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52 results for “health insurance”
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>
When Less is More: Improving Choices in Health Insurance Markets
<p>We study the impact of changing choice set size on the quality of choices in health insurance markets. Using novel data on enrollment and medical claims for school district employees in the state of Oregon, we document that the average employee could save $600 by switching to a lower cost plan. Structural modeling reveals large ``choice inconsistencies'' such as non-equalization of the dollar spent on premiums and out of pocket, and a novel form of ``approximate inertia'' where enrollees are excessively likely to switch to other plans that are close to the current plan on the plan design spreadsheet. Variation in the number of plan choices across districts and over time shows that enrollees make lower-cost choices when the choice set is smaller. We show that a curated restriction of choice set size improves choices more than the best available information intervention, partly because approximate inertia lowers gains from new information. We explicitly test and reject the assumption that this is because individuals choose worse from larger choice sets, or ``choice overload''. Rather, we show that this feature arises from the fact that larger choice sets feature worse choices on average that are not offset by individual re-optimization. </p>
A qualitative assessment on the acceptability of providing cash transfers and social health insurance for tuberculosis-affected families in Ho Chi Minh City, Vietnam
<p>Seven transcripts from qualitative interviews about the acceptability of cash transfers and social health insurance in Vietnam. </p>
Dataset: Preliminary analysis of open data pertaining to the services available through the Health Insurance Institute of Slovenia and provided by family medicine
<p>BACKGROUND: The Health Insurance Institute of Slovenia (ZZZS) began publishing service-related data in May 2023, following a directive from the Ministry of Health (MoH). The ZZZS website provides easily accessible information about the services provided by individual doctors, including their names. The user is provided relevant information about the doctor's employer, including whether it is a public or private institution. The data provided is useful for studying the public system's operations and identifying any errors or anomalies. </p> <p>METHODS: The data for services provided in May 2023 was downloaded and analysed. The published data were cross-referenced using the provider's RIZDDZ number with the daily updated data on ambulatory workload from June 9, 2023, published by ZZZS. The data mentioned earlier were found to be inaccurate and were improved using alerts from the zdravniki.sledilnik.org portal. Therefore, they currently provide an accurate representation of the current situation. The total number of services provided by each provider in a given month was determined by adding up the individual services and then assigning them to the corresponding provider. </p> <p>RESULTS: A pivot table was created to identify 307 unique operators, with 15 operators not appearing in both lists. There are 66 public providers, which make up about 72% of the contractual programme in the public system. There are 241 private providers, accounting for about 28% of the contractual programme. In May 2023, public providers accounted for 69% (n=646,236) of services in the family medicine system, while private providers contributed 31% (n=291,660). The total number of services provided by public and private providers was 937,896. Three linear correlations were analysed. The initial analysis of the entire sample yielded a high R-squared value of .998 (adjusted R-squared value of .996) and a significant level below 0.001. The second analysis of the data from private providers showed a high R Squared value of .904 (Adjusted R Squared = .886), indicating a strong correlation between the variables. Furthermore, the significance level was < 0.001, providing additional support for the statistical significance of the results. The third analysis used data from public providers and showed a strong level of explanatory power, with a R Squared value of 1.000 (Adjusted R Squared = 1.000). Furthermore, the statistical significance of the findings was established with a p-value < 0.001. </p> <p>CONCLUSION: Our analysis shows a strong linear correlation between contract size of the program signed and number services rendered by family medicine providers. A stronger linear correlation is observed among providers in the public system compared to those in the private system. Our study found that private providers generally offer more services than public providers. However, it is important to acknowledge that the evaluation framework for assessing services may have inherent flaws when examining the data. Prescribing a prescription and resuscitating a patient are both assigned a rating of one service. It is crucial to closely monitor trends and identify comparable databases for pairing at the secondary and tertiary levels.</p>
Developing a Health Insurance Navigation Program for Survivors of Childhood Cancer
ClinicalTrials.gov study NCT04520061. IPD Sharing: NO. Countries: 1. Publications: 10.
Rewarding Fitness Tracking – The Communication and Promotion of Health Insurers' Bonus Programs and the Use of Self-Tracking Data
<p>The data set offers additional information for the study on "Rewarding Fitness Tracking – The Communication and Promotion of Health Insurers’ Bonus Programs and the Usage of Self-Monitored Data", to be submitted at HCII 2018.</p> <p>The data set includes the full lists of German and Australian Health Insurers investigated, including a link to their apps.</p> <p>This study aims at giving an overview on the current status quo of health insurances that investigate self-tracking opportunities and possible rewards for customers that share their fitness and health activities. We are interested in how insurers promote their health and well-being programs (intended program goals) and motivate customers to live healthier (incentives). We introduce research in progress while firstly focusing on the countries Germany and Australia. We discuss the current situation of health insurance clients’ data use, data security issues as well as long-term health benefits regarding those programs based on recent research on self-tracking activities. The research questions are: </p> <ol> <li>Which health insurers offer options for client to self-track health and fitness data?</li> <li>How do insurers communicate about the programs?</li> <li>How do those insurers communicate about data security?</li> </ol>
Supporting Decisions About Health Insurance to Improve Care for the Uninsured
ClinicalTrials.gov study NCT02522624. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Navigating Health Insurance Selection for IVF Benefits
ClinicalTrials.gov study NCT05663645. IPD Sharing: YES. Countries: 1. Publications: 1.
Improving Health Insurance Experiences for Adolescent and Young Adult Cancer Patients
ClinicalTrials.gov study NCT04448678. IPD Sharing: NO. Countries: 1. Publications: 2.
Virtual Health Insurance Navigation Pilot Program for Colorectal Survivors
ClinicalTrials.gov study NCT05002608. IPD Sharing: NO. Countries: 1. Publications: 7.
Data on community-based health insurance enrollment trends in northeast Ethiopia
Background <p>The term "community-based health insurance" refers to a broad range of nonprofit, prepaid health financing models designed to meet the health financing needs of disadvantaged populations, particularly those in the rural and informal sectors. Due to their voluntary nature, such initiatives suffer from persistently low coverage in low- and middle-income countries. In Ethiopia, the schemes' membership growth has not been well investigated so far. This study sought to examine the scheme's enrollment trend over a five-year period, and to explore the various challenges that underpin membership growth from the perspectives of various key stakeholders.</p> Results <p>Over the course of the study period, enrollment in the scheme at both districts exhibited non-linear trends with both positive and negative growth rates being identified. Overall, the scheme in Tehulederie has a relatively higher population coverage and better membership retention, which could be due to the strong foundation laid by a rigorous public awareness campaign and technical support during the pilot phase. The challenges contributing to the observed level of performance have been summarized under four main themes that include quality of health care, claims reimbursement for insurance holders, governance practices, and community awareness and acceptability. </p> Conclusions <p>The scheme experienced negative growth ratios in both districts, indicating that it is not functionally viable. It will fail to meet its mission unless relevant stakeholders at all levels of government demonstrate political will and commitment to its implementation, as well as advocate for the community. Interventions should target on the highlighted challenges in order to boost membership growth and ensure the scheme's viability.</p>
Perceived quality of care among households ever enrolled in a community-based health insurance scheme in two districts of northeast Ethiopia: A community-based, cross-sectional study
<p class="MsoNormal"><strong><span>Objectives: </span></strong><span>The purpose of this study was to examine how clients perceived the quality of health care they received and to identify associated factors both at the individual and facility levels.</span></p> <p class="MsoNormal"><strong><span>Design: </span></strong><span>A community-based, cross-sectional study.<span> </span></span></p> <p class="MsoNormal"><strong><span>Setting:</span></strong><span> Two rural districts of northeast Ethiopia</span><span>, Tehulederie and Kallu.</span></p> <p class="MsoNormal"><strong><span>Participants:</span></strong><span> 1081 rural households who had ever been enrolled in community-based health insurance and visited a health center at least once in the previous 12 months.<span> </span>Furthermore, 194 health care providers participated in the study to provide cluster-level data. <a name="_Hlk84940584"></a></span></p> <p class="MsoNormal"><span><strong><span>Outcome measures</span></strong></span><span><span>: The outcome variable of interest was the perceived quality of care, which was measured using a 17-item scale. </span></span><span>Respondents were asked to rate the degree to which they agreed on 5-point response items relating to their experiences with health care in the outpatient departments of nearby health centers. A multilevel linear regression analysis was used to identify predictors of perceived quality of care. </span></p> <p class="MsoNormal"><strong><span>Results:</span></strong><span> The mean perceived quality of care was 70.28 (SD=8.39). <a name="_Hlk85270466"></a>Five dimensions of perceived quality of care were extracted from the factor analysis, with the patient-provider communication dimension having the highest <span>mean score (M=77.84, SD=10.12), and information provision having the lowest (M=64.67, SD=13.87). Wealth status, current insurance status, perceived health status, presence of chronic illness, and time to a recent health center visit </span></span><span><span>were individual level variables that showed a significant association with the perceived quality of care. At the cluster level, the work experience of health care providers, patient volume, and an interaction term between patient volume and staff job satisfaction also showed a significant association</span><span>.</span></span><span><span> <span> </span></span></span></p> <p class="MsoNormal"><strong><span>Conclusions:</span></strong><span> Much work remains to improve the quality of care, especially on information provision and access to care quality dimensions. A range of individual and cluster-level characteristics influence the perceived quality of care. For a better quality of care, it is vital to optimize the patient-provider ratio and enhance staff job satisfaction.</span></p>
Supplementary data for paper titled "Examining the Effects of Environmental Knowledge and Health Insurance Coverage on Health Status"
<p>Codebook, raw data, Stata dataset, Stata code, Stata results</p>
Replication package for: Optimal Long-Term Health Insurance Contracts: Characterization, Computation, and Welfare Effects
<p>This is a replication package for: </p> <p>Ghili, S., Handel, B., Hendel, I. and Whinston, M. "Optimal Long-Term Health Insurance Contracts: Characterization, Computation, and Welfare Effects." </p> <p>The replication package .zip file contains a ReadMe, which provides instructions, and data matrices and code for the paper. </p>
Evaluating the Impact of the National Health Insurance Scheme of Ghana on Surgical Care
ClinicalTrials.gov study NCT03604458. IPD Sharing: NO. Countries: 1. Publications: 1.
Post-COVID-19 Monitoring in Routine Health Insurance Data With Focus on Autoimmune Diseases (POINTED-AD)
ClinicalTrials.gov study NCT05606198. IPD Sharing: NO. Countries: 1. Publications: 1.
Interest of the Return to Primary Care of the "Low Back Pain Booklet" of Health Insurance in the Recovery of Common Acute Low Back Pain
ClinicalTrials.gov study NCT03953625. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Post-COVID-19 Monitoring in Routine Health Insurance Data
ClinicalTrials.gov study NCT05074953. IPD Sharing: NO. Countries: 1. Publications: 2.
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.
Does the National Health Insurance Card Allow us to Predict Antibiotic Resistance?
ClinicalTrials.gov study NCT02292160. IPD Sharing: Not stated. Countries: 1. Publications: 2.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.