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Anforderung von radiologischer Diagnostik in der Unfallchirurgie mittels mobiler Endgeräte

  • Konrad F. Fuchs,
  • Fabian Kerwagen,
  • Andreas S. Kunz,
  • Andrés Schulze,
  • Melanie Ullrich,
  • Maximilian Ertl,
  • Fabian Gilbert

摘要

Background

Time is a scarce resource for physicians. One medical task is the request for radiological diagnostics. This process is characterized by high administrative complexity and sometimes considerable time consumption. Measures that lead to an administrative relief in favor of patient care have so far been lacking.

Aim of the study

Process optimization of the request for radiological diagnostics. As a proof of concept the request for radiological diagnostics was conducted using a mobile, smartphone and tablet-based application with dedicated voice recognition software in the Department of Trauma Surgery at the University Hospital of Würzburg (UKW).

Material and methods

In a prospective study, time differences and efficiency of the mobile app-based method (ukw.mobile based Application = UMBA) compared to the PC-based method (PC-based application = PCBA) for requesting radiological services were analyzed. The time from the indications to the completed request and the time required to create the request on the device were documented and assessed. Due to the non-normal distribution of the data, a Mann-Whitney U test was performed.

Results

The time from the indications to the completed request was significantly (p < 0.05) reduced using UMBA compared to PCBA (PCBA: mean ± standard difference [SD] 19.57 ± 33.24 min, median 3.00 min, interquartile range [IQR] 1.00–30.00 min vs. UMBA: 9.33 ± 13.94 min, median 1.00 min, IQR 0.00–20.00 min). The time to complete the request on the device was also significantly reduced using UMBA (PCBA: mean ± SD 63.77 ± 37.98 s, median 51.96 s, IQR 41.68–68.93 s vs. UMBA: 25.21 ± 11.18 s, median 20.00 s, IQR 17.27–29.00 s).

Conclusion

The mobile, voice-assisted request process leads to a considerable time reduction in daily clinical routine and illustrates the potential of user-oriented, targeted digitalization in healthcare. In future, the process will be supported by artificial intelligence.