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Modeling Flow Sheet Data to Support Secondary Use

CIN: Computers, Informatics, Nursing: September 2017 - Volume 35 - Issue 9 - p 489–490
doi: 10.1097/01.NCN.0000525579.10283.b8


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Back to Top | Article Outline


GENERAL PURPOSE: To present a study designed to create data-driven information models from flow sheet data using a data-driven consensus-based method.

LEARNING OBJECTIVES/OUTCOMES: After completing this continuing education activity, you should be able to:

1. Describe the need for data-driven information models.

2. Discuss the methodology, results, and implications of the study.

  1. Secondary use of standardized flow sheet data includes all of the following except
    1. quality improvement.
    2. research.
    3. individual patient tracking.
  2. The types of information captured in flow sheets are called flow sheet
    1. standards.
    2. measures.
    3. summaries.
  3. Creation of the data-driven information model enables analysis of how interprofessional care is related to
    1. organizational goals.
    2. patient outcomes.
    3. resource allocation.
  4. Challenges encountered in normalizing data in clinical data repositories (CDRs) include
    1. data acquisition.
    2. local customization.
    3. standardized information architecture.
  5. Which of the following might result in multiple semantically equivalent flow sheet measures?
    1. customizing value sets by discipline
    2. reusing existing measures instead of creating new ones
    3. tracking measures during software upgrades
  6. To have valid conclusions about quality measures or research, semantically equivalent flow sheet measures must be
    1. linked.
    2. reused.
    3. separated.
  7. What is needed to map semantically equivalent concepts from flow sheet data?
    1. conceptual models
    2. information models
    3. value models
  8. For the study presented in this article, which term is used to represent assessment questions and interventions performed about a clinical topic?
    1. concepts
    2. structures
    3. topics
  9. Which information model created in the study had the greatest number of flow sheet measures mapped to it?
    1. Cardiovascular system
    2. Falls
    3. Pain
  10. The information models simplified the representation of the content in flow sheet data from 1552 flow sheet measures to 557
    1. classes.
    2. concepts.
    3. topics.
  11. The information models are intended to support data delivery when electronic health record (EHR) data are needed
    1. across units or settings.
    2. by single disciplines.
    3. for a unique unit or setting.
  12. Which tool is used by academic health centers to query their CDRs in comparable ways across systems?
    1. the Harris data-driven mapping model
    2. Informatics for Integrating Biology & the Bedside (i2b2)
    3. Mind Mapper
  13. The information models in this study build upon the model proposed by
    1. Chow.
    2. Kim.
    3. Warren.
  14. When EHR software vendors do not have common information models and support customization of their systems, the result is
    1. inconsistent data within and across systems.
    2. inability to communicate among systems.
    3. loss of data between departments.
  15. The authors of the study suggest that future research is needed to
    1. identify missing components of the concepts.
    2. increase the generalizability of the findings.
    3. use an electronic process to create the information models.
  16. Which terminology will be used to code the value sets for assessments, problems, and interventions?
    1. LOINC
    2. PCORI
    3. SNOMED-CT
  17. As described by the authors, using a manual process to create information models may increase
    1. redundancy.
    2. errors.
    3. inefficiency.
  18. Flow sheet data are challenging for secondary use because of multiple
    1. concepts represented.
    2. data entry points.
    3. semantically equivalent measures.
  19. The value of information models is that flow sheet data can be mapped and extracted regardless of the
    1. clinical experience of the researcher.
    2. information technology skills of the extractor.
    3. time frame, discipline, or setting in which documentation occurred.
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