AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
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AMIA Annu Symp Proc · Jan 2017
Comparative StudyClinical Named Entity Recognition Using Deep Learning Models.
Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP) task to extract important concepts (named entities) from clinical narratives. Researchers have extensively investigated machine learning models for clinical NER. Recently, there have been increasing efforts to apply deep learning models to improve the performance of current clinical NER systems. ⋯ The evaluation results showed that the RNN model trained with the word embeddings achieved a new state-of-the- art performance (a strict F1 score of 85.94%) for the defined clinical NER task, outperforming the best-reported system that used both manually defined and unsupervised learning features. This study demonstrates the advantage of using deep neural network architectures for clinical concept extraction, including distributed feature representation, automatic feature learning, and long-term dependencies capture. This is one of the first studies to compare the two widely used deep learning models and demonstrate the superior performance of the RNN model for clinical NER.
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AMIA Annu Symp Proc · Jan 2017
Quantifying the Impact of Trainee Providers on Outpatient Clinic Workflow using Secondary EHR Data.
Providers today face productivity challenges including increased patient loads, increased clerical burdens from new government regulations and workflow impacts of electronic health records (EHR). Given these factors, methods to study and improve clinical workflow continue to grow in importance. ⋯ The purpose of this study is to demonstrate that secondary EHR data can be used to quantify that impact, with potentially important results for clinic efficiency and provider reimbursement models. Key findings from this study are that (1) Secondary EHR data can be used to reflect in clinic trainee activity, (2) presence of trainees, particularly in high-volume clinic sessions, is associated with longer session lengths, and (3) The timing of trainee appointments within clinic sessions impacts the session length.
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AMIA Annu Symp Proc · Jan 2017
Interactive Visualization and Exploration of Patient Progression in a Hospital Setting.
As medical organizations increasingly adopt the use of electronic health records (EHRs), large volumes of clinical data are being captured on a daily basis. These data provide comprehensive information about patients and have the potential to improve a wide range of application domains in healthcare. Physicians and clinical researchers are interested in finding effective ways to understand this abundance of data. ⋯ Through the use of optimized data structures and progressive visualization techniques, we allow users to interactively explore how patients and their progression change over time. Compared to existing techniques, our work provides additional flexibility in analyzing patient data and has the potential to be used in a real-time hospital setting. Finally, we demonstrate the utility of our approach using a publicly available intensive care unit (ICU) database.
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AMIA Annu Symp Proc · Jan 2016
Checklist as a Memory Externalization Tool during a Critical Care Process.
We analyzed user interactions with a paper-based checklist in a regional trauma center to inform the design of digital cognitive aids for safety-critical medical teamwork. An initial review of paper checklists from actual trauma resuscitations revealed that trauma team leaders frequently wrote notes on the checklist. To understand this notetaking practice, we performed content analysis of 163 checklists collected over the period of four months. ⋯ An analysis of types and amount of notes written by leaders of different experience levels showed that more experienced leaders recorded more patient values and physical findings, while less experienced leaders recorded more notes about their activities and task completion status. These findings suggested that a checklist designed for a high-risk, fast-paced medical event has evolved into a dual function tool, serving both as a compliance and memory aid. Based on these findings, we derived requirements for designing digital cognitive aids to support safety-critical medical teamwork.
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AMIA Annu Symp Proc · Jan 2015
Impact of Robotic Surgery on Decision Making: Perspectives of Surgical Teams.
There has been rapid growth in the purchase of surgical robots in both North America and Europe in recent years. Whilst this technology promises many benefits for patients, the introduction of such a complex interactive system into healthcare practice often results in unintended consequences that are difficult to predict. ⋯ Drawing on the approach of realist evaluation, we conducted a multi-site interview study across nine hospitals, interviewing 44 operating room personnel with experience of robotic surgery to gather their perspectives on how robotic surgery impacts surgeon decision making. The findings reveal both potential benefits and challenges of robotic surgery for decision making.