Objective: Tobacco use is the leading cause of preventable morbidity and mortality in the United States. Quitlines are effective telephone-based tobacco cessation services but are underutilized. The goal of this project was to describe current clinical workflows for Quitline referral and design an optimal electronic health record (EHR)-based workflow for Ask-Advice-Connect (AAC), an evidence-based intervention to increase Quitline referrals.
Materials And Methods: Ten Community Health Center systems (CHC), which use three different EHRs, participated in this study. Methods included: 9 group discussions with CHC leaders; 33 observations/interviews of clinical teams' workflow; surveys with 57 clinical staff; and assessment of the EHR ecosystem in each CHC. Data across these methods were integrated and coded according to the Fit between Individual, Task, Technology and Environment (FITTE) framework. The current and optimal workflow were notated using Business Process Modelling Notation. We compared the requirements of the optimal workflow with EHR capabilities.
Results: Current workflows are inefficient in data collection, variable in who, how, and when tobacco cessation advice and referral are enacted, and lack communication between referring clinics and the Quitline. In the optimal workflow, medical assistants deliver a standardized AAC intervention during the visit intake. Referrals are submitted electronically, and there is bidirectional communication between the clinic and Quitline. We implemented AAC within all three EHRs; however, deviations from the optimal workflow were necessary.
Conclusion: Current workflows for Quitline referral are inefficient and ineffective. We propose an optimal workflow and discuss improvements in EHR capabilities that would improve the implementation of AAC.
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http://dx.doi.org/10.1093/jamiaopen/ooaa070 | DOI Listing |
CJC Open
January 2025
Genetics and Genome Biology, Research Institute, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, Ontario, Canada.
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University of Houston College of Pharmacy, Health 2, 4349 Martin Luther King Blvd, Houston, TX 77204, United States.
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View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.
Computer-aided drug discovery (CADD) utilizes computational methods to accelerate the identification and optimization of potential drug candidates. Free energy perturbation (FEP) and thermodynamic integration (TI) play a critical role in predicting differences in protein binding affinities between drug molecules. Here, we implement SPONGE-FEP, which incorporates selective integrated tempering sampling (SITS) to enhance sampling efficiency and contains an automated workflow for relative binding free energy (RBFE) calculations.
View Article and Find Full Text PDFJ Vis Exp
January 2025
Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota;
Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the characterization of microbial proteins present in low abundance relative to host proteins is difficult.
View Article and Find Full Text PDFBacteremia is a serious clinical condition in which pathogenic bacteria enter the bloodstream, putting patients at risk of septic shock and necessitating antibiotic treatment. Choosing the most effective antibiotic is crucial not only for resolving the infection but also for minimizing side effects, such as dysbiosis in the healthy microbiome and reducing the selection pressure for antibiotic resistance. This requires prompt identification of the pathogen and antibiotic susceptibility testing, yet these processes are inherently slow in standard clinical microbiology labs due to reliance on growth-based assays.
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