Benchmarking and measuring the comparative efficiency of emergency medical services in major US cities
Purpose - The purpose of this paper is to benchmark and measure the comparative efficiency of emergency medical services (EMS) in major US cities (populations greater than 100,000). In so doing, this paper aims to develop a benchmark that can be emulated by cities lagging in EMS efficiency. Also, it...
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Veröffentlicht in: | Benchmarking : an international journal 2009-07, Vol.16 (4), p.543-561 |
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description | Purpose - The purpose of this paper is to benchmark and measure the comparative efficiency of emergency medical services (EMS) in major US cities (populations greater than 100,000). In so doing, this paper aims to develop a benchmark that can be emulated by cities lagging in EMS efficiency. Also, it seeks to develop a profile of cities that are successful in providing highly efficient EMS as benchmarks.Design methodology approach - Data envelopment analysis is used to measure the EMS efficiency of 127 selected large cities in the USA under the premise of a constant-return to scales method of service delivery. In addition, to identify factors influencing the US cities' EMS efficiency and then to predict their efficiency scores, a Tobit regression analysis is employed, which tended to result in a smaller standard error, a smaller bias, and a smaller mean squared error than ordinary least squares.Findings - This paper examines whether more densely settled and populated areas have greater efficiency in delivering EMS. After controlling variables such as weather and climate, income, population growth, the age of a residential home, and geographic size of a city in land area, It is found that more densely settled, geographically large, and high income cities show more efficient provision of EMS.Originality value - This paper is the first to develop in a comprehensive way EMS benchmark performance standards for municipal governments and elaborate on a host of factors which are associated with the success of EMS deliveries. By setting such standards and identifying factors affecting EMS efficiency, this paper helps municipal governments to continuously improve their EMS and develop more efficient public policy. |
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In so doing, this paper aims to develop a benchmark that can be emulated by cities lagging in EMS efficiency. Also, it seeks to develop a profile of cities that are successful in providing highly efficient EMS as benchmarks.Design methodology approach - Data envelopment analysis is used to measure the EMS efficiency of 127 selected large cities in the USA under the premise of a constant-return to scales method of service delivery. In addition, to identify factors influencing the US cities' EMS efficiency and then to predict their efficiency scores, a Tobit regression analysis is employed, which tended to result in a smaller standard error, a smaller bias, and a smaller mean squared error than ordinary least squares.Findings - This paper examines whether more densely settled and populated areas have greater efficiency in delivering EMS. After controlling variables such as weather and climate, income, population growth, the age of a residential home, and geographic size of a city in land area, It is found that more densely settled, geographically large, and high income cities show more efficient provision of EMS.Originality value - This paper is the first to develop in a comprehensive way EMS benchmark performance standards for municipal governments and elaborate on a host of factors which are associated with the success of EMS deliveries. 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In so doing, this paper aims to develop a benchmark that can be emulated by cities lagging in EMS efficiency. Also, it seeks to develop a profile of cities that are successful in providing highly efficient EMS as benchmarks.Design methodology approach - Data envelopment analysis is used to measure the EMS efficiency of 127 selected large cities in the USA under the premise of a constant-return to scales method of service delivery. In addition, to identify factors influencing the US cities' EMS efficiency and then to predict their efficiency scores, a Tobit regression analysis is employed, which tended to result in a smaller standard error, a smaller bias, and a smaller mean squared error than ordinary least squares.Findings - This paper examines whether more densely settled and populated areas have greater efficiency in delivering EMS. After controlling variables such as weather and climate, income, population growth, the age of a residential home, and geographic size of a city in land area, It is found that more densely settled, geographically large, and high income cities show more efficient provision of EMS.Originality value - This paper is the first to develop in a comprehensive way EMS benchmark performance standards for municipal governments and elaborate on a host of factors which are associated with the success of EMS deliveries. By setting such standards and identifying factors affecting EMS efficiency, this paper helps municipal governments to continuously improve their EMS and develop more efficient public policy.</description><subject>Benchmarking</subject><subject>Benchmarks</subject><subject>Brain damage</subject><subject>Brain research</subject><subject>Cardiac arrest</subject><subject>Data analysis</subject><subject>Efficiency</subject><subject>Emergency medical care</subject><subject>Emergency services</subject><subject>Fire departments</subject><subject>Fire prevention</subject><subject>Jurisdiction</subject><subject>Medical care</subject><subject>Medical personnel</subject><subject>Occupational accidents</subject><subject>Performance standards</subject><subject>Population density</subject><subject>Public services</subject><subject>Queuing theory</subject><subject>Response time</subject><subject>Rural areas</subject><subject>Service delivery</subject><subject>Studies</subject><subject>United States of America</subject><subject>Urban areas</subject><subject>Workloads</subject><issn>1463-5771</issn><issn>1758-4094</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp1kEtPAyEUhSdGE58_wB1x4cpRGJhhZqlVWxOjxkdcEgoXpc6jwrTRf--tNZpoXMEN3zmce5Jkl9FDxmh5xETBcylpxWglM5HTlWSDybxMBa3EKt7xPUWArSebMU4opQUrs43EnUBrnhsdXnz7RHRrSQM6zsJi6p-BmK6Z6qB7PwcCznnjkX8nnSPQQHj6HBqw3uiaRAhzbyAS35JGT7pAHu6I8b2HuJ2sOV1H2Pk6t5KH87P7wSi9vB5eDI4vU4OR-xQEl6KsKKNMODfmeWXBFmPHrbSCG11KXKHAveR4XBpdyEoDcJdLbnjGKsu3kv2l7zR0rzOIvWp8NFDXuoVuFhUvkOM5R3DvFzjpZqHFbCpjkstMVhlCbAmZ0MUYwKlp8NjVu2JULWpXf2pHTbrU-NjD27cAC1YF-uZKPGbqasQH-fD0Rt0if7DkF33q2v588dtaTa1DnP6D_5voA5twn7g</recordid><startdate>20090710</startdate><enddate>20090710</enddate><creator>Lambert, Thomas E</creator><creator>Min, Hokey</creator><creator>Srinivasan, Arun K</creator><general>Emerald Group Publishing Limited</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>7TA</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X5</scope><scope>7XB</scope><scope>8AO</scope><scope>8FD</scope><scope>8FI</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>F~G</scope><scope>JG9</scope><scope>K6~</scope><scope>K8~</scope><scope>L.-</scope><scope>L.0</scope><scope>M0C</scope><scope>M0T</scope><scope>PQBIZ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope></search><sort><creationdate>20090710</creationdate><title>Benchmarking and measuring the comparative efficiency of emergency medical services in major US cities</title><author>Lambert, Thomas E ; Min, Hokey ; Srinivasan, Arun K</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c450t-e43748901014ffb359ded6bf3d7d43ca8757761757bb8ca679aee3f573c3219d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Benchmarking</topic><topic>Benchmarks</topic><topic>Brain damage</topic><topic>Brain research</topic><topic>Cardiac arrest</topic><topic>Data analysis</topic><topic>Efficiency</topic><topic>Emergency medical care</topic><topic>Emergency services</topic><topic>Fire departments</topic><topic>Fire prevention</topic><topic>Jurisdiction</topic><topic>Medical care</topic><topic>Medical personnel</topic><topic>Occupational accidents</topic><topic>Performance standards</topic><topic>Population density</topic><topic>Public services</topic><topic>Queuing theory</topic><topic>Response time</topic><topic>Rural areas</topic><topic>Service delivery</topic><topic>Studies</topic><topic>United States of America</topic><topic>Urban areas</topic><topic>Workloads</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lambert, Thomas E</creatorcontrib><creatorcontrib>Min, Hokey</creatorcontrib><creatorcontrib>Srinivasan, Arun K</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>Materials Business File</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Entrepreneurship Database</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Materials Research Database</collection><collection>ProQuest Business Collection</collection><collection>DELNET Management Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ABI/INFORM Global</collection><collection>Healthcare Administration Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Benchmarking : an international journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lambert, Thomas E</au><au>Min, Hokey</au><au>Srinivasan, Arun K</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Benchmarking and measuring the comparative efficiency of emergency medical services in major US cities</atitle><jtitle>Benchmarking : an international journal</jtitle><date>2009-07-10</date><risdate>2009</risdate><volume>16</volume><issue>4</issue><spage>543</spage><epage>561</epage><pages>543-561</pages><issn>1463-5771</issn><eissn>1758-4094</eissn><abstract>Purpose - The purpose of this paper is to benchmark and measure the comparative efficiency of emergency medical services (EMS) in major US cities (populations greater than 100,000). In so doing, this paper aims to develop a benchmark that can be emulated by cities lagging in EMS efficiency. Also, it seeks to develop a profile of cities that are successful in providing highly efficient EMS as benchmarks.Design methodology approach - Data envelopment analysis is used to measure the EMS efficiency of 127 selected large cities in the USA under the premise of a constant-return to scales method of service delivery. In addition, to identify factors influencing the US cities' EMS efficiency and then to predict their efficiency scores, a Tobit regression analysis is employed, which tended to result in a smaller standard error, a smaller bias, and a smaller mean squared error than ordinary least squares.Findings - This paper examines whether more densely settled and populated areas have greater efficiency in delivering EMS. 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subjects | Benchmarking Benchmarks Brain damage Brain research Cardiac arrest Data analysis Efficiency Emergency medical care Emergency services Fire departments Fire prevention Jurisdiction Medical care Medical personnel Occupational accidents Performance standards Population density Public services Queuing theory Response time Rural areas Service delivery Studies United States of America Urban areas Workloads |
title | Benchmarking and measuring the comparative efficiency of emergency medical services in major US cities |
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