Development of predictive scoring model for risk stratification of no-show at a public hospital specialist outpatient clinic

Aim: No-shows are patients who miss scheduled specialist outpatient clinic (SOC) appointments. A predictive scoring model for the risk stratification of no-shows was developed to improve the utilisation of resources. Method: The administrative records of new SOC appointments for subsidised patients...

Full description

Saved in:
Bibliographic Details
Published inProceedings of Singapore healthcare Vol. 28; no. 2; pp. 96 - 104
Main Authors Chua, Siang Li, Chow, Wai Leng
Format Journal Article
LanguageEnglish
Published London, England SAGE Publications 01.06.2019
Sage Publications Ltd
SAGE Publishing
Subjects
Online AccessGet full text

Cover

Loading…
Abstract Aim: No-shows are patients who miss scheduled specialist outpatient clinic (SOC) appointments. A predictive scoring model for the risk stratification of no-shows was developed to improve the utilisation of resources. Method: The administrative records of new SOC appointments for subsidised patients in 2013 were analysed. Univariate analysis was performed on 16 variables comprising patient demographics, appointment/visit records and historical outpatient records. Multiple logistic regression (MLR) was applied to determine independent risk factors of no-shows. The adjusted parameter estimates from MLR were used to develop a predictive model for risk stratification of no-show. Model validation was performed using 2014 data. Result: Out of 75,677 appointments in 2013, 28.6% were no-shows. Univariate analysis showed that 11 variables were associated with no-shows. Six variables (age, race, specialty, lead time, referral source, previous visit status) remained independently associated with no-shows in the MLR model, and their odds ratios were used to develop the weighted predictive scoring model. Weighted scores were 0 to 19, and five levels of no-show risk were derived: extremely low (score: 0–4; odds ratio (OR): 1.0); low (5–6; OR: 2.5); medium (7–8; OR: 5.6); high (9–10; OR: 9.2); and extremely high (11–19; OR: 16.7). The predictive ability of the model was tested using receiver operation curve analysis, where the area under curve (AUC) was 72%. AUC remained at 72% upon validation with 2014 data. Conclusion: The prediction model developed using only administrative data was robust and can be used for the risk stratification of SOC no-show for better resource utilisation to improve access to care.
AbstractList Aim: No-shows are patients who miss scheduled specialist outpatient clinic (SOC) appointments. A predictive scoring model for the risk stratification of no-shows was developed to improve the utilisation of resources. Method: The administrative records of new SOC appointments for subsidised patients in 2013 were analysed. Univariate analysis was performed on 16 variables comprising patient demographics, appointment/visit records and historical outpatient records. Multiple logistic regression (MLR) was applied to determine independent risk factors of no-shows. The adjusted parameter estimates from MLR were used to develop a predictive model for risk stratification of no-show. Model validation was performed using 2014 data. Result: Out of 75,677 appointments in 2013, 28.6% were no-shows. Univariate analysis showed that 11 variables were associated with no-shows. Six variables (age, race, specialty, lead time, referral source, previous visit status) remained independently associated with no-shows in the MLR model, and their odds ratios were used to develop the weighted predictive scoring model. Weighted scores were 0 to 19, and five levels of no-show risk were derived: extremely low (score: 0–4; odds ratio (OR): 1.0); low (5–6; OR: 2.5); medium (7–8; OR: 5.6); high (9–10; OR: 9.2); and extremely high (11–19; OR: 16.7). The predictive ability of the model was tested using receiver operation curve analysis, where the area under curve (AUC) was 72%. AUC remained at 72% upon validation with 2014 data. Conclusion: The prediction model developed using only administrative data was robust and can be used for the risk stratification of SOC no-show for better resource utilisation to improve access to care.
Aim: No-shows are patients who miss scheduled specialist outpatient clinic (SOC) appointments. A predictive scoring model for the risk stratification of no-shows was developed to improve the utilisation of resources. Method: The administrative records of new SOC appointments for subsidised patients in 2013 were analysed. Univariate analysis was performed on 16 variables comprising patient demographics, appointment/visit records and historical outpatient records. Multiple logistic regression (MLR) was applied to determine independent risk factors of no-shows. The adjusted parameter estimates from MLR were used to develop a predictive model for risk stratification of no-show. Model validation was performed using 2014 data. Result: Out of 75,677 appointments in 2013, 28.6% were no-shows. Univariate analysis showed that 11 variables were associated with no-shows. Six variables (age, race, specialty, lead time, referral source, previous visit status) remained independently associated with no-shows in the MLR model, and their odds ratios were used to develop the weighted predictive scoring model. Weighted scores were 0 to 19, and five levels of no-show risk were derived: extremely low (score: 0–4; odds ratio (OR): 1.0); low (5–6; OR: 2.5); medium (7–8; OR: 5.6); high (9–10; OR: 9.2); and extremely high (11–19; OR: 16.7). The predictive ability of the model was tested using receiver operation curve analysis, where the area under curve (AUC) was 72%. AUC remained at 72% upon validation with 2014 data. Conclusion: The prediction model developed using only administrative data was robust and can be used for the risk stratification of SOC no-show for better resource utilisation to improve access to care.
Author Chow, Wai Leng
Chua, Siang Li
Author_xml – sequence: 1
  givenname: Siang Li
  orcidid: 0000-0001-9494-5221
  surname: Chua
  fullname: Chua, Siang Li
  email: siang_li_chua@cgh.com.sg
  organization: Health Services Research, Changi General Hospital, Singapore
– sequence: 2
  givenname: Wai Leng
  surname: Chow
  fullname: Chow, Wai Leng
  organization: Health Services Research, Changi General Hospital, Singapore
BookMark eNp1kc1rHSEUxaWkkDTNPkuh62m8Oo7jsqRfgUA37Vr06rz4Om-cqi-l0D--Tl5poVCE61XP-R3hviBnS1oCIdfAXgModcMZtCVHGJUWIOUzcsGZ1B0XXJ9tPbBuez8nV6XsGWMwCAXAL8jPt-ExzGk9hKXSNNE1Bx-xxsdAC6Yclx09JB9mOqVMcyxfaanZ1jhFbDUtm2dJXXlI36mt1NL16OaI9CGVNVY707IGjHaOpeGPdW2mLQnnuER8SZ5Pdi7h6vd-Sb68f_f59mN3_-nD3e2b-w57ULWDQYPiyAfXqhq5R-09Ki-01-1gEVHiOCreq2BVLznHoe_16DSAU96JS3J34vpk92bN8WDzD5NsNE8XKe-MzTXiHMwgHQoXpOyB9UMYR-60lYAWghuQ9Y316sRac_p2DKWafTrmpX3fcAFCtUwpmoqdVJhTKTlMf1KBmW1k5t-RNUt3shS7C3-h_9X_AgSXmKQ
CitedBy_id crossref_primary_10_1016_j_healthplace_2020_102496
crossref_primary_10_1186_s12913_023_10418_6
crossref_primary_10_1016_j_surg_2022_10_034
crossref_primary_10_2214_AJR_19_22594
crossref_primary_10_1186_s12913_022_07865_y
crossref_primary_10_1016_j_surg_2024_05_030
crossref_primary_10_1186_s12913_022_08784_8
crossref_primary_10_1016_j_asoc_2019_105866
crossref_primary_10_3390_e22060675
crossref_primary_10_1186_s13054_020_03408_1
crossref_primary_10_1111_joes_12534
Cites_doi 10.1287/msom.1090.0272
10.1016/j.healthpol.2018.02.002
10.1080/07408170802165823
10.1111/poms.12401
10.1016/j.ejor.2014.06.034
10.1007/s10729-011-9148-9
10.1007/s10479-009-0569-5
10.1080/07408170802165880
ContentType Journal Article
Copyright The Author(s) 2018
The Author(s) 2018. This work is licensed under the Creative Commons Attribution – Non-Commercial License http://www.creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: The Author(s) 2018
– notice: The Author(s) 2018. This work is licensed under the Creative Commons Attribution – Non-Commercial License http://www.creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID AFRWT
AAYXX
CITATION
3V.
7RV
7XB
8C1
8FI
8FJ
8FK
ABUWG
AFKRA
AZQEC
BENPR
CCPQU
DWQXO
FYUFA
GHDGH
KB0
NAPCQ
PIMPY
PQEST
PQQKQ
PQUKI
PRINS
DOA
DOI 10.1177/2010105818793155
DatabaseName Sage Journals GOLD Open Access 2024
CrossRef
ProQuest Central (Corporate)
Nursing & Allied Health Database
ProQuest Central (purchase pre-March 2016)
Public Health Database
Hospital Premium Collection
Hospital Premium Collection (Alumni Edition)
ProQuest Central (Alumni) (purchase pre-March 2016)
ProQuest Central (Alumni)
ProQuest Central
ProQuest Central Essentials
AUTh Library subscriptions: ProQuest Central
ProQuest One Community College
ProQuest Central Korea
Health Research Premium Collection
Health Research Premium Collection (Alumni)
Nursing & Allied Health Database (Alumni Edition)
Nursing & Allied Health Premium
Publicly Available Content Database
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Academic
ProQuest One Academic UKI Edition
ProQuest Central China
Directory of Open Access Journals
DatabaseTitle CrossRef
Publicly Available Content Database
ProQuest Public Health
ProQuest Central Essentials
ProQuest One Academic Eastern Edition
ProQuest Central (Alumni Edition)
ProQuest One Community College
ProQuest Nursing & Allied Health Source
ProQuest Hospital Collection
Health Research Premium Collection (Alumni)
ProQuest Central China
ProQuest Hospital Collection (Alumni)
ProQuest Central
Nursing & Allied Health Premium
Health Research Premium Collection
ProQuest One Academic UKI Edition
ProQuest Central Korea
ProQuest Nursing & Allied Health Source (Alumni)
ProQuest One Academic
ProQuest Central (Alumni)
DatabaseTitleList

Publicly Available Content Database
Database_xml – sequence: 1
  dbid: DOA
  name: Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: AFRWT
  name: SAGE Open Access Journals
  url: http://journals.sagepub.com/
  sourceTypes: Publisher
– sequence: 3
  dbid: BENPR
  name: ProQuest Central
  url: https://www.proquest.com/central
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
EISSN 2059-2329
EndPage 104
ExternalDocumentID oai_doaj_org_article_65bc3be5541046e882b9a51ca1eb6c04
10_1177_2010105818793155
10.1177_2010105818793155
GeographicLocations Singapore
GeographicLocations_xml – name: Singapore
GroupedDBID 0R~
54M
5VS
7RV
8C1
8FI
8FJ
AAJPV
AAJQC
AAQQG
AATBZ
ABAFQ
ABAWP
ABNCE
ABQXT
ABUWG
ABVFX
ABXGC
ACARO
ACGFS
ACGZU
ACROE
ACSIQ
ADBBV
ADOGD
ADZYD
AEFTW
AEUHG
AEUIJ
AEWDL
AEWHI
AFCOW
AFKRA
AFKRG
AFRWT
AJUZI
ALIPV
ALMA_UNASSIGNED_HOLDINGS
AUTPY
AYAKG
BCNDV
BDDNI
BENPR
BKEYQ
BPHCQ
BSEHC
BVXVI
CCPQU
DC.
DF.
DIK
DV7
EBS
EIHBH
EJD
FYUFA
GROUPED_DOAJ
GROUPED_SAGE_PREMIER_JOURNAL_COLLECTION
J8X
K.F
KQ8
NAPCQ
O9-
OK1
PIMPY
PQQKQ
ROL
SFC
UKHRP
AASGM
AAYXX
CITATION
H13
3V.
7XB
8FK
AZQEC
DWQXO
PQEST
PQUKI
PRINS
ID FETCH-LOGICAL-c417t-169172c26b72c782dc9ddc7d39d92dcaccc5c887247ea74522c64498b911b7db3
IEDL.DBID DOA
ISSN 2010-1058
IngestDate Tue Oct 22 14:53:17 EDT 2024
Mon Nov 18 22:45:59 EST 2024
Fri Dec 06 08:00:09 EST 2024
Tue Jul 16 20:52:04 EDT 2024
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 2
Keywords scheduling
Non-attendance
healthcare operations
logistic regression
Language English
License This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c417t-169172c26b72c782dc9ddc7d39d92dcaccc5c887247ea74522c64498b911b7db3
ORCID 0000-0001-9494-5221
OpenAccessLink https://doaj.org/article/65bc3be5541046e882b9a51ca1eb6c04
PQID 2313791153
PQPubID 4451074
PageCount 9
ParticipantIDs doaj_primary_oai_doaj_org_article_65bc3be5541046e882b9a51ca1eb6c04
proquest_journals_2313791153
crossref_primary_10_1177_2010105818793155
sage_journals_10_1177_2010105818793155
PublicationCentury 2000
PublicationDate 20190600
2019-06-00
20190601
2019-06-01
PublicationDateYYYYMMDD 2019-06-01
PublicationDate_xml – month: 6
  year: 2019
  text: 20190600
PublicationDecade 2010
PublicationPlace London, England
PublicationPlace_xml – name: London, England
– name: London
PublicationTitle Proceedings of Singapore healthcare
PublicationYear 2019
Publisher SAGE Publications
Sage Publications Ltd
SAGE Publishing
Publisher_xml – name: SAGE Publications
– name: Sage Publications Ltd
– name: SAGE Publishing
References Liu, Ziya, Vidyadhar 2010; 12
Gupta, Denton 2008; 40
Liu 2016; 25
Muthuraman, Lawley 2008; 40
Zeng, Turkcan, Lin 2010; 178
Samorani, LaGanga 2015; 240
Alaeddini, Yang, Reddy 2011; 14
Dantas, Fleck, Cyrino Oliveira 2018; 122
bibr6-2010105818793155
bibr1-2010105818793155
bibr10-2010105818793155
bibr7-2010105818793155
bibr4-2010105818793155
bibr5-2010105818793155
bibr11-2010105818793155
Samorani M (bibr2-2010105818793155)
bibr3-2010105818793155
bibr8-2010105818793155
bibr9-2010105818793155
References_xml – volume: 122
  start-page: 412
  year: 2018
  end-page: 421
  article-title: No-shows in appointment scheduling – a systematic literature review
  publication-title: Health Policy
  contributor:
    fullname: Cyrino Oliveira
– volume: 178
  start-page: 121
  year: 2010
  end-page: 144
  article-title: Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities
  publication-title: Ann Oper Res
  contributor:
    fullname: Lin
– volume: 12
  start-page: 347
  year: 2010
  end-page: 364
  article-title: Dynamic scheduling of outpatient appointments under patient no-shows and cancellations, department of statistics and operations research
  publication-title: Manuf Serv Oper Manag
  contributor:
    fullname: Vidyadhar
– volume: 14
  start-page: 146
  year: 2011
  end-page: 157
  article-title: A probabilistic model for predicting the probability of no-show in hospital appointments
  publication-title: Health Care Manag Sci
  contributor:
    fullname: Reddy
– volume: 40
  start-page: 800
  year: 2008
  end-page: 819
  article-title: Appointment scheduling in health care: challenges and opportunities
  publication-title: IIE Trans
  contributor:
    fullname: Denton
– volume: 25
  start-page: 128
  year: 2016
  end-page: 142
  article-title: Optimal choice for appointment scheduling window under patient no-show behaviour
  publication-title: Prod Oper Manag
  contributor:
    fullname: Liu
– volume: 240
  start-page: 245
  year: 2015
  end-page: 257
  article-title: Outpatient appointment scheduling given individual day-dependent no-show predictions
  publication-title: Eur J Oper Res
  contributor:
    fullname: LaGanga
– volume: 40
  start-page: 820
  year: 2008
  end-page: 837
  article-title: A stochastic overbooking model for outpatient clinical scheduling with no-shows
  publication-title: IIE Trans
  contributor:
    fullname: Lawley
– ident: bibr6-2010105818793155
  doi: 10.1287/msom.1090.0272
– ident: bibr1-2010105818793155
  doi: 10.1016/j.healthpol.2018.02.002
– ident: bibr9-2010105818793155
– ident: bibr8-2010105818793155
  doi: 10.1080/07408170802165823
– ident: bibr7-2010105818793155
  doi: 10.1111/poms.12401
– volume-title: “Operations Management: The Enabling Link” proceedings of the 22nd annual POMS (Production and Operations Management Society) conference
  ident: bibr2-2010105818793155
  contributor:
    fullname: Samorani M
– ident: bibr3-2010105818793155
  doi: 10.1016/j.ejor.2014.06.034
– ident: bibr4-2010105818793155
  doi: 10.1007/s10729-011-9148-9
– ident: bibr5-2010105818793155
  doi: 10.1007/s10479-009-0569-5
– ident: bibr11-2010105818793155
  doi: 10.1080/07408170802165880
– ident: bibr10-2010105818793155
SSID ssj0001637112
Score 2.2505414
Snippet Aim: No-shows are patients who miss scheduled specialist outpatient clinic (SOC) appointments. A predictive scoring model for the risk stratification of...
Aim: No-shows are patients who miss scheduled specialist outpatient clinic (SOC) appointments. A predictive scoring model for the risk stratification of...
SourceID doaj
proquest
crossref
sage
SourceType Open Website
Aggregation Database
Publisher
StartPage 96
SubjectTerms Outpatient care facilities
Patient compliance
Risk factors
Scheduling
SummonAdditionalLinks – databaseName: Public Health Database
  dbid: 8C1
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1NS8QwEA26XryIouL6RQ4ieAjbNGnSnkQXRQQ9KXgrSZq6p-26rXjxxzuTZt1V0EuhaVLKTDJ5nZm8IeRM5EldJ1XKlMwLJmvtWV4rxZxJbeKzhJtAsfHwqO6e5f1L9hIdbm1Mq1zYxGCoq8ahj3wEOERoWJmZuJy9MawahdHVWEJjnWxw3OjwpPiYL30sSmgeAp4h5gtQIl9GKkfYhk1Yb1twPOu3sjMFAv8fqHMl0SvsPbfbZCuCRnrVa3mHrPnpLvlcyfehTU1nc4y5oPWirQtpdTSUuaEASylmkNOeIreOXjocM21YO2k-qOmooT3jNZ3ESiK07UvTwzygzXsXCVhpf5Ryjzzf3jyN71ispcCc5LpjyImjU5cqC1dABZUrqsrpShRVATfGOZc5MDip1N5opFl3gJSK3ILIra6s2CeDaTP1B4QajI2mjmthpEyMLayXtYeOVhkBA4bkYiHHctZTZpQ8sor_lvmQXKOgv_sh2XVoaOavZVw7pcqsE9YD8MGAtId_AluYjDvDvVUukUNyvFBTGVdgWy7ny5Cco-qWj_76mMP_33NENmFE0eeJHZNBN3_3J4BIOnsapt0XXhrbUg
  priority: 102
  providerName: ProQuest
– databaseName: Sage Journals GOLD Open Access 2024
  dbid: AFRWT
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LS8QwEA4-Ll5EUXF1lRxE8BBtmzRpT7KKiwh6kF30VpI01YvtsttFBH-8M23q-kDwUmiakJDn1_km3xByxJOgKII8YlIkKROFciwppGRWRyZwcRDqRmLj9k5ej8XNY_y4RMruLozvwdkpulVBi5rNGlc3WqPPPMl4hhQuAIMEQ2VzOBHP5_VL1lq7u6AamIL09PwFmW2L_pBvrLvdtkxWYaVKWBGrg-H9w2hhlZFchQ1F2rDEWMeC2_xV7bezrJH8_4ZTv7iGNafVcIOse5hJB-282CRLrtwi7188hGhV0MkUWRrc7yg0HQ18tAmMQwHIUvQ5p62obuHtelimrNjsuXqluqaathrZ9NnHHqGzNpg9zBxazWsv2Urby5fbZDy8Gl1eMx99gVkRqpqhio6KbCQNPAFH5DbNc6tynuYpvGhrbWxhi4qEclqhMLsFbJUmBrZPo3LDd8hKWZVul1CNbGpkQ8W1EIE2qXGicJDRSM2hQI-cdP2YTVqRjSz0OuQ_-7xHLrCjP_OhPHaTUE2fMr_aMhkby40DqIQUtoO_CJPqOLQ6dEbaQPRIvxumrJtxGSBdrqDxMe-RYxy6xae_GrP334z7ZA2S0tbHrE9W6uncHQCaqc2hn4IfH9juxw
  priority: 102
  providerName: SAGE Publications
Title Development of predictive scoring model for risk stratification of no-show at a public hospital specialist outpatient clinic
URI https://journals.sagepub.com/doi/full/10.1177/2010105818793155
https://www.proquest.com/docview/2313791153
https://doaj.org/article/65bc3be5541046e882b9a51ca1eb6c04
Volume 28
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NS8MwFA9-XLyIouL8GDmI4KHYNGnSHFU2RHDI2NBbSdKUndaxdXjxj_flQ62CePHS0jQpj_fS5Je8l99D6IIWaV2nVZZwVsiE1cImRc15YlSmU5unRHmKjccRv5-yh5f8pZPqy8WEBXrgoLhrnmtDtYVZz3kjLQBCLVVOjCJWcxOZQNOss5jyuyucCuJdnd7bCyCi-PJRXrsyV-QybVPiTvl15iRP3f8Nb3ZCvPysM9xDuxEu4psg5j7asPMD9NaJ9MFNjRdL521x4xZeGR9Qh32CGwyAFLvYcRzIceu4P-fazJtkNWtesWqxwoHrGs9iDhG8CknpoQfgZt1G6lUcDlEeoulwMLm7T2IWhcQwItrEseGIzGRcwxXwQGVkVRlRUVlJeFDGmNzAUJMxYZVwBOsGMJIsNAyDWlSaHqGteTO3xwgr5xXNDBFUMZYqLbVltYWKmisKDXro6kOP5SKQZZQk8on_1HkP3TpFf9ZzNNe-AIxfRuOXfxm_h84-zFTGf29VAmKlAoTPaQ9dOtN9vfpNmJP_EOYU7cB3ZYgjO0Nb7XJtzwGxtLqPNos70kfbN8Px8wTut4PR07jvu-w73eHoJA
link.rule.ids 314,780,784,864,2102,12223,21388,21966,27853,27924,27925,33266,33744,43579,43805,44945,45333
linkProvider Directory of Open Access Journals
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3BTtwwELXK9lAuiKpULCytD1UlDhZx7NjJCQEq2rYLJ5C4RbbjwGmzbLLiwscz43ibBYleIsWxo2g8Hr_MjN8Q8kPkSV0nVcqUzAsma-1ZXivFnElt4rOEm0CxcXWtprfyz112Fx1ubUyrXNvEYKirxqGP_ARwiNCwMjNxunhkWDUKo6uxhMYW-Qjbvkatzi_44GNRQvMQ8AwxX4AS-RCpPME2bMJ624LjWb-NnSkQ-L9CnRuJXmHvudwlOxE00rN-lj-TD37-hTxv5PvQpqaLJcZc0HrR1oW0OhrK3FCApRQzyGlPkVtHLx2OmTesfWieqOmooT3jNX2IlURo25emBz2gzaqLBKy0P0q5R24vf91cTFmspcCc5LpjyImjU5cqC1dABZUrqsrpShRVATfGOZc5MDip1N5opFl3gJSK3ILIra6s-EpG82bu9wk1GBtNHdfCSJkYW1gvaw8drTICBozJ8VqO5aKnzCh5ZBV_K_MxOUdB_-uHZNehoVnel3HtlCqzTlgPwAcD0h7-CWxhMu4M91a5RI7JZD1NZVyBbTnoy5j8xKkbHr33MQf_f8938ml6czUrZ7-v_x6SbRhd9DljEzLqlit_BOiks9-CCr4A90TeTg
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3BTtwwEB2VRaq4oKJSsUCLDwiJQyCJHTs-0pYVpQUhBIJbZDsOnDar3SAufDwzjpeFVki9REpsR9bYGb_4jd8A7PIybZq0zhMpSp2IRvmkbKRMnMlt6os0M0Fi4-xcnlyL09viNsbm0FmYaMHZAYVVYY-Cs6ave1I3h5FjPCQGF3FBSZmyOS6IS7AshBbFAJaPRpc3V4tNFslVFhjPQPpSmwVV-c9r3ixNQcH_Dex8FekVFp_RJ1iNqJEd9cO8Bh_8-DM8vQr4YW3DJlMiXch9sZkLcXUs5LlhiEsZhZCzXiO3idt01GbcJrP79pGZjhnWS16z-5hKhM363PQ4EVj70EUFVtafpVyH69Hx1Y-TJCZTSJzIVJeQKI7KXS4tXhEW1E7XtVM117XGG-OcKxx6nFwobxTprDuESrq06A2tqi3_AoNxO_YbwAyRo7nLFDdCpMZq60XjsaKVhmODIezP7VhNes2MKouy4n_bfAjfydAv9UjtOjxop3dV_HgqWVjHrUfkQ4y0x58Cq02ROZN5K10qhrA9H6ZqPoEqBK5cYecLPoQ9GrpF0Xud2fzfijvw8eLnqPrz6_z3Fqxgqe6jx7Zh0E0f_FfEKZ39FmfjM_Bz3UM
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Development+of+predictive+scoring+model+for+risk+stratification+of+no-show+at+a+public+hospital+specialist+outpatient+clinic&rft.jtitle=Proceedings+of+Singapore+healthcare&rft.au=Siang+Li+Chua&rft.au=Wai+Leng+Chow&rft.date=2019-06-01&rft.pub=SAGE+Publishing&rft.issn=2010-1058&rft.eissn=2059-2329&rft.volume=28&rft_id=info:doi/10.1177%2F2010105818793155&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_65bc3be5541046e882b9a51ca1eb6c04
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2010-1058&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2010-1058&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2010-1058&client=summon