Background:Studies on nurse competence on alarm management are a few and tend to be focused on limited skills. In response to Phase II of implementing the National Patient Safety Goal on clinical alarm systems safety, this study assessed nurses’ perceived competence on physiologic monitors use in intensive care units (ICUs) and developed and validated a tool for this purpose.Methods:This descriptive study took place in a Magnet hospital in a Southwestern state of the U.S. A Nurse Competence on Philips Physiologic Monitors Use Survey was created and went through validation by 13 expert ICU nurses. The survey included 5 subscales with 59 rated items and two open-ended questions. Items on the first 4 subscales reflect most common tasks nurses perform using physiologic monitors. Items on the fifth subscale (advanced functions) reflect rarely used skills and were included to understand the scope of utilizing advanced physiologic monitors’ features. Thirty nurses from 4 adult ICUs were invited to respond to the survey.Results:Thirty nurses (100%) responded to the survey. The majority of nurses were from Neuro (47%) and Surgical Trauma (37%) ICUs. The data supported the high reliability and construct validity of the survey. At least one (3%) to 8 nurses (27%) reported lack of confidence on each item on the survey. On the first four subscales, 3% - 40% of the nurses reported they had never heard of or used 27 features/functions on the monitors. No relationships were found between subscales’ scores and demographic characteristics (p > .05). Nurses asked for training on navigating the central-station monitor and troubleshooting alarms, and the use of unit-specific super users to tailor training to users’ needs.Conclusion:This is the first study to create and test a list of competencies for physiologic monitors use. Rigorous, periodic and individualized training is essential for safe and appropriate use of physiologic monitors and to decrease alarm fatigue. Training should be comprehensive to include all necessary skills and should not assume proficiency on basic skills. Special attention should be focused on managing technical alarms. Increasing the number of super users is a recommended strategy for individualized and unit-specific training. There is a need for a usability testing of complex IT-equipped medical devices, such as physiologic monitors, for effective, efficient and safe navigation of the monitors.

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Background and purpose: Radiotherapy has been associated with late dose-dependent cardiovascular toxicity. In this cross-sectional pilot study, radiation dose distributions were correlated with areas of localized and diffuse myocardial fibrosis as measured by novel cardiac MRI (CMR) sequences including late gadolinium enhancement (LGE) and T1 mapping with the goal to identify early markers of myocardial damage. Materials and methods: Twenty-eight patients with chest tumors including lung, breast, esophagus, and lymphoma underwent CMR per study protocol on average 46.4 months (range 1.7-344.5) after radiotherapy. Patients without pretreatment cardiac history were included if the volume of heart receiving 5 Gy or more was at least 10% (V5Gy ≥ 10%). The association of LGE with cardiac dosimetric factors, clinical factors (e.g., tumor type, smoking history, BMI), and T1 values was analyzed. Results: Cardiac maximum (Dmax) and mean dose (Dmean) equivalent to doses delivered in 2 Gy fractions (EQD2) were on average 50.9 Gy (range 6.2-108.0) and 8.2 Gy (range 1.0-35.7), respectively, compared to 60.8 Gy (40.8-108.0) and 6.8 Gy (1.8-21.8) among the 9 patients with LGE. Doses were not different between patients with and without LGE (p = 0.16 and 0.56, respectively). The average T1 value of the left ventricle myocardium was 1009 ms (range 933-1117). No significant correlation was seen for heart Dmax and Dmean and T1 values (p = 0.14 and 0.58, respectively). In addition, no significant association between clinical factors and the development of LGE was identified. Conclusions: No relation between cardiac doses, the presence of LGE or T1 values was observed. Further study is needed to determine the benefit of CMR for detecting radiotherapy-related myocardial fibrosis.

Background Critically ill patients require constant point-of-care blood glucose testing to guide insulin-related decisions. Transcribing these values from glucometers into a paper log and the electronic medical record is very common yet error-prone in intensive care units, given the lack of connectivity between glucometers and the electronic medical record in many US hospitals. Objective We examined (1) transcription errors of glucometer blood glucose values documented in the paper log and in the electronic medical record vital signs flow sheet in a surgical trauma intensive care unit, (2) insulin errors resulting from transcription errors, (3) lack of documenting these values in the paper log and the electronic medical record vital signs flow sheet, and (4) average time for docking the glucometer. Methods This secondary data analysis examined 5049 point-of-care blood glucose tests. We obtained values of blood glucose tests from bidirectional interface software that transfers the meters’ data to the electronic medical record, the paper log, and the vital signs flow sheet. We obtained patient demographic and clinical-related information from the electronic medical record. Results Of the 5049 blood glucose tests, which were pertinent to 234 patients, the total numbers of undocumented or untranscribed tests were 608 (12.04%) in the paper log, 2064 (40.88%) in the flow sheet, and 239 (4.73%) in both. The numbers of transcription errors for the documented tests were 98 (2.21% of 4441 documented tests) in the paper log, 242 (8.11% of 2985 tests) in the flow sheet, and 43 (1.64% of 2616 tests) in both. The numbers of transcription errors per patient were 0.4 (98 errors/234 patients) in the paper log, 1 (242 errors/234 patients) in the flow sheet, and 0.2 in both (43 errors/234 patients). Transcription errors in the paper log, the flow sheet, and in both resulted in 8, 24, and 2 insulin errors, respectively. As a consequence, patients were given a lower or higher insulin dose than the dose they should have received had there been no errors. Discrepancies in insulin doses were 2 to 8 U lower doses in paper log transcription errors, 10 U lower to 3 U higher doses in flow sheet transcription errors, and 2 U lower in transcription errors in both. Overall, 30 unique insulin errors affected 25 of 234 patients (10.7%). The average time from point-of-care testing to meter docking was 8 hours (median 5.5 hours), with some taking 56 hours (2.3 days) to be uploaded. Conclusions Given the high dependence on glucometers for point-of-care blood glucose testing in intensive care units, full electronic medical record-glucometer interoperability is required for complete, accurate, and timely documentation of blood glucose values and elimination of transcription errors and the subsequent insulin-related errors in intensive care units.