N538 Module 1 assignment: nursing informatics language and body of knowledge paper, a full sample

What the Problem List Leaves Out: Standardized Nursing Language and the Body of Knowledge of Nursing Informatics

Student Name

Master of Science in Nursing Program, Aspen University

N538: Advanced Health Care Informatics

Instructor Name

Month Day, Year

What the Problem List Leaves Out: Standardized Nursing Language and the Body of Knowledge of Nursing Informatics

On the medical unit of a composite 300-bed community hospital, nurses assess every patient for aspiration risk on admission and every shift after. The finding lives in a flowsheet row and, when it is positive, in a nursing care plan built from a vendor list of problems. It does not appear on the patient's shared problem list, the list that physicians, therapists, pharmacists and the next facility actually read. When the same patient is transferred to a rehabilitation hospital, the receiving team gets a summary of care document that lists pneumonia, stroke and diabetes, but not the nursing judgment that most shaped the patient's last five days of meals.

That gap is the subject of this paper. It is a language problem before it is a technology problem, and it sits at the center of the body of knowledge that defines nursing informatics as a specialty. The paper describes that body of knowledge, explains why nursing data have been hard to share, reviews the evidence behind standardized nursing terminologies, examines how SNOMED CT and LOINC, the two reference terminologies that matter most here, carry nursing concepts across systems, and closes with recommendations an informatics nurse specialist could carry to the hospital's clinical documentation committee.

Nursing Informatics as a Specialty and a Body of Knowledge

According to the American Nurses Association (2022), nursing informatics as a specialty joining nursing science to the information, computer and analytical sciences, so that nurses can identify, manage and communicate what they know at every level, from a raw value on a flowsheet to the judgment that changes a protocol. The definition matters because it places the work in nursing, not in the information technology department. The informatics nurse is accountable for whether the data nurses record mean the same thing to everyone who later uses them.

The body of knowledge the specialty draws on has three strands. The first is nursing's own content: the phenomena nurses assess, diagnose and treat, and the outcomes they are responsible for. The second is the science of representing that content in a computable way, which includes terminologies, classifications, data models and standards. The third is the sociotechnical knowledge of how systems change work, including usability, workflow and implementation. A nurse who knows only the first strand documents well but cannot explain why the data vanish downstream. A nurse who knows only the second can build a data dictionary that no clinician will use.

For this course, the second strand is where the advanced work begins. Every module that follows, whether on interoperability, integration, health information exchange or quality reporting, rests on the question of whether a concept recorded in one place keeps its meaning in another.

Why Nursing Data Disappear

Nurses generate more documentation than any other discipline in the hospital record, yet very little of it is available for reuse. Westra et al. (2015) reported that essential standardized nursing data are seldom integrated into electronic health records and clinical data repositories, and that many separate efforts to implement standardized nursing languages had gone uncoordinated, producing duplicate work instead of shared resources. The result is familiar at the bedside: a hospital can report how many patients had a urinary catheter on a given day but not how many were assessed as being at risk for aspiration, or what nurses did about it.

Three causes explain most of the loss. First, vendor content is often local. A flowsheet row labeled "aspiration precautions" may have a different definition in each hospital that builds it, so the data cannot be combined. Second, nursing care plans are frequently stored in a separate module from the interdisciplinary problem list, so a nursing diagnosis never enters the document that travels with the patient. Third, nursing terms that are coded at all are often coded in an interface terminology that the receiving system does not recognize. Each cause has a different remedy, which is why the specialty treats language as a design decision rather than a clerical one.

The Evidence Behind Standardized Nursing Terminologies

The ANA recognizes several terminologies that support nursing practice, among them interface terminologies built for documentation, such as NANDA International, the Nursing Interventions Classification, the Nursing Outcomes Classification, the Omaha System, the International Classification for Nursing Practice and the Clinical Care Classification, and reference terminologies with broader scope, SNOMED CT and LOINC among them. Interface terminologies are designed to be readable by nurses at the point of care. Reference terminologies are designed to be computable, with each concept carrying a permanent identifier and formal relationships to other concepts.

The research base for the interface terminologies is large but uneven. In a systematic review of 312 studies, Tastan et al. (2014) found that 72.4% were descriptive, 18.9% observational and only 8.7% intervention studies, and that 72.1% of the reports concerned NANDA International, the Nursing Interventions Classification, the Nursing Outcomes Classification or a combination of them. The review supports two conclusions for practice. Nurses can use these terminologies reliably enough to describe care, but the evidence that coded nursing data improve patient outcomes is still thin, largely because so few systems have kept the coded data long enough to study them. A hospital that adopts a standardized terminology is therefore investing in future evidence as much as in current documentation.

Reference Terminologies and the Shared Problem List

The problem list is where nursing language most needs to meet the rest of the record. Recognizing this, Matney et al. (2012) developed a nursing problem list subset of SNOMED CT by querying the Unified Medical Language System for nursing diagnoses across four nursing terminologies that were also represented in SNOMED CT. From 1,320 initial concepts the team arrived at 369 SNOMED CT concepts suitable for the problem list, with the stated goal of letting nursing diagnoses sit beside medical diagnoses in one interdisciplinary list.

The subset has grown, but coverage remains incomplete. Kim et al. (2019) mapped four nursing terminologies against the 2017 edition of the subset and identified 1,470 unique nursing diagnosis concepts, of which the subset covered approximately 43%. Coverage varied by source: 90% for the Clinical Care Classification, 59% for NANDA International, 47% for the International Classification for Nursing Practice and 32% for the Omaha System. For the hospital in the opening case, that finding is practical rather than academic. Before the nursing diagnosis of aspiration risk can appear on the shared problem list, someone must confirm that the hospital's local term maps to a SNOMED CT concept in the subset, and must decide what to do with local terms that do not map.

Assessment data follow a parallel path through LOINC, which assigns codes to observations and to the questions on assessment instruments. When a swallow screen or a risk scale is represented in LOINC and its answers in SNOMED CT, a receiving system can read both the question that was asked and the answer that was recorded, instead of a label that only the sending hospital understands.

Applying the Body of Knowledge to the Case

An informatics nurse specialist asked to fix the missing aspiration risk would work through the three strands in order. The nursing content comes first: the documentation committee agrees on what aspiration risk means on this unit, which screening instrument establishes it, and which interventions follow a positive screen. The representation comes second: the screen's question and answer are bound to LOINC and SNOMED CT codes, the nursing diagnosis is mapped to a concept in the nursing problem list subset, and the care plan entry is configured to propose that concept for the interdisciplinary problem list when the screen is positive. The sociotechnical work comes last and takes longest: the proposal must be placed in the nurse's existing workflow rather than added as a separate step, and physicians must agree that nursing diagnoses belong on the shared list.

Success can be measured with data the hospital already has. The committee can track the percentage of patients with a positive swallow screen whose problem list carries the corresponding concept at discharge, and the percentage of transfer summaries that include it. A reasonable first target is that eight in ten positive screens reach the problem list within three months of go-live.

Recommendations

Three recommendations follow from the analysis. First, the hospital should adopt one interface terminology for nursing care plans across all units rather than allowing unit-built lists, and should choose it partly for its mapping to SNOMED CT, since coverage of the problem list subset differs widely between terminologies (Kim et al., 2019). Second, nursing diagnoses that affect care after discharge, such as aspiration risk, impaired skin integrity and fall risk, should be eligible for the interdisciplinary problem list and included in summary of care documents. Third, the hospital should contribute its unmapped local terms to national harmonization work instead of maintaining them privately, which is the coordinated approach the national action plan calls for (Westra et al., 2015).

Conclusion

The body of knowledge of nursing informatics is more than familiarity with software. It joins nursing's clinical content to the science of representing that content so it can be shared, and to the practical knowledge of how documentation changes are made to stick. The missing aspiration risk on a transfer summary shows why the combination matters. Standardized terminologies exist, the evidence for them is growing, and reference terminologies now provide a path for nursing diagnoses to sit on the same problem list as everyone else's. What remains is the local work of choosing, mapping and measuring, which is the work this course prepares an informatics nurse to lead.

References

American Nurses Association. (2022). Nursing informatics: Scope and standards of practice (3rd ed.). American Nurses Association.

Kim, J., Yao, Y., Macieira, T. G. R., & Keenan, G. (2019). An examination of the coverage of the SNOMED CT coded nursing problem list subset. JAMIA Open, 2(3), 386-391. https://doi.org/10.1093/jamiaopen/ooz023

Matney, S. A., Warren, J. J., Evans, J. L., Kim, T. Y., Coenen, A., & Auld, V. A. (2012). Development of the nursing problem list subset of SNOMED CT. Journal of Biomedical Informatics, 45(4), 683-688. https://doi.org/10.1016/j.jbi.2011.12.003

Tastan, S., Linch, G. C. F., Keenan, G. M., Stifter, J., McKinney, D., Fahey, L., Lopez, K. D., Yao, Y., & Wilkie, D. J. (2014). Evidence for the existing American Nurses Association-recognized standardized nursing terminologies: A systematic review. International Journal of Nursing Studies, 51(8), 1160-1170. https://doi.org/10.1016/j.ijnurstu.2013.12.004

Westra, B. L., Latimer, G. E., Matney, S. A., Park, J. I., Sensmeier, J., Simpson, R. L., Swanson, M. J., Warren, J. J., & Delaney, C. W. (2015). A national action plan for sharable and comparable nursing data to support practice and translational research for transforming health care. Journal of the American Medical Informatics Association, 22(3), 600-607. https://doi.org/10.1093/jamia/ocu011