Journal of the American Medical Informatics Association : JAMIA
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J Am Med Inform Assoc · Nov 2011
The influence of computerized decision support on prescribing during ward-rounds: are the decision-makers targeted?
To assess whether a low level of decision support within a hospital computerized provider order entry system has an observable influence on the medication ordering process on ward-rounds and to assess prescribers' views of the decision support features. ⋯ The computerized alerts failed to target the doctors who were making the prescribing decisions on ward-rounds. Senior doctors were the decision makers, yet the junior doctors who used the system received the alerts. As a result, the alert information was generally ignored and not incorporated into the decision-making processes on ward-rounds. The greatest value of decision support in this setting may be in non-ward-round situations where senior doctors are less influential. Identifying how prescribing systems are used during different clinical activities can guide the design of decision support that effectively supports users in different situations. If confirmed, the findings reported here present a specific focus and user group for designers of medication decision support.
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To provide an overview and tutorial of natural language processing (NLP) and modern NLP-system design. ⋯ We describe the historical evolution of NLP, and summarize common NLP sub-problems in this extensive field. We then provide a synopsis of selected highlights of medical NLP efforts. After providing a brief description of common machine-learning approaches that are being used for diverse NLP sub-problems, we discuss how modern NLP architectures are designed, with a summary of the Apache Foundation's Unstructured Information Management Architecture. We finally consider possible future directions for NLP, and reflect on the possible impact of IBM Watson on the medical field.
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J Am Med Inform Assoc · Sep 2011
Multicenter StudyEnrollment into a time sensitive clinical study in the critical care setting: results from computerized septic shock sniffer implementation.
Recruitment of patients into time sensitive clinical trials in intensive care units (ICU) poses a significant challenge. Enrollment is limited by delayed recognition and late notification of research personnel. The objective of the present study was to evaluate the effectiveness of the implementation of electronic screening (septic shock sniffer) regarding enrollment into a time sensitive (24 h after onset) clinical study of echocardiography in severe sepsis and septic shock. ⋯ Automated electronic medical records screening improves the efficiency of enrollment and should be a routine tool for the recruitment of patients into time sensitive clinical trials in the ICU setting.
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Electronic personal health record systems (PHRs) support patient centered healthcare by making medical records and other relevant information accessible to patients, thus assisting patients in health self-management. We reviewed the literature on PHRs including design, functionality, implementation, applications, outcomes, and benefits. We found that, because primary care physicians play a key role in patient health, PHRs are likely to be linked to physician electronic medical record systems, so PHR adoption is dependent on growth in electronic medical record adoption. ⋯ These must be provided to support self-management and disease prevention if improvements in health outcomes are to be expected. Differences in patient motivation to use PHRs exist, but an overall low adoption rate is to be expected, except for the disabled, chronically ill, or caregivers for the elderly. Finally, trials of PHR effectiveness and sustainability for patient self-management are needed.
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To introduce the availability of grant-to-article linkage data associated with National Institutes of Health (NIH) grants and to perform a high-level analysis of the publication outputs and impacts associated with those grants. ⋯ The US PHS is effective at funding research with a higher-than-average impact. The data are amenable to further and much more detailed analysis.