Evidence map›Paper›PMID 42220827›Full record

ArticleCureus2026

The Impact of Documented Electronic Referrals to the Smoking Cessation Clinic Before Discharge From Medical Wards on Patients' Adherence to Clinic Follow-Up.

Naveed Uddin, Muhammad Sohaib Ejaz Khan, Alia Abotaleb, Saleh Y Alhedyan, Usman Ghani, Dheyya F Alnajar, Yara Alqurashi

Abstract read
In one paragraph

Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Naveed UddinInternal Medicine, King Fahad Armed Forces Hospital, Jeddah, SAU.
Muhammad Sohaib Ejaz KhanInternal Medicine, King Fahad Armed Forces Hospital, Jeddah, SAU.
Alia AbotalebMedicine, King Fahad Armed Forces Hospital, Jeddah, SAU.
Saleh Y AlhedyanInternal Medicine, King Fahad Armed Forces Hospital, Jeddah, SAU.
Usman GhaniInternal Medicine, King Fahad Armed Forces Hospital, Jeddah, SAU.
Dheyya F AlnajarInternal Medicine, King Fahad Armed Forces Hospital, Jeddah, SAU.
Yara AlqurashiInternal Medicine, King Fahad Armed Forces Hospital, Jeddah, SAU.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective The objective of this study is to evaluate whether the implementation of a mandatory electronic inpatient referral pathway to the Smoking Cessation Clinic (SCC) improves referral completion and post-discharge clinic attendance. Methods A six-month prospective quality improvement study was conducted from May to October 2025 in the Department of Medicine at King Fahad Armed Forces Hospital (KFAFH), Jeddah. This study aimed to identify hospitalized smokers, assess readiness to quit, and implement documented electronic referrals to the Smoking Cessation Clinic (SCC) prior to discharge. Baseline data were collected from 27 medical inpatients with a smoking history who agreed to referral. The intervention incorporated systematic smoking status documentation, motivational counseling using the "5 As" framework, provider education, and audit-feedback cycles. Clinic attendance was assessed within 90 days of discharge. Results Among the 27 admitted patients with a six-month follow-up, 100% were identified as current smokers. Readiness and motivation to quit were assessed in all the patients. One hundred percent (27/27) were referred to a Smoking Cessation Clinic, with 100.0% (27/27) of referrals made electronically. A documented appointment was booked for all 27 patients prior to discharge. The primary outcome, clinic attendance within 90 days, was achieved in 100.0% (27/27) of patients. The end-to-end success rate (identification → assessment → referral → appointment → attendance) was 100.0%. Conclusions Embedding a mandatory electronic smoking cessation referral pathway within inpatient workflows resulted in complete adherence to referral and follow-up processes. Integration within the electronic health record (EHR), pre-booked appointments, and provider engagement were key enablers. This model is scalable and supports the implementation of a hospital-wide mandatory smoking cessation pathway for admitted smokers.

Indexed as

cigarette smokingclinic adherenceelectronic referral systemsfollow-up appointmentpost-discharge

Identifiers

PMID42220827
PMCPMC13221615

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.