NUR 6310 Module 4 Testing Paper: Pediatric Weight-Based Dosing Scenarios Before an Order Entry Upgrade Example

Testing the Orders That Could Kill: A Pediatric Dosing Test Plan Before an Order Entry Upgrade in Long Branch

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M.S. in Nursing, Nursing Informatics, Thomas Edison State University

NUR-6310: Nursing Informatics: Systems Life Cycle

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Month Day, Year

Testing the Orders That Could Kill: A Pediatric Dosing Test Plan Before an Order Entry Upgrade in Long Branch

The Upgrade

The hospital and its numbers are invented for this paper. A 260-bed hospital in Long Branch has a 20-bed pediatric unit and a pediatric section of its emergency department. The vendor of its electronic health record has released an upgrade that changes how medication orders are built, including new dose calculators and a new drug database. The upgrade will reach all units on one weekend. The vendor has tested the software, and the hospital's information technology team has confirmed that screens load and orders transmit to pharmacy. The informatics nurse assigned to pediatrics argued that neither kind of test checks the thing most likely to hurt a child: a wrong dose that the system fails to stop.

Why Testing Must Target Danger

Children's doses are calculated by weight, and errors of tenfold or more can occur when a decimal is misplaced or a weight is entered in pounds instead of kilograms. Adult defaults, such as a standard dose that appears when a drug is selected, can be dangerous for a small child. Decision support exists to catch these errors, but only if it is configured and working.

Kilbridge et al. (2006) described the method developed for the Leapfrog Group to evaluate order entry systems as hospitals had actually set them up. Hospital staff create test patients in their live system, with ages, weights, allergies, diagnoses and laboratory results, and then enter test orders designed to cause adverse drug events of known types, such as drug-allergy conflicts, excessive doses, drug-drug interactions, drug-diagnosis contraindications and drug-laboratory conflicts. The tool records whether the system generated an alert or stopped the order. The method tests the configured system, not the vendor's product in general.

Metzger et al. (2010) reported results from 62 hospitals that used the tool. On average, the systems detected only about half of the test orders that would have caused a fatal adverse drug event, and detection of orders that would have caused serious adverse events varied widely from hospital to hospital. Results did not depend much on which vendor's product a hospital used, suggesting that local configuration and decision support choices mattered more than the software brand.

What Can Go Wrong at Go-Live

Han et al. (2005) reported that after a children's hospital implemented a commercially sold order entry system, mortality among children transferred in from other hospitals rose from 2.80 percent to 6.57 percent over the following months. The authors pointed to changes in workflow, such as the system requiring patients to be registered before orders could be entered and medications being dispensed from a central pharmacy rather than from the unit, which delayed treatment for critically ill children. The implementation had been completed over six days. The study was observational, and other hospitals using similar systems did not report the same result, but it showed that a system change can harm children through workflow delays as well as through dosing errors.

The Test Plan

The plan has three parts.

Dangerous dose scenarios. The informatics nurse, a pediatric pharmacist and a pediatric hospitalist created twelve test patients from a newborn of 3.2 kilograms to an adolescent of 70 kilograms, including a child with kidney impairment and one with a documented allergy. For each, they wrote orders designed to cause harm: a tenfold overdose of morphine, acetaminophen above the daily maximum, gentamicin without adjustment for kidney function, an adult dose of an antibiotic for a toddler, a weight entered in pounds, a penicillin ordered for a child with a penicillin allergy and a drug interaction known to cause dangerous heart rhythms. In all, 46 test orders.

Workflow timing scenarios. The team timed how long it takes, in the test environment, to register an unidentified child arriving by ambulance, enter emergency orders and see them released by pharmacy, comparing the upgrade with the current system.

Regression scenarios. The team re-ran the hospital's existing pediatric order sets to confirm that their doses, frequencies and default weights calculated correctly in the new version.

Go or No-Go Criteria

The upgrade reaches pediatrics only if every test order that would cause a fatal event is stopped or produces a hard alert, at least 90 percent of serious-harm orders are caught, every order set calculates correctly and emergency registration and ordering take no longer than in the current system. If any criterion fails, pediatrics stays on the current version while the problem is fixed, even if adult units proceed.

Results of the First Run

In the first test run, 43 of 46 orders produced alerts or stops. Three did not: gentamicin without adjustment for kidney function, because the new database used a different creatinine field; a weight entered in pounds, which the system accepted without question; and one interaction. All three were fixed and re-tested before the go-live date, and a hard stop was added for weights that differ by more than 20 percent from the last recorded weight.

Limits

Test scenarios cover known dangers and may miss new ones. Testing takes pharmacist and physician time that is hard to spare. Passing tests does not prove safety in real use, so the plan includes extra pharmacist review of pediatric orders through the first fourteen days of live use.

Conclusion

Testing that confirms screens load does not show that a system protects children from dangerous doses. The Leapfrog method offered a way to test the configured system, national results showed that decision support often misses fatal orders and a pediatric case showed that workflow delays can harm children too. A plan with dangerous scenarios, timing tests and go or no-go criteria found three gaps before any child was exposed.

References

Han, Y. Y., Carcillo, J. A., Venkataraman, S. T., Clark, R. S. B., Watson, R. S., Nguyen, T. C., Bayir, H., & Orr, R. A. (2005). Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics, 116(6), 1506-1512. https://doi.org/10.1542/peds.2005-1287

Kilbridge, P. M., Welebob, E. M., & Classen, D. C. (2006). Development of the Leapfrog methodology for evaluating hospital implemented inpatient computerized physician order entry systems. Quality and Safety in Health Care, 15(2), 81-84. https://doi.org/10.1136/qshc.2005.014969

Metzger, J., Welebob, E., Bates, D. W., Lipsitz, S., & Classen, D. C. (2010). Mixed results in the safety performance of computerized physician order entry. Health Affairs, 29(4), 655-663. https://doi.org/10.1377/hlthaff.2010.0160