RESUMO
BACKGROUND: The medical teams in intensive care units (ICUs) spend increasing amounts of time at computer systems for data processing, input, and interpretation purposes. As each patient creates about 1000 data points per hour, the available information is abundant, making the interpretation difficult and time-consuming. This data flood leads to a decrease in time for evidence-based, patient-centered care. Information systems, such as patient data management systems (PDMSs), are increasingly used at ICUs. However, they often create new challenges arising from the increasing documentation burden. OBJECTIVE: New concepts, such as artificial intelligence (AI)-based assistant systems, are hence introduced to the workflow to cope with these challenges. However, there is a lack of standardized, published metrics in order to compare the various data input and management systems in the ICU setting. The objective of this study is to compare established documentation and retrieval processes with newer methods, such as PDMSs and voice information and documentation systems (VIDSs). METHODS: In this crossover study, we compare traditional, paper-based documentation systems with PDMSs and newer AI-based VIDSs in terms of performance (required time), accuracy, mental workload, and user experience in an intensive care setting. Performance is assessed on a set of 6 standardized, typical ICU tasks, ranging from documentation to medical interpretation. RESULTS: A total of 60 ICU-experienced medical professionals participated in the study. The VIDS showed a statistically significant advantage compared to the other 2 systems. The tasks were completed significantly faster with the VIDS than with the PDMS (1-tailed t59=12.48; Cohen d=1.61; P<.001) or paper documentation (t59=20.41; Cohen d=2.63; P<.001). Significantly fewer errors were made with VIDS than with the PDMS (t59=3.45; Cohen d=0.45; P=.03) and paper-based documentation (t59=11.2; Cohen d=1.45; P<.001). The analysis of the mental workload of VIDS and PDMS showed no statistically significant difference (P=.06). However, the analysis of subjective user perception showed a statistically significant perceived benefit of the VIDS compared to the PDMS (P<.001) and paper documentation (P<.001). CONCLUSIONS: The results of this study show that the VIDS reduced error rate, documentation time, and mental workload regarding the set of 6 standardized typical ICU tasks. In conclusion, this indicates that AI-based systems such as the VIDS tested in this study have the potential to reduce this workload and improve evidence-based and safe patient care.
RESUMO
Achieving the combination of delayed and immediate release of a vaccine from a delivery device without applying external triggers remains elusive in implementing single administration vaccination strategies. Here a means of vaccine delivery is presented, which exploits osmosis to trigger delayed burst release of an active compound. Poly(ε-caprolactone) capsules of 2 mm diameter were prepared by dip-coating, and their burst pressure and release characteristics were evaluated. Burst pressures (in bar) increased with wall thickness (t in mm) following Pburst = 131(.) t + 3(.) 4 (R(2) = 0.93). Upon immersion in PBS, glucose solution-filled capsules burst after 8.7 ± 2.9 days. Copolymers of hydrophobic ε -caprolactone and hydrophilic polyethylene glycol were synthesized and their physico-chemical properties were assessed. With increasing hydrophilic content, the copolymer capsules showed increased water uptake rates and maximum weight increase, while the burst release was earlier: 5.6 ± 2.0 days and 1.9 ± 0.2 days for 5 and 10 wt% polyethylene glycol, respectively. The presented approach enables the reproducible preparation of capsules with high versatility in materials and properties, while these vaccine delivery vehicles can be prepared separately from, and independently of the active compound.
