Objective: To evaluate selected Children's Health Insurance Program Reauthorization Act claims-based quality measures using claims data alone, electronic health record (EHR) data alone, and both data sources combined.
Methods: Our population included pediatric patients from 46 clinics in the OCHIN network of community health centers, who were continuously enrolled in Oregon's public health insurance program during 2010. Within this population, we calculated selected pediatric care quality measures according to the Children's Health Insurance Program Reauthorization Act technical specifications within administrative claims. We then calculated these measures in the same cohort, by using EHR data, by using the technical specifications plus clinical data previously shown to enhance capture of a given measure. We used the k statistic to determine agreement in measurement when using claims versus EHR data. Finally, we measured quality of care delivered to the study population, when using a combined dataset of linked, patient-level administrative claims and EHR data.
Results: When using administrative claims data, 1.0% of children (aged 3-17) had a BMI percentile recorded, compared with 71.9% based on the EHR data (k agreement [k] # 0.01), and 72.0% in the combined dataset. Among children turning 2 in 2010, 20.2% received all recommended immunizations according to the administrative claims data, 17.2% according to the EHR data (k = 0.82), and 21.4% according to the combined dataset.
Conclusions: Children's care quality measures may not be accurate when assessed using only administrative claims. Adding EHR data to administrative claims data may yield more complete measurement.
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http://dx.doi.org/10.1542/peds.2013-4277 | DOI Listing |
Eur Heart J Digit Health
January 2025
School of Life Course & Population Sciences, King's College London, SE1 1UL London, UK.
Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction.
View Article and Find Full Text PDFJAMIA Open
February 2025
Institute for Informatics, Data Science and Biostatistics, Washington University, Saint Louis, MO 63110, United States.
Objective: Dimensionality reduction techniques aim to enhance the performance of machine learning (ML) models by reducing noise and mitigating overfitting. We sought to compare the effect of different dimensionality reduction methods for comorbidity features extracted from electronic health records (EHRs) on the performance of ML models for predicting the development of various sub-phenotypes in children with Neurofibromatosis type 1 (NF1).
Materials And Methods: EHR-derived data from pediatric subjects with a confirmed clinical diagnosis of NF1 were used to create 10 unique comorbidities code-derived feature sets by incorporating dimensionality reduction techniques using raw International Classification of Diseases codes, Clinical Classifications Software Refined, and Phecode mapping schemes.
Am J Epidemiol
January 2025
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Multiple imputation (MI) models can be improved with auxiliary covariates (AC), but their performance in high-dimensional data remains unclear. We aimed to develop and compare high-dimensional MI (HDMI) methods using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation with acute kidney injury as outcome and simulated 100 cohorts with a null treatment effect, incorporating creatinine labs, atrial fibrillation (AFib), and other investigator-derived confounders in the outcome generation.
View Article and Find Full Text PDFRes Pract Thromb Haemost
January 2025
Section of Hematology & Medical Oncology, Boston University School of Medicine, Boston, Massachusetts, USA.
Background: Cancer-associated thrombosis (CAT) is a leading cause of death in patients diagnosed with cancer. However, pharmacologic thromboprophylaxis use in cancer patients must be carefully evaluated due to a 2-fold increased risk of experiencing a major bleeding event within this population. The electronic health record CAT (EHR-CAT) risk assessment model (RAM) was recently developed, and reports improved performance over the widely used Khorana score.
View Article and Find Full Text PDFNPJ Parkinsons Dis
January 2025
Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder. However, current treatments only manage symptoms and lack the ability to slow or prevent disease progression. We utilized a systems genetics approach to identify potential risk genes and repurposable drugs for PD.
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