Background: Uncoded diagnoses in computerized health insurance claims are excluded from statistical summaries of health-related risks and other factors. The effects of these uncoded diagnoses, coded according to ICD-10 disease categories, have not been investigated to date in Japan.
Methods: I obtained all computerized health insurance claims (outpatient medical care, inpatient medical care, and diagnosis procedure-combination per-diem payment system [DPC/PDPS] claims) submitted to the National Health Insurance Organization of Kumamoto Prefecture in May 2010. These were classified according to the disease categories of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10). I used accompanying text documentation related to the uncoded diagnoses to classify these diagnoses. Using these classifications, I calculated the proportion of uncoded diagnoses by ICD-10 category.
Results: The number of analyzed diagnoses was 3,804,246, with uncoded diagnoses accounting for 9.6% of the total. The proportion of uncoded diagnoses in claims for outpatient medical care, inpatient medical care, and DPC/PDPS were 9.3%, 10.9%, and 14.2%, respectively. Among the diagnoses, Congenital malformations, deformations, and chromosomal abnormalities had the highest proportion of uncoded diagnoses (19.3%), and Diseases of the respiratory system had the lowest proportion of uncoded diagnoses (4.7%).
Conclusions: The proportion of uncoded diagnoses differed by the type of health insurance claim and disease category. These findings indicate that Japanese health statistics computed using computerized health insurance claims might be biased by the exclusion of uncoded diagnoses.
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http://dx.doi.org/10.2188/jea.je20130194 | DOI Listing |
Proc (IEEE Int Conf Healthc Inform)
June 2024
Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.
Delirium is an acute decline or fluctuation in attention, awareness, or other cognitive function that can lead to serious adverse outcomes. Despite the severe outcomes, delirium is frequently unrecognized and uncoded in patients' electronic health records (EHRs) due to its transient and diverse nature. Natural language processing (NLP), a key technology that extracts medical concepts from clinical narratives, has shown great potential in studies of delirium outcomes and symptoms.
View Article and Find Full Text PDFBMC Prim Care
July 2024
Institute of primary care, University and University Hospital Zurich, Pestalozzistr. 24, Zürich, 8091, Switzerland.
Background: Diagnoses entered by general practitioners into electronic medical records have great potential for research and practice, but unfortunately, diagnoses are often in uncoded format, making them of little use. Natural language processing (NLP) could assist in coding free-text diagnoses, but NLP models require local training data to unlock their potential. The aim of this study was to develop a framework of research-relevant diagnostic codes, to test the framework using free-text diagnoses from a Swiss primary care database and to generate training data for NLP modelling.
View Article and Find Full Text PDFEur J Surg Oncol
July 2024
Thoracic Surgery Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health - DSCTV, University of Padova, 35128, Italy.
Introduction: Tumor Inflammatory microenvironment (TIME) encompasses several immune pathways modulating cancer development and escape that are not entirely uncoded. The results achieved with immunotherapy elicited the scientific debate on TIME also in non-small cell lung cancer (NSCLC). We aimed to investigate whether TIME (in terms of PD-L1 expression and/or Tumor Infiltrating Lymphocytes - TILs) played a separate role in terms of survival (OS) in resected upstaged lung adenocarcinomas (ADCs), excluding other perioperative variables as confounders.
View Article and Find Full Text PDFBMC Nephrol
March 2024
Department of Medicine and Geriatrics, Tuen Mun Hospital, Hong Kong, China.
Background: Chronic kidney disease (CKD) requires accurate prediction of renal replacement therapy (RRT) initiation risk. This study developed deep learning algorithms (DLAs) to predict RRT risk in CKD patients by incorporating medical history and prescriptions in addition to biochemical investigations.
Methods: A multi-centre retrospective cohort study was conducted in three major hospitals in Hong Kong.
J Clin Nurs
March 2024
Research Associate, Center of Clinical Nursing Science, University Hospital Zurich, Zurich, Switzerland.
Aims: To describe the point prevalence, risk factors and possible outcomes of delirium in inpatients.
Design: A cross-sectional point prevalence study.
Background: Delirium is an acute brain syndrome that negatively affects patients, healthcare professionals and institutions alike; it is common in inpatient settings and is preventable in about one third of cases.
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