HLTH 2120 Week 3 Patient Weight Data Flow Map Example

Following One Admission Weight Through the Hospital: A Data Flow Map With Every Hop Named and What Can Go Wrong at Each

Student Name

Walden University

HLTH 2120: Health Informatics

Instructor Name

Month Day, Year

Following One Admission Weight Through the Hospital: A Data Flow Map With Every Hop Named and What Can Go Wrong at Each

The Element and Why It Was Chosen

This map follows a single data element, the admission weight of an adult patient on a medical-surgical unit, from the moment it is entered to the reports where it is read by people who never meet the patient. Weight was chosen because it is ordinary and because it matters. Many medications are dosed by weight, including some anticoagulants and antibiotics, and an error in the weight becomes an error in the dose. Medication safety experts recommend weighing patients on admission and recording weight in metric units only, because mixing pounds and kilograms has caused serious dosing errors (Institute for Safe Medication Practices, 2024). The hospital, unit and systems described are a composite of common designs, not a specific institution.

The Map

Table 1 sets out each hop: where the weight goes, who or what moves it, the rules attached and what can go wrong.

Table 1

Hops Taken by an Admission Weight From Entry to Report

Note. EHR = electronic health record. Hops and rules are typical of common designs and are illustrative.

Where the Weight Loses Its Context

The map shows that the number survives every hop, but its context does not. At hop 1, the nurse records the method, which tells every later reader whether to trust the value. That method field is used at hop 2, where it decides whether the value becomes the dosing weight, and it is visible at hop 3 if the pharmacist opens the flowsheet. By hop 5, the nightly extract drops it, so the quality department receives a number with a unit and a time but no indication of whether anyone put the patient on a scale. The most important caveat of all, a family member's guess about a patient who cannot stand, never left the nursing note.

This pattern, in which data are reused for purposes far from the one they were collected for, is a known source of data quality problems in health records. Assessments of electronic record data quality describe dimensions such as completeness, correctness and currency, and they note that data fit for one use may not be fit for another (Weiskopf & Weng, 2013). A stated weight may be good enough for a nutrition screen and not good enough for dosing an anticoagulant.

The Highest-Risk Hop

Hop 2 carries the most risk, because it is where a value becomes the dosing weight, the number that drives medication calculations. The automatic copy is safe when the value was measured. The risk lies in the hard stop that requires a dosing weight before some orders can be placed. A nurse admitting a patient who cannot stand, with orders waiting, may confirm a stated weight to clear the stop. The system then treats that value as if it were measured. Nothing about the downstream display tells the pharmacist that the weight came from a daughter's estimate.

Researchers studying computerized order entry have described this kind of problem as an unintended consequence of hard stops and required fields: staff find ways to satisfy the system that the designers did not anticipate (Campbell et al., 2006). The hard stop at hop 2 was added to prevent missing weights, and it succeeds at that. It also makes a guessed weight look like a measured one.

A second risk sits at hop 3, where a weight from a previous admission can appear if no current weight has been entered. Weight can change substantially between admissions, especially for patients with heart failure or kidney disease, and a pharmacist who does not notice the date may verify a dose against a value months out of date.

Three Changes, Ranked by Risk Reduced

Three changes follow from the map, in order of how much risk they reduce. First, carry the method of measurement with the weight through every hop, so the pharmacy display, the dietitian's form and the extract all show whether the value was measured. Second, require a measured weight within a set time after admission for patients receiving weight-based high-alert medications, with a flag on the medication record until it is entered. Third, change the quality report to use the first measured weight rather than the first weight of any kind.

None of these changes requires new technology. Each requires deciding that the context of a value is part of the value. That is the lesson of following one weight: data move easily between systems, but meaning does not move unless someone designs it to. Next week's evaluation of a medication administration system will look at the bedside end of the same chain, where the dose calculated from this weight reaches the patient.

References

Campbell, E. M., Sittig, D. F., Ash, J. S., Guappone, K. P., & Dykstra, R. H. (2006). Types of unintended consequences related to computerized provider order entry. Journal of the American Medical Informatics Association, 13(5), 547-556. https://doi.org/10.1197/jamia.M2042

Institute for Safe Medication Practices. (2024). Targeted medication safety best practices for hospitals. https://www.ismp.org/guidelines/best-practices-hospitals

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