225 research outputs found
Challenges of developing a digital scribe to reduce clinical documentation burden.
Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms
SPEECH TO CHART: SPEECH RECOGNITION AND NATURAL LANGUAGE PROCESSING FOR DENTAL CHARTING
Typically, when using practice management systems (PMS), dentists perform data entry by utilizing an assistant as a transcriptionist. This prevents dentists from interacting directly with the PMSs. Speech recognition interfaces can provide the solution to this problem. Existing speech interfaces of PMSs are cumbersome and poorly designed. In dentistry, there is a desire and need for a usable natural language interface for clinical data entry. Objectives. (1) evaluate the efficiency, effectiveness, and user satisfaction of the speech interfaces of four dental PMSs, (2) develop and evaluate a speech-to-chart prototype for charting naturally spoken dental exams. Methods. We evaluated the speech interfaces of four leading PMSs. We manually reviewed the capabilities of each system and then had 18 dental students chart 18 findings via speech in each of the systems. We measured time, errors, and user satisfaction. Next, we developed and evaluated a speech-to-chart prototype which contained the following components: speech recognizer; post-processor for error correction; NLP application (ONYX) and; graphical chart generator. We evaluated the accuracy of the speech recognizer and the post-processor. We then performed a summative evaluation on the entire system. Our prototype charted 12 hard tissue exams. We compared the charted exams to reference standard exams charted by two dentists. Results. Of the four systems, only two allowed both hard tissue and periodontal charting via speech. All interfaces required using specific commands directly comparable to using a mouse. The average time to chart the nine hard tissue findings was 2:48 and the nine periodontal findings was 2:06. There was an average of 7.5 errors per exam. We created a speech-to-chart prototype that supports natural dictation with no structured commands. On manually transcribed exams, the system performed with an average 80% accuracy. The average time to chart a single hard tissue finding with the prototype was 7.3 seconds. An improved discourse processor will greatly enhance the prototype's accuracy. Conclusions. The speech interfaces of existing PMSs are cumbersome, require using specific speech commands, and make several errors per exam. We successfully created a speech-to-chart prototype that charts hard tissue findings from naturally spoken dental exams
Improving speech recognition accuracy for clinical conversations
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 73-74).Accurate and comprehensive data form the lifeblood of health care. Unfortunately, there is much evidence that current data collection methods sometimes fail. Our hypothesis is that it should be possible to improve the thoroughness and quality of information gathered through clinical encounters by developing a computer system that (a) listens to a conversation between a patient and a provider, (b) uses automatic speech recognition technology to transcribe that conversation to text, (c) applies natural language processing methods to extract the important clinical facts from the conversation, (d) presents this information in real time to the participants, permitting correction of errors in understanding, and (e) organizes those facts into an encounter note that could serve as a first draft of the note produces by the clinician. In this thesis, we present our attempts to measure the performances of two state-of-the-art automatic speech recognizers (ASRs) for the task of transcribing clinical conversations, and explore the potential ways of optimizing these software packages for the specific task. In the course of this thesis, we have (1) introduced a new method for quantitatively measuring the difference between two language models and showed that conversational and dictational speech have different underlying language models, (2) measured the perplexity of clinical conversations and dictations and shown that spontaneous speech has a higher perplexity than dictational speech, (3) improved speech recognition accuracy by language adaptation using a conversational corpus, and (4) introduced a fast and simple algorithm for cross talk elimination in two speaker settings.by Burkay GĂĽr.M.Eng
DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record
Machine learning (ML) applications in healthcare are extensively researched,
but successful translations to the bedside are scant. Healthcare institutions
are establishing frameworks to govern and promote the implementation of
accurate, actionable and reliable models that integrate with clinical workflow.
Such governance frameworks require an accompanying technical framework to
deploy models in a resource efficient manner. Here we present DEPLOYR, a
technical framework for enabling real-time deployment and monitoring of
researcher created clinical ML models into a widely used electronic medical
record (EMR) system. We discuss core functionality and design decisions,
including mechanisms to trigger inference based on actions within EMR software,
modules that collect real-time data to make inferences, mechanisms that
close-the-loop by displaying inferences back to end-users within their
workflow, monitoring modules that track performance of deployed models over
time, silent deployment capabilities, and mechanisms to prospectively evaluate
a deployed model's impact. We demonstrate the use of DEPLOYR by silently
deploying and prospectively evaluating twelve ML models triggered by clinician
button-clicks in Stanford Health Care's production instance of Epic. Our study
highlights the need and feasibility for such silent deployment, because
prospectively measured performance varies from retrospective estimates. By
describing DEPLOYR, we aim to inform ML deployment best practices and help
bridge the model implementation gap
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