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Evaluating the Patient Boarding during Omicron Surge in Hong Kong: Time Series Analysis

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Abstract

The fifth wave of COVID-19 outbreaks in Hong Kong (HK) from January to March 2022 has the highest confirmed cases and deaths compared with previous waves. Severe hospital boarding (to inpatient wards) was noted in various Emergency Departments (EDs). Our objective is to identify factors associated with hospital boarding during Omicron surge in HK. We conducted a retrospective cohort study including all ED visits and inpatient (IP) ward admissions from January 1st to March 31st, 2022. Vector Autoregression model evaluated the effects of a single variable on the targeted hospital boarding variables. Admissions from elderly homes with 6 lag days held the highest positive value of statistical significance (t-stat = 2.827, P < .05) caused prolonged admission waiting time, while medical patients with 4 lag days had the highest statistical significance (t-stat = 2.530, P < .05) caused an increased number of boarding patients. Within one week after impulses, medical occupancy’s influence on the waiting time varied from 0.289 on the 1st day to -0.315 on the 7th day. While occupancy of medical wards always positively affected blocked number of patients, and its response was maximized at 0.309 on the 2nd day. Number of confirmed COVID-19 cases was not the sole significant contributor, while occupancy of medical wards was still a critical factor associated with patient boarding. Increasing ward capacity and controlling occupancy were suggested during the outbreak. Moreover, streamlining elderly patients in ED could be an approach to relieve pressure on the healthcare system.

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Data Availability

We performed a territory-wide, time-series study using data from an electronic administrative health care repository—the Clinical Data Analysis and Reporting System (CDARS), which is an electronic database that connects to electronic health records since 1995 from all public hospitals and clinics managed by Hospital Authority in Hong Kong. It protects patients’ personal data through anonymized hospital ward, clinic and emergency department admissions records, which covers different areas in patient care, namely, diagnosis, patient activities, laboratory and microbiology tests and prescription and dispensing. The patients and their episodes of service utilization were uniquely labelled respectively.

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Acknowledgements

The authors would like to express our sincere gratitude to all health care workers at all positions in Hong Kong who served our patients during the difficult period. The research of Qihao Wu and Yong-Hong Kuo is supported by Early Career Scheme (ECS), Research Grants Council (RGC) of Hong Kong (Ref: 27200419). The funder had no role in study design, data collection/analysis/interpretation or manuscript preparation.

Funding

The research of Qihao Wu and Yong-Hong Kuo is supported by Early Career Scheme (ECS), Research Grants Council (RGC) of Hong Kong (Ref: 27200419). The funder had no role in study design, data collection/analysis/interpretation or manuscript preparation.

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Authors and Affiliations

  1. Department of Emergency Medicine, The University of Hong Kong, Hong Kong, China

    Sunny Ching-long Chan, Teddy Tai-loy Lee, Kevin Wang-leong So, Omar Wai-kiu Tsui, Timothy Hudson Rainer & Abraham Ka-chung Wai

  2. Accident & Emergency Department, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China

    Abraham Ka-chung Wai

  3. Accident & Emergency Department, Queen Mary Hospital, Hong Kong, China

    Timothy Hudson Rainer & Abraham Ka-chung Wai

  4. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China

    Qihao Wu & Yong-Hong Kuo

  5. HKU Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong, China

    Yong-Hong Kuo

Authors
  1. Qihao Wu
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  2. Sunny Ching-long Chan
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Contributions

(1) Abraham Ka-chung Wai, Yong-Hong Kuo and Qihao Wu proposed the concept and design. (2) Sunny Ching-long Chan, Teddy Tai-loy Lee, Kevin Wang-leong So and Omar Wai-kiu Tsui finished acquisition of data. (3) Qihao Wu completed the analysis, interpretation of data, and also drafted the manuscript. (4) Timothy Hudson Rainer, Yong-Hong Kuo and Abraham Ka-chung Wai supervised critical revision for important intellectual content. All authors had full access to the data, contributed to the study, approved the final version for publication, and take responsibility for its accuracy and integrity.

Corresponding author

Correspondence to Abraham Ka-chung Wai.

Ethics declarations

Ethics Approval

The study protocol was approved by the Institutional Review Board of the Hospital Authority, Hong Kong West Cluster (reference UW 20–112). Informed consent was not required from participants as all data provided by the Clinical Data Analysis and Reporting System (CDARS) were de-identified.

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All authors have disclosed no conflicts of interest.

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Wu, Q., Chan, S.Cl., Lee, T.Tl. et al. Evaluating the Patient Boarding during Omicron Surge in Hong Kong: Time Series Analysis. J Med Syst 47, 76 (2023). https://doi.org/10.1007/s10916-023-01964-x

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