J Med Syst
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The widely used American Society of Anesthesiologists Physical Status (ASA PS) classification is subjective, requires manual clinician review to score, and has limited granularity. Our objective was to develop a system that automatically generates an ASA PS with finer granularity by creating a continuous ASA PS score. Supervised machine learning methods were used to create a model that predicts a patient's ASA PS on a continuous scale using the patient's home medications and comorbidities. ⋯ A model consisting of three random forest classifiers (split model) achieved the best Cohen's Kappa. The model's agreement with our anesthesiologists on the ASA PS 3 case pairs yielded fair to moderate Kappa values. The results suggest that the random forest split classification model can predict ASA PS with agreement similar to that of anesthesiologists reported in literature and produce a continuous score in which agreement in accurately judging granularity is fair to moderate.
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The health care reform initiative led by the Hong Kong government's Food and Health Bureau has started the implementation of an electronic sharing platform to provide an information infrastructure that enables public hospitals and private clinics to share their electronic medical records (EMRs) for improved access to patients' health care information. However, previous attempts to convince the private clinics to adopt EMRs to document health information have faced challenges, as the EMR adoption has been voluntary. The lack of electronic data shared by private clinics carries direct impacts to the efficacy of electronic record sharing between public and private healthcare providers. ⋯ The adoption factors identified are multifaceted, ranging from technological characteristics, clinician-technology interactions, skills and knowledge, and the user-workflow-technology fit. Other findings, which have been relatively underrepresented in previous studies, contribute unique insights about the influence of work and social environment on the adoption of EMRs, including limited clinic space and the effects of physicians' decision to use the technology on clinical staffs' adoption decisions. Potential strategies to address the concerns, overcome adoption barriers, and define relevant policies are discussed.
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The current state of clinical trials underscores a need for timely interventions to reduce the cost and length of the average trial. Newly developed health informatics technologies-including electronic health records, telemedicine systems, and mobile health applications-have recently been employed in a wide range of clinical trials in an effort to improve different aspects of the clinical trial process. The current review will focus on the observed benefits and drawbacks of using such technology to improve various patient-centered aspects of the clinical trial process, namely its potential to improve patient recruitment, patient retention, and data collection. Broad future challenges and opportunities in the field as a whole will also be covered.