NURS 6412 Week 4 data quality audit example

Is Allergy Status Fit to Use? A Repeatable Data Quality Audit of One Element Across 60 Constructed Admissions

Student Name

College of Nursing, Walden University

NURS 6412: Information and Knowledge Management

Instructor Name

Month Day, Year

Is Allergy Status Fit to Use? A Repeatable Data Quality Audit of One Element Across 60 Constructed Admissions

The Element

The element audited is documented allergy status: the structured allergy list in the record, including an explicit entry of no known allergies when the patient has none. It is defined here as it would be in a dictionary entry: a coded list of substances to which the patient reports or has had a reaction, with reaction type, or a single explicit entry stating that the patient has no known allergies, recorded and reviewed during the current encounter. An empty allergy list is not the same as no known allergies. The first means nobody recorded anything; the second means somebody asked. The distinction matters because allergy lists drive medication alerts, and large record reviews show that reported drug allergies are common, with about a third of patients in one health system carrying at least one (Zhou et al., 2016).

Dimensions Measured

Three dimensions are measured, drawn from a harmonized framework for assessing record data quality (Kahn et al., 2016) and a review of the dimensions researchers use when reusing record data (Weiskopf & Weng, 2013). Completeness: is an allergy status present at all, either a list with at least one entry or an explicit no known allergies? Plausibility: is what is present believable, meaning an entry with a substance and a reaction, not an entry of unknown, a free-text note in the substance field or a no known allergies entry alongside a listed allergy? Timeliness: was the status recorded or reviewed within four hours of arrival on the unit, the point at which the first scheduled medications are usually due?

Method

Sample: 60 adult inpatient admissions to two medical-surgical units. The records are constructed for this exercise, built from the patterns described in the literature and in our unit's orientation materials, because a completeness rate taken from a live employer system is that organization's figure and does not belong in a course paper. Inclusion rule: every admission of an adult to either unit during a four-week period, excluding direct transfers from another hospital, since their allergy status is often carried from the sending record. Selection: all 60 admissions in the period, not a subset. Procedure: for each record, a single reviewer checked the allergy list at four hours after unit arrival and classified it using the rules above; a second reviewer independently classified 15 records, chosen by taking every fourth record, and the two agreed on 14 of 15. Anyone with the same records and these rules could repeat the count.

Findings

Table 1

Allergy Status in 60 Constructed Admissions, by Dimension

The timeliness counts apply only to the 53 records with a status present, and the remaining 7 had none at the four-hour check.

Interpretation

What the record shows is that allergy status is usually present but less often usable and timely. Only 44 of 60 records, about three in four, held a status that a medication alert could rely on, and fewer than two thirds held it by the time scheduled medications were likely to be given. The nine implausible entries fell into three groups. Five had free text such as see old chart or pt unsure in the substance field, which an alert cannot read. Three listed an allergy and also carried no known allergies, usually because a later reviewer added an allergy without removing the earlier entry. One recorded unknown, which is honest but leaves the question open.

What the record implies about practice is a separate question, and the audit can only suggest answers. The empty lists and late entries cluster in admissions that arrived between 1900 and 0700, which may reflect night staffing, or may reflect patients arriving from the emergency department with their allergy history already asked there and not re-entered. The contradictory entries suggest that the screen allows two incompatible states to exist at once, which is a design problem rather than a nursing one. Neither explanation can be confirmed from the records alone; each would need observation or interviews.

Two recommendations follow directly from the findings. The system should block saving a listed allergy while a no known allergies entry is active, prompting the user to remove one. And the four-hour check should become part of the admission task list, so that a missing status appears as an open task rather than an empty field. The same audit, repeated with the same rules three months after both changes, would show whether they worked.

Limits of This Audit

Four limits should be stated so that no one reads more into the counts than they can carry. The records are constructed, so the percentages illustrate how the method works rather than describing any real hospital; the method is the part meant to transfer. The sample covers four weeks on two units, which cannot show seasonal variation or differences on other units. Plausibility was judged against the record alone, so an allergy that was listed correctly but was not a true allergy, such as nausea recorded as an allergy to an opioid, would pass this audit even though it limits future prescribing. And the four-hour timeliness window is a practical choice tied to medication timing, not a published standard; a different window would change the timeliness counts. Each limit points to a next step: repeating the audit on real data under the hospital's own approvals, extending the period, and adding a check of reaction types. A reviewer who repeats the audit should keep the same rules even if they seem imperfect, so that the second count can be compared with the first.

References

Kahn, M. G., Callahan, T. J., Barnard, J., Bauck, A. E., Brown, J., Davidson, B. N., Estiri, H., Goerg, C., Holve, E., Johnson, S. G., Liaw, S.-T., Hamilton-Lopez, M., Meeker, D., Ong, T. C., Ryan, P., Shang, N., Weiskopf, N. G., Weng, C., Zozus, M. N., & Schilling, L. (2016). A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data. eGEMs, 4(1), Article 18. https://doi.org/10.13063/2327-9214.1244

Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. https://doi.org/10.1136/amiajnl-2011-000681

Zhou, L., Dhopeshwarkar, N., Blumenthal, K. G., Goss, F., Topaz, M., Slight, S. P., & Bates, D. W. (2016). Drug allergies documented in electronic health records of a large healthcare system. Allergy, 71(9), 1305-1313. https://doi.org/10.1111/all.12881