Thoracic Imaging 2003 - Society of Thoracic Radiology

Voice Recognition
Theresa C. McLoud, M.D.
Associate Radiologist-in-Chief, Director of Education; Thoracic Radiologist, Massachusetts General Hospital
Professor of Radiology, Harvard Medical School, Boston, Massachusetts
Voice recognition technology allows radiologists to achieve
efficiency goals and become more competitive in the digital
environment. It provides a link that can improve the speed of
communication between radiologists and their referring physicians.
Speech recognition systems were introduced into main
stream large radiology departments in the United States in the
mid 1990’s. Most systems require limited amounts of learning
and adaptation compared to other transcription systems and
methods. Most software packages allow easy integration into
existing radiology and hospital information systems. The major
disadvantage of voice recognition comes from radiologists’
resistance to change and fear of technology. In addition, voice
recognition does require more radiologist time and secretarial
skills. However, the benefits to the hospital, referring clinicians
and patients, are impressive because radiology reports are
immediately available on the radiology and hospital information
systems.
Voice recognition software at the human interface level comprises
four core technologies; 1) the recognition of spoken
human speech, 2) the synthesis of the spoken speech into readable
characters, 3) the identification of the speaker and author
verification, and 3) the understanding of the recognized word.
These technologies are often referred to as speech recognition
or speech-to-text; speech synthesis or text-to-speech; speaker
identification and verification; and natural language understanding.
Most voice recognition systems have certain hardware
requirements. These include specific processor and speeds, usually
a Pentium 200 MHz chip as well as an operating system
usually a Windows NT platform. In addition to individual
workstations, the system requires integration into a network.
The network allows integration into the radiology information
system and ultimately into PACS and the hospital information
system. Most systems also require the use of high quality
microphones and sound cards to achieve high accuracy rates.
Integration of a voice recognition system into a radiology
department is not a trivial process. Most systems offer several
key features. These include an RIS interface, i.e. an interface
with links to the radiology information system, the hospital
information system, PACS and the billing system. The second
component consists of standardized reports. These allow radiologists
to create pre-defined reports for individual radiologists or
the institution. The third components are templates/macros.
Most systems allow the creation of not only standard reports but
customizable templates and fields. The template capability
gives the radiologist the flexibility to create a form with blank
areas that vary with each dictation. Feature filled macros allow
standard phrases or components of report to be easily inserted in
the text. These functions allow greater efficiency. The fourth
component is customizable fields. Most packages permit customized
definitions by the institution and multiple fields associated
with the report. These fields may include ICD-9, CPT, BI-
RADS, ACR /NEMA codes or ACR pathology identifiers. The
fifth component includes bar code interfaces. Most voice recognition
software packages support the use of a bar code laser
reader or integrated microphone laser reader into the system.
The final component is a system for security.
The most important feature of speech recognition is report
turnaround. In our own institution, the previously utilized dictation
system required an average of over three days to complete.
This included transcription, review by the radiologist and
the resident. The introduction of voice recognition eliminates
the transcription and correction steps and a report by a staff
radiologist becomes immediately finalized after being dictated
and edited. The turnaround time in our own department has
now dropped to 0.4 days.
REFERENCES:
Mehta A. Voice Recognition. In: PACS. A Guide to the Digital
Revolution.
Eds: Dreyer KJ, Mehta A, Thrall JH. Springer-Verlag New York,
Inc., 2002.
Chapter 11, pages 281-302.
179
TUESDAY