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Quality of care and outcomes in internal medicine patients bedspaced to noninternal medicine units
Hospital overcrowding has led to a practice known as bedspacing (in which admitted patients are placed on a different specialty's inpatient ward), yet little is known about the impact of this practice on healthcare quality.We investigated whether hospital outcome measures differ between bedspac...
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Published in: | Medicine (Baltimore) 2021-05, Vol.100 (18), p.e25737-e25737 |
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description | Hospital overcrowding has led to a practice known as bedspacing (in which admitted patients are placed on a different specialty's inpatient ward), yet little is known about the impact of this practice on healthcare quality.We investigated whether hospital outcome measures differ between bedspaced general internal medicine (GIM) patients vs nonbedspaced patients.Our retrospective study included patients admitted to GIM wards at 2 academic hospitals (2012-2014), comparing bedspaced to nonbedspaced patients, and identifying adverse events from the hospital's Electronic Patient Record.We compared these groups with respect to actual length of stay vs the expected length of stay (% ELOS), which is defined as length of stay (LOS) divided by expected length of stay (ELOS), 30-day readmission, adverse events (falls, medication-related incidents, equipment-related incidents, first treatment related incidents, laboratory-related incidents, and operative/invasive events), and in-hospital mortality.There were 22,519 patients analyzed with 15,985 (71%) discharged from a medical ward and 6534 (29%) discharged from a non-medical ward. Bedspaced patients had shorter lengths of stay (4.1 vs 6.2 days, P |
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Bedspaced patients had shorter lengths of stay (4.1 vs 6.2 days, P < .001) and expected lengths of stay (ELOS) (6.1 vs 6.4 days, P < .001). Bedspaced patients had a lower percentage of ELOS (% ELOS) than nonbedspaced patients (70% vs 91%, P < .001), similar readmission rates (9.8 vs 10.3 events per 100 patients, P = .24), lower in-hospital mortality rates (2.6 vs 3.3 events per 100 patients, P = .003) and fewer adverse events (0.20 vs 0.60 events per 100 patient days, P < .01).Bedspacing of patients is common. Patients who are bedspaced to off-service wards have better outcomes. This may relate to preferential allocation practices.</description><identifier>ISSN: 0025-7974</identifier><identifier>EISSN: 1536-5964</identifier><identifier>DOI: 10.1097/MD.0000000000025737</identifier><identifier>PMID: 33950957</identifier><language>eng</language><publisher>United States: Lippincott Williams & Wilkins</publisher><subject><![CDATA[Aged ; Aged, 80 and over ; Female ; Hospital Bed Capacity - statistics & numerical data ; Hospital Mortality ; Hospitals, University - organization & administration ; Hospitals, University - statistics & numerical data ; Humans ; Internal Medicine - organization & administration ; Internal Medicine - statistics & numerical data ; Length of Stay - statistics & numerical data ; Male ; Middle Aged ; Observational Study ; Ontario - epidemiology ; Patient Admission - statistics & numerical data ; Patient Readmission - statistics & numerical data ; Quality of Health Care - statistics & numerical data ; Retrospective Studies ; Tertiary Care Centers - organization & administration ; Tertiary Care Centers - statistics & numerical data]]></subject><ispartof>Medicine (Baltimore), 2021-05, Vol.100 (18), p.e25737-e25737</ispartof><rights>Lippincott Williams & Wilkins</rights><rights>Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.</rights><rights>Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4505-c2b92f7325b77ff200b3652cea7f1e8915869b700323f2a3d00944c21f0530463</citedby><cites>FETCH-LOGICAL-c4505-c2b92f7325b77ff200b3652cea7f1e8915869b700323f2a3d00944c21f0530463</cites><orcidid>0000-0002-7999-7223</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104304/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104304/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33950957$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bogler, Orly</creatorcontrib><creatorcontrib>Liu, Jessica</creatorcontrib><creatorcontrib>Cadesky, Ben</creatorcontrib><creatorcontrib>Bell, Chaim M.</creatorcontrib><title>Quality of care and outcomes in internal medicine patients bedspaced to noninternal medicine units</title><title>Medicine (Baltimore)</title><addtitle>Medicine (Baltimore)</addtitle><description>Hospital overcrowding has led to a practice known as bedspacing (in which admitted patients are placed on a different specialty's inpatient ward), yet little is known about the impact of this practice on healthcare quality.We investigated whether hospital outcome measures differ between bedspaced general internal medicine (GIM) patients vs nonbedspaced patients.Our retrospective study included patients admitted to GIM wards at 2 academic hospitals (2012-2014), comparing bedspaced to nonbedspaced patients, and identifying adverse events from the hospital's Electronic Patient Record.We compared these groups with respect to actual length of stay vs the expected length of stay (% ELOS), which is defined as length of stay (LOS) divided by expected length of stay (ELOS), 30-day readmission, adverse events (falls, medication-related incidents, equipment-related incidents, first treatment related incidents, laboratory-related incidents, and operative/invasive events), and in-hospital mortality.There were 22,519 patients analyzed with 15,985 (71%) discharged from a medical ward and 6534 (29%) discharged from a non-medical ward. Bedspaced patients had shorter lengths of stay (4.1 vs 6.2 days, P < .001) and expected lengths of stay (ELOS) (6.1 vs 6.4 days, P < .001). Bedspaced patients had a lower percentage of ELOS (% ELOS) than nonbedspaced patients (70% vs 91%, P < .001), similar readmission rates (9.8 vs 10.3 events per 100 patients, P = .24), lower in-hospital mortality rates (2.6 vs 3.3 events per 100 patients, P = .003) and fewer adverse events (0.20 vs 0.60 events per 100 patient days, P < .01).Bedspacing of patients is common. Patients who are bedspaced to off-service wards have better outcomes. This may relate to preferential allocation practices.</description><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Female</subject><subject>Hospital Bed Capacity - statistics & numerical data</subject><subject>Hospital Mortality</subject><subject>Hospitals, University - organization & administration</subject><subject>Hospitals, University - statistics & numerical data</subject><subject>Humans</subject><subject>Internal Medicine - organization & administration</subject><subject>Internal Medicine - statistics & numerical data</subject><subject>Length of Stay - statistics & numerical data</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Observational Study</subject><subject>Ontario - epidemiology</subject><subject>Patient Admission - statistics & numerical data</subject><subject>Patient Readmission - statistics & numerical data</subject><subject>Quality of Health Care - statistics & numerical data</subject><subject>Retrospective Studies</subject><subject>Tertiary Care Centers - organization & administration</subject><subject>Tertiary Care Centers - statistics & numerical data</subject><issn>0025-7974</issn><issn>1536-5964</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNplkVtrVDEUhYModqz-AkHy6MupO7eTyYsgrTdoKQV9Djk5iRPNJGOS09J_39SptWrYENh77S-LLIReEjgioOSbs5Mj-HOokEw-Qisi2DgINfLHaHXbHaSS_AA9q_U7AGGS8qfogDElQAm5QtPFYmJo1zh7bE1x2KQZ56XZvHUVh9SruZJMxFs3BxuSwzvTgkut4snNdWesm3HLOOX0v3RJodXn6Ik3sboXd_ch-vrh_ZfjT8Pp-cfPx-9OB8sFiMHSSVEvGRWTlN5TgImNglpnpCdurYhYj2qSAIwyTw2bARTnlhIPggEf2SF6u-fulqk7sN1jMVHvStiacq2zCfrvSQob_S1f6jUB3gkd8PoOUPLPxdWmt6FaF6NJLi9VU0HpSBSRokvZXmpLrrU4f_8MAX2bjj470f-m07dePXR4v_M7ji7ge8FVjv0v64-4XLmiN87EtvnFE1LRgQIlIEDC0DtcsBvH4psA</recordid><startdate>20210507</startdate><enddate>20210507</enddate><creator>Bogler, Orly</creator><creator>Liu, Jessica</creator><creator>Cadesky, Ben</creator><creator>Bell, Chaim M.</creator><general>Lippincott Williams & Wilkins</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>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-7999-7223</orcidid></search><sort><creationdate>20210507</creationdate><title>Quality of care and outcomes in internal medicine patients bedspaced to noninternal medicine units</title><author>Bogler, Orly ; Liu, Jessica ; Cadesky, Ben ; Bell, Chaim M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4505-c2b92f7325b77ff200b3652cea7f1e8915869b700323f2a3d00944c21f0530463</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Female</topic><topic>Hospital Bed Capacity - statistics & numerical data</topic><topic>Hospital Mortality</topic><topic>Hospitals, University - organization & administration</topic><topic>Hospitals, University - statistics & numerical data</topic><topic>Humans</topic><topic>Internal Medicine - organization & administration</topic><topic>Internal Medicine - statistics & numerical data</topic><topic>Length of Stay - statistics & numerical data</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Observational Study</topic><topic>Ontario - epidemiology</topic><topic>Patient Admission - statistics & numerical data</topic><topic>Patient Readmission - statistics & numerical data</topic><topic>Quality of Health Care - statistics & numerical data</topic><topic>Retrospective Studies</topic><topic>Tertiary Care Centers - organization & administration</topic><topic>Tertiary Care Centers - statistics & numerical data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bogler, Orly</creatorcontrib><creatorcontrib>Liu, Jessica</creatorcontrib><creatorcontrib>Cadesky, Ben</creatorcontrib><creatorcontrib>Bell, Chaim M.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Medicine (Baltimore)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bogler, Orly</au><au>Liu, Jessica</au><au>Cadesky, Ben</au><au>Bell, Chaim M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quality of care and outcomes in internal medicine patients bedspaced to noninternal medicine units</atitle><jtitle>Medicine (Baltimore)</jtitle><addtitle>Medicine (Baltimore)</addtitle><date>2021-05-07</date><risdate>2021</risdate><volume>100</volume><issue>18</issue><spage>e25737</spage><epage>e25737</epage><pages>e25737-e25737</pages><issn>0025-7974</issn><eissn>1536-5964</eissn><abstract>Hospital overcrowding has led to a practice known as bedspacing (in which admitted patients are placed on a different specialty's inpatient ward), yet little is known about the impact of this practice on healthcare quality.We investigated whether hospital outcome measures differ between bedspaced general internal medicine (GIM) patients vs nonbedspaced patients.Our retrospective study included patients admitted to GIM wards at 2 academic hospitals (2012-2014), comparing bedspaced to nonbedspaced patients, and identifying adverse events from the hospital's Electronic Patient Record.We compared these groups with respect to actual length of stay vs the expected length of stay (% ELOS), which is defined as length of stay (LOS) divided by expected length of stay (ELOS), 30-day readmission, adverse events (falls, medication-related incidents, equipment-related incidents, first treatment related incidents, laboratory-related incidents, and operative/invasive events), and in-hospital mortality.There were 22,519 patients analyzed with 15,985 (71%) discharged from a medical ward and 6534 (29%) discharged from a non-medical ward. Bedspaced patients had shorter lengths of stay (4.1 vs 6.2 days, P < .001) and expected lengths of stay (ELOS) (6.1 vs 6.4 days, P < .001). Bedspaced patients had a lower percentage of ELOS (% ELOS) than nonbedspaced patients (70% vs 91%, P < .001), similar readmission rates (9.8 vs 10.3 events per 100 patients, P = .24), lower in-hospital mortality rates (2.6 vs 3.3 events per 100 patients, P = .003) and fewer adverse events (0.20 vs 0.60 events per 100 patient days, P < .01).Bedspacing of patients is common. Patients who are bedspaced to off-service wards have better outcomes. This may relate to preferential allocation practices.</abstract><cop>United States</cop><pub>Lippincott Williams & Wilkins</pub><pmid>33950957</pmid><doi>10.1097/MD.0000000000025737</doi><orcidid>https://orcid.org/0000-0002-7999-7223</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Aged Aged, 80 and over Female Hospital Bed Capacity - statistics & numerical data Hospital Mortality Hospitals, University - organization & administration Hospitals, University - statistics & numerical data Humans Internal Medicine - organization & administration Internal Medicine - statistics & numerical data Length of Stay - statistics & numerical data Male Middle Aged Observational Study Ontario - epidemiology Patient Admission - statistics & numerical data Patient Readmission - statistics & numerical data Quality of Health Care - statistics & numerical data Retrospective Studies Tertiary Care Centers - organization & administration Tertiary Care Centers - statistics & numerical data |
title | Quality of care and outcomes in internal medicine patients bedspaced to noninternal medicine units |
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