{"id":3380,"date":"2010-02-01T00:00:00","date_gmt":"2010-02-01T05:00:00","guid":{"rendered":"https:\/\/icesontario.wpengine.com\/journal-articles\/risk-adjustment-using-administrative-data-based-and-survey-derived-methods-for-explaining-physician-utilization\/"},"modified":"2023-06-14T19:45:25","modified_gmt":"2023-06-14T23:45:25","slug":"risk-adjustment-using-administrative-data-based-and-survey-derived-methods-for-explaining-physician-utilization","status":"publish","type":"journal_article","link":"https:\/\/www.ices.on.ca\/fr\/publications\/journal-articles\/risk-adjustment-using-administrative-data-based-and-survey-derived-methods-for-explaining-physician-utilization\/","title":{"rendered":"Risk adjustment using administrative data-based and survey-derived methods for explaining physician utilization"},"content":{"rendered":"<p><strong>Objectives<\/strong> &#x2014; The objective of this study was to evaluate an administrative data-based risk adjustment method for predicting physician utilization and the contribution of survey-derived indicators of health status. The results of this study will support the use of administrative data for planning, reimbursement, and assessing equity of physician utilization.<\/p>\n<p><strong>Methods<\/strong> &#x2014; The Ontario portion of the 2000-2001 Canadian Community Health Survey was linked with administrative physician claims data from 2002-2003 and 2003-2004. Explanatory models of family physician (FP) and specialist physician (SP) utilization were run using demographic information and The Johns Hopkins University Adjusted Clinical Groups (ACG) Case-mix System. Survey-based measures of health status were then added to the models. The coefficient of determination, R, indicated the models&apos; explanatory power.<\/p>\n<p><strong>Results<\/strong> &#x2014; The study sample consisted of 25,558 individuals aged 20 to 79 years representing approximately 7.8 million people. Over the 2 years of study period, 82.5% of the study population had a FP visit with a median of 6 visits and 53.2% had a SP visit with a median of 1 visit. The R values based on administrative data alone were 33% and 21% for the frequency of FP and SP visits and 16% and 35% for having one or more visit to an FPs and SPs, respectively. The addition of the survey-based measures to the administrative data-based models produced less than a 2% increase in explanatory power for any outcome.<\/p>\n<p><strong>Conclusion<\/strong> &#x2014; Administrative data-based measures of morbidity burden are valid and useful indicators of future physician utilization. The survey-derived measures used in this study did not contribute significantly to models on the basis of administrative data-based measures. These findings support the future use of administrative data-based data and Adjusted Clinical Groups for planning, reimbursement, and research.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Objectives &#x2014; The objective of this study was to evaluate an administrative data-based risk adjustment method for predicting physician utilization and the contribution of survey-derived indicators of health status. The results of this study will support the use of administrative data for planning, reimbursement, and assessing equity of physician utilization. Methods &#x2014; The Ontario portion [&hellip;]<\/p>\n","protected":false},"template":"","migration-helper-automated":[],"migration-manual":[],"topic":[62,19],"migration-helper-qa-sample-set":[],"class_list":["post-3380","journal_article","type-journal_article","status-publish","hentry","topic-health-services-research","topic-marginalized-populations"],"acf":{"citation":"Sibley LM, Moineddin R, Agha MM, Glazier RH. <em>Med Care<\/em>. 2010; 48(2):175-82.","source_url":"","ices_scientist":[1327,1384,1250],"site":[6733],"research_program":[],"news_release":[],"journal_article":[],"atlas":[],"research_report":[],"infographic":[],"video":[],"downloads":null,"links":null,"sitecore_item_id":"958BF579-D4F6-4758-8B21-7D6293701802","sitecore_item_name":"Risk-adjustment-using-administrative-data-based-and-survey-derived-methods-for-explaining","sitecore_field_values":"{\n  \"Title\": \"Risk adjustment using administrative data-based and survey-derived methods for explaining physician utilization\",\n  \"Short title\": \"Risk adjustment using administrative\",\n  \"Citation\": \"<p>Sibley LM, Moineddin R, Agha MM, Glazier RH. <em>Med Care<\/em>. 2010; 48(2):175-82.<\/p>\",\n  \"Abstract\": \"<p><strong>Objectives<\/strong> &mdash; The objective of this study was to evaluate an administrative data-based risk adjustment method for predicting physician utilization and the contribution of survey-derived indicators of health status. The results of this study will support the use of administrative data for planning, reimbursement, and assessing equity of physician utilization.<\/p>rn<p><strong>Methods<\/strong> &mdash; The Ontario portion of the 2000-2001 Canadian Community Health Survey was linked with administrative physician claims data from 2002-2003 and 2003-2004. Explanatory models of family physician (FP) and specialist physician (SP) utilization were run using demographic information and The Johns Hopkins University Adjusted Clinical Groups (ACG) Case-mix System. Survey-based measures of health status were then added to the models. The coefficient of determination, R, indicated the models' explanatory power.<\/p>rn<p><strong>Results<\/strong> &mdash; The study sample consisted of 25,558 individuals aged 20 to 79 years representing approximately 7.8 million people. Over the 2 years of study period, 82.5% of the study population had a FP visit with a median of 6 visits and 53.2% had a SP visit with a median of 1 visit. The R values based on administrative data alone were 33% and 21% for the frequency of FP and SP visits and 16% and 35% for having one or more visit to an FPs and SPs, respectively. The addition of the survey-based measures to the administrative data-based models produced less than a 2% increase in explanatory power for any outcome.<\/p>rn<p><strong>Conclusion<\/strong> &mdash; Administrative data-based measures of morbidity burden are valid and useful indicators of future physician utilization. The survey-derived measures used in this study did not contribute significantly to models on the basis of administrative data-based measures. These findings support the future use of administrative data-based data and Adjusted Clinical Groups for planning, reimbursement, and research.<\/p>\",\n  \"Keywords\": \"{80AF17F9-2AF8-43F7-9CA1-8A45542CDF7C}|{C854EE01-6CCA-41CE-9A24-52AA5AFA4C3F}|{263A4422-83C0-4DA7-9594-07606A469124}\",\n  \"Research Programs\": \"{5B1AF319-EC9B-4BF0-A9CD-D066ABE49D71}\",\n  \"ICES Locations\": \"{4FCAABBA-14A5-42E6-8F33-BC6C2F1D9908}\",\n  \"ICES Scientists\": \"{D20A13C2-789B-4AEB-9C05-64AF13F2068E}|{21862FB9-DD44-41AF-B1A5-A3B5D2474169}|{2C81C93C-B401-432B-8741-83841744D4CA}\",\n  \"Posted Date\": \"20100201T000000\"\n}","previous_url":"https:\/\/www.ices.on.ca\/Publications\/Journal-Articles\/2010\/January\/Risk-adjustment-using-administrative-data-based-and-survey-derived-methods-for-explaining"},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>ICES | Risk adjustment using administrative data-based and survey-derived methods for explaining physician utilization<\/title>\n<meta name=\"description\" content=\"Objectives &#x2014; 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