인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Weather parameters impact patient admission to emergency departments (EDs) and due to pressures of overcrowding, it is imperative to better forecast admission levels. This retrospective single-centre study aimed to assess whether ambient temperature independently predicts emergency department (ED) admissions, beyond typical seasonal variation. We analysed 150,751 admissions to the ED of the University Hospital Klinikum rechts der Isar (MRI) in Munich, Germany from 2019-2022. Patients were divided into either surgical or internal medicine groups. Their post-treatment status as well as discharge type ('outpatient' versus 'inpatient') was also recorded. Descriptive statistics as well as linear models were used to identify and test statistically significant correlations of ED and weather variables. Patient admissions of the surgery group were directly correlated with changes in ambient temperature. This relationship persisted consistently across all seasons, suggesting a temperature effect that is independent of typical seasonal fluctuations. Whilst ED patient intake of the internal medicine group decreased during some holidays, spring as well as on weekends, sharp increases could be seen during the Oktoberfest period and Christmas. Despite minor variances, this was not the case in the surgery group. Here, the overall direct correlation of temperature and surgical patient levels for the period between 2019-22 seems highly significant. Temperature changes lead to more surgical cases irrespective of the season and more outpatient discharges, whilst inpatient admissions generally seem rather unresponsive to weather changes. A better allocation of resources in ED departments results from superior understandings of trigger factors and temperature has thus been rendered a particularly potent one.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
