Optimizing appointment template and number of staff of an OB/GYN clinic--micro and macro simulation analyses
The Department of Obstetrics and Gynecology (OB/GYN) at the University of Arkansas for Medical Sciences (UAMS) tested various, new system-restructuring ideas such as varying number of different types of nurses to reduce patient wait times for its outpatient clinic, often with little or no effect on...
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| Veröffentlicht in: | BMC health services research 2015-09, Vol.15 (1), p.387-387, Article 387 |
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| creator | Lenin, R B Lowery, Curtis L Hitt, Wilbur C Manning, Nirvana A Lowery, Peter Eswaran, Hari |
| description | The Department of Obstetrics and Gynecology (OB/GYN) at the University of Arkansas for Medical Sciences (UAMS) tested various, new system-restructuring ideas such as varying number of different types of nurses to reduce patient wait times for its outpatient clinic, often with little or no effect on waiting time. Witnessing little progress despite these time-intensive interventions, we sought an alternative way to intervene the clinic without affecting the normal clinic operations.
The aim is to identify the optimal (1) time duration between appointments and (2) number of nurses to reduce wait time of patients in the clinic.
We developed a discrete-event computer simulation model for the OB/GYN clinic. By using the patient tracker (PT) data, appropriate probability distributions of service times of staff were fitted to model different variability in staff service times. These distributions were used to fine-tune the simulation model. We then validated the model by comparing the simulated wait times with the actual wait times calculated from the PT data. The validated model was then used to carry out "what-if" analyses.
The best scenario yielded 16 min between morning appointments, 19 min between afternoon appointments, and addition of one medical assistant. Besides removing all peak wait times and bottlenecks around noon and late in the afternoon, the best scenario yielded 39.84 % (p |
| doi_str_mv | 10.1186/s12913-015-1007-9 |
| format | Article |
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The aim is to identify the optimal (1) time duration between appointments and (2) number of nurses to reduce wait time of patients in the clinic.
We developed a discrete-event computer simulation model for the OB/GYN clinic. By using the patient tracker (PT) data, appropriate probability distributions of service times of staff were fitted to model different variability in staff service times. These distributions were used to fine-tune the simulation model. We then validated the model by comparing the simulated wait times with the actual wait times calculated from the PT data. The validated model was then used to carry out "what-if" analyses.
The best scenario yielded 16 min between morning appointments, 19 min between afternoon appointments, and addition of one medical assistant. Besides removing all peak wait times and bottlenecks around noon and late in the afternoon, the best scenario yielded 39.84 % (p<.001), 30.31 % (p<.001), and 15.12 % (p<.001) improvement in patients' average wait times for providers in the exam rooms, average total wait time at various locations and average total spent time in the clinic, respectively. This is achieved without any compromise in the utilization of the staff and in serving all patients by 5 pm.
A discrete-event simulation model is developed, validated, and used to carry out "what-if" scenarios to identify the optimal time between appointments and number of nurses. Using the model, we achieved a significant improvement in wait time of patients in the clinic, which the clinic management initially had difficulty achieving through manual interventions. The model provides a tool for the clinic management to test new ideas to improve the performance of other UAMS OB/GYN clinics.</description><identifier>ISSN: 1472-6963</identifier><identifier>EISSN: 1472-6963</identifier><identifier>DOI: 10.1186/s12913-015-1007-9</identifier><identifier>PMID: 26376782</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Ambulatory Care Facilities - manpower ; Appointments and Schedules ; Clinics ; Computer Simulation ; Female ; Gynecology ; Health care ; Health care delivery ; Health care industry ; Humans ; Internal medicine ; Linear programming ; Models, Organizational ; Nurses ; Obstetrics ; Optimization techniques ; Outpatient care facilities ; Patient satisfaction ; Physicians ; Queuing theory ; Schedules ; Scheduling ; Simulation ; Simulation Training ; Ultrasonic imaging ; Vital signs ; Womens health</subject><ispartof>BMC health services research, 2015-09, Vol.15 (1), p.387-387, Article 387</ispartof><rights>COPYRIGHT 2015 BioMed Central Ltd.</rights><rights>Copyright BioMed Central 2015</rights><rights>Lenin et al. 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c494t-9370eb692df75b61770fb53fbce87ee05600ec6048c651d140c3c734b8e3b4c43</citedby><cites>FETCH-LOGICAL-c494t-9370eb692df75b61770fb53fbce87ee05600ec6048c651d140c3c734b8e3b4c43</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/PMC4572647/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4572647/$$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/26376782$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lenin, R B</creatorcontrib><creatorcontrib>Lowery, Curtis L</creatorcontrib><creatorcontrib>Hitt, Wilbur C</creatorcontrib><creatorcontrib>Manning, Nirvana A</creatorcontrib><creatorcontrib>Lowery, Peter</creatorcontrib><creatorcontrib>Eswaran, Hari</creatorcontrib><title>Optimizing appointment template and number of staff of an OB/GYN clinic--micro and macro simulation analyses</title><title>BMC health services research</title><addtitle>BMC Health Serv Res</addtitle><description>The Department of Obstetrics and Gynecology (OB/GYN) at the University of Arkansas for Medical Sciences (UAMS) tested various, new system-restructuring ideas such as varying number of different types of nurses to reduce patient wait times for its outpatient clinic, often with little or no effect on waiting time. Witnessing little progress despite these time-intensive interventions, we sought an alternative way to intervene the clinic without affecting the normal clinic operations.
The aim is to identify the optimal (1) time duration between appointments and (2) number of nurses to reduce wait time of patients in the clinic.
We developed a discrete-event computer simulation model for the OB/GYN clinic. By using the patient tracker (PT) data, appropriate probability distributions of service times of staff were fitted to model different variability in staff service times. These distributions were used to fine-tune the simulation model. We then validated the model by comparing the simulated wait times with the actual wait times calculated from the PT data. The validated model was then used to carry out "what-if" analyses.
The best scenario yielded 16 min between morning appointments, 19 min between afternoon appointments, and addition of one medical assistant. Besides removing all peak wait times and bottlenecks around noon and late in the afternoon, the best scenario yielded 39.84 % (p<.001), 30.31 % (p<.001), and 15.12 % (p<.001) improvement in patients' average wait times for providers in the exam rooms, average total wait time at various locations and average total spent time in the clinic, respectively. This is achieved without any compromise in the utilization of the staff and in serving all patients by 5 pm.
A discrete-event simulation model is developed, validated, and used to carry out "what-if" scenarios to identify the optimal time between appointments and number of nurses. Using the model, we achieved a significant improvement in wait time of patients in the clinic, which the clinic management initially had difficulty achieving through manual interventions. The model provides a tool for the clinic management to test new ideas to improve the performance of other UAMS OB/GYN clinics.</description><subject>Ambulatory Care Facilities - manpower</subject><subject>Appointments and Schedules</subject><subject>Clinics</subject><subject>Computer Simulation</subject><subject>Female</subject><subject>Gynecology</subject><subject>Health care</subject><subject>Health care delivery</subject><subject>Health care industry</subject><subject>Humans</subject><subject>Internal medicine</subject><subject>Linear programming</subject><subject>Models, Organizational</subject><subject>Nurses</subject><subject>Obstetrics</subject><subject>Optimization techniques</subject><subject>Outpatient care facilities</subject><subject>Patient satisfaction</subject><subject>Physicians</subject><subject>Queuing theory</subject><subject>Schedules</subject><subject>Scheduling</subject><subject>Simulation</subject><subject>Simulation Training</subject><subject>Ultrasonic imaging</subject><subject>Vital signs</subject><subject>Womens health</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>eNptUk1v1TAQtBCIlsIP4IIiceHi1o4dO7kgtVVbKlV9l3LgZDnO-uHKHyFOkMqvx-GVfiDkg0frmVnvahB6T8khpa04yrTuKMOENpgSInH3Au1TLmssOsFePsF76E3Ot4RQ2dbyNdqrBZOi4H3kN-Psgvvl4rbS45hcnAPEuZohjF7PUOk4VHEJPUxVslWetbUr0LHanBxdfLuujHfRGYyDM1P6Qw96RdmFpTi4FEtR-7sM-S16ZbXP8O7-PkBfz89uTr_gq83F5enxFTa84zPumCTQi64erGx6QaUktm-Y7Q20EoA0ghAwgvDWiIYOlBPDjGS8b4H13HB2gD7vfMelDzCYMtCkvRonF_R0p5J26vlLdN_VNv1UvJG14LIYfLo3mNKPBfKsgssGvNcR0pIVlZR1XEoqCvXjP9TbtExl4JXVElKXz_JH1lZ7UC7aVPqa1VQdN5xyLjvWFtbhf1jlDFC2myJYV-rPBHQnKAvPeQL7MCMlao2I2kVElYioNSKqK5oPT5fzoPibCfYb-Ei2TA</recordid><startdate>20150916</startdate><enddate>20150916</enddate><creator>Lenin, R B</creator><creator>Lowery, Curtis L</creator><creator>Hitt, Wilbur C</creator><creator>Manning, Nirvana A</creator><creator>Lowery, Peter</creator><creator>Eswaran, Hari</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>20150916</creationdate><title>Optimizing appointment template and number of staff of an OB/GYN clinic--micro and macro simulation analyses</title><author>Lenin, R B ; Lowery, Curtis L ; Hitt, Wilbur C ; Manning, Nirvana A ; Lowery, Peter ; Eswaran, Hari</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c494t-9370eb692df75b61770fb53fbce87ee05600ec6048c651d140c3c734b8e3b4c43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Ambulatory Care Facilities - manpower</topic><topic>Appointments and Schedules</topic><topic>Clinics</topic><topic>Computer Simulation</topic><topic>Female</topic><topic>Gynecology</topic><topic>Health care</topic><topic>Health care delivery</topic><topic>Health care industry</topic><topic>Humans</topic><topic>Internal medicine</topic><topic>Linear programming</topic><topic>Models, Organizational</topic><topic>Nurses</topic><topic>Obstetrics</topic><topic>Optimization techniques</topic><topic>Outpatient care facilities</topic><topic>Patient satisfaction</topic><topic>Physicians</topic><topic>Queuing theory</topic><topic>Schedules</topic><topic>Scheduling</topic><topic>Simulation</topic><topic>Simulation Training</topic><topic>Ultrasonic imaging</topic><topic>Vital signs</topic><topic>Womens health</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lenin, R B</creatorcontrib><creatorcontrib>Lowery, Curtis L</creatorcontrib><creatorcontrib>Hitt, Wilbur C</creatorcontrib><creatorcontrib>Manning, Nirvana A</creatorcontrib><creatorcontrib>Lowery, Peter</creatorcontrib><creatorcontrib>Eswaran, Hari</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>Access via ProQuest (Open Access)</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>Lenin, R B</au><au>Lowery, Curtis L</au><au>Hitt, Wilbur C</au><au>Manning, Nirvana A</au><au>Lowery, Peter</au><au>Eswaran, Hari</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimizing appointment template and number of staff of an OB/GYN clinic--micro and macro simulation analyses</atitle><jtitle>BMC health services research</jtitle><addtitle>BMC Health Serv Res</addtitle><date>2015-09-16</date><risdate>2015</risdate><volume>15</volume><issue>1</issue><spage>387</spage><epage>387</epage><pages>387-387</pages><artnum>387</artnum><issn>1472-6963</issn><eissn>1472-6963</eissn><abstract>The Department of Obstetrics and Gynecology (OB/GYN) at the University of Arkansas for Medical Sciences (UAMS) tested various, new system-restructuring ideas such as varying number of different types of nurses to reduce patient wait times for its outpatient clinic, often with little or no effect on waiting time. Witnessing little progress despite these time-intensive interventions, we sought an alternative way to intervene the clinic without affecting the normal clinic operations.
The aim is to identify the optimal (1) time duration between appointments and (2) number of nurses to reduce wait time of patients in the clinic.
We developed a discrete-event computer simulation model for the OB/GYN clinic. By using the patient tracker (PT) data, appropriate probability distributions of service times of staff were fitted to model different variability in staff service times. These distributions were used to fine-tune the simulation model. We then validated the model by comparing the simulated wait times with the actual wait times calculated from the PT data. The validated model was then used to carry out "what-if" analyses.
The best scenario yielded 16 min between morning appointments, 19 min between afternoon appointments, and addition of one medical assistant. Besides removing all peak wait times and bottlenecks around noon and late in the afternoon, the best scenario yielded 39.84 % (p<.001), 30.31 % (p<.001), and 15.12 % (p<.001) improvement in patients' average wait times for providers in the exam rooms, average total wait time at various locations and average total spent time in the clinic, respectively. This is achieved without any compromise in the utilization of the staff and in serving all patients by 5 pm.
A discrete-event simulation model is developed, validated, and used to carry out "what-if" scenarios to identify the optimal time between appointments and number of nurses. Using the model, we achieved a significant improvement in wait time of patients in the clinic, which the clinic management initially had difficulty achieving through manual interventions. The model provides a tool for the clinic management to test new ideas to improve the performance of other UAMS OB/GYN clinics.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>26376782</pmid><doi>10.1186/s12913-015-1007-9</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Ambulatory Care Facilities - manpower Appointments and Schedules Clinics Computer Simulation Female Gynecology Health care Health care delivery Health care industry Humans Internal medicine Linear programming Models, Organizational Nurses Obstetrics Optimization techniques Outpatient care facilities Patient satisfaction Physicians Queuing theory Schedules Scheduling Simulation Simulation Training Ultrasonic imaging Vital signs Womens health |
| title | Optimizing appointment template and number of staff of an OB/GYN clinic--micro and macro simulation analyses |
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