, CHAPTER LIST
Section I: Building Blocks of Health Informatics
Chapter 1: Informatics, Disciplinary Science, and the Foundation of Knowledge
Chapter 2: Introduction to Information, Information Science, and Information Systems
Chapter 3: Computer Science and the Foundation of Knowledge Model
Chapter 4: Introduction to Cognitive Science and Cognitive Informatics
Chapter 5: Ethical and Legal Aspects of Health Informatics
Section II: Choosing and Using Information Systems
Chapter 6: Systems Development Life Cycle
Chapter 7: Administrative Information Systems
Chapter 8: The Human-Technology Interface
Chapter 9: Electronic Security
Chapter 10: Workflow and Beyond Meaningful Use
Section III: Informatics Applications for Care Delivery
Chapter 11: The Electronic Health Record and Clinical Informatics
Chapter 12: Informatics Tools to Promote Patient Safety, Quality Outcomes, and
Interdisciplinary Collaboration
Chapter 13: Patient Engagement and Connected Health
Chapter 14: Using Informatics to Promote Community and Population Health
Section IV: Advanced Concepts in Health Informatics
Chapter 15: Informatics Tools to Support Healthcare Professional Education and
Continuing Education
Chapter 16: Data Mining as a Research Tool
Chapter 17: Finding, Understanding, and Applying Research Evidence in Practice
Chapter 18: Bioinformatics, Biomedical Informatics, and Computational Biology
Section V: Practice in the Future
Chapter 19: The Art of Delivering Patient-Centered Care in Technology-Laden
Environments
Chapter 20: Generating and Managing Organizational Knowledge
,SECTION I – BUILDING BLOCKS OF HEALTH INFORMATICS
Chapter 1 – Informatics, Disciplinary Science, and the Foundation of Knowledge
Question 1
A hospital quality improvement team collects patient fall incident reports, tallies the
number of falls per unit per month, and posts the numbers on a whiteboard. Nursing
staff glance at the numbers but do not change practice. A newly hired nurse
informaticist reviews the situation and notes that the organization has data but no
mechanism to transform it into actionable guidance.
Which step in the DIKW hierarchy is the organization failing to execute, and what is the
most accurate characterization of its deficiency?
A. The organization has information but lacks the structural framework to convert it into
knowledge applicable to fall prevention interventions.
B. The organization lacks data because incident reports are qualitative and cannot be
quantified without a formal taxonomy.
C. The organization has wisdom but is choosing not to apply it due to institutional
resistance.
D. The organization has knowledge but lacks the technology infrastructure to
disseminate it to bedside nurses.
Answer: A
Rationale: The organization has successfully aggregated raw counts (data) and
presented them in context (information), but has not synthesized patterns, relationships,
or causal reasoning that would constitute knowledge. The failure is at the data-to-
knowledge transformation stage. The incident numbers are quantifiable and have been
quantified, eliminating option B. Wisdom represents the integration of knowledge with
ethical and experiential judgment to guide action; there is no evidence that wisdom-
, level synthesis has occurred, making option C incorrect. Option D incorrectly
presupposes knowledge already exists in a transmissible form.
Key Words: DIKW hierarchy, data transformation, knowledge generation, fall prevention
informatics, information contextualization, knowledge deficit, quality improvement
Question 2
A clinical informatics specialist is asked to evaluate why a newly implemented clinical
decision support (CDS) system has not reduced medication error rates despite
containing extensive drug interaction rules. Upon investigation, she finds that alerts fire
for nearly every medication order, that nurses override 94% of alerts without reading
them, and that the underlying logic was not developed with clinician input.
Which DIKW-level failure most precisely characterizes this situation?
A. The CDS system contains data without information because the drug interaction rules
were never validated.
B. The system possesses information and knowledge but fails at the wisdom level
because alert design did not incorporate experiential clinical judgment.
C. The system lacks data because medication orders are not being captured with
sufficient granularity.
D. The failure is at the information level because the system cannot distinguish relevant
alerts from irrelevant ones.
Answer: B
Rationale: The CDS system contains validated drug interaction rules (knowledge), but
the design failed to incorporate the contextual, experiential, and ethical dimensions that
characterize wisdom-level application. Wisdom in the DIKW model involves knowing
when and how to act based on integrated understanding, including understanding
human factors such as alert fatigue. The rules are validated, refuting option A. Data
capture of medication orders is not identified as the deficiency, eliminating option C.
The system does distinguish alert types based on its rule base, but the failure is not at
the information level—it is at the wisdom-to-action interface, making option D
imprecise.
Section I: Building Blocks of Health Informatics
Chapter 1: Informatics, Disciplinary Science, and the Foundation of Knowledge
Chapter 2: Introduction to Information, Information Science, and Information Systems
Chapter 3: Computer Science and the Foundation of Knowledge Model
Chapter 4: Introduction to Cognitive Science and Cognitive Informatics
Chapter 5: Ethical and Legal Aspects of Health Informatics
Section II: Choosing and Using Information Systems
Chapter 6: Systems Development Life Cycle
Chapter 7: Administrative Information Systems
Chapter 8: The Human-Technology Interface
Chapter 9: Electronic Security
Chapter 10: Workflow and Beyond Meaningful Use
Section III: Informatics Applications for Care Delivery
Chapter 11: The Electronic Health Record and Clinical Informatics
Chapter 12: Informatics Tools to Promote Patient Safety, Quality Outcomes, and
Interdisciplinary Collaboration
Chapter 13: Patient Engagement and Connected Health
Chapter 14: Using Informatics to Promote Community and Population Health
Section IV: Advanced Concepts in Health Informatics
Chapter 15: Informatics Tools to Support Healthcare Professional Education and
Continuing Education
Chapter 16: Data Mining as a Research Tool
Chapter 17: Finding, Understanding, and Applying Research Evidence in Practice
Chapter 18: Bioinformatics, Biomedical Informatics, and Computational Biology
Section V: Practice in the Future
Chapter 19: The Art of Delivering Patient-Centered Care in Technology-Laden
Environments
Chapter 20: Generating and Managing Organizational Knowledge
,SECTION I – BUILDING BLOCKS OF HEALTH INFORMATICS
Chapter 1 – Informatics, Disciplinary Science, and the Foundation of Knowledge
Question 1
A hospital quality improvement team collects patient fall incident reports, tallies the
number of falls per unit per month, and posts the numbers on a whiteboard. Nursing
staff glance at the numbers but do not change practice. A newly hired nurse
informaticist reviews the situation and notes that the organization has data but no
mechanism to transform it into actionable guidance.
Which step in the DIKW hierarchy is the organization failing to execute, and what is the
most accurate characterization of its deficiency?
A. The organization has information but lacks the structural framework to convert it into
knowledge applicable to fall prevention interventions.
B. The organization lacks data because incident reports are qualitative and cannot be
quantified without a formal taxonomy.
C. The organization has wisdom but is choosing not to apply it due to institutional
resistance.
D. The organization has knowledge but lacks the technology infrastructure to
disseminate it to bedside nurses.
Answer: A
Rationale: The organization has successfully aggregated raw counts (data) and
presented them in context (information), but has not synthesized patterns, relationships,
or causal reasoning that would constitute knowledge. The failure is at the data-to-
knowledge transformation stage. The incident numbers are quantifiable and have been
quantified, eliminating option B. Wisdom represents the integration of knowledge with
ethical and experiential judgment to guide action; there is no evidence that wisdom-
, level synthesis has occurred, making option C incorrect. Option D incorrectly
presupposes knowledge already exists in a transmissible form.
Key Words: DIKW hierarchy, data transformation, knowledge generation, fall prevention
informatics, information contextualization, knowledge deficit, quality improvement
Question 2
A clinical informatics specialist is asked to evaluate why a newly implemented clinical
decision support (CDS) system has not reduced medication error rates despite
containing extensive drug interaction rules. Upon investigation, she finds that alerts fire
for nearly every medication order, that nurses override 94% of alerts without reading
them, and that the underlying logic was not developed with clinician input.
Which DIKW-level failure most precisely characterizes this situation?
A. The CDS system contains data without information because the drug interaction rules
were never validated.
B. The system possesses information and knowledge but fails at the wisdom level
because alert design did not incorporate experiential clinical judgment.
C. The system lacks data because medication orders are not being captured with
sufficient granularity.
D. The failure is at the information level because the system cannot distinguish relevant
alerts from irrelevant ones.
Answer: B
Rationale: The CDS system contains validated drug interaction rules (knowledge), but
the design failed to incorporate the contextual, experiential, and ethical dimensions that
characterize wisdom-level application. Wisdom in the DIKW model involves knowing
when and how to act based on integrated understanding, including understanding
human factors such as alert fatigue. The rules are validated, refuting option A. Data
capture of medication orders is not identified as the deficiency, eliminating option C.
The system does distinguish alert types based on its rule base, but the failure is not at
the information level—it is at the wisdom-to-action interface, making option D
imprecise.
