* Read the article on page 215.
* Take the test, recording your answers in the test answers section (Section B) of the CE enrollment form. Each question has only one correct answer.
* Complete registration information (Section A) and course evaluation (Section C).
* Mail completed test with registration fee to: Lippincott Williams & Wilkins, CE Group, 333 7th Avenue, 19th Floor, New York, NY 10001.
* Within 4-6 weeks after your CE enrollment form is received, you will be notified of your test results.
* If you pass, you will receive a certificate of earned contact hours and answer key. If you fail, you have the option of taking the test again at no additional cost.
* A passing score for this test is 13 correct answers.
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Registration Deadline: August 31, 2011
Lippincott William & Wilkins, publisher of CIN: Computers, Informatics, Nursing, will award 2.3 contact hours for this continuing nursing education activity.
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CE TEST QUESTIONS
GENERAL PURPOSE STATEMENT: To familiarize the registered professional nurse with natural language processing as an approach for capturing data from narratives and creating structured reports for computer processing.
LEARNING OBJECTIVES: After reading this article and taking this test, the nurse will be able to:
1. Describe the use of natural language processing for data collection.
2. Outline the methodology, results, and implications of the study described in this article.
1. Electronic health records capture data by
a. unstructured, coded formats only.
b. structured, coded formats only.
c. free-text formats only.
d. both structured, coded formats and free-text formats.
2. Using only structured, coded approaches for data entry into electronic health record systems may result in
a. inaccurate data collection.
b. duplicate data collection.
c. loss of information contained in narratives (free-text data).
d. increase in the number of medical errors.
3. Natural language processing offers an approach for capturing data from narratives and
a. creating structured reports.
b. creating free-text or unstructured reports.
c. providing evidence-based data.
d. providing clinical decision support.
4. Which natural language processing system was primarily discussed in this article?
5. A study by Bakken et al stated that nursing narratives are rich in
d. complete sentences.
6. In which hospital unit was this study conducted?
a. medical-surgical unit
b. pediatrics units
c. oncology unit
d. intensive care unit
7. The study analysis focused on extraction of data for quality and safety purposes and extraction of data
a. in general.
b. for nursing assessments.
c. for nursing interventions.
d. for patient education.
8. Which routine preprocessing is a required technical step for the natural language processing system used in the study?
a. defining all abbreviations
b. defining all medical words
c. placing a period at the end of each sentence in the text data
d. placing an additional space between each sentence in the text data
9. Which patient safety measure was one selected by the investigators to be extracted from free-text data?
b. pain management
c. wound management
d. medication adverse effects
10. Which terms were selected as a source of terms of relevance to support the gold standards necessary to compare terms extracted through natural language processing?
a. medical dictionary
b. hospital terminology list
c. clinical practice guidelines
d. JCAHO terminology list
11. How many nursing progress notes were processed by the natural language processing system?
12. One example of an abbreviation in a predefined hospital abbreviation list that is not in the natural language processing system used in the study is
13. Which of the following best describes an example of abbreviation that commonly appeared in the nursing free texts and could be interpreted with more than one meaning?
14. The term tylenol was selected from the clinical practice guidelines as related to
b. pain management.
15. About what percentage of terms extracted using the natural language processing system were matched with the terms of relevance to pain management?
16. Which terms frequently appeared in nursing free text but not in the natural language processing system as identified in this article?
17. While using abbreviations may be convenient and efficient, they may
a. result in inaccurate data processing.
b. hinder the use of the electronic health record.
c. be confusing to the clinician.
d. be a detriment to patient safety.
18. According to the authors of the study, natural language processing may provide a method to do all of the following except
a. assess nursing outcomes not captured through structured data entry.
b. assess the use of clinical decision support for patient care.
c. assist nurses in evaluating patient process.
d. assist nurses in determining which interventions are effective or ineffective.
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