An analytical study of queues in medical sector
Abstract In general, we do not like to wait but each of us has spent a great deal of time waiting in lines even in medical sector also and queuing has become a symbol of inefficiency of a hospital. Managing the length of the line is one of the challenges facing most hospitals. A few of factors that...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Agarwal, Rashmi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Operational Research Society of India 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Opsearch - New Delhi : Springer India, 1997, 55(2017), 2 vom: 05. Sept., Seite 268-287 |
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| Übergeordnetes Werk: |
volume:55 ; year:2017 ; number:2 ; day:05 ; month:09 ; pages:268-287 |
| Links: |
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| DOI / URN: |
10.1007/s12597-017-0324-7 |
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SPR026243067 |
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| 520 | |a Abstract In general, we do not like to wait but each of us has spent a great deal of time waiting in lines even in medical sector also and queuing has become a symbol of inefficiency of a hospital. Managing the length of the line is one of the challenges facing most hospitals. A few of factors that are responsible for long waiting lines or delays in providing services are lack of passion of hospital staff and overloading of available doctors as they are attached in more than one clinic etc. This paper is based on the consideration that most of these problems can be managed by using queuing model to measure the waiting line performances as average arrival rate of patients, average service rate of patients, system utilization etc. The purpose of this study is to provide insight of general background of queuing theory, and how queuing theory can be used by policy makers to increase efficiency of services and to improve the quality of care of patients in hospitals, also understanding cost factor for getting optimum profit. | ||
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| 650 | 4 | |a Performance measures and cost factor of queuing system |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Singh, B. K. |4 aut | |
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10.1007/s12597-017-0324-7 doi (DE-627)SPR026243067 (SPR)s12597-017-0324-7-e DE-627 ger DE-627 rakwb eng Agarwal, Rashmi verfasserin (orcid)0000-0002-5246-6676 aut An analytical study of queues in medical sector 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Operational Research Society of India 2017 Abstract In general, we do not like to wait but each of us has spent a great deal of time waiting in lines even in medical sector also and queuing has become a symbol of inefficiency of a hospital. Managing the length of the line is one of the challenges facing most hospitals. A few of factors that are responsible for long waiting lines or delays in providing services are lack of passion of hospital staff and overloading of available doctors as they are attached in more than one clinic etc. This paper is based on the consideration that most of these problems can be managed by using queuing model to measure the waiting line performances as average arrival rate of patients, average service rate of patients, system utilization etc. The purpose of this study is to provide insight of general background of queuing theory, and how queuing theory can be used by policy makers to increase efficiency of services and to improve the quality of care of patients in hospitals, also understanding cost factor for getting optimum profit. Queuing theory (dpeaa)DE-He213 Priority queues (dpeaa)DE-He213 Waiting time (dpeaa)DE-He213 Performance measures and cost factor of queuing system (dpeaa)DE-He213 Singh, B. K. aut Enthalten in Opsearch New Delhi : Springer India, 1997 55(2017), 2 vom: 05. Sept., Seite 268-287 (DE-627)609775766 (DE-600)2516085-0 0975-0320 nnns volume:55 year:2017 number:2 day:05 month:09 pages:268-287 https://dx.doi.org/10.1007/s12597-017-0324-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 55 2017 2 05 09 268-287 |
| spelling |
10.1007/s12597-017-0324-7 doi (DE-627)SPR026243067 (SPR)s12597-017-0324-7-e DE-627 ger DE-627 rakwb eng Agarwal, Rashmi verfasserin (orcid)0000-0002-5246-6676 aut An analytical study of queues in medical sector 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Operational Research Society of India 2017 Abstract In general, we do not like to wait but each of us has spent a great deal of time waiting in lines even in medical sector also and queuing has become a symbol of inefficiency of a hospital. Managing the length of the line is one of the challenges facing most hospitals. A few of factors that are responsible for long waiting lines or delays in providing services are lack of passion of hospital staff and overloading of available doctors as they are attached in more than one clinic etc. This paper is based on the consideration that most of these problems can be managed by using queuing model to measure the waiting line performances as average arrival rate of patients, average service rate of patients, system utilization etc. The purpose of this study is to provide insight of general background of queuing theory, and how queuing theory can be used by policy makers to increase efficiency of services and to improve the quality of care of patients in hospitals, also understanding cost factor for getting optimum profit. Queuing theory (dpeaa)DE-He213 Priority queues (dpeaa)DE-He213 Waiting time (dpeaa)DE-He213 Performance measures and cost factor of queuing system (dpeaa)DE-He213 Singh, B. K. aut Enthalten in Opsearch New Delhi : Springer India, 1997 55(2017), 2 vom: 05. Sept., Seite 268-287 (DE-627)609775766 (DE-600)2516085-0 0975-0320 nnns volume:55 year:2017 number:2 day:05 month:09 pages:268-287 https://dx.doi.org/10.1007/s12597-017-0324-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 55 2017 2 05 09 268-287 |
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Agarwal, Rashmi misc Queuing theory misc Priority queues misc Waiting time misc Performance measures and cost factor of queuing system An analytical study of queues in medical sector |
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An analytical study of queues in medical sector |
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Abstract In general, we do not like to wait but each of us has spent a great deal of time waiting in lines even in medical sector also and queuing has become a symbol of inefficiency of a hospital. Managing the length of the line is one of the challenges facing most hospitals. A few of factors that are responsible for long waiting lines or delays in providing services are lack of passion of hospital staff and overloading of available doctors as they are attached in more than one clinic etc. This paper is based on the consideration that most of these problems can be managed by using queuing model to measure the waiting line performances as average arrival rate of patients, average service rate of patients, system utilization etc. The purpose of this study is to provide insight of general background of queuing theory, and how queuing theory can be used by policy makers to increase efficiency of services and to improve the quality of care of patients in hospitals, also understanding cost factor for getting optimum profit. © Operational Research Society of India 2017 |
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Abstract In general, we do not like to wait but each of us has spent a great deal of time waiting in lines even in medical sector also and queuing has become a symbol of inefficiency of a hospital. Managing the length of the line is one of the challenges facing most hospitals. A few of factors that are responsible for long waiting lines or delays in providing services are lack of passion of hospital staff and overloading of available doctors as they are attached in more than one clinic etc. This paper is based on the consideration that most of these problems can be managed by using queuing model to measure the waiting line performances as average arrival rate of patients, average service rate of patients, system utilization etc. The purpose of this study is to provide insight of general background of queuing theory, and how queuing theory can be used by policy makers to increase efficiency of services and to improve the quality of care of patients in hospitals, also understanding cost factor for getting optimum profit. © Operational Research Society of India 2017 |
| abstract_unstemmed |
Abstract In general, we do not like to wait but each of us has spent a great deal of time waiting in lines even in medical sector also and queuing has become a symbol of inefficiency of a hospital. Managing the length of the line is one of the challenges facing most hospitals. A few of factors that are responsible for long waiting lines or delays in providing services are lack of passion of hospital staff and overloading of available doctors as they are attached in more than one clinic etc. This paper is based on the consideration that most of these problems can be managed by using queuing model to measure the waiting line performances as average arrival rate of patients, average service rate of patients, system utilization etc. The purpose of this study is to provide insight of general background of queuing theory, and how queuing theory can be used by policy makers to increase efficiency of services and to improve the quality of care of patients in hospitals, also understanding cost factor for getting optimum profit. © Operational Research Society of India 2017 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR026243067</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230331232907.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12597-017-0324-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR026243067</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12597-017-0324-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Agarwal, Rashmi</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-5246-6676</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An analytical study of queues in medical sector</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Operational Research Society of India 2017</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In general, we do not like to wait but each of us has spent a great deal of time waiting in lines even in medical sector also and queuing has become a symbol of inefficiency of a hospital. Managing the length of the line is one of the challenges facing most hospitals. A few of factors that are responsible for long waiting lines or delays in providing services are lack of passion of hospital staff and overloading of available doctors as they are attached in more than one clinic etc. This paper is based on the consideration that most of these problems can be managed by using queuing model to measure the waiting line performances as average arrival rate of patients, average service rate of patients, system utilization etc. The purpose of this study is to provide insight of general background of queuing theory, and how queuing theory can be used by policy makers to increase efficiency of services and to improve the quality of care of patients in hospitals, also understanding cost factor for getting optimum profit.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Queuing theory</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Priority queues</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Waiting time</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Performance measures and cost factor of queuing system</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, B. 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