Question:

A 3-year-old boy is hospitalized in the pediatric intensive care unit due to management of septic shock. The patient's hospital course is complicated by central line–associated deep venous thrombosis in the right subclavian vein, and preparations are made for a continuous infusion of intravenous heparin. The dose of heparin is calculated in units/kg and prepared from a premixed bag of intravenous heparin. Shortly after the infusion of heparin has started, the patient becomes unresponsive and cannot be resuscitated. The team discovers that an excessively high dose of heparin was infused, resulting in overdose and massive cerebral bleeding. Which of the following strategies is most likely to reduce future occurrences of this error?

Adopting shared governance organizational principles

Conducting time-out prior to beginning infusion

Requiring 2 providers to calculate dose and rate

Reviewing checklists prior to drug administration

Using infusion pumps with dosing algorithms and preset limits

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Explanation:

Human factors engineering strategies
Reliability	Strategy	Description & examples
Highest	Forcing functions	Hard stops in design or process to eliminate risk of incorrect use Example: each anesthesia gas fits only one compatible socket & is not interchangeable
	Computerized automation	Automated processes to remove human effort & variations that cause error Example: automated vital signs monitoring
	Environment & physical layout	Workspace design to facilitate correct action & minimize error Example: look-alike drugs stocked in different locations
High	Standardization & simplification	Uniform processes to minimize variation, complexity & learning curve Example: every hospital unit follows the same process for heparin administration
High	Human-machine redundancy	Repetitive step to confirm correct action in an error-prone process Example: barcode scanning of medications in addition to visual inspection
Medium	Reminders, alerts & double-checks	Processes prompting providers to check actions to reduce errors Examples: drug-drug interaction alerts; time-out before procedures
Human factors engineering seeks to reduce error risk by designing systems based on expected human behaviors. Less reliable HFE strategies include trainings, policy changes & education.

Heparin administration is a high-risk process, often requiring manual preparation of infusion solution from premixed bags or vials, which may look alike despite highly variable concentrations (ranging from 1 to 20,000 units/mL). Infusion protocols (eg, units/hr, mL/hr, units/kg/hr) may differ even within the same hospital, necessitating dose conversions and increasing risk of mathematical error.

Human factors engineering (HFE) reduces error risk by designing processes responsive to expected human behaviors. According to the HFE hierarchy of effectiveness, strategies that remove or greatly reduce human effort for correct action are most effective. Infusion pumps with dosing algorithms and preset limits (ie, "smart pumps") apply the most effective HFE strategies to reduce heparin-associated errors:

Computerized automation replaces human effort with technology to reduce harmful process variation. Dosing algorithm software automates conversion between infusion protocols, preventing mathematical errors. Automation is appropriate for constant, predictable processes (eg, constant heparin infusion on a hospital floor) but is less suitable for dynamic processes requiring critical analysis and frequent adjustments (eg, administering heparin boluses in an operating room).
Forcing functions are hard stops in a design or process that eliminate the risk of incorrect action. Preconfigured dose limits represent a hard stop blocking infusion of excessive heparin doses.

(Choices A, B, C, and D) HFE strategies requiring greater human effort for correct action are comparatively less effective than forcing functions and automation, including:

Checklists (eg, reviewing list of sterile precautions prior to central line insertion)
"Forced pauses" or time-out (eg, prior to surgery to verify procedure and patient)
Double-checks (eg, requiring 2 providers to calculate an infusion dose)
Organizational policy change, including promoting shared governance (eg, employee involvement in structuring organizational culture)

Although these strategies are important and do reduce error risk, they are vulnerable to human factors such as confirmation bias (eg, tendency to interpret information toward an anticipated outcome, such as believing all items on a checklist are complete) and workarounds (bypassing protocols to increase efficiency, such as skipping certain components of a time-out to expedite a procedure).

Educational objective:
According to human factors engineering, the most effective error-prevention strategies include computerized automation and forcing functions, which promote correct action with minimal human effort. An example is a heparin infusion pump with dosing algorithms (automating mathematical conversions) and preset limits ("blocking" excess drug administration).