admitted to the modified ward is higher. Furthermore, the independent variable coefficient for ACH in the outpatient channel is negative, while other variables exhibit positive coefficients. These contrasting results from the heterogeneity analysis demonstrate significant differences compared with the regression results obtained from the benchmark models using the full sample.

Table 5

Results of regional heterogeneity analysis

Discussion

This study focuses on verifying the specific impact of the ICU bed capacity optimisation method, an emergency measure implemented to address the shortage of medical resources, using the DID model. The analysis is based on data obtained from the information management system of a tertiary hospital in southwest China, particularly within the context of China’s open policy in response to the COVID-19 pandemic. Unlike the period characterised by relatively stable infection case numbers under the strict control policies implemented by local governments, the implementation of the open policy in December 2022 introduced a different scenario. This policy mandated the avoidance of various temporary containment measures and allowed for unrestricted movement of people, facilitating the resumption of normal activities. Consequently, there was an increase in population mobility and a surge in infection cases across the region, posing challenges to the adequate protection of medical resources. This situation could potentially impact the timely diagnosis and treatment of critically ill patients.14

Numerous studies have substantiated the adoption of diverse measures by countries worldwide to address the challenges posed by this emergency. For instance, several European countries have implemented strategies such as postponing scheduled treatments and surgeries, prioritising them based on COVID-19 patients’ differentiated conditions. Additionally, field hospitals have been established in Madrid, Spain, while Denmark has established specialised departments and clinics that can be swiftly integrated back into regular healthcare structures when the number of cases subsides.29 These are just a few examples of the multifaceted approaches employed in response to this kind of crisis. However, the existing body of research primarily relies on qualitative data or subjective quantitative data, such as questionnaires and interviews, to evaluate the effectiveness of various medical resource management and optimisation measures implemented by different countries. Consequently, obtaining an objective and accurate assessment of the impact of these measures poses a challenge. Moreover, this study diverges from previous research, which primarily focused on routine hospital care structures and capacity expansion. Instead, this study specifically concentrates on optimising bed allocation for critically ill patients in ICUs, aiming to enhance the capacity and efficiency of diagnosis and treatment for these specialised cases. Furthermore, this study also acknowledges the heterogeneity in ALS and ACH among ICU patients admitted through different channels. This finding underscores the importance of hospitals adopting standardised and proactive measures when allocating medical resources. Such measures ensure that critically ill patients receive timely and comprehensive diagnosis and treatment, while maintaining a reasonable level of control over resource allocation.

Therefore, the selection of the large tertiary hospital in southwest China as the research setting in this study serves a crucial purpose. The implementation of China’s open policy provides a unique opportunity to scientifically evaluate the specific effects of intervention measures adopted after the policy’s implementation. This evaluation not only contributes to establishing a solid and evidence-based foundation for proposing, implementing, promoting and evaluating relevant optimisation measures, but also sheds light on their efficacy. The utilisation of the DID model and methodology allows for a robust comparison between different groups (non-modified ICUs and modified ICUs) before and after the open policy, providing an accurate measurement and assessment of the intervention’s implementation effect. The application of this model also offers a methodological reference for future studies in the field.

However, it is important to acknowledge the limitations of this study. Due to the constraints of survey data availability, the analysis and investigation were limited to the data from the selected hospital, focusing specifically on critically ill patients in the ICUs. Therefore, the ability to extend and generalise the findings to other care units, hospitals, regions or even countries requires further verification. Additionally, the study’s reliance on data collected by the hospital’s information management system imposes certain limitations. Although efforts were made to include relevant factors under investigation and control the influence of pertinent variables, there is scope for considering additional influential factors to obtain more precise answers in future research endeavours. Nonetheless, it is important to highlight that these limitations were minimised within the models, and multiple robustness testing approaches were employed to ensure the reliability and stability of the results.

Conclusions

Theoretically, the present study contributes to the existing literature by demonstrating, for the first time, the significant impact of the ICU bed capacity optimisation method on patient hospitalisation efficacy and medical expenditure within the context of the epidemic open policy. Employing the DID models, this study reveals that the modification method effectively reduces the ALS and ACH for ICU patients, a finding that withstands rigorous robustness tests. While previous research has evaluated and analysed various optimisation schemes for hospital bed capacity in the aftermath of the COVID-19 pandemic,1 5 8 11 12 there has been a lack of quantitative comparison regarding the specific implementation effects of these improvement methods. This study fills this theoretical gap by providing empirical evidence for the effectiveness of the ICU bed capacity optimisation method. Furthermore, through heterogeneity analysis, this study unveils that the impact of the optimisation method is heterogeneous among patients admitted through different channels, namely outpatient and emergency access.

Methodologically, this study takes a pioneering approach by constructing an econometric model using DID models. This allows for the empirical examination of the effectiveness of the ICU bed capacity optimisation method. Moreover, the utilisation of a quasi-natural experiment estimation approach in this study successfully mitigates endogeneity issues and addresses the limitation of relying solely on subjective qualitative analysis without causal reasoning.33 34 This finding contributes to the advancement of a more scientific and accurate method for effectively identifying and managing hospital bed resource optimisation.

Practically, the findings of this study have managerial implications for medical departments in other regions. They can draw upon the bed capacity optimisation and resource management methods employed by this large tertiary hospital as a reference. By considering their own unique circumstances, these departments can devise appropriate transformation plans to effectively address the challenges of medical resource allocation and shortages during public health emergencies.13 15 38 39 It is recommended that hospitals dynamically adjust their bed allocation schemes, aiming to reduce ALS and increase the turnover rate of beds. This adjustment would enhance the hospital’s treatment capacity for patients with infectious disease while simultaneously reducing their average waiting time and ACH.6 30 40 41 Furthermore, hospital management departments and decision-makers should actively implement prevention and control management strategies to minimise the impact on patients from different admission channels. Scientific and priority scheduling should be employed to address the admission issues faced by emergency patients and outpatients.36 37 42–44

Data availability statement

No data are available.

Ethics statements

Patient consent for publication

Ethics approval

This study was approved by the Ethics Committee of West China Hospital, Sichuan University (ID: KCRC-20-0004). According to the Chinese Ethical Guidelines for Biomedical Research Involving Human Subjects, this study fell within the scope of research, did not involve interventions and did not use human samples. The protocol was performed in accordance with the relevant guidelines and regulations (Declaration of Helsinki). Prior to admission, each patient or their legal guardian in the tertiary hospital is required to provide informed consent, which includes the acknowledgement that the collected data may be used for scientific research purposes. Consequently, all patient-related data employed in this study were obtained with the informed consent of the subjects or their legal guardians, ensuring compliance with ethical guidelines and privacy regulations.

References

Footnotes

  • QZ and ZZ contributed equally.

  • Contributors QZ—formal analysis, methodology, writing (original draft). ZZ—data curation, software, validation. XT—data curation, methodology, validation. LM (Guarantor)—conceptualisation, supervision, funding acquisition. All authors have critically revised the manuscript for important intellectual content, approve of the final version to be published and agree to be accountable for all aspects of the work.

  • Funding This research was jointly supported by the Key R&D Plan of Chengdu Science and Technology Bureau (2022-YF05-01884-SN), the West China Nursing Discipline Development Special Fund Project, Sichuan University (HXHL21009) and the Health System Research Fund Project of Long Quanyi District in Chengdu (WJKY020 and WJKY008). The funders mentioned above provided financial assistance.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.