Administrative health care databases contain valuable patient information generated by health care encounters. These "big data" repositories have been increasingly used in epidemiological health research internationally in recent years as they are easily accessible and cost-efficient and cover large populations for long periods. Despite these beneficial characteristics, it is also important to consider the limitations that administrative health research presents, such as issues related to data incompleteness and the limited sensitivity of the variables. These barriers potentially lead to unwanted biases and pose threats to the validity of the research being conducted. In this review, we discuss the effectiveness of health administrative data in understanding the epidemiology of and outcomes after acute kidney injury (AKI) among adults and children. In addition, we describe various validation studies of AKI diagnostic or procedural codes among adults and children. These studies reveal challenges of AKI research using administrative data and the lack of this type of research in children and other subpopulations. Additional pediatric-specific validation studies of administrative health data are needed to promote higher volume and increased validity of this type of research in pediatric AKI, to elucidate the large-scale epidemiology and patient and health systems impacts of AKI in children, and to devise and monitor programs to improve clinical outcomes and process of care.
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http://dx.doi.org/10.3389/fped.2021.742888 | DOI Listing |
AAPS PharmSciTech
January 2025
OSIS, Silver Spring, Maryland, U.S.A.
Travel restrictions during the novel coronavirus, SARS-CoV-2 (COVID-19) public health emergency affected the U.S. Food and Drug Administration's (FDA) ability to conduct on-site bioavailability/bioequivalence (BA/BE) and Good Laboratory Practice (GLP) nonclinical inspections.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Community Medicine, VMMC and Safdarjung Hospital, New Delhi, India.
User satisfaction with Assistive Technology (AT) is one of the crucial factors in the success of any AT service. The current study aimed to estimate satisfaction with AT and the reasons for dissatisfaction and unsuitability among persons with functional difficulties in India. Using the WHO Rapid Assistive Technology Assessment tool, a cross-sectional study was conducted in eight districts, representing four zones of India.
View Article and Find Full Text PDFObjectives: Patient-sharing networks based on administrative data are used to understand the organisation of healthcare. We examined the patient-sharing networks between different professionals taking care of patients with mental health or substance use problems.
Design: Register study based on the Register of Primary Health Care visits (Avohilmo) that covers all outpatient primary health care visits in Finland.
JMIR Form Res
December 2024
Pharmacy Department, Gold Coast Hospital and Health Service, Southport, Australia.
Background: Artificial intelligence (AI) has the potential to address growing logistical and economic pressures on the health care system by reducing risk, increasing productivity, and improving patient safety; however, implementing digital health technologies can be disruptive. Workforce perception is a powerful indicator of technology use and acceptance, however, there is little research available on the perceptions of allied health professionals (AHPs) toward AI in health care.
Objective: This study aimed to explore AHP perceptions of AI and the opportunities and challenges for its use in health care delivery.
J Affect Disord
January 2025
School of Population Health, Curtin University, Perth, WA, Australia; enAble Institute, Curtin University, Perth, Western Australia, Australia.
Background: This study aims to examine the relationship between maternal antenatal and postnatal depressive disorders and the risk of disruptive behavioural disorders (DBDs) in offspring, including conduct disorder (CD) and oppositional defiant disorder (ODD), to enhance understanding and address gaps in the literature.
Methods: We utilised a large administrative health dataset from New South Wales (NSW), Australia. Maternal perinatal depressive disorders and offspring DBDs were identified using International Classification of Diseases (ICD-10) codes.
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