{"id":1098,"date":"2024-03-25T15:40:07","date_gmt":"2024-03-25T15:40:07","guid":{"rendered":"https:\/\/eliteacademicessays.com\/free-essay-examples\/?p=1098"},"modified":"2024-03-25T15:40:08","modified_gmt":"2024-03-25T15:40:08","slug":"health-insurance-and-healthcare-outcomes-data","status":"publish","type":"post","link":"https:\/\/eliteacademicessays.com\/free-essay-examples\/health-insurance-and-healthcare-outcomes-data\/","title":{"rendered":"Health Insurance and Healthcare Outcomes Data"},"content":{"rendered":"\n<p>Consistent past reports indicated that health insurance coverage impacts healthcare outcomes. The health insurance data choice presumes that health insurance coincides with better health outcomes and appropriate utilization of healthcare resources. The health insurance and healthcare outcomes data could be sampled from varied sources, for instance, patient surveys, medical records as well as from the administrative database that the US government uses in healthcare management and medical coverage. The main data elements include whether a person is insured or not and their medical health outcome as either good or bad. The two data elements could classify as categorical data types where the insurance gets denoted with either \u201c1\u201d for \u201c<em>insured<\/em>\u201d and \u201c0\u201d for \u201c<em>not insured<\/em>\u201d.<\/p>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>Missing Values<\/strong><\/h1>\n\n\n\n<p>One can opt to fill the missing values about the healthcare data using \u201cbest guess\u201d. According to Harrington and Estes, (2018), backfilling and averages could assist in predicting the most suitable data values. Absent values might distort the conclusions about the health insurance and health insurance data. The research could alternatively delete the rows that contain missing data from the analysis. If an entire column for instance entails missing data, then the analysis could eliminate the entire column. The report might also compute different formats for the missing values.<\/p>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>Outlier Detection and Treatment<\/strong><strong><\/strong><\/h1>\n\n\n\n<p>Outliers indicate broken data collection outcomes or an interesting perspective. Harrington and Estes, (2018) stated that the outliers could be removed from the analysis because they raise or lower the average which distorts the conclusion. Any values past the upper percentile could be removed, and values below the lower percentile should also be removed. The research could also group the data into segments so that the outliers find themselves in different groups. Different statistical methods could be used to eradicate the outliers. Trimmed means and weighted means could deal with outliers without necessarily excluding them from the analysis.<\/p>\n\n\n\n<h1 class=\"wp-block-heading\"><strong>References<\/strong><strong><\/strong><\/h1>\n\n\n\n<p>Bureau, U. S. C. (2021, October 18). <em>Health insurance coverage in the United States: 2020<\/em>. Census.gov. Retrieved January 16, 2022, from https:\/\/www.census.gov\/library\/publications\/2021\/demo\/p60-274.html<\/p>\n\n\n\n<p>&nbsp;Harrington, C., &amp; Estes, C. L. (2018).&nbsp;<em>Health policy: Crisis and reform in the U.S. health care delivery system<\/em>. Sudbury, Mass: Jones and Bartlett Pub.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Consistent past reports indicated that health insurance coverage impacts healthcare outcomes. The health insurance data choice presumes that health insurance coincides with better health outcomes and appropriate utilization of healthcare resources. The health insurance and healthcare outcomes data could be sampled from varied sources, for instance, patient surveys, medical records as well as from the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[],"class_list":["post-1098","post","type-post","status-publish","format-standard","hentry","category-computer-science"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Health Insurance and Healthcare Outcomes Data<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/eliteacademicessays.com\/free-essay-examples\/health-insurance-and-healthcare-outcomes-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Health Insurance and Healthcare Outcomes Data\" \/>\n<meta property=\"og:description\" content=\"Consistent past reports indicated that health insurance coverage impacts healthcare outcomes. 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