AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
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AMIA Annu Symp Proc · Jan 2009
Video-mediated communication in hospice interdisciplinary team meetings: examining technical quality and content.
This study aims to determine how videoconferencing quality impacts the style and content of communication between members of hospice interdisciplinary teams and patients and their families. We videotaped video-calls between hospice teams and family caregivers based on the use of low-cost videophones. We assessed their audio and video quality using both a form that was filled out on site and a protocol for retrospective analysis. ⋯ The time spent on general informal talk was significantly correlated to the video and audio quality of the session (r=0.43 and 0.41 respectively, p<0.001). The time spent addressing psychosocial issues and on caregiver education correlated significantly to video and audio quality. This study demonstrates the potential of video-mediated communication that supports shared decision making in hospice.
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AMIA Annu Symp Proc · Jan 2009
The cognitive basis of effective team performance: features of failure and success in simulated cardiac resuscitation.
Despite a body of research on teams in other fields relatively little is known about measuring teamwork in healthcare. The aim of this study is to characterize the qualitative dimensions of team performance during cardiac resuscitation that results in good and bad outcomes. ⋯ Results suggest that deviation from the sequence suggested by the ACLS protocol had no impact on the outcome as the successful team deviated more from this sequence than the unsuccessful team. It isn't the deviation from the protocol per se that appears to be important, but how the leadership flexibly adapts to the situational changes with deviations is the crucial factor in team competency.
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AMIA Annu Symp Proc · Jan 2009
Care coordination and electronic health records: connecting clinicians.
To examine the association between use of electronic health records (EHR) and care coordination. ⋯ EHR use is associated with aspects of care coordination involving information transfer and communication of treatment goals.
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AMIA Annu Symp Proc · Jan 2009
Using Bayesian networks and rule-based trending to predict patient status in the intensive care unit.
Multivariate Bayesian models trained with machine learning, in conjunction with rule-based time-series statistical techniques, are explored for the purpose of improving patient monitoring. Three vital sign data streams and known outcomes for 36 intensive care unit (ICU) patients were captured retrospectively and used to train a set of Bayesian net models and to construct time-series models. Models were validated on a reserved dataset from 16 additional patients. ⋯ The model's AUC for predicting declining outcome increased from 70% to 85% when the model was indexed to personalized baselines for each patient. The rule-based trending and alerting system was accurate 100% of the time in alerting a subsequent decline in condition. These techniques promise to improve the monitoring of ICU patients with high-sensitivity alerts, fewer false alarms, and earlier intervention.
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AMIA Annu Symp Proc · Jan 2009
Inductive creation of an annotation schema and a reference standard for de-identification of VA electronic clinical notes.
Accessing both structured and unstructured clinical data is a high priority for research efforts. However, HIPAA requires that data meet or exceed a deidentification standard to assure that protected health information (PHI) is removed. This is a particularly difficult problem in the case of unstructured clinical free text and natural language processing (NLP) systems can be trained to automatically de-identify clinical text. ⋯ Annotation schema must be created that can be used to build reliable and valid reference standards to evaluate NLP systems for the deidentification task. We describe the inductive creation of an annotation schema and subsequent reference standard. We also provide estimates of the accuracy of human annotators for this particular task.