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dc.contributor.authorChen, Jinying
dc.contributor.authorCutrona, Sarah L
dc.contributor.authorDharod, Ajay
dc.contributor.authorBunch, Stephanie C
dc.contributor.authorFoley, Kristie L
dc.contributor.authorOstasiewski, Brian
dc.contributor.authorHale, Erica R
dc.contributor.authorBridges, Aaron
dc.contributor.authorMoses, Adam
dc.contributor.authorDonny, Eric C
dc.contributor.authorSutfin, Erin L
dc.contributor.authorHouston, Thomas K
dc.date.accessioned2023-03-17T17:52:24Z
dc.date.available2023-03-17T17:52:24Z
dc.date.issued2023-03-02
dc.identifier.citationChen J, Cutrona SL, Dharod A, Bunch SC, Foley KL, Ostasiewski B, Hale ER, Bridges A, Moses A, Donny EC, Sutfin EL, Houston TK; iDAPT Implementation Science Center for Cancer Control. Monitoring the Implementation of Tobacco Cessation Support Tools: Using Novel Electronic Health Record Activity Metrics. JMIR Med Inform. 2023 Mar 2;11:e43097. doi: 10.2196/43097. PMID: 36862466.en_US
dc.identifier.issn2291-9694
dc.identifier.doi10.2196/43097en_US
dc.identifier.pmid36862466
dc.identifier.urihttp://hdl.handle.net/20.500.14038/51846
dc.description.abstractBackground: Clinical decision support (CDS) tools in electronic health records (EHRs) are often used as core strategies to support quality improvement programs in the clinical setting. Monitoring the impact (intended and unintended) of these tools is crucial for program evaluation and adaptation. Existing approaches for monitoring typically rely on health care providers' self-reports or direct observation of clinical workflows, which require substantial data collection efforts and are prone to reporting bias. Objective: This study aims to develop a novel monitoring method leveraging EHR activity data and demonstrate its use in monitoring the CDS tools implemented by a tobacco cessation program sponsored by the National Cancer Institute's Cancer Center Cessation Initiative (C3I). Methods: We developed EHR-based metrics to monitor the implementation of two CDS tools: (1) a screening alert reminding clinic staff to complete the smoking assessment and (2) a support alert prompting health care providers to discuss support and treatment options, including referral to a cessation clinic. Using EHR activity data, we measured the completion (encounter-level alert completion rate) and burden (the number of times an alert was fired before completion and time spent handling the alert) of the CDS tools. We report metrics tracked for 12 months post implementation, comparing 7 cancer clinics (2 clinics implemented the screening alert and 5 implemented both alerts) within a C3I center, and identify areas to improve alert design and adoption. Results: The screening alert fired in 5121 encounters during the 12 months post implementation. The encounter-level alert completion rate (clinic staff acknowledged completion of screening in EHR: 0.55; clinic staff completed EHR documentation of screening results: 0.32) remained stable over time but varied considerably across clinics. The support alert fired in 1074 encounters during the 12 months. Providers acted upon (ie, not postponed) the support alert in 87.3% (n=938) of encounters, identified a patient ready to quit in 12% (n=129) of encounters, and ordered a referral to the cessation clinic in 2% (n=22) of encounters. With respect to alert burden, on average, both alerts fired over 2 times (screening alert: 2.7; support alert: 2.1) before completion; time spent postponing the screening alert was similar to completing (52 vs 53 seconds) the alert, and time spent postponing the support alert was more than completing (67 vs 50 seconds) the alert per encounter. These findings inform four areas where the alert design and use can be improved: (1) improving alert adoption and completion through local adaptation, (2) improving support alert efficacy by additional strategies including training in provider-patient communication, (3) improving the accuracy of tracking for alert completion, and (4) balancing alert efficacy with the burden. Conclusions: EHR activity metrics were able to monitor the success and burden of tobacco cessation alerts, allowing for a more nuanced understanding of potential trade-offs associated with alert implementation. These metrics can be used to guide implementation adaptation and are scalable across diverse settings.en_US
dc.language.isoenen_US
dc.relation.ispartofJMIR Medical Informaticsen_US
dc.relation.urlhttps://doi.org/10.2196/43097en_US
dc.rights©Jinying Chen, Sarah L Cutrona, Ajay Dharod, Stephanie C Bunch, Kristie L Foley, Brian Ostasiewski, Erica R Hale, Aaron Bridges, Adam Moses, Eric C Donny, Erin L Sutfin, Thomas K Houston, iDAPT Implementation Science Center for Cancer Control. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 02.03.2023. 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 JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.en_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectEHR metricsen_US
dc.subjectalert burdenen_US
dc.subjectalertsen_US
dc.subjectclinical decision supporten_US
dc.subjectdecision toolen_US
dc.subjectelectronic health recordsen_US
dc.subjectimplementation scienceen_US
dc.subjectmedical informaticsen_US
dc.subjectmonitoringen_US
dc.subjectsmoking cessationen_US
dc.subjecttobacco cessationen_US
dc.titleMonitoring the Implementation of Tobacco Cessation Support Tools: Using Novel Electronic Health Record Activity Metricsen_US
dc.typeJournal Articleen_US
dc.source.journaltitleJMIR medical informatics
dc.source.volume11
dc.source.beginpagee43097
dc.source.endpage
dc.source.countryCanada
dc.identifier.journalJMIR medical informatics
refterms.dateFOA2023-03-17T17:52:25Z
dc.contributor.departmentPopulation and Quantitative Health Sciencesen_US


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©Jinying Chen, Sarah L Cutrona, Ajay Dharod, Stephanie C Bunch, Kristie L Foley, Brian Ostasiewski, Erica R Hale, Aaron
Bridges, Adam Moses, Eric C Donny, Erin L Sutfin, Thomas K Houston, iDAPT Implementation Science Center for Cancer
Control. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 02.03.2023. 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 JMIR
Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on
https://medinform.jmir.org/, as well as this copyright and license information must be included.
Except where otherwise noted, this item's license is described as ©Jinying Chen, Sarah L Cutrona, Ajay Dharod, Stephanie C Bunch, Kristie L Foley, Brian Ostasiewski, Erica R Hale, Aaron Bridges, Adam Moses, Eric C Donny, Erin L Sutfin, Thomas K Houston, iDAPT Implementation Science Center for Cancer Control. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 02.03.2023. 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 JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.