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Building a Foundation for High-Quality Health Data: Multihospital Case Study in Belgium.
JMIR Med Inform
December 2024
Department of Public Health and Primary Care, Unit of Medical Informatics and Statistics, Ghent University, Ghent, Belgium.
Background: Data quality is fundamental to maintaining the trust and reliability of health data for both primary and secondary purposes. However, before the secondary use of health data, it is essential to assess the quality at the source and to develop systematic methods for the assessment of important data quality dimensions.
Objective: This case study aims to offer a dual aim-to assess the data quality of height and weight measurements across 7 Belgian hospitals, focusing on the dimensions of completeness and consistency, and to outline the obstacles these hospitals face in sharing and improving data quality standards.
Every Day I Write the Book: Transforming EHR Data from Daily Practice into New Knowledge.
J Am Board Fam Med
December 2024
From The Center for Professionalism & Value in Health Care, American Board of Family Medicine, Washington, DC (RLP); Research & Policy, American Board of Family Medicine, Washington, DC; and Center for Professionalism & Value in Health Care Washington, DC (AWB).
Despite producing mountains of data in the daily course of care, the documentation labors of frontline clinicians currently return very little value to them or to the health system. The potential of these painstakingly collected data are enormous and clinical registries can extract the extraordinary capacity of these data and transform them into research-ready datasets while protecting the confidentiality of the patients and clinicians. Clinical registries represent transformative tools for primary care research, bringing together the dimensions of clinical practice, research, quality improvement, and policy impact from a large, nationally reflective, diverse sample of practices and patients.
View Article and Find Full Text PDFEnhancing patient representation learning with inferred family pedigrees improves disease risk prediction.
J Am Med Inform Assoc
December 2024
Statistical Modeling, Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riβ 88400, Germany.
Background: Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research has so far adopted a limited view of family relations, essentially treating patients as independent samples in the analysis.
Methods: To address this gap, we present ALIGATEHR, which models inferred family relations in a graph attention network augmented with an attention-based medical ontology representation, thus accounting for the complex influence of genetics, shared environmental exposures, and disease dependencies.
Big brother or big opportunity? Utilization of EHR use metadata in the inpatient setting: A mixed methods study at 16 organizations.
J Hosp Med
December 2024
Division of Hospital Medicine, University of Kentucky, Lexington, Kentucky, USA.
Background: Clinician electronic actions within the electronic health record (EHR) are captured seamlessly in real-time during regular work activities in all major EHRs. Analysis of this EHR use metadata, such as audit log data, is increasingly used to understand the impact of work design on critical patient, workforce, and organizational outcomes.
Objective: Understand experiences and perspectives influencing the use and implementation of audit log data into practice.
Visualizing Patient Pathways and Identifying Data Repositories in a UK Neurosciences Center: Exploratory Study.
JMIR Med Inform
December 2024
Lancaster Medical School, Lancaster University, Bailrigg, Lancaster, LA1 4YW, United Kingdom, 44 01524 594547.
Background: Health and clinical activity data are a vital resource for research, improving patient care and service efficiency. Health care data are inherently complex, and their acquisition, storage, retrieval, and subsequent analysis require a thorough understanding of the clinical pathways underpinning such data. Better use of health care data could lead to improvements in patient care and service delivery.
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