The relative contribution of provider and ED-level factors to variation among the top 15 reasons for ED admission
Abstract Study objective We examine adult emergency department (ED) admission rates for the top 15 most frequently admitted conditions, and assess the relative contribution in admission rate variation attributable to the provider and hospital. Methods This was a retrospective, cross-sectional study...
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Veröffentlicht in: | The American journal of emergency medicine 2017-09, Vol.35 (9), p.1291-1297 |
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creator | Khojah, Imad, MD Li, Suhui, PhD Luo, Qian Davis, Griffin, MD, MPH, MBA Galarraga, Jessica E., MD, MPH Granovsky, Michael, MD Litvak, Ori, MBA Davis, Samuel Shesser, Robert, MD, MPH Pines, Jesse M., MD, MBA, MSCE |
description | Abstract Study objective We examine adult emergency department (ED) admission rates for the top 15 most frequently admitted conditions, and assess the relative contribution in admission rate variation attributable to the provider and hospital. Methods This was a retrospective, cross-sectional study of ED encounters (≥ 18 years) from 19 EDs and 603 providers (January 2012–December 2013), linked to the Area Health Resources File for county-level information on healthcare resources. “Hospital admission” was the outcome, a composite of inpatient, observation, or intra-hospital transfer. We studied the 15 most commonly admitted conditions, and calculated condition-specific risk-standardized hospital admission rates (RSARs) using multi-level hierarchical generalized linear models. We then decomposed the relative contribution of provider-level and hospital-level variation for each condition. Results The top 15 conditions made up 34% of encounters and 49% of admissions. After adjustment, the eight conditions with the highest hospital-level variation were: 1) injuries, 2) extremity fracture (except hip fracture), 3) skin infection, 4) lower respiratory disease, 5) asthma/chronic obstructive pulmonary disease (A&C), 6) abdominal pain, 7) fluid/electrolyte disorders, and 8) chest pain. Hospital-level intra-class correlation coefficients (ICC) ranged from 0.042 for A&C to 0.167 for extremity fractures. Provider-level ICCs ranged from 0.026 for abdominal pain to 0.104 for chest pain. Several patient, hospital, and community factors were associated with admission rates, but these varied across conditions. Conclusion For different conditions, there were different contributions to variation at the hospital- and provider-level. These findings deserve consideration when designing interventions to optimize admission decisions and in value-based payment programs. |
doi_str_mv | 10.1016/j.ajem.2017.03.074 |
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Methods This was a retrospective, cross-sectional study of ED encounters (≥ 18 years) from 19 EDs and 603 providers (January 2012–December 2013), linked to the Area Health Resources File for county-level information on healthcare resources. “Hospital admission” was the outcome, a composite of inpatient, observation, or intra-hospital transfer. We studied the 15 most commonly admitted conditions, and calculated condition-specific risk-standardized hospital admission rates (RSARs) using multi-level hierarchical generalized linear models. We then decomposed the relative contribution of provider-level and hospital-level variation for each condition. Results The top 15 conditions made up 34% of encounters and 49% of admissions. After adjustment, the eight conditions with the highest hospital-level variation were: 1) injuries, 2) extremity fracture (except hip fracture), 3) skin infection, 4) lower respiratory disease, 5) asthma/chronic obstructive pulmonary disease (A&C), 6) abdominal pain, 7) fluid/electrolyte disorders, and 8) chest pain. Hospital-level intra-class correlation coefficients (ICC) ranged from 0.042 for A&C to 0.167 for extremity fractures. Provider-level ICCs ranged from 0.026 for abdominal pain to 0.104 for chest pain. Several patient, hospital, and community factors were associated with admission rates, but these varied across conditions. Conclusion For different conditions, there were different contributions to variation at the hospital- and provider-level. These findings deserve consideration when designing interventions to optimize admission decisions and in value-based payment programs.</description><identifier>ISSN: 0735-6757</identifier><identifier>EISSN: 1532-8171</identifier><identifier>DOI: 10.1016/j.ajem.2017.03.074</identifier><identifier>PMID: 28410917</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adolescent ; Adult ; Age ; Asthma ; Chest ; Chronic infection ; Chronic obstructive pulmonary disease ; Codes ; Comorbidity ; Correlation coefficient ; Correlation coefficients ; Cost control ; Cross-Sectional Studies ; Economic indicators ; Emergencies - epidemiology ; Emergency ; Emergency medical care ; Emergency medical services ; Emergency Service, Hospital - statistics & numerical data ; Expenditures ; Female ; Fractures ; Fractures, Bone - epidemiology ; Generalized linear models ; Health care policy ; Health Resources ; Hip ; Hospitals ; Humans ; Infections ; Lung diseases ; Male ; Medicaid ; Medicare ; Middle Aged ; Obstructive lung disease ; Pain ; Patient Admission - statistics & numerical data ; Patient admissions ; Pediatrics ; Pneumonia ; Respiratory diseases ; Retrospective Studies ; Skin Diseases, Infectious - epidemiology ; Trauma ; United States ; Wounds and Injuries - epidemiology ; Young Adult</subject><ispartof>The American journal of emergency medicine, 2017-09, Vol.35 (9), p.1291-1297</ispartof><rights>2017 Elsevier Inc.</rights><rights>Copyright © 2017 Elsevier Inc. All rights reserved.</rights><rights>Copyright Elsevier Limited 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c439t-6306be6acf6d2fea461730eba4ac15897c2419f28cc0912622bcf75d6d8509533</citedby><cites>FETCH-LOGICAL-c439t-6306be6acf6d2fea461730eba4ac15897c2419f28cc0912622bcf75d6d8509533</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1945871183?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976,64364,64366,64368,72218</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28410917$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Khojah, Imad, MD</creatorcontrib><creatorcontrib>Li, Suhui, PhD</creatorcontrib><creatorcontrib>Luo, Qian</creatorcontrib><creatorcontrib>Davis, Griffin, MD, MPH, MBA</creatorcontrib><creatorcontrib>Galarraga, Jessica E., MD, MPH</creatorcontrib><creatorcontrib>Granovsky, Michael, MD</creatorcontrib><creatorcontrib>Litvak, Ori, MBA</creatorcontrib><creatorcontrib>Davis, Samuel</creatorcontrib><creatorcontrib>Shesser, Robert, MD, MPH</creatorcontrib><creatorcontrib>Pines, Jesse M., MD, MBA, MSCE</creatorcontrib><title>The relative contribution of provider and ED-level factors to variation among the top 15 reasons for ED admission</title><title>The American journal of emergency medicine</title><addtitle>Am J Emerg Med</addtitle><description>Abstract Study objective We examine adult emergency department (ED) admission rates for the top 15 most frequently admitted conditions, and assess the relative contribution in admission rate variation attributable to the provider and hospital. Methods This was a retrospective, cross-sectional study of ED encounters (≥ 18 years) from 19 EDs and 603 providers (January 2012–December 2013), linked to the Area Health Resources File for county-level information on healthcare resources. “Hospital admission” was the outcome, a composite of inpatient, observation, or intra-hospital transfer. We studied the 15 most commonly admitted conditions, and calculated condition-specific risk-standardized hospital admission rates (RSARs) using multi-level hierarchical generalized linear models. We then decomposed the relative contribution of provider-level and hospital-level variation for each condition. Results The top 15 conditions made up 34% of encounters and 49% of admissions. After adjustment, the eight conditions with the highest hospital-level variation were: 1) injuries, 2) extremity fracture (except hip fracture), 3) skin infection, 4) lower respiratory disease, 5) asthma/chronic obstructive pulmonary disease (A&C), 6) abdominal pain, 7) fluid/electrolyte disorders, and 8) chest pain. Hospital-level intra-class correlation coefficients (ICC) ranged from 0.042 for A&C to 0.167 for extremity fractures. Provider-level ICCs ranged from 0.026 for abdominal pain to 0.104 for chest pain. Several patient, hospital, and community factors were associated with admission rates, but these varied across conditions. Conclusion For different conditions, there were different contributions to variation at the hospital- and provider-level. These findings deserve consideration when designing interventions to optimize admission decisions and in value-based payment programs.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Age</subject><subject>Asthma</subject><subject>Chest</subject><subject>Chronic infection</subject><subject>Chronic obstructive pulmonary disease</subject><subject>Codes</subject><subject>Comorbidity</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Cost control</subject><subject>Cross-Sectional Studies</subject><subject>Economic indicators</subject><subject>Emergencies - epidemiology</subject><subject>Emergency</subject><subject>Emergency medical care</subject><subject>Emergency medical services</subject><subject>Emergency Service, Hospital - statistics & numerical data</subject><subject>Expenditures</subject><subject>Female</subject><subject>Fractures</subject><subject>Fractures, Bone - epidemiology</subject><subject>Generalized linear models</subject><subject>Health care policy</subject><subject>Health Resources</subject><subject>Hip</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Infections</subject><subject>Lung diseases</subject><subject>Male</subject><subject>Medicaid</subject><subject>Medicare</subject><subject>Middle Aged</subject><subject>Obstructive lung disease</subject><subject>Pain</subject><subject>Patient Admission - statistics & numerical data</subject><subject>Patient admissions</subject><subject>Pediatrics</subject><subject>Pneumonia</subject><subject>Respiratory diseases</subject><subject>Retrospective Studies</subject><subject>Skin Diseases, Infectious - epidemiology</subject><subject>Trauma</subject><subject>United States</subject><subject>Wounds and Injuries - epidemiology</subject><subject>Young Adult</subject><issn>0735-6757</issn><issn>1532-8171</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kk-L1TAUxYsoznP0C7iQgBs3rblJm6QggozjHxhw4bgOaXqrqW3zJkkL8-1NfaPCLFzdze8c7rnnFsVzoBVQEK_Hyow4V4yCrCivqKwfFAdoOCsVSHhYHKjkTSlkI8-KJzGOlALUTf24OGOqBtqCPBQ31z-QBJxMchsS65cUXLcm5xfiB3IMfnM9BmKWnly-LyfccCKDscmHSJInmwnO_KbN7JfvJGW35I8Emmxqol8iGXzIUmL62cWYyafFo8FMEZ_dzfPi24fL64tP5dWXj58v3l2VtuZtKgWnokNh7CB6NqCpBUhOsTO1sdCoVlpWQzswZW1OwgRjnR1k04teNbRtOD8vXp18c4ibFWPSeQGL02QW9GvUoJQSijHWZvTlPXT0a1jydhraulESQO2G7ETZ4GMMOOhjcLMJtxqo3gvRo94L0XshmnKdC8miF3fWazdj_1fyp4EMvDkBmG-xOQw6WoeLxd4FtEn33v3f_-09uZ3c4qyZfuItxn85dGSa6q_7S-wfsR-TCWj5L5OzsEM</recordid><startdate>20170901</startdate><enddate>20170901</enddate><creator>Khojah, Imad, MD</creator><creator>Li, Suhui, PhD</creator><creator>Luo, Qian</creator><creator>Davis, Griffin, MD, MPH, MBA</creator><creator>Galarraga, Jessica E., MD, MPH</creator><creator>Granovsky, Michael, MD</creator><creator>Litvak, Ori, MBA</creator><creator>Davis, Samuel</creator><creator>Shesser, Robert, MD, MPH</creator><creator>Pines, Jesse M., MD, MBA, MSCE</creator><general>Elsevier Inc</general><general>Elsevier Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7T5</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20170901</creationdate><title>The relative contribution of provider and ED-level factors to variation among the top 15 reasons for ED admission</title><author>Khojah, Imad, MD ; Li, Suhui, PhD ; Luo, Qian ; Davis, Griffin, MD, MPH, MBA ; Galarraga, Jessica E., MD, MPH ; Granovsky, Michael, MD ; Litvak, Ori, MBA ; Davis, Samuel ; Shesser, Robert, MD, MPH ; Pines, Jesse M., MD, MBA, MSCE</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c439t-6306be6acf6d2fea461730eba4ac15897c2419f28cc0912622bcf75d6d8509533</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Age</topic><topic>Asthma</topic><topic>Chest</topic><topic>Chronic infection</topic><topic>Chronic obstructive pulmonary disease</topic><topic>Codes</topic><topic>Comorbidity</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Cost control</topic><topic>Cross-Sectional Studies</topic><topic>Economic indicators</topic><topic>Emergencies - epidemiology</topic><topic>Emergency</topic><topic>Emergency medical care</topic><topic>Emergency medical services</topic><topic>Emergency Service, Hospital - statistics & numerical data</topic><topic>Expenditures</topic><topic>Female</topic><topic>Fractures</topic><topic>Fractures, Bone - epidemiology</topic><topic>Generalized linear models</topic><topic>Health care policy</topic><topic>Health Resources</topic><topic>Hip</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Infections</topic><topic>Lung diseases</topic><topic>Male</topic><topic>Medicaid</topic><topic>Medicare</topic><topic>Middle Aged</topic><topic>Obstructive lung disease</topic><topic>Pain</topic><topic>Patient Admission - statistics & numerical data</topic><topic>Patient admissions</topic><topic>Pediatrics</topic><topic>Pneumonia</topic><topic>Respiratory diseases</topic><topic>Retrospective Studies</topic><topic>Skin Diseases, Infectious - epidemiology</topic><topic>Trauma</topic><topic>United States</topic><topic>Wounds and Injuries - epidemiology</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Khojah, Imad, MD</creatorcontrib><creatorcontrib>Li, Suhui, PhD</creatorcontrib><creatorcontrib>Luo, Qian</creatorcontrib><creatorcontrib>Davis, Griffin, MD, MPH, MBA</creatorcontrib><creatorcontrib>Galarraga, Jessica E., MD, MPH</creatorcontrib><creatorcontrib>Granovsky, Michael, MD</creatorcontrib><creatorcontrib>Litvak, Ori, MBA</creatorcontrib><creatorcontrib>Davis, Samuel</creatorcontrib><creatorcontrib>Shesser, Robert, MD, MPH</creatorcontrib><creatorcontrib>Pines, Jesse M., MD, MBA, MSCE</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Immunology Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</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><collection>MEDLINE - Academic</collection><jtitle>The American journal of emergency medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Khojah, Imad, MD</au><au>Li, Suhui, PhD</au><au>Luo, Qian</au><au>Davis, Griffin, MD, MPH, MBA</au><au>Galarraga, Jessica E., MD, MPH</au><au>Granovsky, Michael, MD</au><au>Litvak, Ori, MBA</au><au>Davis, Samuel</au><au>Shesser, Robert, MD, MPH</au><au>Pines, Jesse M., MD, MBA, MSCE</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The relative contribution of provider and ED-level factors to variation among the top 15 reasons for ED admission</atitle><jtitle>The American journal of emergency medicine</jtitle><addtitle>Am J Emerg Med</addtitle><date>2017-09-01</date><risdate>2017</risdate><volume>35</volume><issue>9</issue><spage>1291</spage><epage>1297</epage><pages>1291-1297</pages><issn>0735-6757</issn><eissn>1532-8171</eissn><abstract>Abstract Study objective We examine adult emergency department (ED) admission rates for the top 15 most frequently admitted conditions, and assess the relative contribution in admission rate variation attributable to the provider and hospital. Methods This was a retrospective, cross-sectional study of ED encounters (≥ 18 years) from 19 EDs and 603 providers (January 2012–December 2013), linked to the Area Health Resources File for county-level information on healthcare resources. “Hospital admission” was the outcome, a composite of inpatient, observation, or intra-hospital transfer. We studied the 15 most commonly admitted conditions, and calculated condition-specific risk-standardized hospital admission rates (RSARs) using multi-level hierarchical generalized linear models. We then decomposed the relative contribution of provider-level and hospital-level variation for each condition. Results The top 15 conditions made up 34% of encounters and 49% of admissions. After adjustment, the eight conditions with the highest hospital-level variation were: 1) injuries, 2) extremity fracture (except hip fracture), 3) skin infection, 4) lower respiratory disease, 5) asthma/chronic obstructive pulmonary disease (A&C), 6) abdominal pain, 7) fluid/electrolyte disorders, and 8) chest pain. Hospital-level intra-class correlation coefficients (ICC) ranged from 0.042 for A&C to 0.167 for extremity fractures. Provider-level ICCs ranged from 0.026 for abdominal pain to 0.104 for chest pain. Several patient, hospital, and community factors were associated with admission rates, but these varied across conditions. Conclusion For different conditions, there were different contributions to variation at the hospital- and provider-level. These findings deserve consideration when designing interventions to optimize admission decisions and in value-based payment programs.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>28410917</pmid><doi>10.1016/j.ajem.2017.03.074</doi><tpages>7</tpages></addata></record> |
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subjects | Adolescent Adult Age Asthma Chest Chronic infection Chronic obstructive pulmonary disease Codes Comorbidity Correlation coefficient Correlation coefficients Cost control Cross-Sectional Studies Economic indicators Emergencies - epidemiology Emergency Emergency medical care Emergency medical services Emergency Service, Hospital - statistics & numerical data Expenditures Female Fractures Fractures, Bone - epidemiology Generalized linear models Health care policy Health Resources Hip Hospitals Humans Infections Lung diseases Male Medicaid Medicare Middle Aged Obstructive lung disease Pain Patient Admission - statistics & numerical data Patient admissions Pediatrics Pneumonia Respiratory diseases Retrospective Studies Skin Diseases, Infectious - epidemiology Trauma United States Wounds and Injuries - epidemiology Young Adult |
title | The relative contribution of provider and ED-level factors to variation among the top 15 reasons for ED admission |
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