Workflow analysis for design of an electronic health record-based tobacco cessation intervention in community health centers

JAMIA Open. 2021 Feb 11;4(3):ooaa070. doi: 10.1093/jamiaopen/ooaa070. eCollection 2021 Jul.

ABSTRACT

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.

PMID:34514352 | PMC:PMC8423419 | DOI:10.1093/jamiaopen/ooaa070

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