The Adolescent Primary Care Visit: Time to Build on Strengths

AbstractIntroductionMost tobacco treatment efforts target healthcare settings, because about 75% of smokers in the United States visit a primary care provider annually. Yet, 25% of patients may be missed by such targeting.AimsTo describe patients who smoke but infrequently visit primary care – their characteristics, rates of successful telephone contact, and acceptance of tobacco treatment.MethodsTobacco Cessation Outreach Specialists ‘cold-called’ those without a primary care visit in the past year, offering tobacco dependence treatment. Age, sex, insurance status, race, ethnicity, electronic health record (EHR) patient-portal status and outreach outcomes were reported.ResultsOf 3,407 patients identified as smokers in a health system registry, 565 (16.6%) had not seen any primary care provider in the past year. Among 271 of those called, 143 (53%) were successfully reached and 33 (23%) set a quit date. Those without visits tended to be younger, male, some-day versus every-day smokers (42 vs. 44 years, P = 0.004; 48% vs. 40% female, P = 0.0002, and 21% vs. 27% some-day, P = 0.003), and less active on the EHR patient portal (33% vs. 40%, P = 0.001).ConclusionsA substantial proportion of patients who smoke are missed by traditional tobacco treatment interventions that require a primary care visit, yet many are receptive to quit smoking treatment offers.

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Natural language processing for automated annotation of medication mentions in primary care visit conversations

JAMIA Open ◽

10.1093/jamiaopen/ooab071 ◽

2021 ◽

Vol 4 (3) ◽

Author(s):

Craig H Ganoe ◽

Weiyi Wu ◽

Paul J Barr ◽

William Haslett ◽

Michelle D Dannenberg ◽

...

Keyword(s):

Primary Care ◽

Natural Language Processing ◽

Natural Language ◽

Information Extraction ◽

Language Processing ◽

Primary Care Visit ◽

Data Set ◽

Test Set ◽

Medication Information ◽

Care Visit

Abstract Objectives The objective of this study is to build and evaluate a natural language processing approach to identify medication mentions in primary care visit conversations between patients and physicians. Materials and Methods Eight clinicians contributed to a data set of 85 clinic visit transcripts, and 10 transcripts were randomly selected from this data set as a development set. Our approach utilizes Apache cTAKES and Unified Medical Language System controlled vocabulary to generate a list of medication candidates in the transcribed text and then performs multiple customized filters to exclude common false positives from this list while including some additional common mentions of the supplements and immunizations. Results Sixty-five transcripts with 1121 medication mentions were randomly selected as an evaluation set. Our proposed method achieved an F-score of 85.0% for identifying the medication mentions in the test set, significantly outperforming existing medication information extraction systems for medical records with F-scores ranging from 42.9% to 68.9% on the same test set. Discussion Our medication information extraction approach for primary care visit conversations showed promising results, extracting about 27% more medication mentions from our evaluation set while eliminating many false positives in comparison to existing baseline systems. We made our approach publicly available on the web as an open-source software. Conclusion Integration of our annotation system with clinical recording applications has the potential to improve patients’ understanding and recall of key information from their clinic visits, and, in turn, to positively impact health outcomes.

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Vision Screening

Pediatric Care Online ◽

10.1542/aap.ppcqr.396022 ◽

2020 ◽

Keyword(s):

Primary Care ◽

Early Detection ◽

Physical Examination ◽

Vision Loss ◽

Light Reflection ◽

Primary Care Visit ◽

Vision Screening ◽

Care Visit ◽

Test Instrument ◽

Key Points

Key Points Vision screening should be a routine part of every primary care visit, with physical examination from birth and formal acuity testing from 3 years on. Elements of the physical examination include inspection, red reflex evaluation, corneal light reflection, cover-uncover test. Instrument-based testing can begin at 1 year of age until chart-based acuity testing is possible from 3 years on. Amblyopia, which results in permanent vision loss, is preventable with early detection—before 5 years of age.

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Patient-Reported Use of the After Visit Summary in a Primary Care Internal Medicine Practice

Journal of Patient Experience ◽

10.1177/2374373519879286 ◽

2019 ◽

Vol 7 (5) ◽

pp. 703-707

Author(s):

Sarita Pathak ◽

Gregory Summerville ◽

Celia P Kaplan ◽

Sarah S Nouri ◽

Leah S Karliner

Keyword(s):

Primary Care ◽

Multivariable Analysis ◽

Free Text ◽

Primary Care Visit ◽

Communication Tool ◽

Cross Sectional Survey ◽

Cross Sectional ◽

Care Visit ◽

Patient Reported ◽

Communication Errors

Participants completed a cross-sectional survey about their use of the after visit summary (AVS) at a previous primary care visit. Of 355 participants, 294 (82.8%) recalled receiving it, 67.4% consulted it, 45.9% consulted it more than once, and 31.6% shared the AVS. In multivariable analysis, higher education and older age were associated with AVS consultation. Among the subset of 133 patients recalling personalized free-text instructions, 96% found them easy to understand and 94.4% found them useful. Our findings suggest that the AVS is a useful communication tool and improvement efforts should emphasize clarity for those most vulnerable to communication errors.

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