Literature DB >> 31592972

Effect of an Electronic Health Record Decision Support Alert to Decrease Excess Cervical Cancer Screening.

Deanna Teoh1, Rachel I Vogel1, Adam Langer1, Jinai Bharucha1, Melissa A Geller1, Eileen Harwood2, Shalini Kulasingam2, Genevieve B Melton3,4.   

Abstract

OBJECTIVE: Cervical cancer screening is often conducted in excess of current screening guidelines. The objective of this study was to evaluate the effect of an electronic health record (EHR) clinical decision support alert to decrease guideline-nonadherent cervical cancer screening beyond the age limits of screening or posthysterectomy.
MATERIALS AND METHODS: The proportion of guideline-nonadherent Pap tests in women younger than 21 years or older than 65 years or posthysterectomy were compared 4 months before and 3 months after implementation of an EHR clinical decision support alert warning providers that a Pap test is not indicated. Providers could cancel the Pap test or override the alert and place the order. Provider characteristics and Pap test indications were summarized by preintervention/postintervention period using descriptive statistics. The proportions of nonindicated Pap tests were compared by intervention period and provider characteristics using generalized estimating equation models.
RESULTS: In women beyond the screening age limits or posthysterectomy, a total of 388 Pap tests were ordered before intervention, and 313 tests were ordered after intervention. Proportion of guideline-nonadherent tests was similar before (62%) and after intervention (63%); thus, implementation of the clinical decision support alert did not change the proportion of guideline-nonadherent Pap tests ordered (OR = 1.08, 95% CI = 0.77-1.52). It is notable that 52% of guideline-nonadherent tests were ordered by 11 providers. Even when controlling for providers who ordered more than 1 test during the study period, multivariate analysis showed that male providers were more likely to order guideline-nonadherent Pap tests (OR = 2.30, 95% CI = 1.36-3.89); no other differences by provider characteristics were observed.
CONCLUSIONS: An EHR clinical decision support alert does not decrease guideline-nonadherent cervical cancer screening. These data suggest efforts to optimize clinical decision support should be focused on other aspects of cervical cancer prevention.

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Year:  2019        PMID: 31592972      PMCID: PMC6941655          DOI: 10.1097/LGT.0000000000000484

Source DB:  PubMed          Journal:  J Low Genit Tract Dis        ISSN: 1089-2591            Impact factor:   3.842


  18 in total

1.  Differences among primary care physicians' adherence to 2009 ACOG guidelines for cervical cancer screening.

Authors:  Jennifer Corbelli; Sonya Borrero; Rachel Bonnema; Megan McNamara; Kevin Kraemer; Doris Rubio; Irina Karpov; Melissa McNeil
Journal:  J Womens Health (Larchmt)       Date:  2013-12-31       Impact factor: 2.681

2.  Rethinking How to Measure the Appropriateness of Cervical Cancer Screening.

Authors:  Natasha K Parekh; Julie M Donohue; Aiju Men; Jennifer Corbelli; Marian Jarlenski
Journal:  Ann Intern Med       Date:  2017-07-11       Impact factor: 25.391

3.  Cancer statistics, 2012.

Authors:  Rebecca Siegel; Deepa Naishadham; Ahmedin Jemal
Journal:  CA Cancer J Clin       Date:  2012-01-04       Impact factor: 508.702

4.  Single Health System Adherence to 2012 Cervical Cancer Screening Guidelines at Extremes of Age and Posthysterectomy.

Authors:  Deanna Teoh; Rachel Isaksson Vogel; Gretchen Hultman; Minnu Monu; Levi Downs; Melissa A Geller; Chap Le; Genevieve Melton-Meaux; Shalini Kulasingam
Journal:  Obstet Gynecol       Date:  2017-03       Impact factor: 7.661

5.  Adherence to the 2012 national cervical cancer screening guidelines: a pilot study.

Authors:  Deanna G K Teoh; Amity E Marriott; Rachel Isaksson Vogel; Ryan T Marriott; Charles W Lais; Levi S Downs; Shalini L Kulasingam
Journal:  Am J Obstet Gynecol       Date:  2014-06-30       Impact factor: 8.661

6.  Challenges in cervical cancer prevention: a survey of U.S. obstetrician-gynecologists.

Authors:  Rebecca B Perkins; Britta L Anderson; Sherri Sheinfeld Gorin; Jay A Schulkin
Journal:  Am J Prev Med       Date:  2013-08       Impact factor: 5.043

7.  Discontent and Confusion: Primary Care Providers' Opinions and Understanding of Current Cervical Cancer Screening Recommendations.

Authors:  Emily Boone; LaVonna Lewis; Michael Karp
Journal:  J Womens Health (Larchmt)       Date:  2015-11-05       Impact factor: 2.681

8.  Overuse of papanicolaou testing among older women and among women without a cervix.

Authors:  Deanna Kepka; Nancy Breen; Jessica B King; Vicki B Benard; Mona Saraiya
Journal:  JAMA Intern Med       Date:  2014-02-01       Impact factor: 21.873

9.  Evaluating alert fatigue over time to EHR-based clinical trial alerts: findings from a randomized controlled study.

Authors:  Peter J Embi; Anthony C Leonard
Journal:  J Am Med Inform Assoc       Date:  2012-04-25       Impact factor: 4.497

10.  Can automated alerts within computerized physician order entry improve compliance with laboratory practice guidelines for ordering Pap tests?

Authors:  Lydia Pleotis Howell; Scott MacDonald; Jacqueline Jones; Daniel J Tancredi; Joy Melnikow
Journal:  J Pathol Inform       Date:  2014-09-30
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  3 in total

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Authors:  Jennifer L Moss; Siddhartha Roy; Chan Shen; Joie D Cooper; Robert P Lennon; Eugene J Lengerich; Alan Adelman; William Curry; Mack T Ruffin
Journal:  JAMA Netw Open       Date:  2020-07-01

2.  Implementation of an Anticoagulation Practice Guideline for COVID-19 via a Clinical Decision Support System in a Large Academic Health System and Its Evaluation: Observational Study.

Authors:  Surbhi Shah; Sean Switzer; Nathan D Shippee; Pamela Wogensen; Kathryn Kosednar; Emma Jones; Deborah L Pestka; Sameer Badlani; Mary Butler; Brittin Wagner; Katie White; Joshua Rhein; Bradley Benson; Mark Reding; Michael Usher; Genevieve B Melton; Christopher James Tignanelli
Journal:  JMIR Med Inform       Date:  2021-11-18

3.  Optimization of Cervical Cancer Screening: A Stacking-Integrated Machine Learning Algorithm Based on Demographic, Behavioral, and Clinical Factors.

Authors:  Lin Sun; Lingping Yang; Xiyao Liu; Lan Tang; Qi Zeng; Yuwen Gao; Qian Chen; Zhaohai Liu; Bin Peng
Journal:  Front Oncol       Date:  2022-02-15       Impact factor: 6.244

  3 in total

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