RNCase ReportJournal of emergency nursing2021

Use of the Donabedian Model as a Framework for COVID-19 Response at a Hospital in Suburban Westchester County, New York: A Facility-Level Case Report.

Cairenn Binder, Rafael E Torres, Diana Elwell

PMID 33317860

WHAT IT FOUND

A suburban hospital emergency department used the Donabedian model to guide pandemic response, creating negative pressure zones and universal N95 use.

Staff sick calls peaked at 18.5% and 11% of staff tested positive for antibodies, while patient return rates remained stable at 1.1%.

Key findings

01Staff sick calls peaked at 18.5% of scheduled nursing staff, and 11% of emergency department staff tested positive for COVID-19 antibodies.

02The rate of patients sent home who returned within 48 hours requiring hospitalization remained stable at 1.1%.

03Structural changes increased isolation capacity from 7 patients to 46 patients by creating low-acuity and high-acuity zones.

STILL TO COME

How it was doneWhat they foundWhat it means for RNs

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What it does not show

This is a single-facility case report with no control group, so it cannot prove that the interventions caused the outcomes. The study is retrospective and relies on administrative data, which may be incomplete or inaccurate. The setting is a private hospital in a wealthy suburban area with specific architectural features that allowed for easy conversion of spaces, limiting generalizability to other settings. Key outcomes such as inpatient mortality and long-term complications were not measured. The 11% staff antibody positivity rate is interpreted by the authors as evidence of protection, but the lack of a comparison group makes this difficult to assess.

The easy way to misread this

Do not interpret the 11% staff antibody positivity rate as evidence that the interventions were successful in preventing infection, as there was no comparison group and the rate may reflect community exposure rather than workplace transmission. The positive outcomes reported are descriptive and do not demonstrate causality.

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