Using Theories, Models, and Frameworks to Inform Implementation Cycles of Computerized Clinical Decision Support Systems in Tertiary Health Care Settings: Scoping Review
Computerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care settings such as hospitals. Theories, models, and frameworks may assist in facilitating the implementation processes associated with CDSS innovatio...
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| Published in | Journal of medical Internet research Vol. 25; no. 1; p. e45163 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Canada
Journal of Medical Internet Research
18.10.2023
Gunther Eysenbach MD MPH, Associate Professor JMIR Publications |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Computerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care settings such as hospitals. Theories, models, and frameworks may assist in facilitating the implementation processes associated with CDSS innovation and its use within these care settings. These processes include context assessments to identify key determinants, implementation plans for adoption, promoting ongoing uptake, adherence, and long-term evaluation. However, there has been no prior review synthesizing the literature regarding the theories, models, and frameworks that have informed the implementation and adoption of CDSSs within hospitals.
This scoping review aims to identify the theory, model, and framework approaches that have been used to facilitate the implementation and adoption of CDSSs in tertiary health care settings, including hospitals. The rationales reported for selecting these approaches, including the limitations and strengths, are described.
A total of 5 electronic databases were searched (CINAHL via EBSCOhost, PubMed, Scopus, PsycINFO, and Embase) to identify studies that implemented or adopted a CDSS in a tertiary health care setting using an implementation theory, model, or framework. No date or language limits were applied. A narrative synthesis was conducted using full-text publications and abstracts. Implementation phases were classified according to the "Active Implementation Framework stages": exploration (feasibility and organizational readiness), installation (organizational preparation), initial implementation (initiating implementation, ie, training), full implementation (sustainment), and nontranslational effectiveness studies.
A total of 81 records (42 full text and 39 abstracts) were included. Full-text studies and abstracts are reported separately. For full-text studies, models (18/42, 43%), followed by determinants frameworks (14/42,33%), were most frequently used to guide adoption and evaluation strategies. Most studies (36/42, 86%) did not list the limitations associated with applying a specific theory, model, or framework.
Models and related quality improvement methods were most frequently used to inform CDSS adoption. Models were not typically combined with each other or with theory to inform full-cycle implementation strategies. The findings highlight a gap in the application of implementation methods including theories, models, and frameworks to facilitate full-cycle implementation strategies for hospital CDSSs. |
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| AbstractList | Computerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care settings such as hospitals. Theories, models, and frameworks may assist in facilitating the implementation processes associated with CDSS innovation and its use within these care settings. These processes include context assessments to identify key determinants, implementation plans for adoption, promoting ongoing uptake, adherence, and long-term evaluation. However, there has been no prior review synthesizing the literature regarding the theories, models, and frameworks that have informed the implementation and adoption of CDSSs within hospitals. This scoping review aims to identify the theory, model, and framework approaches that have been used to facilitate the implementation and adoption of CDSSs in tertiary health care settings, including hospitals. The rationales reported for selecting these approaches, including the limitations and strengths, are described. A total of 5 electronic databases were searched (CINAHL via EBSCOhost, PubMed, Scopus, PsycINFO, and Embase) to identify studies that implemented or adopted a CDSS in a tertiary health care setting using an implementation theory, model, or framework. No date or language limits were applied. A narrative synthesis was conducted using full-text publications and abstracts. Implementation phases were classified according to the "Active Implementation Framework stages": exploration (feasibility and organizational readiness), installation (organizational preparation), initial implementation (initiating implementation, ie, training), full implementation (sustainment), and nontranslational effectiveness studies. A total of 81 records (42 full text and 39 abstracts) were included. Full-text studies and abstracts are reported separately. For full-text studies, models (18/42, 43%), followed by determinants frameworks (14/42,33%), were most frequently used to guide adoption and evaluation strategies. Most studies (36/42, 86%) did not list the limitations associated with applying a specific theory, model, or framework. Models and related quality improvement methods were most frequently used to inform CDSS adoption. Models were not typically combined with each other or with theory to inform full-cycle implementation strategies. The findings highlight a gap in the application of implementation methods including theories, models, and frameworks to facilitate full-cycle implementation strategies for hospital CDSSs. Background:Computerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care settings such as hospitals. Theories, models, and frameworks may assist in facilitating the implementation processes associated with CDSS innovation and its use within these care settings. These processes include context assessments to identify key determinants, implementation plans for adoption, promoting ongoing uptake, adherence, and long-term evaluation. However, there has been no prior review synthesizing the literature regarding the theories, models, and frameworks that have informed the implementation and adoption of CDSSs within hospitals.Objective:This scoping review aims to identify the theory, model, and framework approaches that have been used to facilitate the implementation and adoption of CDSSs in tertiary health care settings, including hospitals. The rationales reported for selecting these approaches, including the limitations and strengths, are described.Methods:A total of 5 electronic databases were searched (CINAHL via EBSCOhost, PubMed, Scopus, PsycINFO, and Embase) to identify studies that implemented or adopted a CDSS in a tertiary health care setting using an implementation theory, model, or framework. No date or language limits were applied. A narrative synthesis was conducted using full-text publications and abstracts. Implementation phases were classified according to the “Active Implementation Framework stages”: exploration (feasibility and organizational readiness), installation (organizational preparation), initial implementation (initiating implementation, ie, training), full implementation (sustainment), and nontranslational effectiveness studies.Results:A total of 81 records (42 full text and 39 abstracts) were included. Full-text studies and abstracts are reported separately. For full-text studies, models (18/42, 43%), followed by determinants frameworks (14/42,33%), were most frequently used to guide adoption and evaluation strategies. Most studies (36/42, 86%) did not list the limitations associated with applying a specific theory, model, or framework.Conclusions:Models and related quality improvement methods were most frequently used to inform CDSS adoption. Models were not typically combined with each other or with theory to inform full-cycle implementation strategies. The findings highlight a gap in the application of implementation methods including theories, models, and frameworks to facilitate full-cycle implementation strategies for hospital CDSSs. BackgroundComputerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care settings such as hospitals. Theories, models, and frameworks may assist in facilitating the implementation processes associated with CDSS innovation and its use within these care settings. These processes include context assessments to identify key determinants, implementation plans for adoption, promoting ongoing uptake, adherence, and long-term evaluation. However, there has been no prior review synthesizing the literature regarding the theories, models, and frameworks that have informed the implementation and adoption of CDSSs within hospitals. ObjectiveThis scoping review aims to identify the theory, model, and framework approaches that have been used to facilitate the implementation and adoption of CDSSs in tertiary health care settings, including hospitals. The rationales reported for selecting these approaches, including the limitations and strengths, are described. MethodsA total of 5 electronic databases were searched (CINAHL via EBSCOhost, PubMed, Scopus, PsycINFO, and Embase) to identify studies that implemented or adopted a CDSS in a tertiary health care setting using an implementation theory, model, or framework. No date or language limits were applied. A narrative synthesis was conducted using full-text publications and abstracts. Implementation phases were classified according to the “Active Implementation Framework stages”: exploration (feasibility and organizational readiness), installation (organizational preparation), initial implementation (initiating implementation, ie, training), full implementation (sustainment), and nontranslational effectiveness studies. ResultsA total of 81 records (42 full text and 39 abstracts) were included. Full-text studies and abstracts are reported separately. For full-text studies, models (18/42, 43%), followed by determinants frameworks (14/42,33%), were most frequently used to guide adoption and evaluation strategies. Most studies (36/42, 86%) did not list the limitations associated with applying a specific theory, model, or framework. ConclusionsModels and related quality improvement methods were most frequently used to inform CDSS adoption. Models were not typically combined with each other or with theory to inform full-cycle implementation strategies. The findings highlight a gap in the application of implementation methods including theories, models, and frameworks to facilitate full-cycle implementation strategies for hospital CDSSs. Computerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care settings such as hospitals. Theories, models, and frameworks may assist in facilitating the implementation processes associated with CDSS innovation and its use within these care settings. These processes include context assessments to identify key determinants, implementation plans for adoption, promoting ongoing uptake, adherence, and long-term evaluation. However, there has been no prior review synthesizing the literature regarding the theories, models, and frameworks that have informed the implementation and adoption of CDSSs within hospitals. This scoping review aims to identify the theory, model, and framework approaches that have been used to facilitate the implementation and adoption of CDSSs in tertiary health care settings, including hospitals. The rationales reported for selecting these approaches, including the limitations and strengths, are described. A total of 5 electronic databases were searched (CINAHL via EBSCOhost, PubMed, Scopus, PsycINFO, and Embase) to identify studies that implemented or adopted a CDSS in a tertiary health care setting using an implementation theory, model, or framework. No date or language limits were applied. A narrative synthesis was conducted using full-text publications and abstracts. Implementation phases were classified according to the "Active Implementation Framework stages": exploration (feasibility and organizational readiness), installation (organizational preparation), initial implementation (initiating implementation, ie, training), full implementation (sustainment), and nontranslational effectiveness studies. A total of 81 records (42 full text and 39 abstracts) were included. Full-text studies and abstracts are reported separately. For full-text studies, models (18/42, 43%), followed by determinants frameworks (14/42,33%), were most frequently used to guide adoption and evaluation strategies. Most studies (36/42, 86%) did not list the limitations associated with applying a specific theory, model, or framework. Models and related quality improvement methods were most frequently used to inform CDSS adoption. Models were not typically combined with each other or with theory to inform full-cycle implementation strategies. The findings highlight a gap in the application of implementation methods including theories, models, and frameworks to facilitate full-cycle implementation strategies for hospital CDSSs. Computerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care settings such as hospitals. Theories, models, and frameworks may assist in facilitating the implementation processes associated with CDSS innovation and its use within these care settings. These processes include context assessments to identify key determinants, implementation plans for adoption, promoting ongoing uptake, adherence, and long-term evaluation. However, there has been no prior review synthesizing the literature regarding the theories, models, and frameworks that have informed the implementation and adoption of CDSSs within hospitals.BACKGROUNDComputerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care settings such as hospitals. Theories, models, and frameworks may assist in facilitating the implementation processes associated with CDSS innovation and its use within these care settings. These processes include context assessments to identify key determinants, implementation plans for adoption, promoting ongoing uptake, adherence, and long-term evaluation. However, there has been no prior review synthesizing the literature regarding the theories, models, and frameworks that have informed the implementation and adoption of CDSSs within hospitals.This scoping review aims to identify the theory, model, and framework approaches that have been used to facilitate the implementation and adoption of CDSSs in tertiary health care settings, including hospitals. The rationales reported for selecting these approaches, including the limitations and strengths, are described.OBJECTIVEThis scoping review aims to identify the theory, model, and framework approaches that have been used to facilitate the implementation and adoption of CDSSs in tertiary health care settings, including hospitals. The rationales reported for selecting these approaches, including the limitations and strengths, are described.A total of 5 electronic databases were searched (CINAHL via EBSCOhost, PubMed, Scopus, PsycINFO, and Embase) to identify studies that implemented or adopted a CDSS in a tertiary health care setting using an implementation theory, model, or framework. No date or language limits were applied. A narrative synthesis was conducted using full-text publications and abstracts. Implementation phases were classified according to the "Active Implementation Framework stages": exploration (feasibility and organizational readiness), installation (organizational preparation), initial implementation (initiating implementation, ie, training), full implementation (sustainment), and nontranslational effectiveness studies.METHODSA total of 5 electronic databases were searched (CINAHL via EBSCOhost, PubMed, Scopus, PsycINFO, and Embase) to identify studies that implemented or adopted a CDSS in a tertiary health care setting using an implementation theory, model, or framework. No date or language limits were applied. A narrative synthesis was conducted using full-text publications and abstracts. Implementation phases were classified according to the "Active Implementation Framework stages": exploration (feasibility and organizational readiness), installation (organizational preparation), initial implementation (initiating implementation, ie, training), full implementation (sustainment), and nontranslational effectiveness studies.A total of 81 records (42 full text and 39 abstracts) were included. Full-text studies and abstracts are reported separately. For full-text studies, models (18/42, 43%), followed by determinants frameworks (14/42,33%), were most frequently used to guide adoption and evaluation strategies. Most studies (36/42, 86%) did not list the limitations associated with applying a specific theory, model, or framework.RESULTSA total of 81 records (42 full text and 39 abstracts) were included. Full-text studies and abstracts are reported separately. For full-text studies, models (18/42, 43%), followed by determinants frameworks (14/42,33%), were most frequently used to guide adoption and evaluation strategies. Most studies (36/42, 86%) did not list the limitations associated with applying a specific theory, model, or framework.Models and related quality improvement methods were most frequently used to inform CDSS adoption. Models were not typically combined with each other or with theory to inform full-cycle implementation strategies. The findings highlight a gap in the application of implementation methods including theories, models, and frameworks to facilitate full-cycle implementation strategies for hospital CDSSs.CONCLUSIONSModels and related quality improvement methods were most frequently used to inform CDSS adoption. Models were not typically combined with each other or with theory to inform full-cycle implementation strategies. The findings highlight a gap in the application of implementation methods including theories, models, and frameworks to facilitate full-cycle implementation strategies for hospital CDSSs. |
| Audience | Academic |
| Author | Tyack, Zephanie McPhail, Steven M Fernando, Manasha Abell, Bridget Naicker, Sundresan Donovan, Thomasina |
| AuthorAffiliation | 2 Digital Health and Informatics Directorate Metro South Health Brisbane Australia 1 Australian Centre for Health Services Innovation and Centre for Healthcare Transformation School of Public Health and Social Work, Faculty of Health Queensland University of Technology Brisbane Australia |
| AuthorAffiliation_xml | – name: 1 Australian Centre for Health Services Innovation and Centre for Healthcare Transformation School of Public Health and Social Work, Faculty of Health Queensland University of Technology Brisbane Australia – name: 2 Digital Health and Informatics Directorate Metro South Health Brisbane Australia |
| Author_xml | – sequence: 1 givenname: Manasha orcidid: 0000-0001-9506-600X surname: Fernando fullname: Fernando, Manasha organization: Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia – sequence: 2 givenname: Bridget orcidid: 0000-0002-1324-4536 surname: Abell fullname: Abell, Bridget organization: Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia – sequence: 3 givenname: Zephanie orcidid: 0000-0003-3376-5731 surname: Tyack fullname: Tyack, Zephanie organization: Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia – sequence: 4 givenname: Thomasina orcidid: 0000-0002-0127-0091 surname: Donovan fullname: Donovan, Thomasina organization: Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia – sequence: 5 givenname: Steven M orcidid: 0000-0002-1463-662X surname: McPhail fullname: McPhail, Steven M organization: Digital Health and Informatics Directorate, Metro South Health, Brisbane, Australia – sequence: 6 givenname: Sundresan orcidid: 0000-0002-2392-4981 surname: Naicker fullname: Naicker, Sundresan organization: Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37851492$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1186/s13012-017-0656-y 10.1177/10436596221129226 10.1136/bmjoq-2018-000408 10.1016/j.pedhc.2021.07.009 10.1186/s12913-018-3671-z 10.1186/s40537-019-0217-0 10.2196/26323 10.1002/ejhf.277 10.2196/36501 10.1177/1062860619879981 10.1186/1748-5908-5-14 10.1016/j.jclinepi.2020.01.008 10.2105/ajph.89.9.1322 10.1136/bmj-2021-069271 10.1186/s12911-020-01376-8 10.1136/bmj.h1258 10.3390/cancers12041032 10.2196/jmir.8775 10.1017/9781139136365.015 10.1007/s00198-019-05090-8 10.1097/PCC.0000000000002733 10.1186/s40635-022-00469-0 10.1542/peds.141.1ma2.177 10.1186/s13012-019-0852-z 10.1017/cem.2019.133 10.2196/medinform.4640 10.1136/bmjoq-2018-000397 10.1016/j.jbi.2013.07.005 10.1093/jamiaopen/ooad019 10.1186/1748-5908-1-1 10.1016/j.jamcollsurg.2019.08.752 10.1007/978-3-319-24277-4 10.1186/s13012-016-0437-z 10.3414/ME11-01-0099 10.1111/pan.13811 10.1542/peds.2020-000570 10.1016/j.ahjo.2022.100185 10.1186/s12913-021-06236-3 10.3233/jifs-182926 10.1186/1748-5908-4-50 10.1542/peds.147.3ma1.31 10.2196/resprot.2417 10.1186/1748-5908-8-139 10.1186/s13643-016-0384-4 10.1186/s12913-020-05355-7 10.1016/j.conctc.2018.07.003 10.1002/jac5.1204 10.1503/cmaj.150056 10.2196/27532 10.1186/s43058-021-00201-1 10.1016/j.ijmedinf.2015.12.007 10.1002/jac5.1481 10.2196/32215 10.1001/jama.2018.9471 10.1186/s12885-020-6611-3 10.1093/jamia/ocaa296 10.1016/j.crad.2022.09.109 10.2196/18500 10.1037//0033-295x.84.2.191 10.1186/s13012-022-01199-3 10.1111/bcp.12190 10.1186/s13012-017-0644-2 10.1111/jep.13600 10.3233/mgs-170260 10.1136/bmjopen-2022-060810 10.1186/s13012-015-0242-0 10.1016/j.sapharm.2017.05.006 10.2196/18388 10.1093/jamiaopen/ooab041 10.1016/s1386-5056(01)00189-7 10.1186/s12911-016-0241-3 10.1542/peds.142.1ma4.384 10.1001/jamanetworkopen.2019.17094 10.1186/s13756-017-0201-4 10.1177/1049731509335549 10.1371/journal.pmed.1000360 10.1136/bmjoq-2018-000572 10.1136/bmj.n71 10.1016/j.jhin.2018.07.034 10.1002/jhm.12825 10.2196/28023 10.1197/jamia.m3170 10.1016/j.cmi.2017.02.028 10.1177/1460458219900452 10.7861/clinmedicine.14-4-338 10.1111/jgs.16483 10.1136/bmjopen-2021-053886 10.1136/bmj.e3935 10.13140/RG.2.2.28768.99845 10.1097/pq9.0000000000000184 10.2196/44977 10.1097/pq9.0000000000000503 10.1097/won.0000000000000650 10.1007/s11121-021-01286-9 10.1007/s13167-019-00188-9 10.21203/rs.3.rs-34358/v1 10.1136/bmjqs-2014-003627 10.1111/j.1742-6723.2008.01149.x 10.1097/01.ccm.0000474656.39318.ac 10.3389/frhs.2021.817750 10.1007/s11096-011-9602-2 10.1016/j.hjdsi.2021.100598 10.2196/15573 10.1055/s-0041-1729983 10.1542/peds.144.2ma2.160 10.1016/s1549-3741(03)29041-9 10.2196/22866 10.1136/bmj.i6795 10.1097/CIN.0000000000000196 10.1186/s13012-019-0922-2 10.1186/s13012-016-0375-9 10.1071/ah060380 10.1080/1364557032000119616 10.1136/bmj.n1010 10.1164/ajrccm-conference.2011.183.1_meetingabstracts.a2600 10.1016/j.annemergmed.2014.07.306 10.1055/s-0040-1701256 10.1016/s1532-0464(03)00060-1 10.1016/j.psychres.2019.06.036 10.1007/s10916-014-0038-9 10.1111/trf.14924 10.1002/pbc.26591 10.1093/intqhc/mzw104.13 10.1007/s00134-010-1778-8 10.11124/JBIES-20-00167 10.1016/j.sapharm.2019.04.045 10.1186/s43058-020-00023-7 10.1080/00185868.2022.2060155 10.1136/bmjqs-2013-001862 10.1136/bmj.d6054 10.4324/9781498757461 10.1186/s12913-019-4015-3 10.3109/02699052.2016.1162060 10.1001/jama.282.1.67 10.1002/hpm.2334 10.1007/s11255-021-02817-7 10.1097/PEC.0000000000001998 10.1038/s41746-020-0221-y 10.1186/1748-5908-7-37 10.1136/amiajnl-2013-002252 |
| ContentType | Journal Article |
| Copyright | Manasha Fernando, Bridget Abell, Zephanie Tyack, Thomasina Donovan, Steven M McPhail, Sundresan Naicker. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.10.2023. COPYRIGHT 2023 Journal of Medical Internet Research 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Manasha Fernando, Bridget Abell, Zephanie Tyack, Thomasina Donovan, Steven M McPhail, Sundresan Naicker. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.10.2023. 2023 |
| Copyright_xml | – notice: Manasha Fernando, Bridget Abell, Zephanie Tyack, Thomasina Donovan, Steven M McPhail, Sundresan Naicker. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.10.2023. – notice: COPYRIGHT 2023 Journal of Medical Internet Research – notice: 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: Manasha Fernando, Bridget Abell, Zephanie Tyack, Thomasina Donovan, Steven M McPhail, Sundresan Naicker. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.10.2023. 2023 |
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| Keywords | models frameworks mobile phone theories computerized clinical decision support systems hospital CDSS implementation science |
| Language | English |
| License | Manasha Fernando, Bridget Abell, Zephanie Tyack, Thomasina Donovan, Steven M McPhail, Sundresan Naicker. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.10.2023. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
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| References | ref57 Wickham, H (ref60) 2016 ref59 ref58 Wilson, KM (ref31) 2011; 8 ref53 ref52 ref55 Ash, JS (ref28) 2007; 2007 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref100 ref101 ref40 ref35 ref34 ref37 ref148 Scott, P (ref12) 2019 ref30 ref149 ref33 ref146 ref32 ref147 ref39 ref38 ref155 ref156 ref153 ref154 ref151 ref152 ref150 ref24 ref23 ref26 ref25 ref20 Norris, RS (ref125) 2018; 13 ref22 ref157 ref21 ref27 ref29 Osheroff, JA (ref54) 2012 Kuo, MC (ref88) 2014; 201 Dalal, AK (ref111) 2019; 13 ref13 ref15 ref128 ref14 ref129 ref97 ref126 ref96 ref127 ref11 ref99 ref124 ref10 ref98 ref17 ref16 ref19 ref18 Khatri, SB (ref121) 2015; 191 Bernonille, S (ref69) 2011; 166 ref93 ref92 ref134 ref95 ref131 ref94 ref132 ref91 ref90 ref89 ref139 Karim, F (ref130) 2018; 113 ref86 ref137 ref85 ref138 ref135 ref87 ref136 Kibert, J (ref144) 2017; 37 Glauser, J (ref56) 2017 ref82 ref81 ref145 ref84 ref142 ref83 ref143 ref140 Green, LW (ref36) 2005 ref141 ref80 ref79 ref108 ref78 ref109 ref106 ref107 ref75 ref104 ref74 ref105 ref77 ref102 ref76 ref103 ref2 ref1 ref71 ref70 ref73 ref72 ref110 Disabato, JA (ref112) 2013; 14 Sanchez, EL (ref122) 2015; 191 ref68 ref119 ref67 ref117 ref118 ref64 ref115 ref63 ref116 ref66 ref113 ref65 ref114 Yeh, DM (ref133) 2022; 149 ref123 ref62 ref120 ref61 |
| References_xml | – ident: ref42 doi: 10.1186/s13012-017-0656-y – ident: ref62 – ident: ref136 doi: 10.1177/10436596221129226 – ident: ref65 doi: 10.1136/bmjoq-2018-000408 – ident: ref124 doi: 10.1016/j.pedhc.2021.07.009 – ident: ref46 doi: 10.1186/s12913-018-3671-z – ident: ref2 doi: 10.1186/s40537-019-0217-0 – ident: ref3 doi: 10.2196/26323 – ident: ref145 doi: 10.1002/ejhf.277 – ident: ref103 doi: 10.2196/36501 – ident: ref108 doi: 10.1177/1062860619879981 – year: 2005 ident: ref36 publication-title: Health Program Planning: An Educational and Ecological Approach contributor: fullname: Green, LW – ident: ref152 doi: 10.1186/1748-5908-5-14 – ident: ref51 doi: 10.1016/j.jclinepi.2020.01.008 – ident: ref35 doi: 10.2105/ajph.89.9.1322 – ident: ref98 doi: 10.1136/bmj-2021-069271 – volume: 13 issue: 4 year: 2018 ident: ref125 publication-title: J Intern Med contributor: fullname: Norris, RS – volume: 14 start-page: 151 year: 2013 ident: ref112 publication-title: Epilepsy Curr contributor: fullname: Disabato, JA – ident: ref21 doi: 10.1186/s12911-020-01376-8 – ident: ref153 doi: 10.1136/bmj.h1258 – ident: ref20 doi: 10.3390/cancers12041032 – ident: ref155 doi: 10.2196/jmir.8775 – start-page: 62 year: 2017 ident: ref56 publication-title: Observation Medicine: Clinical Setting and Education doi: 10.1017/9781139136365.015 contributor: fullname: Glauser, J – volume: 191 start-page: A4251 year: 2015 ident: ref121 publication-title: Am J Respir Crit Care Med contributor: fullname: Khatri, SB – ident: ref118 doi: 10.1007/s00198-019-05090-8 – ident: ref87 doi: 10.1097/PCC.0000000000002733 – ident: ref107 doi: 10.1186/s40635-022-00469-0 – ident: ref113 doi: 10.1542/peds.141.1ma2.177 – ident: ref85 doi: 10.1186/s13012-019-0852-z – ident: ref132 doi: 10.1017/cem.2019.133 – ident: ref74 doi: 10.2196/medinform.4640 – ident: ref80 doi: 10.1136/bmjoq-2018-000397 – ident: ref92 doi: 10.1016/j.jbi.2013.07.005 – ident: ref71 doi: 10.1093/jamiaopen/ooad019 – ident: ref29 doi: 10.1186/1748-5908-1-1 – ident: ref140 doi: 10.1016/j.jamcollsurg.2019.08.752 – year: 2016 ident: ref60 publication-title: ggplot2: Elegant Graphics for Data Analysis doi: 10.1007/978-3-319-24277-4 contributor: fullname: Wickham, H – ident: ref43 doi: 10.1186/s13012-016-0437-z – ident: ref96 doi: 10.3414/ME11-01-0099 – ident: ref68 doi: 10.1111/pan.13811 – volume: 149 start-page: 550 issue: 1 year: 2022 ident: ref133 publication-title: Pediatrics contributor: fullname: Yeh, DM – ident: ref75 doi: 10.1542/peds.2020-000570 – ident: ref67 doi: 10.1016/j.ahjo.2022.100185 – ident: ref102 doi: 10.1186/s12913-021-06236-3 – ident: ref63 doi: 10.3233/jifs-182926 – ident: ref32 doi: 10.1186/1748-5908-4-50 – ident: ref119 doi: 10.1542/peds.147.3ma1.31 – ident: ref137 doi: 10.2196/resprot.2417 – ident: ref45 doi: 10.1186/1748-5908-8-139 – ident: ref58 doi: 10.1186/s13643-016-0384-4 – ident: ref141 doi: 10.1186/s40635-022-00469-0 – ident: ref26 doi: 10.1186/s12913-020-05355-7 – ident: ref93 doi: 10.1016/j.conctc.2018.07.003 – ident: ref139 doi: 10.1002/jac5.1204 – ident: ref55 doi: 10.1503/cmaj.150056 – ident: ref134 doi: 10.2196/27532 – ident: ref156 doi: 10.1186/s43058-021-00201-1 – ident: ref95 doi: 10.1016/j.ijmedinf.2015.12.007 – ident: ref138 doi: 10.1002/jac5.1481 – ident: ref37 doi: 10.2196/32215 – ident: ref57 doi: 10.1001/jama.2018.9471 – ident: ref66 doi: 10.1186/s12885-020-6611-3 – ident: ref10 doi: 10.1093/jamia/ocaa296 – ident: ref129 doi: 10.1016/j.crad.2022.09.109 – ident: ref41 doi: 10.2196/18500 – ident: ref33 doi: 10.1037//0033-295x.84.2.191 – ident: ref22 doi: 10.1186/s13012-022-01199-3 – ident: ref91 doi: 10.1111/bcp.12190 – ident: ref24 doi: 10.1186/s13012-017-0644-2 – ident: ref38 doi: 10.1111/jep.13600 – ident: ref64 doi: 10.3233/mgs-170260 – ident: ref99 doi: 10.1136/bmjopen-2022-060810 – ident: ref30 doi: 10.1186/s13012-015-0242-0 – ident: ref48 doi: 10.1016/j.sapharm.2017.05.006 – ident: ref149 doi: 10.2196/18388 – ident: ref89 doi: 10.1093/jamiaopen/ooab041 – ident: ref105 doi: 10.1016/s1386-5056(01)00189-7 – ident: ref104 doi: 10.1186/s12911-016-0241-3 – ident: ref126 doi: 10.1542/peds.142.1ma4.384 – ident: ref6 doi: 10.1001/jamanetworkopen.2019.17094 – ident: ref131 doi: 10.1186/s13756-017-0201-4 – ident: ref47 doi: 10.1177/1049731509335549 – year: 2019 ident: ref12 publication-title: Applied Interdisciplinary Theory in Health Informatics: A Knowledge Base for Practitioners contributor: fullname: Scott, P – ident: ref27 doi: 10.1371/journal.pmed.1000360 – ident: ref73 doi: 10.1136/bmjoq-2018-000572 – volume: 13 start-page: 8 issue: 4 year: 2019 ident: ref111 publication-title: J Hosp Med contributor: fullname: Dalal, AK – ident: ref61 doi: 10.1136/bmj.n71 – ident: ref78 doi: 10.1016/j.jhin.2018.07.034 – ident: ref17 doi: 10.1002/jhm.12825 – ident: ref23 doi: 10.2196/28023 – ident: ref16 doi: 10.1197/jamia.m3170 – ident: ref19 doi: 10.1016/j.cmi.2017.02.028 – volume: 8 start-page: A46 issue: 2 year: 2011 ident: ref31 publication-title: Prev Chronic Dis contributor: fullname: Wilson, KM – ident: ref8 doi: 10.1177/1460458219900452 – ident: ref13 doi: 10.7861/clinmedicine.14-4-338 – ident: ref79 doi: 10.1111/jgs.16483 – ident: ref18 doi: 10.1136/bmjopen-2021-053886 – ident: ref81 doi: 10.1136/bmj.e3935 – ident: ref77 doi: 10.13140/RG.2.2.28768.99845 – ident: ref70 doi: 10.1097/pq9.0000000000000184 – ident: ref100 doi: 10.2196/44977 – ident: ref72 doi: 10.1097/pq9.0000000000000503 – ident: ref109 doi: 10.1097/won.0000000000000650 – ident: ref154 doi: 10.1007/s11121-021-01286-9 – ident: ref5 doi: 10.1007/s13167-019-00188-9 – ident: ref25 doi: 10.21203/rs.3.rs-34358/v1 – ident: ref44 doi: 10.1136/bmjqs-2014-003627 – volume: 166 start-page: 180 year: 2011 ident: ref69 publication-title: Stud Health Technol Inform contributor: fullname: Bernonille, S – ident: ref4 doi: 10.1111/j.1742-6723.2008.01149.x – ident: ref142 doi: 10.1097/01.ccm.0000474656.39318.ac – ident: ref150 doi: 10.3389/frhs.2021.817750 – ident: ref128 doi: 10.1007/s11096-011-9602-2 – ident: ref101 doi: 10.1016/j.hjdsi.2021.100598 – ident: ref97 doi: 10.2196/15573 – ident: ref127 doi: 10.1055/s-0041-1729983 – ident: ref123 doi: 10.1542/peds.144.2ma2.160 – ident: ref94 doi: 10.1016/s1549-3741(03)29041-9 – ident: ref90 doi: 10.2196/22866 – ident: ref157 doi: 10.1136/bmj.i6795 – ident: ref40 doi: 10.1097/CIN.0000000000000196 – ident: ref53 doi: 10.1186/s13012-019-0922-2 – ident: ref86 doi: 10.1186/s13012-016-0375-9 – volume: 191 start-page: A3808 year: 2015 ident: ref122 publication-title: Am J Respir Crit Care Med contributor: fullname: Sanchez, EL – ident: ref83 doi: 10.1071/ah060380 – ident: ref50 doi: 10.1080/1364557032000119616 – volume: 2007 start-page: 26 year: 2007 ident: ref28 publication-title: AMIA Annu Symp Proc contributor: fullname: Ash, JS – volume: 113 start-page: 75 issue: S1 year: 2018 ident: ref130 publication-title: Vox Sang contributor: fullname: Karim, F – ident: ref151 doi: 10.1136/bmj.n1010 – ident: ref114 doi: 10.1164/ajrccm-conference.2011.183.1_meetingabstracts.a2600 – ident: ref120 doi: 10.1016/j.annemergmed.2014.07.306 – volume: 201 start-page: 441 year: 2014 ident: ref88 publication-title: Stud Health Technol Inform contributor: fullname: Kuo, MC – ident: ref84 doi: 10.1055/s-0040-1701256 – ident: ref106 doi: 10.1016/s1532-0464(03)00060-1 – ident: ref39 doi: 10.1016/j.psychres.2019.06.036 – ident: ref9 doi: 10.1007/s10916-014-0038-9 – ident: ref76 doi: 10.1111/trf.14924 – ident: ref143 doi: 10.1002/pbc.26591 – volume: 37 start-page: e216 issue: 12 year: 2017 ident: ref144 publication-title: Pharmacotherapy contributor: fullname: Kibert, J – ident: ref117 doi: 10.1093/intqhc/mzw104.13 – ident: ref7 doi: 10.1007/s00134-010-1778-8 – ident: ref59 – ident: ref49 doi: 10.11124/JBIES-20-00167 – ident: ref116 doi: 10.1016/j.sapharm.2019.04.045 – ident: ref146 doi: 10.1186/s43058-020-00023-7 – ident: ref52 doi: 10.1080/00185868.2022.2060155 – ident: ref147 doi: 10.1136/bmjqs-2013-001862 – ident: ref14 doi: 10.1136/bmj.d6054 – year: 2012 ident: ref54 publication-title: Improving Outcomes with Clinical Decision Support: An Implementer's Guide. 2nd edition doi: 10.4324/9781498757461 contributor: fullname: Osheroff, JA – ident: ref148 doi: 10.1186/s12913-019-4015-3 – ident: ref135 doi: 10.3109/02699052.2016.1162060 – ident: ref11 doi: 10.1001/jama.282.1.67 – ident: ref115 doi: 10.1002/hpm.2334 – ident: ref82 doi: 10.1007/s11255-021-02817-7 – ident: ref110 doi: 10.1097/PEC.0000000000001998 – ident: ref1 doi: 10.1038/s41746-020-0221-y – ident: ref34 doi: 10.1186/1748-5908-7-37 – ident: ref15 doi: 10.1136/amiajnl-2013-002252 |
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| Snippet | Computerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care... Background Computerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute... Background:Computerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute... BackgroundComputerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute... |
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| SubjectTerms | Acute services Cell Phone Clinical decision making Clinical outcomes Computerization Computerized decision support systems Decision support systems Decision Support Systems, Clinical Decision-making Delivery of Health Care Electronic records Feasibility Frame analysis Health care Health care reform Hospitals Humans Implementation Implementation Science Information technology Innovations Installation Literature reviews Medical records Models, Theoretical Narration Quality Improvement Quality management Review Systematic review Taxonomy Technological change Technology Acceptance Model Tertiary Healthcare Uptake Web applications |
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| Title | Using Theories, Models, and Frameworks to Inform Implementation Cycles of Computerized Clinical Decision Support Systems in Tertiary Health Care Settings: Scoping Review |
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