Evidence map›Paper›PMID 39838447›Full record

ArticleHealth research policy and systems2025

Lessons from the pandemic: Relationship between patient visits and patient length of stay in California's health system.

David D Cho, Yu Wang

Abstract read
In one paragraph

Article in Health research policy and systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

David D ChoDepartment of Management, College of Business and Economics, California State University, Fullerton, Fullerton, CA, 92831, United States of America. davcho@fullerton.edu.
Yu WangCongdon School of Supply Chain, Business Analytics, and Information Systems, University of North Carolina Wilmington, Wilmington, NC, 28403, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe coronavirus disease 2019 (COVID-19) pandemic placed a heavy strain on the United States healthcare system. Common hospital operational performances were impacted to varying degrees by the pandemic. This study examined the healthcare operational measures during COVID-19 pandemic.

methodsThis cross-sectional study examined the COVID-19 cases and death counts of 56 California counties and hospital-level utilization data of 397 hospitals in California from January 2019 to March 2021.

resultsA total of 56 California counties were analysed, of which 37 counties were urban and 19 counties were rural. Average patient length of stay was positively associated with the number of intensive care unit visits by COVID-19 patients for all counties, as well as urban and rural counties separately. However, average patient length of stay was negatively associated with the number of inpatient visits by COVID-19 patients for all counties and urban counties.

conclusionsThe findings suggest that, while the need for additional beds and nursing staff in intensive care units exceeded initial estimates, there were also opportunities to streamline the care process for improved efficiency in regular acute care units. The understanding of factors impacting average patient length of stay would be valuable for hospital administrators in optimizing resource allocation and utilization to balance patient outcomes with financial sustainability during disruptive events.

Indexed as

COVID-19Intensive Care UnitsLength of StayCaliforniaCross-Sectional StudiesHospitalizationHumansPandemicsSARS-CoV-2Average patient length of stayCalifornia hospitalsCOVID-19 pandemicHealth care utilizationQuality of care

Identifiers

PMID39838447
PMCPMC11748248

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.