Objective The quality of colonoscopy procedures for colorectal cancer screening is often inadequate and varies widely among physicians. Routine measurement of quality is limited by the costs of manual review of free-t...
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Objective The quality of colonoscopy procedures for colorectal cancer screening is often inadequate and varies widely among physicians. Routine measurement of quality is limited by the costs of manual review of free-text patient charts. Our goal was to develop a natural language processing (NLP) application to measure colonoscopy quality. Materials and methods Using a set of quality measures published by physician specialty societies, we implemented an NLP engine that extracts 21 variables for 19 quality measures from free-text colonoscopy and pathology reports. We evaluated the performance of the NLP engine on a test set of 453 colonoscopy reports and 226 pathology reports, considering accuracy in extracting the values of the target variables from text, and the reliability of the outcomes of the quality measures as computed from the NLP-extracted information. Results The average accuracy of the NLP engine over all variables was 0.89 (range: 0.62-1.0) and the average F measure over all variables was 0.74 (range: 0.49-0.89). The average agreement score, measured as Cohen's kappa, between the manually established and NLP-derived outcomes of the quality measures was 0.62 (range: 0.09-0.86). Discussion For nine of the 19 colonoscopy quality measures, the agreement score was 0.70 or above, which we consider a sufficient score for the NLP-derived outcomes of these measures to be practically useful for quality measurement. Conclusion The use of NLP for information extraction from free-text colonoscopy and pathology reports creates opportunities for large scale, routine quality measurement, which can support quality improvement in colonoscopy care.
Background The re-use of patient data from electronic healthcare record systems can provide tremendous benefits for clinical research, but measures to protect patient privacy while utilizing these records have many ch...
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Background The re-use of patient data from electronic healthcare record systems can provide tremendous benefits for clinical research, but measures to protect patient privacy while utilizing these records have many challenges. Some of these challenges arise from a misperception that the problem should be solved technically when actually the problem needs a holistic solution. Objective The authors' experience with informatics for integrating biology and the bedside (i2b2) use cases indicates that the privacy of the patient should be considered on three fronts: technical de-identification of the data, trust in the researcher and the research, and the security of the underlying technical platforms. methods The security structure of i2b2 is implemented based on consideration of all three fronts. It has been supported with several use cases across the USA, resulting in five privacy categories of users that serve to protect the data while supporting the use cases. Results The i2b2 architecture is designed to provide consistency and faithfully implement these user privacy categories. These privacy categories help reflect the policy of both the Health Insurance Portability and Accountability Act and the provisions of the National Research Act of 1974, as embodied by current institutional review boards. Conclusion By implementing a holistic approach to patient privacy solutions, i2b2 is able to help close the gap between principle and practice.
Objective The Child Health Improvement through Computer Automation (CHICA) system is a decision-support and electronic-medical-record system for pediatric health maintenance and disease management. The purpose of this...
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Objective The Child Health Improvement through Computer Automation (CHICA) system is a decision-support and electronic-medical-record system for pediatric health maintenance and disease management. The purpose of this study was to explore CHICA's ability to screen patients for disorders that have validated screening criteria specifically tuberculosis (TB) and iron-deficiency anemia. Design Children between 0 and 11 years were randomized by the CHICA system. In the intervention group, parents were asked about TB and iron-deficiency risk, and physicians received a tailored prompt. In the control group, no screens were performed, and the physician received a generic prompt about these disorders. Results 1123 participants were randomized to the control group and 1116 participants to the intervention group. Significantly more people reported positive risk factors for iron-deficiency anemia in the intervention group (17.5% vs 3.1%, OR 6.6, 95% CI 4.5 to 9.5). In general, far fewer parents reported risk factors for TB than for iron-deficiency anemia. Again, there were significantly higher detection rates of positive risk factors in the intervention group (1.8% vs 0.8%, OR 2.3, 95% CI 1.0 to 5.0). Limitations It is possible that there may be more positive screens without improving outcomes. However, the guidelines are based on studies that have evaluated the questions the authors used as sensitive and specific, and there is no reason to believe that parents misunderstood them. Conclusions Many screening tests are risk-based, not universal, leaving physicians to determine who should have a further workup. This can be a time-consuming process. The authors demonstrated that the CHICA system performs well in assessing risk automatically for TB and iron-deficiency anemia.
Nearly a decade since the completion of the first draft of the human genome, the biomedical community is positioned to usher in a new era of scientific inquiry that links fundamental biological insights with clinical ...
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Nearly a decade since the completion of the first draft of the human genome, the biomedical community is positioned to usher in a new era of scientific inquiry that links fundamental biological insights with clinical knowledge. Accordingly, holistic approaches are needed to develop and assess hypotheses that incorporate genotypic, phenotypic, and environmental knowledge. This perspective presents translational bioinformatics as a discipline that builds on the successes of bioinformatics and health informatics for the study of complex diseases. The early successes of translational bioinformatics are indicative of the potential to achieve the promise of the Human Genome Project for gaining deeper insights to the genetic underpinnings of disease and progress toward the development of a new generation of therapies.
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