Context: Candidates taking the Comprehensive Osteopathic Medical Licensing Examination-USA Level 2-Performance Evaluation (COMLEX-USA Level 2-PE) are currently evaluated on their ability to document clinical findings using a handwritten postencounter note. However, keyboard data entry is increasingly used for medical documentation.
Objective: To determine the use and perception of keyboard data entry among osteopathic medical students and residents in educational and clinical settings.
Methods: A Web-based survey regarding frequency of and preference for keyboard data entry was distributed to 9801 osteopathic medical students, 17,268 osteopathic residents, and 34 clinical deans of colleges of osteopathic medicine (COMs). In addition, 31 COMs' clinical skills center directors were contacted to participate in a telephone survey about the use of keyboard data entry in their centers.
Results: A total of 1711 students, 1198 residents, 14 clinical deans, and 17 clinical skills center directors responded to the surveys. The majority of students (872 [51%]) reported using electronic keyboard data entry at their COM's clinical skills center for postencounter notes. Among respondents, 379 students (23%), 77 residents (9%), and 1 clinical dean reported that electronic keyboard data entry is never or rarely used during clinical rotations. Most trainees (1592 students [93%], 864 residents [94%]) reported that they were either comfortable or very comfortable with typing. Given the option of recording methods for SOAP (subjective, objective, assessment, plan) note findings on the COMLEX-USA Level 2-PE, 7 clinical deans were unsure of their students' preferences, while the remaining favored keyboard data entry (5) over handwriting (2). The majority of student and resident respondents would choose keyboard data entry (1009 [60%] and 511 [55%], respectively).
Conclusion: Osteopathic medical students and residents are comfortable with typing; they are exposed to and would prefer using an electronic form of entry for medical documentation. These results support a conversion from written postencounter notes to keyboard data entry of notes on the COMLEX-USA Level 2-PE.
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http://dx.doi.org/10.7556/jaoa.2014.053 | DOI Listing |
Stat Biopharm Res
July 2024
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Conventionally, dose finding trials are based on dose-limiting toxicity (DLT) that only captures the most severe toxicities, e.g., treatment related grade 3 or higher toxicity according to the NCI Common Terminology Criteria for Adverse Events.
View Article and Find Full Text PDFiScience
November 2024
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.
In the field of steady-state visual evoked potential (SSVEP), stimulus paradigms are regularly arranged or mimic the style of a keyboard with the same size. However, stimulation paradigms have important effects on the performance of SSVEP systems, which correlate with the electroencephalogram (EEG) signal amplitude and recognition accuracy. This paper provides MP dataset that was acquired using a 12-target BCI speller.
View Article and Find Full Text PDFComput Biol Med
January 2025
Area 2 AI Corporation, 245 Main Street, Cambridge, 02142, MA, United States.
Objective: This paper aims to introduce and assess KeyGAN, a generative modeling-based keystroke data synthesizer. The synthesizer is designed to generate realistic synthetic keystroke data capturing the nuances of fine motor control and cognitive processes that govern finger-keyboard kinematics, thereby paving the way to support biomarker development for psychomotor impairment due to neurodegeneration.
Methods: KeyGAN is designed with two primary objectives: (i) to ensure high realism in the synthetic distributions of the keystroke features and (ii) to analyze its ability to replicate the subtleties of natural typing for enhancing biomarker development.
J Med Internet Res
October 2024
Department of Occupational Therapy, College of Medical Science, Soonchunhyang University, Asan, Republic of Korea.
J Med Internet Res
October 2024
Department of Psychiatry, University of Illinois Chicago, Chicago, IL, United States.
Background: Passive sensing through smartphone keyboard data can be used to identify and monitor symptoms of mood disorders with low participant burden. Behavioral phenotyping based on mobile keystroke data can aid in clinical decision-making and provide insights into the individual symptoms of mood disorders.
Objective: This study aims to derive digital phenotypes based on smartphone keyboard backspace use among 128 community adults across 2948 observations using a Bayesian mixture model.
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