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CE Connection

The promise of big data

Improving patient safety and nursing practice

Author Information
doi: 10.1097/01.NURSE.0000482860.31028.8f
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INSTRUCTIONS The promise of big data: Improving patient safety and nursing practice


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Lippincott Williams & Wilkins, publisher of Nursing2016 journal, will award 2.0 contact hours for this continuing nursing education activity.

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The promise of big data: Improving patient safety and nursing practice

GENERAL PURPOSE: To provide information about how big data can positively impact patient care. LEARNING OBJECTIVES: After completing this continuing-education activity, you should be able to: 1. Compare current initiatives that focus on data collection. 2. Identify how big data can enhance nursing practice. 3. Describe the role of the nursing informatics specialist.

  1. One component of the Triple Aim of healthcare is to improve
    1. the patient experience.
    2. patient education.
    3. provider knowledge.
  2. Which is a strategic priority of the National Quality Strategy?
    1. clinical outcomes
    2. patient engagement
    3. wellness standards
  3. Clinical analytics are used to convey what to clinical users?
    1. data sets
    2. meaningful information
    3. use cases
  4. Which term best describes finding useful information or patterns in large data sets?
    1. data aggregation
    2. data normalization
    3. data mining
  5. Over the past few years, the focus in HIT has been
    1. implementing EHRs.
    2. integrating data.
    3. locating clinical data sources.
  6. Which “V” in the Four Vs of big data technologies refers to an integrated pool of multiple databases?
    1. variety
    2. veracity
    3. volume
  7. To create a big data “ecosystem” in nursing, numerous databases will need to be collected into a(n)
    1. electronic dashboard.
    2. interoperable program.
    3. single data platform.
  8. Which initiative launched in 2015 has the aim of building a massive research cohort of 1 million de-identified patient data sets?
    1. the Precision Medicine Initiative
    2. the National Database Initiative
    3. the Individualized Health and Medicine Initiative
  9. Which term describes the ability to identify specific patients at risk for adverse nurse-sensitive care outcomes?
    1. database-informed nursing
    2. individualized nursing
    3. precision nursing
  10. The most important application of advanced data technologies in nursing is its ability to
    1. reduce costs.
    2. provide clinical decision support.
    3. support patient safety.
  11. The IOM's 2010 report, The Future of Nursing, calls on professional nurses to assume which roles at the local, regional, state, and federal levels?
    1. educational
    2. leadership
    3. management
  12. Which of the following is accurate about the role of the nursing informatics specialist?
    1. The role encompasses all conceivable clinical data and information systems.
    2. The Department of Health and Human Services mandates that every acute care organization have a nursing informatics specialist.
    3. It's a board-certified specialty under the HIMSS.
  13. Which strategic priority is included in the “Guiding Principles for Big Data in Nursing” report?
    1. uses of data
    2. information security
    3. ethical dilemmas
  14. Using big data at the macro level is hindered by the lack of
    1. clinician involvement.
    2. information security.
    3. shared data definitions.
  15. Which is the chief strategic aim for nursing and nursing informatics related to big data technologies?
    1. set data interoperability
    2. formalize nursing standards
    3. provide clinical decision support
  16. A 2021 goal of the ONC is to achieve health data exchanges that
    1. integrate with patient home monitoring device databases.
    2. are reimbursable.
    3. provide clinical decision support.
  17. The biggest hurdle in achieving health data exchanges that are interoperable, secure, and standardized is a lack of
    1. funding.
    2. clinical data.
    3. stakeholders.
  18. Early adopters of big data in healthcare have focused on use cases supporting quality goals tied to
    1. decreased adverse events.
    2. improved care outcomes.
    3. reimbursement incentives.
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