How many operating rooms are needed to manage non-elective surgical cases? A Monte Carlo simulation study
Patients often wait to have urgent or emergency surgery. The number of operating rooms (ORs) needed to minimize waiting time while optimizing resources can be determined using queuing theory and computer simulation. We developed a computer program using Monte Carlo simulation to determine the number...
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| Veröffentlicht in: | BMC health services research 2015-10, Vol.15 (1), p.487-487, Article 487 |
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| creator | Antognini, Joseph M O'Brien Antognini, Joseph F Khatri, Vijay |
| description | Patients often wait to have urgent or emergency surgery. The number of operating rooms (ORs) needed to minimize waiting time while optimizing resources can be determined using queuing theory and computer simulation. We developed a computer program using Monte Carlo simulation to determine the number of ORs needed to minimize patient wait times while optimizing resources.
We used patient arrival data and surgical procedure length from our institution, a tertiary-care academic medical center that serves a large diverse population. With ~4800 patients/year requiring non-elective surgery, and mean procedure length 185 min (median 150 min) we determined the number of ORs needed during the day and evening (0600-2200) and during the night (2200-0600) that resulted in acceptable wait times.
Simulation of 4 ORs at day/evening and 3 ORs at night resulted in median wait time = 0 min (mean = 19 min) for emergency cases requiring surgery within 2 h, with wait time at the 95th percentile = 109 min. Median wait time for urgent cases needing surgery within 8-12 h was 34 min (mean = 136 min), with wait time at the 95th percentile = 474 min. The effect of changes in surgical length and volume on wait times was determined with sensitivity analysis.
Monte Carlo simulation can guide decisions on how to balance resources for elective and non-elective surgical procedures. |
| doi_str_mv | 10.1186/s12913-015-1148-x |
| format | Article |
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We used patient arrival data and surgical procedure length from our institution, a tertiary-care academic medical center that serves a large diverse population. With ~4800 patients/year requiring non-elective surgery, and mean procedure length 185 min (median 150 min) we determined the number of ORs needed during the day and evening (0600-2200) and during the night (2200-0600) that resulted in acceptable wait times.
Simulation of 4 ORs at day/evening and 3 ORs at night resulted in median wait time = 0 min (mean = 19 min) for emergency cases requiring surgery within 2 h, with wait time at the 95th percentile = 109 min. Median wait time for urgent cases needing surgery within 8-12 h was 34 min (mean = 136 min), with wait time at the 95th percentile = 474 min. The effect of changes in surgical length and volume on wait times was determined with sensitivity analysis.
Monte Carlo simulation can guide decisions on how to balance resources for elective and non-elective surgical procedures.</description><identifier>ISSN: 1472-6963</identifier><identifier>EISSN: 1472-6963</identifier><identifier>DOI: 10.1186/s12913-015-1148-x</identifier><identifier>PMID: 26507265</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Aged ; Analysis ; Anesthesiology ; Computer Simulation ; Computer-generated environments ; Efficiency ; Emergency Treatment - statistics & numerical data ; Hospital costs ; Humans ; Medical centers ; Monte Carlo Method ; Monte Carlo simulation ; Operating Rooms - statistics & numerical data ; Operations management ; Operative Time ; Queuing theory ; Surgery ; Surgical Procedures, Operative - statistics & numerical data ; Time-to-Treatment - statistics & numerical data ; Waiting Lists</subject><ispartof>BMC health services research, 2015-10, Vol.15 (1), p.487-487, Article 487</ispartof><rights>COPYRIGHT 2015 BioMed Central Ltd.</rights><rights>Copyright BioMed Central 2015</rights><rights>Antognini et al. 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c494t-72a599c78028d84d680c9032ba6cecb3c4c70145dd2e84b133416b7f8913c8433</citedby><cites>FETCH-LOGICAL-c494t-72a599c78028d84d680c9032ba6cecb3c4c70145dd2e84b133416b7f8913c8433</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4624654/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4624654/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26507265$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Antognini, Joseph M O'Brien</creatorcontrib><creatorcontrib>Antognini, Joseph F</creatorcontrib><creatorcontrib>Khatri, Vijay</creatorcontrib><title>How many operating rooms are needed to manage non-elective surgical cases? A Monte Carlo simulation study</title><title>BMC health services research</title><addtitle>BMC Health Serv Res</addtitle><description>Patients often wait to have urgent or emergency surgery. The number of operating rooms (ORs) needed to minimize waiting time while optimizing resources can be determined using queuing theory and computer simulation. We developed a computer program using Monte Carlo simulation to determine the number of ORs needed to minimize patient wait times while optimizing resources.
We used patient arrival data and surgical procedure length from our institution, a tertiary-care academic medical center that serves a large diverse population. With ~4800 patients/year requiring non-elective surgery, and mean procedure length 185 min (median 150 min) we determined the number of ORs needed during the day and evening (0600-2200) and during the night (2200-0600) that resulted in acceptable wait times.
Simulation of 4 ORs at day/evening and 3 ORs at night resulted in median wait time = 0 min (mean = 19 min) for emergency cases requiring surgery within 2 h, with wait time at the 95th percentile = 109 min. Median wait time for urgent cases needing surgery within 8-12 h was 34 min (mean = 136 min), with wait time at the 95th percentile = 474 min. The effect of changes in surgical length and volume on wait times was determined with sensitivity analysis.
Monte Carlo simulation can guide decisions on how to balance resources for elective and non-elective surgical procedures.</description><subject>Aged</subject><subject>Analysis</subject><subject>Anesthesiology</subject><subject>Computer Simulation</subject><subject>Computer-generated environments</subject><subject>Efficiency</subject><subject>Emergency Treatment - statistics & numerical data</subject><subject>Hospital costs</subject><subject>Humans</subject><subject>Medical centers</subject><subject>Monte Carlo Method</subject><subject>Monte Carlo simulation</subject><subject>Operating Rooms - statistics & numerical data</subject><subject>Operations management</subject><subject>Operative Time</subject><subject>Queuing theory</subject><subject>Surgery</subject><subject>Surgical Procedures, Operative - statistics & numerical data</subject><subject>Time-to-Treatment - statistics & numerical data</subject><subject>Waiting Lists</subject><issn>1472-6963</issn><issn>1472-6963</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptkk9rFTEUxQdRbK1-ADcScONmav4ns1Eej2qFlm50HTKZO2PKTPJMZmrft2-GV2srEkjCze-cyw2nqt4SfEqIlh8zoQ1hNSaiJoTr-vZZdUy4orVsJHv-6H5Uvcr5GmOiNFUvqyMqBVZlO678efyNJhv2KO4g2dmHAaUYp4xsAhQAOujQHFfEDqUQQw0juNnfAMpLGryzI3I2Q_6MNugyhhnQ1qYxouynZSyGMaA8L93-dfWit2OGN_fnSfXjy9n37Xl9cfX123ZzUTve8LlW1IqmcUpjqjvNO6mxazCjrZUOXMscdwoTLrqOguYtYYwT2apel59wmjN2Un06-O6WdoLOQZiTHc0u-cmmvYnWm6cvwf80Q7wxXFIuBS8GH-4NUvy1QJ7N5LODcbQB4pINUVRToThRBX3_D3odlxTKeIVSjdRCyOYvNdgRjA99LH3damo2ghPONVZr29P_UGV1MHkXA_S-1J8IyEHgUsw5Qf8wI8FmzYc55MOUfJg1H-a2aN49_pwHxZ9AsDuU47VS</recordid><startdate>20151028</startdate><enddate>20151028</enddate><creator>Antognini, Joseph M O'Brien</creator><creator>Antognini, Joseph F</creator><creator>Khatri, Vijay</creator><general>BioMed Central Ltd</general><general>BioMed Central</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>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88C</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>KB0</scope><scope>L.-</scope><scope>M0C</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>NAPCQ</scope><scope>PIMPY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20151028</creationdate><title>How many operating rooms are needed to manage non-elective surgical cases? A Monte Carlo simulation study</title><author>Antognini, Joseph M O'Brien ; Antognini, Joseph F ; Khatri, Vijay</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c494t-72a599c78028d84d680c9032ba6cecb3c4c70145dd2e84b133416b7f8913c8433</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Aged</topic><topic>Analysis</topic><topic>Anesthesiology</topic><topic>Computer Simulation</topic><topic>Computer-generated environments</topic><topic>Efficiency</topic><topic>Emergency Treatment - statistics & numerical data</topic><topic>Hospital costs</topic><topic>Humans</topic><topic>Medical centers</topic><topic>Monte Carlo Method</topic><topic>Monte Carlo simulation</topic><topic>Operating Rooms - statistics & numerical data</topic><topic>Operations management</topic><topic>Operative Time</topic><topic>Queuing theory</topic><topic>Surgery</topic><topic>Surgical Procedures, Operative - statistics & numerical data</topic><topic>Time-to-Treatment - statistics & numerical data</topic><topic>Waiting Lists</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Antognini, Joseph M O'Brien</creatorcontrib><creatorcontrib>Antognini, Joseph F</creatorcontrib><creatorcontrib>Khatri, Vijay</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>Access via ABI/INFORM (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Healthcare Administration Database (Alumni)</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>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</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><collection>PubMed Central (Full Participant titles)</collection><jtitle>BMC health services research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Antognini, Joseph M O'Brien</au><au>Antognini, Joseph F</au><au>Khatri, Vijay</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>How many operating rooms are needed to manage non-elective surgical cases? A Monte Carlo simulation study</atitle><jtitle>BMC health services research</jtitle><addtitle>BMC Health Serv Res</addtitle><date>2015-10-28</date><risdate>2015</risdate><volume>15</volume><issue>1</issue><spage>487</spage><epage>487</epage><pages>487-487</pages><artnum>487</artnum><issn>1472-6963</issn><eissn>1472-6963</eissn><abstract>Patients often wait to have urgent or emergency surgery. The number of operating rooms (ORs) needed to minimize waiting time while optimizing resources can be determined using queuing theory and computer simulation. We developed a computer program using Monte Carlo simulation to determine the number of ORs needed to minimize patient wait times while optimizing resources.
We used patient arrival data and surgical procedure length from our institution, a tertiary-care academic medical center that serves a large diverse population. With ~4800 patients/year requiring non-elective surgery, and mean procedure length 185 min (median 150 min) we determined the number of ORs needed during the day and evening (0600-2200) and during the night (2200-0600) that resulted in acceptable wait times.
Simulation of 4 ORs at day/evening and 3 ORs at night resulted in median wait time = 0 min (mean = 19 min) for emergency cases requiring surgery within 2 h, with wait time at the 95th percentile = 109 min. Median wait time for urgent cases needing surgery within 8-12 h was 34 min (mean = 136 min), with wait time at the 95th percentile = 474 min. The effect of changes in surgical length and volume on wait times was determined with sensitivity analysis.
Monte Carlo simulation can guide decisions on how to balance resources for elective and non-elective surgical procedures.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>26507265</pmid><doi>10.1186/s12913-015-1148-x</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Aged Analysis Anesthesiology Computer Simulation Computer-generated environments Efficiency Emergency Treatment - statistics & numerical data Hospital costs Humans Medical centers Monte Carlo Method Monte Carlo simulation Operating Rooms - statistics & numerical data Operations management Operative Time Queuing theory Surgery Surgical Procedures, Operative - statistics & numerical data Time-to-Treatment - statistics & numerical data Waiting Lists |
| title | How many operating rooms are needed to manage non-elective surgical cases? A Monte Carlo simulation study |
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