No abstract

Background Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice. Objective The purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework. Methods An artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents. Results The Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (χ262=36.984; P=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension. Conclusions User acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

Roasted Trichosanthes kirilowii seeds have much more intense flavor than the raw seeds, and are commonly used as food and in the preparations of many medicinal formulations. Volatile constituents in the raw and roasted T. kirilowii seeds were separated by simultaneous distillation and extraction, and analyzed by gas chromatography-mass spectrometry on two capillary gas chromatography columns of different polarities (DB-WAX and HP-1). A total of 40 volatile compounds were identified in the raw seeds, with pentanal, 2-pentanol, styrene, (Z)-2-heptenal, (+)-calarene, and α-muurolene being the predominant compounds; 40 volatile compounds were also identified in the roasted seeds, with 3-methylbutanal, ethanol, 2-butanol, 2,3-butanediol, (E,E)-2,4-nonadienal, and 2-isopropyl-5-methyl-9-methylene-bicyclo[4.4.0]dec-1-ene being the most abundant compounds. A total of 15 compounds, mostly aldehydes, were common in both seeds. Roasting of T. kirilowii seeds resulted in a significant decrease in the levels of sesquiterpenes and short-chain aliphatic aldehydes. By contrast, high concentrations of 3-methylbutanal, ethanol, 2-butanol, and alkyl pyrazines were generated, which was responsible for the unique flavor of the roasted seeds. The study results may be useful for optimizing the roasting process and oil processing of T. kirilowii seeds.

No abstract