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Impact of a hospital policy to redistribute admission flow across clinical services for capacity relief during COVID-19 surges.
Safavi, Kyan C; Copenhaver, Martin S; Moore, Amber; Bravard, Marjory A; Britton, O'Neil; Dunn, Peter.
Afiliação
  • Safavi KC; Healthcare Systems Engineering, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Copenhaver MS; Healthcare Systems Engineering, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Moore A; Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Bravard MA; Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Britton O; Mass General Brigham, Harvard Medical School, Boston, Massachusetts, USA.
  • Dunn P; Healthcare Systems Engineering, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
J Hosp Med ; 18(7): 568-575, 2023 07.
Article em En | MEDLINE | ID: mdl-36788630
ABSTRACT

BACKGROUND:

Increased hospital admissions due to COVID-19 place a disproportionate strain on inpatient general medicine service (GMS) capacity compared to other services.

OBJECTIVE:

To study the impact on capacity and safety of a hospital-wide policy to redistribute admissions from GMS to non-GMS based on admitting diagnosis during surge periods. DESIGN, SETTING, AND

PARTICIPANTS:

Retrospective case-controlled study at a large teaching hospital. The intervention included adult patients admitted to general care wards during two surge periods (January-February 2021 and 2022) whose admission diagnosis was impacted by the policy. The control cohort included admissions during a matched number of days preceding the intervention. MAIN OUTCOMES AND

MEASURES:

Capacity measures included average daily admissions and hospital census occupied on GMS. Safety measures included length of stay (LOS) and adverse outcomes (death, rapid response, floor-to-intensive care unit transfer, and 30-day readmission).

RESULTS:

In the control cohort, there were 365 encounters with 299 (81.9%) GMS admissions and 66 (18.1%) non-GMS versus the intervention with 384 encounters, including 94 (24.5%) GMS admissions and 290 (75.5%) non-GMS (p < .001). The average GMS census decreased from 17.9 and 21.5 during control periods to 5.5 and 8.5 during intervention periods. An interrupted time series analysis confirmed a decrease in GMS daily admissions (p < .001) and average daily hospital census (p = .014; p < .001). There were no significant differences in LOS (5.9 vs. 5.9 days, p = .059) or adverse outcomes (53, 14.5% vs. 63, 16.4%; p = .482).

CONCLUSION:

Admission redistribution based on diagnosis is a safe lever to reduce capacity strain on GMS during COVID-19 surges.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Admissão do Paciente / COVID-19 Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Admissão do Paciente / COVID-19 Idioma: En Ano de publicação: 2023 Tipo de documento: Article