Beyond Hospital-Level Aggregated Data

人员配备 背景(考古学) 医学 急症护理 单位(环理论) 医疗保健 医疗急救 护理部 急诊医学 心理学 经济增长 生物 数学教育 古生物学 经济
作者
Christine Yang,Mark Kuebeler,Ruhong Jiang,Melissa K. Knox,Janine J. Wong,Paras Mehta,Lynette E. Dorsey,Laura A. Petersen
出处
期刊:Medical Care [Lippincott Williams & Wilkins]
卷期号:62 (3): 189-195
标识
DOI:10.1097/mlr.0000000000001972
摘要

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.

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