MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports.

Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient's chest, but requires specialized training for proper interpretation. With the advent of high performance general purpose computer vision algorithms, the accurate automated analysis of chest radiographs is becoming increasingly of interest to researchers. Here we describe MIMIC-CXR, a large dataset of 227,835 imaging studies for 65,379 patients presenting to the Beth Israel Deaconess Medical Center Emergency Department between 2011-2016. Each imaging study can contain one or more images, usually a frontal view and a lateral view. A total of 377,110 images are available in the dataset. Studies are made available with a semi-structured free-text radiology report that describes the radiological findings of the images, written by a practicing radiologist contemporaneously during routine clinical care. All images and reports have been de-identified to protect patient privacy. The dataset is made freely available to facilitate and encourage a wide range of research in computer vision, natural language processing, and clinical data mining.

Improving documentation of presenting problems in the emergency department using a domain-specific ontology and machine learning-driven user interfaces.

OBJECTIVES: To determine the effect of a domain-specific ontology and machine learning-driven user interfaces on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED). METHODS: As part of a quality improvement project, we simultaneously implemented three interventions: a domain-specific ontology, contextual autocomplete, and top five suggestions. Contextual autocomplete is a user interface that ranks concepts by their predicted probability which helps nurses enter data about a patient's presenting problems. Nurses were also given a list of top five suggestions to choose from. These presenting problems were represented using a consensus ontology mapped to SNOMED CT. Predicted probabilities were calculated using a previously derived model based on triage vital signs and a brief free text note. We evaluated the percentage and quality of structured data captured using a mixed methods retrospective before-and-after study design. RESULTS: A total of 279,231 consecutive patient encounters were analyzed. Structured data capture improved from 26.2% to 97.2% (p < 0.0001). During the post-implementation period, presenting problems were more complete (3.35 vs 3.66; p = 0.0004) and higher in overall quality (3.38 vs. 3.72; p = 0.0002), but showed no difference in precision (3.59 vs. 3.74; p = 0.1). Our system reduced the mean number of keystrokes required to document a presenting problem from 11.6 to 0.6 (p < 0.0001), a 95% improvement. DISCUSSION: We demonstrated a technique that captures structured data on nearly all patients. We estimate that our system reduces the number of man-hours required annually to type presenting problems at our institution from 92.5 h to 4.8 h. CONCLUSION: Implementation of a domain-specific ontology and machine learning-driven user interfaces resulted in improved structured data capture, ontology usage compliance, and data quality.

Consensus Development of a Modern Ontology of Emergency Department Presenting Problems-The Hierarchical Presenting Problem Ontology (HaPPy).

OBJECTIVE: Numerous attempts have been made to create a standardized "presenting problem" or "chief complaint" list to characterize the nature of an emergency department visit. Previous attempts have failed to gain widespread adoption as they were not freely shareable or did not contain the right level of specificity, structure, and clinical relevance to gain acceptance by the larger emergency medicine community. Using real-world data, we constructed a presenting problem list that addresses these challenges. MATERIALS AND METHODS: We prospectively captured the presenting problems for 180,424 consecutive emergency department patient visits at an urban, academic, Level I trauma center in the Boston metro area. No patients were excluded. We used a consensus process to iteratively derive our system using real-world data. We used the first 70% of consecutive visits to derive our ontology, followed by a 6-month washout period, and the remaining 30% for validation. All concepts were mapped to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT). RESULTS: Our system consists of a polyhierarchical ontology containing 692 unique concepts, 2,118 synonyms, and 30,613 nonvisible descriptions to correct misspellings and nonstandard terminology. Our ontology successfully captured structured data for 95.9% of visits in our validation data set. DISCUSSION AND CONCLUSION: We present the HierArchical Presenting Problem ontologY (HaPPy). This ontology was empirically derived and then iteratively validated by an expert consensus panel. HaPPy contains 692 presenting problem concepts, each concept being mapped to SNOMED CT. This freely sharable ontology can help to facilitate presenting problem-based quality metrics, research, and patient care.

Pain management framework in the emergency department: patterns in 40 emergency departments worldwide.

Many studies illustrate variation in pain management protocols in emergency medicine. This study examines analgesia frameworks in emergency departments (EDs) in multiple countries, compares them with the recent literature, and illuminates the variability in protocols and treatment. A survey was conducted assessing the pain management framework and practices in a convenience sample of 40 hospitals distributed over 22 countries. Most EDs (80%) indicated that pain intensity was routinely documented, most commonly (42.5%) using a verbal numerical 0-10 scale. Most (57.5%) reported specific protocols for specific conditions, with 56.5% reporting that these protocols were mandatory. Structured training was reported by 27.5% of responders. All (100%) reported analgesia administration in the trauma room. Oral paracetamol (67.5%) and intravenous morphine (92.5%) were the most commonly used analgesics. The variability in the pain management framework is high among EDs worldwide, highlighting the need for more international uniformity in analgesia practices in the ED.