NU 725 Unit 3 data quality report example

Data Quality Report: Admission Weight in Kilograms on an Adult Medical Unit

Student Name

Department of Nursing, Herzing University

NU 725: Technology and Nursing Informatics in Advanced Practice

Instructor Name

February 3, 2026

Data Quality Report: Admission Weight in Kilograms on an Adult Medical Unit

The Element

Field: admission weight, recorded in kilograms in the admission navigator of the electronic health record. Data dictionary definition: "Patient's body weight measured on admission to the unit, in kilograms, with method of measurement." Entry screens: the emergency department triage flowsheet, the admission navigator on the unit, and the vital signs flowsheet. A separate field records the method: standing scale, bed scale, chair scale, stated by patient, or estimated. The field is used downstream by weight-based medication dosing, the pharmacy's dosing calculators, nutrition screening, and the hospital's coding of malnutrition and obesity. This report is written for the unit's practice council and pharmacy and therapeutics committee.

Quality Dimensions, Defined Before Measurement

Three dimensions are measured, using the terms of a harmonized framework for electronic health record data quality (Kahn et al., 2016). Completeness: a weight value is present in the admission field within the first 24 hours of admission. Accuracy, which Kahn and colleagues call plausibility and conformance: the value is recorded in kilograms, not pounds typed into a kilogram field; the method is recorded as measured on a scale rather than stated or estimated; and the value is within 5% of the next measured scale weight within 48 hours. Timeliness: the weight is recorded before the first weight-based medication order is signed. Defining each dimension in advance matters, because data that are adequate for one purpose, such as research on average weights, may be inadequate for another, such as dosing enoxaparin (Weiskopf & Weng, 2013).

Method

A random sample of 200 adult admissions to the medical unit between July and December was drawn from the admissions list using a random number generator. For each, the reviewer, an informatics nurse, recorded whether the admission weight was present, the method recorded, the value, the time it was entered, the time of the first weight-based medication order, and the next scale weight in the flowsheet within 48 hours. A second reviewer independently abstracted 30 of the records; agreement was 97% across fields, and disagreements were resolved by discussion. A pound-for-kilogram error was defined as an admission weight that was about 2.2 times the next measured weight.

Findings, With Counts and Denominators

The table reports each dimension against the number of records it applies to.

Interpretation

The field is usually present but often not measured. Nearly half of admission weights were stated or estimated rather than weighed, and a fifth of those that could be checked differed from the next scale weight by more than 5%. Estimated weights are known to be inaccurate; in a study of patients with acute stroke, weights estimated by physicians and nurses frequently differed from measured weights, which matters when medications are dosed by weight (Breuer et al., 2010). Three records contained a weight entered in pounds, making the recorded weight about 2.2 times the true value. In 31 of 152 admissions with a weight-based order, the order was signed before any weight was recorded, which means the prescriber either used a weight from a prior encounter or estimated one.

Downstream Consequences

Dosing: enoxaparin, heparin infusions, vancomycin, and several chemotherapy agents on this unit are dosed by weight. A 10% weight error produces a 10% dosing error; a pound-for-kilogram error more than doubles the dose. Two of the three unit errors in the sample were followed by weight-based orders, one for enoxaparin; the pharmacist caught the discrepancy in both cases. Reporting: the hospital's obesity prevalence and malnutrition screening rates are calculated from this field, so estimated and wrong-unit weights distort both. Billing: malnutrition and obesity diagnoses supported by body mass index affect coding and reimbursement, and documentation that rests on an estimated weight may not withstand audit. In each case, whatever is wrong in the field is inherited downstream without any flag.

Recommendations

First, require a measured weight at admission for all patients who can stand or sit, using the bed scale for others, and make "estimated" selectable only with a reason. Second, add a hard stop that rejects admission weights more than 30% different from the most recent weight in the record without confirmation, which would catch pound-for-kilogram errors. Third, display the weight method and time beside the weight in the ordering screen, so prescribers can see whether they are dosing on a measured or estimated value. Fourth, repeat this audit in three months using the same method and sample size to measure the effect.

References

Breuer, L., Nowe, T., Huttner, H. B., Blinzler, C., Kollmar, R., Schellinger, P. D., Schwab, S., & Kohrmann, M. (2010). Weight approximation in stroke before thrombolysis: The WAIST-Study: A prospective observational "dose-finding" study. Stroke, 41(12), 2867-2871. https://doi.org/10.1161/STROKEAHA.110.578062

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 1244. 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