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Beyond Hospital-Level Aggregated Data

delete2024-01-04
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PRE
AI
C
Christine Yang
M
Mark Kuebeler
R
Rebecca Jiang
M
Melissa K. Knox
J
Janine J. Wong
P
Paras Mehta
L
Lynette E. Dorsey
L
Laura A. Petersen *
DOI:10.1097/MLR.0000000000001972delete
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摘要

摘要

En 中文
Background:Studies of nurse staffing frequently use data aggregated at the hospital level that do not provide the appropriate context to inform unit-level decisions, such as nurse staffing.Objectives:Describe a method to link patient data collected during the provision of routine care and recorded in the electronic health record (EHR) to the nursing units where care occurred in a national dataset.Research Design:We identified all Veterans Health Administration acute care hospitalizations in the calendar year 2019 nationwide. We linked patient-level EHR and bar code medication administration data to nursing units using a crosswalk. We divided hospitalizations into segments based on the patient's time-stamped location (ward stays). We calculated the number of ward stays and medication administrations linked to a nursing unit and the unit-level and facility-level mean patient risk scores.Results:We extracted data on 1117 nursing units, 3782 EHR patient locations associated with 1,137,391 ward stays, and 67,772 bar code medication administration locations associated with 147,686,996 medication administrations across 125 Veterans Health Administration facilities. We linked 89.46% of ward stays and 93.10% of medication administrations to a nursing unit. The average (standard deviation) unit-level patient severity across all facilities is 4.71 (1.52), versus 4.53 (0.88) at the facility level.Conclusions:Identification of units is indispensable for using EHR data to understand unit-level phenomena in nursing research and can provide the context-specific information needed by managers making frontline decisions about staffing.
Keyword:
databases
health services research
methodology
nursing
veterans

期刊

M
Medical Care
IF:
2.8
论文数:
6.8K
被引数:
2.0W

机构

U
university of houston system
学者数:
1.4W
论文数: 1.4W
被引数: 16
B
Baylor College of Medicine
学者数:
4.1W
论文数: 3.0W
被引数: 4.2W
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