MMHA 6600 Week 4 Weight Data Quality Memo Example

Memorandum

To: Chief Nursing Officer and Director of Pharmacy, Bramble Creek Hospital

From: Health Informatics Analyst (MMHA student)

Date: Month Day, Year

Re: Data quality of patient weight and its path to weight-based dose calculations

Summary

Patient weight is one of the most consequential numbers in our record because the order system uses it to calculate weight-based doses of heparin, enoxaparin, several antibiotics and most pediatric medications. A review of how weight enters the record found that it arrives by three routes, measured, stated by the patient or family, or estimated by staff, and that the record stores all three in the same field with no label. A fourth route, carrying forward a weight from a previous encounter, fills the field when nobody enters a new value. I recommend labeling the source of every weight and requiring a measured weight before the first weight-based dose wherever a scale is available.

How Weight Enters the Record

I traced weight entries on admission for one month across the emergency department and two inpatient units, interviewed triage and floor nurses, and reviewed the field's configuration with the build team. The figures below are illustrative values prepared for this sample memo.

Table 1

Source of the First Recorded Weight on Admission (Illustrative, One Month)

Nearly half of first weights were not measured, and the prescriber sees all four sources as the same plain number.

Why the Source Matters

Data quality frameworks distinguish whether a value is present, whether it is plausible and whether it is correct for the purpose at hand (Weiskopf & Weng, 2013). A stated weight is present and usually plausible, but its correctness for dosing depends on how recently the patient was weighed and how well they remember. A staff estimate is quicker still and less accurate; in an emergency department study, both doctors' and nurses' visual estimates were often wrong by more than ten percent (Menon & Kelly, 2005). A carried-forward weight may be months old and may predate a large change, such as fluid overload in heart failure or weight loss during cancer treatment.

For many drugs, a ten percent error is clinically minor. For weight-based anticoagulants and some antibiotics with narrow margins, it is not. The same number can be fit for one use and unfit for another, which is the core idea in the consumer view of data quality: quality depends on the task the data serve (Wang & Strong, 1996).

The Path From Field to Dose

Once a weight is in the field, it flows without further review. The dose calculator in the order composer reads the most recent weight and displays a calculated dose; the prescriber sees the result, not the source. Pharmacy verification checks the calculation but has no way to see whether the weight was measured. The medication administration record shows the dose, not the weight. At no point after entry does anyone see a label saying stated, estimated or carried forward.

Two safety reports in the past year, both near misses, involved enoxaparin doses calculated from weights carried forward from encounters more than a year earlier. In each case a nurse noticed that the patient looked very different from the weight on file.

Recommendations

The policy change will need exceptions for emergencies, where a dose cannot wait for a scale. In those cases the label itself is the safeguard: a prescriber who sees estimated beside a weight knows to reweigh and recalculate once the patient is stable. The monthly audit uses a measure that data quality frameworks for secondary use already describe, the share of values that conform to a stated rule (Kahn et al., 2016).

Table 2

Recommended Changes, Owner and Timing

The first two changes cost almost nothing and remove the silent default; the third puts the source where the decision is made.

What I Need From You

I ask for two decisions: approval to place the labeling and carry-forward changes in the next configuration cycle, and your joint sponsorship as the measured-weight policy moves through both nursing and pharmacy committees. I will report the first month's audit to both of you.

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), 1244. https://doi.org/10.13063/2327-9214.1244

Menon, S., & Kelly, A.-M. (2005). How accurate is weight estimation in the emergency department? Emergency Medicine Australasia, 17(2), 113-116. https://doi.org/10.1111/j.1742-6723.2005.00701.x

Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-33. https://doi.org/10.1080/07421222.1996.11518099

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