Monitoring the Implementation of Tobacco Cessation Support Tools: Using Novel Electronic Health Record Activity Metrics
Open accessPublished version of a preprint
Abstract
BACKGROUND: 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.
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Alert burden can lead to high rates of alert overrides and physician burnout (both physical and cognitive).
“Alert burden can lead to high rates of alert overrides and physician burnout (both physical and cognitive).9,16,64–66”
From Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling (2025) · cites this paper as [9]
The Discussion section “Balancing Alert Efficacy With Burden” says that an overabundance of interruptive alerts may lead to frequent overrides and that providers experience alert-related fatigue. It does not say that alert burden causes physician burnout, or distinguish physical from cognitive burnout. No page number is available in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredOur recent study found that providers acted upon the support-alert in 87.3% encounters.
“Our recent study found that providers acted upon the support-alert in 87.3% encounters.9”
From Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling (2025) · cites this paper as [9]
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- Authors
- Chen, J., Cutrona, S. L., Dharod, A., Bunch, S. C., Foley, K. L., Ostasiewski, B., Hale, E. R., Bridges, A., Moses, A., Donny, E. C., Sutfin, E. L., Houston, T. K., & iDAPT Implementation Science Center for Cancer Control
- Affiliations
- Boston University (US)
- University of Massachusetts Chan Medical School (US)
- Wake Forest University Health Sciences (US)
- RTI International (US)
- Corresponding author
- Chen, Jinying
- ORCID
- Venue
- JMIR Medical Informatics
- Publisher
- JMIR Publications Inc.
- Published
- 2023-03-02
- Volume
- 11
- Pages
- e43097
- ISSN
- 2291-9694 (ISSN-L)
- PMC
- PMC10020903
- OpenAlex
- W4322757339
- Cited by
- 4
- References
- 50
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 1
- FWCI
- 0.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Informatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (UL1TR001420)
- National Heart, Lung, and Blood Institute (K12HL138049)
- National Cancer Institute (P50CA244693, P30CA012197)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Keywords
- tobacco cessation, clinical decision support, electronic health record alerts, implementation monitoring, smoking screening, alert burden, cancer centers, quality improvement, smoking cessation referral, program evaluation, reporting bias, tobacco control policy evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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