Identifying and Reducing Insulin Errors in the Simulated Military Critical Care Air Transport Environment: A Human Factors Approach.

INTRODUCTION: During high-fidelity simulations in the Critical Care Air Transport (CCAT) Advanced course, we identified a high frequency of insulin medication errors and sought strategies to reduce them using a human factors approach. MATERIALS AND METHODS: Of 169 eligible CCAT simulations, 22 were randomly selected for retrospective audio-video review to establish a baseline frequency of insulin medication errors. Using the Human Factors Analysis Classification System, dosing errors, defined as a physician ordering an inappropriate dose, were categorized as decision-based; administration errors, defined as a clinician preparing and administering a dose different than ordered, were categorized as skill-based. Next, 3 a priori interventions were developed to decrease the frequency of insulin medication errors, and these were grouped into 2 study arms. Arm 1 included a didactic session reviewing a sliding-scale insulin (SSI) dosing protocol and a hands-on exercise requiring all CCAT teams to practice preparing 10 units of insulin including a 2-person check. Arm 2 contained arm 1 interventions and added an SSI cognitive aid available to students during simulation. Frequency and type of insulin medication errors were collected for both arms with 93 simulations for arm 1 (January-August 2021) and 139 for arm 2 (August 2021-July 2022). The frequency of decision-based and skill-based errors was compared across control and intervention arms. RESULTS: Baseline insulin medication error rates were as follows: decision-based error occurred in 6/22 (27.3%) simulations and skill-based error occurred in 6/22 (27.3%). Five of the 6 skill-based errors resulted in administration of a 10-fold higher dose than ordered. The post-intervention decision-based error rates were 9/93 (9.7%) and 23/139 (2.2%), respectively, for arms 1 and 2. Compared to baseline error rates, both arm 1 (P = .04) and arm 2 (P < .001) had a significantly lower rate of decision-based errors. Additionally, arm 2 had a significantly lower decision-based error rate compared to arm 1 (P = .015). For skill-based preparation errors, 1/93 (1.1%) occurred in arm 1 and 4/139 (2.9%) occurred in arm 2. Compared to baseline, this represents a significant decrease in skill-based error in both arm 1 (P < .001) and arm 2 (P < .001). There were no significant differences in skill-based error between arms 1 and 2. CONCLUSIONS: This study demonstrates the value of descriptive error analysis during high-fidelity simulation using audio-video review and effective risk mitigation using training and cognitive aids to reduce medication errors in CCAT. As demonstrated by post-intervention observations, a human factors approach successfully reduced decision-based error by using didactic training and cognitive aids and reduced skill-based error using hands-on training. We recommend the development of a Clinical Practice Guideline including an SSI protocol, guidelines for a 2-person check, and a cognitive aid for implementation with deployed CCAT teams. Furthermore, hands-on training for insulin preparation and administration should be incorporated into home station sustainment training to reduced medication errors in the operational environment.

The Ability of Military Critical Care Air Transport Members to Visually Estimate Percent Systolic Pressure Variation.

INTRODUCTION: Inappropriate fluid management during patient transport may lead to casualty morbidity. Percent systolic pressure variation (%SPV) is one of several technologies that perform a dynamic assessment of fluid responsiveness (FT-DYN). Trained anesthesia providers can visually estimate and use %SPV to limit the incidence of erroneous volume management decisions to 1-4%. However, the accuracy of visually estimated %SPV by other specialties is unknown. The aim of this article is to determine the accuracy of estimated %SPV and the incidence of erroneous volume management decisions for Critical Care Air Transport (CCAT) team members before and after training to visually estimate and utilize %SPV. MATERIAL AND METHODS: In one sitting, CCAT team providers received didactics defining %SPV and indicators of fluid responsiveness and treatment with %SPV ≤7 and ≥14.5 defining a fluid nonresponsive and responsive patient, respectively; they were then shown ten 45-second training arterial waveforms on a simulated Propaq M portable monitor's screen. Study subjects were asked to visually estimate %SPV for each arterial waveform and queried whether they would treat with a fluid bolus. After each training simulation, they were told the true %SPV. Seven days post-training, the subjects were shown a different set of ten 45-second testing simulations and asked to estimate %SPV and choose to treat, or not. Nonparametric limits of agreement for differences between true and estimated %SPV were analyzed using Bland-Altman graphs. In addition, three errors were defined: (1) %SPV visual estimate errors that would label a volume responsive patient as nonresponsive, or vice versa; (2) incorrect treatment decisions based on estimated %SPV (algorithm application errors); and (3) incorrect treatment decisions based on true %SPV (clinically significant treatment errors). For the training and testing simulations, these error rates were compared between, and within, provider groups. RESULTS: Sixty-one physicians (MDs), 64 registered nurses (RNs), and 53 respiratory technicians (RTs) participated in the study. For testing simulations, the incidence and 95% CI for %SPV estimate errors with sufficient magnitude to result in a treatment error were 1.4% (0.5%, 3.2%), 1.6% (0.6%, 3.4%), and 4.1% (2.2%, 6.9%) for MDs, RNs, and RTs, respectively. However, clinically significant treatment errors were statistically more common for all provider types, occurring at a rate of 7%, 10%, and 23% (all P < .05). Finally, students did not show clinically relevant reductions in their errors between training and testing simulations. CONCLUSIONS: Although most practitioners correctly visually estimated %SPV and all students completed the training in interpreting and applying %SPV, all groups persisted in making clinically significant treatment errors with moderate to high frequency. This suggests that the treatment errors were more often driven by misapplying FT-DYN algorithms rather than by inaccurate visual estimation of %SPV. Furthermore, these errors were not responsive to training, suggesting that a decision-making cognitive aid may improve CCAT teams' ability to apply FT-DYN technologies.

Hypopituitarism after multiple concussions: a retrospective case study in an adolescent male.

OBJECTIVE: To describe the development of hypopituitarism in an adolescent athlete after multiple concussions and to raise awareness among sports medicine clinicians concerning the growing concern of hypopituitarism in concussion injury surveillance and management. BACKGROUND: A 14-year-old, previously healthy male athlete suffered 4 head traumas over a 4-month period. The first 3 traumas were considered by the athlete to be minor and were not reported to medical personnel. The fourth trauma was a medically diagnosed concussion suffered during soccer play. Over the next year, the patient noted a decline in strength and conditioning and a failure to grow. DIFFERENTIAL DIAGNOSIS: After physical examination and a full battery of endocrine tests, the patient, then 16.5 years old, was diagnosed with hypopituitarism. Follow-up interviews provided evidence that at least 2 of the 3 head injuries suffered before the last concussion could also be considered concussions, which may have contributed to the severity of the last head injury. TREATMENT: The patient is currently being treated with physiologic replacement hormones (growth hormone, cortisol, and thyroxine), with resumption of linear growth and strength. He is progressing well. UNIQUENESS: In the past few years in the medical literature, increased attention has been drawn to the occult occurrence of hypopituitarism after traumatic brain injury in adults. Initial reports indicate that children are also at risk. To our knowledge, this is the first reported case of hypopituitarism after mild traumatic brain injury in the sports medicine literature. CONCLUSIONS: Symptoms of hypopituitarism are often masked by trauma and postconcussion symptoms and may not appear until months or years after the trauma incident, which can lead to significant delay in proper diagnosis and treatment. We urge greater vigilance by, and training of, sports medicine clinicians toward the goal of recognizing the possibility of pituitary disorders after sports concussion.