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15 results for “Health Claims”
Health Insurance Claims
<p>The dataset is eligible in exploring Health Insurance fraud Claims using machine learning algorithms. Its well suited for students developimg ML models to predict Healthcare insurance claims fraud. </p> <p> </p>
A cross sectional study on adaptation and initial validation of a test to evaluate health claims among high school students – Croatian version
<p>Objectives: We validated the Croatian version of the test using multiple-choice questions (MCQs) from the Claim Evaluation Tools item bank of the Informed Health Choices project, and measured the ability of high school students to appraise health claims.</p> <p>Setting: 16 high schools from the urban agglomeration of the city of Split, Croatia.</p> <p>Participants: Final year high school students of at least 18 years of age.</p> <p>Interventions: 18 MCQs from the item bank considered relevant for high school students were translated. After face-validity testing, the questionnaire was piloted and sent to a convenient sample of 302 high school students.</p> <p>Primary and secondary outcome measures: Difficulty and discrimination indices were calculated for each MCQ to determine the validity of translation and the weight of MCQs. We assessed basic metric characteristics and performed initial validation of the test. Two tests were created, the full (18 MCQs) and the short version (12 MCQs). We analysed differences in test score according to gender and school.</p> <p>Results: The response rate was 96% (75% female respondents). Metric characteristics of both tests were satisfactory (Cronbach α=0.71 for the full and α=0.73 for the short version). The mean score (± standard deviation) for the full version was 11.15±3.43 and 8.13±2.76 for the short version. There were 6 easy and 12 moderately difficult questions. Questions concerning effectiveness and dissimilar comparison groups were answered correctly by fewer than 40% of students. Female students and those from grammar and health schools scored higher on both tests.</p> <p>Conclusions: Both tests showed good metric characteristics and may be used for quick and reliable assessments of adolescents' ability to appraise health claims. They may be used to identify needs and inform development of educational activities to foster critical thinking about health among adolescents</p>
A cross sectional study on adaptation and initial validation of a test to evaluate health claims among high school students – Croatian version
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Data from: Validation of an algorithm for identifying MS cases in administrative health claims datasets
Objective: To develop a valid algorithm for identifying multiple sclerosis (MS) cases in administrative health claims (AHC) datasets. Methods: We used 4 AHC datasets from the Veterans Administration (VA), Kaiser Permanente Southern California (KPSC), Manitoba (Canada), and Saskatchewan (Canada). In the VA, KPSC, and Manitoba, we tested the performance of candidate algorithms based on inpatient, outpatient, and disease-modifying therapy (DMT) claims compared to medical records review using sensitivity, specificity, positive and negative predictive values, and interrater reliability (Youden J statistic) both overall and stratified by sex and age. In Saskatchewan, we tested the algorithms in a cohort randomly selected from the general population. Results: The preferred algorithm required ≥3 MS-related claims from any combination of inpatient, outpatient, or DMT claims within a 1-year time period; a 2-year time period provided little gain in performance. Algorithms including DMT claims performed better than those that did not. Sensitivity (86.6%–96.0%), specificity (66.7%–99.0%), positive predictive value (95.4%–99.0%), and interrater reliability (Youden J = 0.60–0.92) were generally stable across datasets and across strata. Some variation in performance in the stratified analyses was observed but largely reflected changes in the composition of the strata. In Saskatchewan, the preferred algorithm had a sensitivity of 96%, specificity of 99%, positive predictive value of 99%, and negative predictive value of 96%. Conclusions: The performance of each algorithm was remarkably consistent across datasets. The preferred algorithm required ≥3 MS-related claims from any combination of inpatient, outpatient, or DMT use within 1 year. We recommend this algorithm as the standard AHC case definition for MS.
Supplements and underlying data for the study: Using Health Claims to Teach Evidence-Based Practice to Healthcare Students: A Mixed Methods Study
<p>Supplements and English translation of the Norwegian data sets, in addition to checklists for the manuscript.</p>
Survey Assessing How Consumers Adapt Their Diet in Response to Health Claim Messaging
ClinicalTrials.gov study NCT05088863. IPD Sharing: YES. Countries: 1. Publications: 1.
Comparative Study of the Efficacy of Biologics vs Usual Treatment on OCS Reduction for Severe Asthma Patients Using Health Insurance Claim Database
ClinicalTrials.gov study NCT05136547. IPD Sharing: YES. Countries: 1. Publications: 1.
Impact of New Hormonotherapy Drugs in Prostatic Cancer on the Risk of Cardiovascular Events : a Pharmacoepidemiology Study Using the French Health Care Claims Database
ClinicalTrials.gov study NCT06902441. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
U of A/ U of M Beans and Peas Health Claim Project
ClinicalTrials.gov study NCT01661543. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Validation of an algorithm for identifying MS cases in administrative health claims datasets
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Consumer Perceptions of Cannabidiol (CBD) Health Claims
ClinicalTrials.gov study NCT06069713. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: The prevalence of MS in the United States: a population-based estimate using health claims data
Objective: To generate a national multiple sclerosis (MS) prevalence estimate for the United States by applying a validated algorithm to multiple administrative health claims (AHC) datasets. Methods: A validated algorithm was applied to private, military, and public AHC datasets to identify adult cases of MS between 2008 and 2010. In each dataset, we determined the 3-year cumulative prevalence overall and stratified by age, sex, and census region. We applied insurance-specific and stratum-specific estimates to the 2010 US Census data and pooled the findings to calculate the 2010 prevalence of MS in the United States cumulated over 3 years. We also estimated the 2010 prevalence cumulated over 10 years using 2 models and extrapolated our estimate to 2017. Results: The estimated 2010 prevalence of MS in the US adult population cumulated over 10 years was 309.2 per 100,000 (95% confidence interval [CI] 308.1–310.1), representing 727,344 cases. During the same time period, the MS prevalence was 450.1 per 100,000 (95% CI 448.1–451.6) for women and 159.7 (95% CI 158.7–160.6) for men (female:male ratio 2.8). The estimated 2010 prevalence of MS was highest in the 55- to 64-year age group. A US north-south decreasing prevalence gradient was identified. The estimated MS prevalence is also presented for 2017. Conclusion: The estimated US national MS prevalence for 2010 is the highest reported to date and provides evidence that the north-south gradient persists. Our rigorous algorithm-based approach to estimating prevalence is efficient and has the potential to be used for other chronic neurologic conditions.
RCT-Consumer Perceptions of Cannabidiol (CBD) Health Claims
ClinicalTrials.gov study NCT06800066. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: The prevalence of MS in the United States: a population-based estimate using health claims data
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Analysis of Linked Health Insurance Claims and Clinical Registries to Determine Device Surveillance With Paclitaxel-coated Medical Devices
ClinicalTrials.gov study NCT04683458. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
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