: The Application of Data to Problem-Solving
In the modern era, there are few professions that do not to some extent rely on data. Stockbrokers rely on market data to advise clients on financial matters. Meteorologists rely on weather data to forecast weather conditions, while realtors rely on data to advise on the purchase and sale of property. In these and other cases, data not only helps solve problems, but adds to the practitioner’s and the discipline’s body of knowledge.Of course, the nursing profession also relies heavily on data. The field of nursing informatics aims to make sure nurses have access to the appropriate date to solve healthcare problems, make decisions in the interest of patients, and add to knowledge.In this Discussion, you will consider a scenario that would benefit from access to data and how such access could facilitate both problem-solving and knowledge formation.To Prepare:Reflect on the concepts of informatics and knowledge work as presented in the Resources.Consider a hypothetical scenario based on your own healthcare practice or organization that would require or benefit from the access/collection and application of data. Your scenario may involve a patient, staff, or management problem or gap.BELOW IS THE QUESTION——————Post a description of the focus of your scenario. Describe the data that could be used and how the data might be collected and accessed. What knowledge might be derived from that data? How would a nurse leader use clinical reasoning and judgment in the formation of knowledge from this experience.BELOW IS THE REQUIRED READING——————Required ReadingsMcGonigle, D., & Mastrian, K. G. (2017). Nursing informatics and the foundation of knowledge (4th ed.). Burlington, MA: Jones & Bartlett Learning.Chapter 1, “Nursing Science and the Foundation of Knowledge” (pp. 7-19)Chapter 2, “Introduction to Information, Information Science, and Information Systems” (pp. 21-33)Chapter 3, “Computer Science and the Foundation of Knowledge Model” (pp. 35-62)Nagle, L., Sermeus, W., & Junger, A. (2017). Evolving Role of the Nursing Informatics Specialist. In J. Murphy, W. Goosen, & P. Weber (Eds.), Forecasting Competencies for Nurses in the Future of Connected Health (212-221). Clifton, VA: IMIA and IOS Press. Retrieved from https://serval.unil.ch/resource/serval:BIB_4A0FEA56B8CB.P001/REFSweeney, J. (2017). Healthcare informatics. Online Journal of Nursing Informatics, 21(1).Required MediaLaureate Education (Producer). (2018). Health Informatics and Population Health: Trends in Population Health [Video file]. Baltimore, MD: Author.Accessible playerCredit: Provided courtesy of the Laureate International Network of Universities.Public Health Informatics Institute. (2017). Public Health Informatics: “translating” knowledge for health [Video file]. Retrieved from https://www.youtube.com/watch?v=fLUygA8HpfoPLEASE USE 7TH EDITION FORMAT AND ALSO PROVIDE 4 REFERENCIES NOT MORE THAN 5 YEARS. CORE SKILL: walking a specific clinical scenario up the DIKW ladder. The assignment is abstract until you anchor it in one concrete question, so pick the question first.
THE FRAMEWORK — the DIKW HIERARCHY (Data → Information → Knowledge → Wisdom), which is the organizing concept of nursing informatics:
— DATA: discrete, objective, uninterpreted facts. “Temp 38.9. HR 118. WBC 16.2. Lactate 2.4.”
— INFORMATION: data organized, structured, and given CONTEXT so it has meaning. “These values are outside this patient’s baseline and have moved in the same direction over six hours.”
— KNOWLEDGE: information synthesized with clinical understanding, relationships identified, patterns recognized. “This trajectory meets sepsis screening criteria; the pattern is consistent with an evolving infection, likely urinary given the indwelling catheter.”
— WISDOM: knowledge APPLIED with judgment, ethics, and human context. “Initiate the sepsis bundle — but this patient has a DNR and a documented goal of comfort, so I escalate to the provider and the family before proceeding, and I reassess what ‘benefit’ means for her.”
THE PROGRESSION IS THE POINT. Data alone is useless — a number without context. And WISDOM IS THE STEP MACHINES DO NOT TAKE: an algorithm can move from data to knowledge (that is exactly what a predictive sepsis model does), but the application of that knowledge to THIS patient’s values, goals, and circumstances requires a human. Saying this explicitly is the single strongest move available in this assignment, because it defines what the nurse contributes that the system cannot.
STRUCTURE YOUR POST: (1) pose a specific CLINICAL QUESTION you might ask in your practice — a real one (“which of my patients on the unit are at highest risk of a fall in the next shift?” or “is our CLABSI rate actually improving or is that a random fluctuation?”); (2) identify the DATA you would need and WHERE it lives (EHR fields, flowsheets, the incident reporting system, the barcode scanning log, the ADT feed, patient-reported outcomes); (3) describe the KNOWLEDGE you could derive from it; (4) describe how you would apply it with WISDOM, including the ethical and contextual judgment involved.
THE PRACTICAL LAYER worth adding: data QUALITY determines everything downstream. EHR data is captured for BILLING and DOCUMENTATION, not for analysis — it is inconsistently coded, missing not at random, and contaminated by copy-forward. Garbage in, garbage out. A nurse who understands the provenance of the data is far better positioned to interpret it than an analyst who does not.
ALSO NAME: standardized nursing terminologies (NANDA-I, NIC, NOC, SNOMED CT, LOINC) — because data cannot be aggregated or compared across settings unless it is captured in a STANDARD LANGUAGE, and this is precisely why nursing’s contribution has historically been invisible in health data. That point — that unstructured, non-standardized nursing documentation renders nursing’s impact unmeasurable and therefore unfundable — is a genuinely powerful argument and it is the professional stake in the whole informatics enterprise.
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