Literature DB >> 33663756

Lessons From the Free-Text Epidemic: Opportunities to Optimize Deployment of Imaging Clinical Decision Support.

Jessica G Fried1, Jina Pakpoor2, Charles E Kahn3, Hanna M Zafar4.   

Abstract

OBJECTIVE: The Protecting Access to Medicare Act of 2014 requires clinicians to consult Appropriate Use Criteria (AUC) when ordering advanced imaging procedures. Free-text order indications are available when there is no applicable structured indication but are unscored by the AUC. We determined the proportion of free-text indications among all advanced imaging orders and the proportion of free-text indications that could be mapped to a single structured indication.
METHODS: All outpatient advanced diagnostic imaging orders placed in a large multisite health system were recorded after initial AUC deployment (November 20, 2017, to December 19, 2017). Clinicians were prompted upon order entry to select a structured indication or enter a free-text indication. We manually reviewed the two imaging examinations with the highest rate of free-text indications: enhanced CT abdomen/pelvis and unenhanced CT head. Regression analysis examined differences in patient-, imaging-, context-, and provider-level characteristics between scored and unscored examinations.
RESULTS: Among all 39,533 orders for advanced imaging procedures, 59% (23,267 of 39,533) were unscored by the system. The regression model c-statistic (0.50-0.55) demonstrated poor model fit to evaluate for differences between scored and unscored examinations. Free-text indications were found in 71% (16,440 of 23,267) of unscored examinations and 42% (16,440 of 39,533) of all examinations. Manual review of all 1,693 CT abdomen/pelvis and 1,527 CT head examinations with free-text indications revealed that 3,132 free-text indications (97%) could be mapped to a single existing structured indication. DISCUSSION: Of all initially placed outpatient advanced imaging procedure orders, 42% included free-text indications and 97% of manually reviewed free-text indications could be mapped to a single structured indication.
Copyright © 2021 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Appropriate Use Criteria; imaging clinical decision support; implementation science

Mesh:

Year:  2021        PMID: 33663756      PMCID: PMC9187972          DOI: 10.1016/j.jacr.2021.01.002

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   6.240


  16 in total

1.  Increasing the appropriateness of outpatient imaging: effects of a barrier to ordering low-yield examinations.

Authors:  Vartan M Vartanians; Christopher L Sistrom; Jeffrey B Weilburg; Daniel I Rosenthal; James H Thrall
Journal:  Radiology       Date:  2010-06       Impact factor: 11.105

Review 2.  Early Experience With Implementation of a Commercial Decision-Support Product for Imaging Order Entry.

Authors:  Timothy Huber; Cree M Gaskin; Arun Krishnaraj
Journal:  Curr Probl Diagn Radiol       Date:  2015-10-23

3.  Examining clinical decision support integrity: is clinician self-reported data entry accurate?

Authors:  Anurag Gupta; Ali S Raja; Ramin Khorasani
Journal:  J Am Med Inform Assoc       Date:  2013-07-25       Impact factor: 4.497

4.  Impact of a Health Information Technology-Enabled Appropriate Use Criterion on Utilization of Emergency Department CT for Renal Colic.

Authors:  Ali S Raja; Sarvenaz Pourjabbar; Ivan K Ip; Christopher W Baugh; Aaron D Sodickson; Michael O'Leary; Ramin Khorasani
Journal:  AJR Am J Roentgenol       Date:  2018-11-07       Impact factor: 3.959

5.  Medicare Imaging Demonstration Final Evaluation: Report to Congress.

Authors:  Justin W Timbie; Peter S Hussey; Lane F Burgette; Neil S Wenger; Afshin Rastegar; Ian Brantley; Dmitry Khodyakov; Kristin J Leuschner; Beverly A Weidmer; Katherine L Kahn
Journal:  Rand Health Q       Date:  2015-07-15

6.  Application of ACR appropriateness guidelines for spine MRI in the emergency department.

Authors:  Lubdha M Shah; Deanne Long; Diana Sanone; Anne M Kennedy
Journal:  J Am Coll Radiol       Date:  2014-04-24       Impact factor: 5.532

7.  Machine Learning for Automation of Radiology Protocols for Quality and Efficiency Improvement.

Authors:  Angad Kalra; Amit Chakraborty; Benjamin Fine; Joshua Reicher
Journal:  J Am Coll Radiol       Date:  2020-04-09       Impact factor: 5.532

8.  Implementation of a Computerized Decision Support System for Computed Tomography Scan Requests for Nontraumatic Headache in the Emergency Department.

Authors:  Ana Royuela; Cristina Abad; Agustina Vicente; Alfonso Muriel; Rut Romera; Borja M Fernandez-Felix; Jesus Corres; Patricia Fernandez Bustos; Angelica Ortega; Julio Heras-Mosteiro; Raquel Garcia Latorre; Javier Zamora
Journal:  J Emerg Med       Date:  2019-10-04       Impact factor: 1.484

9.  Natural Language Processing of Radiology Reports in Patients With Hepatocellular Carcinoma to Predict Radiology Resource Utilization.

Authors:  A D Brown; J R Kachura
Journal:  J Am Coll Radiol       Date:  2019-03-02       Impact factor: 5.532

10.  Clinical decision support for high-cost imaging: A randomized clinical trial.

Authors:  Joseph Doyle; Sarah Abraham; Laura Feeney; Sarah Reimer; Amy Finkelstein
Journal:  PLoS One       Date:  2019-03-15       Impact factor: 3.240

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