Biology subjects

Nathanson, L. A.

Publications and source records attributed to Nathanson, L. A..

4 recordsLinked to original sources

bioRxiv2017

Evaluation of the Angus ICD9-CM Sepsis Abstraction Criteria

ObjectiveValidate the infection component of the Angus International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9-CM) sepsis abstraction criteria\n\nDesignObservational cohort study\n\nSetting55,000 visits/year Adult Emergency Department (ED)\n\nPatientsAll consecutive ED patient visits between 12/16/2011 and 08/13/2012 were included in the study. Patients were excluded if there was a missing outcome measure.\n\nInterventionsNone.\n\nMeasurements and Main ResultsThe primary outcome measure was suspected infection at conclusion of the ED work-up as judged by the physician. There were 34,796 patients who presented to the ED between 12/16/11 and 8/13/12, of which 31,755 (91%) patients were included and analyzed. The original Angus sepsis abstraction criteria had a sensitivity of 55%, specificity of 97%, PPV of 82%, NPV of 88%, accuracy of 87%, and a F1 score of 0.66. The modified Angus sepsis abstraction criteria which includes codes added after the original publication had a sensitivity of 65%, specificity of 96%, PPV of 81%, NPV of 91%, accuracy of 89%, and F1 score of 0.72.\n\nConclusionsIn our study, the Angus abstraction criteria have high specificity (97%), but moderate sensitivity (55%) in identifying patients with suspected infection as defined by physician at the time of disposition from the emergency department. Given these findings, it is likely that we are underestimating the true incidence of sepsis in the United States and worldwide.

bioinformatics↗

bioRxiv2017

Derivation and Validation of a Record Linkage Algorithm between EMS and the Emergency Department

BackgroundLinking EMS electronic patient care reports (ePCRs) to ED records can provide clinicians access to vital information that can alter management. It can also create rich databases for research and quality improvement. Unfortunately, previous attempts at ePCR - ED record linkage have had limited success.\n\nObjectiveTo derive and validate an automated record linkage algorithm between EMS ePCRs and ED records using supervised machine learning.\n\nMethodsAll consecutive ePCRs from a single EMS provider between June 2013 and June 2015 were included. A primary reviewer matched ePCRs to a list of ED patients to create a gold standard. Age, gender, last name, first name, social security number (SSN), and date of birth (DOB) were extracted. Data was randomly split into 80%/20% training and test data sets. We derived missing indicators, identical indicators, edit distances, and percent differences. A multivariate logistic regression model was trained using 5k fold cross-validation, using label k-fold, L2 regularization, and class re-weighting.\n\nResultsA total of 14,032 ePCRs were included in the study. Inter-rater reliability between the primary and secondary reviewer had a Kappa of 0.9. The algorithm had a sensitivity of 99.4%, a PPV of 99.9% and AUC of 0.99 in both the training and test sets. DOB match had the highest odd ratio of 16.9, followed by last name match (10.6). SSN match had an odds ratio of 3.8.\n\nConclusionsWe were able to successfully derive and validate a probabilistic record linkage algorithm from a single EMS ePCR provider to our hospital EMR.

bioinformatics↗

bioRxiv2017

Consensus Development Of A Modern Ontology Of Emergency Department Presenting Problems: The HierArchical Presenting Problem OntologY (HaPPy)

ObjectiveNumerous 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 none were freely sharable and contained 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.\n\nMaterials and MethodsWe 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 SNOMED-CT.\n\nResultsOur system consists of a polyhierarchical ontology containing 692 unique concepts, 2,118 synonyms, and 30,613 non-visible descriptions to correct misspellings and non-standard terminology. Our ontology successfully captured structured data for 95.9% of visits in our validation dataset.\n\nDiscussion and ConclusionWe present the HierArchical Presenting Problem ontologY (HaPPy). This ontology was empirically derived 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.

bioinformatics↗

bioRxiv2017

Contextual Autocomplete: A Novel User Interface Using Machine Learning to Improve Ontology Usage and Structured Data Capture for Presenting Problems in the Emergency Department

ObjectiveTo determine the effect of contextual autocomplete, a user interface that uses machine learning, on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED).\n\nMaterials and MethodsWe used contextual autocomplete, a user interface that ranks concepts by their predicted probability, to help nurses enter data about a patients reason for visiting the ED. 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 prospective before-and-after study design.\n\nResultsA total of 279,231 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), as precise (3.59 vs. 3.74; p=0.1), and higher in overall quality (3.38 vs. 3.72; p=0.0002). 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.\n\nDiscussionWe have 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 hours to 4.8 hours.\n\nConclusionImplementation of a contextual autocomplete system resulted in improved structured data capture, ontology usage compliance, and data quality.

bioinformatics↗