Evidence map›Paper›PMID 29365341›Full record

ArticleApplied clinical informatics2018

Improving the Accuracy of a Clinical Decision Support System for Cervical Cancer Screening and Surveillance.

K E Ravikumar, Kathy L MacLaughlin, Marianne R Scheitel, Maya Kessler, Kavishwar B Wagholikar, Hongfang Liu, Rajeev Chaudhry

Abstract read
In one paragraph

Article in Applied clinical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

11 citing papers in PubMed.

  1. Process Mapping to Compare and Improve Management of Abnormal Cervical Cancer Screening Results in Two US Healthcare Systems within the PROSPR Consortium.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025
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4 · The record

Corrections and comments

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.

K E Ravikumar
Kathy L MacLaughlin
Marianne R Scheitel
Maya Kessler
Kavishwar B Wagholikar
Hongfang Liu
Rajeev Chaudhry

Funding

A Framework to Enhance Decision Support by Invoking NLP: Methods and ApplicationsR00LM011575 · NLM · MASSACHUSETTS GENERAL HOSPITAL · PI WAGHOLIKAR, KAVISHWAR B. · 2016 to 2018
$749k
NLP-enabled decision support for cervical cancer screening and surveillanceR21HS022911 · AHRQ · MAYO CLINIC ROCHESTER · PI CHAUDHRY, RAJEEV · 2014 to 2015
$290k
AHRQ HHS R21 HS022911NLM NIH HHS R00 LM011575
6 · The paper itself

Abstract

backgroundClinical decision support systems (CDSS) for cervical cancer prevention are generally limited to identifying patients who are overdue for their next routine/next screening, and they do not provide recommendations for follow-up of abnormal results. We previously developed a CDSS to automatically provide follow-up recommendations based on the American Society of Colposcopy and Cervical Pathology (ASCCP) guidelines for women with both previously normal and abnormal test results leveraging information available in the electronic medical record (EMR).

objectiveEnhance the CDSS by improving its accuracy and incorporating changes to reflect the latest revision of the guidelines.

methodsAfter making enhancements to the CDSS, we evaluated the performance of the clinical recommendations on 393 patients selected through stratified sampling from a set of 3,704 patients in a nonclinical setting. We performed chart review of individual patient's record to evaluate the performance of the system. An expert clinician assisted by a resident manually reviewed the recommendation made by the system and verified whether the recommendations were as per the ASCCP guidelines.

resultsThe recommendation accuracy of the enhanced CDSS improved to 93%, which is a substantial improvement over the 84% reported previously. A detailed analysis of errors is presented in this article. We fixed the errors identified in this evaluation that were amenable to correction to further improve the accuracy of the system. The source code of the updated CDSS is available at https://github.com/ohnlp/MayoNlpPapCdss.

conclusionWe made substantial enhancements to our earlier prototype CDSS with the updated ASCCP guidelines and performed a thorough evaluation in a nonclinical setting to improve the accuracy of the CDSS. The CDSS will be further refined as it is utilized in the practice.

Indexed as

Decision Support Systems, ClinicalEarly Detection of CancerFemaleHumansPopulation SurveillanceTime FactorsUterine Cervical Neoplasms

Identifiers

PMID29365341
PMCPMC5801884

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.