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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.

The NP-FEP improved SE and facilitated achievement of goals related to risk factors in patients with cardiovascular disease for at least 1 year.

To solve the problems of high specific energy consumption and excessive harmful ions in the water production of a small reverse osmosis (RO) plant, a desalination system coupling RO and membrane capacitive deionization (MCDI) is proposed in this study. Aiming at producing two cubic meters per day of fresh water with a salt concentration of less than 280 mg L−1, parameter matching optimization was carried out on two desalination system schemes of one-stage two-section RO and one-stage three-section RO coupled with MCDI. The results were compared with the parameter matching optimization results of the one-stage one-section RO and the one-stage two-section pure RO desalination system. The results show that compared with the pure RO desalination mode, the seawater desalination mode coupled with RO and MCDI reduces the specific energy consumption under the same effluent salt concentration. Moreover, it decreases the feed water pressure in front of the RO membrane, which can reduce the standard of high-pressure pump in a small seawater desalination plant. The energy consumption of the one-stage three-section RO and MCDI coupling system is lower than that of the one- stage two-section RO and MCDI coupling system, and the feed water pressure is also lower.