JMIR Publications

ABSTRACT

Background:

The medical team on Intensive Care Units (ICU) 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 decreasing time for evidence-based patient-centred care. Information systems, such as Patient Data Management Systems (PDMS) are increasingly used at ICUs. However, they often create new challenges arising from increasing documentation burden.

Objective:

New concepts, such as artificial intelligence (AI) based, voice guided, 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.

Methods:

In this cross-over study, we compare traditional, paper-based documentation systems, with PDMS and newer AI-based, Voice Information and Documentation Systems (VIDS) in terms of performance (required time), accuracy, mental workload and user experience in an Intensive Care Setting. The performance is assessed on a set of six 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 two systems. The tasks were completed significantly faster with the VIDS than with the PDMS (t59= 12.48; P<.001; d=1.61) or paper documentation (t59 =20.41; P<.001; d=2.63). Significantly less errors were made with VIDS compared to the PDMS (t59=3.45; P=.03; d=0.45) and to paper-based documentation (t59=11.2; P<.001; d=1.45). The analysis of the mental workload of VIDS and PDMS showed no statistically significant difference (P=.06). However, the analysis of the 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 indicate that AI-based systems like the VIDS tested in this study have the potential to reduce this workload and improve evidence-based and safe patient care. Clinical Trial: Trial has been approved by the Ethics commission of the Medical Faculty of Rheinisch-Westfaelische Technische Hochschule Aachen under registration EK370/19.