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A classification approach to coreference in discharge summaries: 2011 i2b2 challenge.

ABSTRACT: To create a highly accurate coreference system in discharge summaries for the 2011 i2b2 challenge. The coreference categories include Person, Problem, Treatment, and Test.An integrated coreference resolution system was developed by exploiting Person attributes, contextual semantic clues, and world knowledge. It includes three subsystems: Person coreference system based on three Person attributes, Problem/Treatment/Test system based on numerous contextual semantic extractors and world knowledge, and Pronoun system based on a multi-class support vector machine classifier. The three Person attributes are patient, relative and hospital personnel. Contextual semantic extractors include anatomy, position, medication, indicator, temporal, spatial, section, modifier, equipment, operation, and assertion. The world knowledge is extracted from external resources such as Wikipedia.Micro-averaged precision, recall and F-measure in MUC, BCubed and CEAF were used to evaluate results.The system achieved an overall micro-averaged precision, recall and F-measure of 0.906, 0.925, and 0.915, respectively, on test data (from four hospitals) released by the challenge organizers. It achieved a precision, recall and F-measure of 0.905, 0.920 and 0.913, respectively, on test data without Pittsburgh data. We ranked the first out of 20 competing teams. Among the four sub-tasks on Person, Problem, Treatment, and Test, the highest F-measure was seen for Person coreference.This system achieved encouraging results. The Person system can determine whether personal pronouns and proper names are coreferent or not. The Problem/Treatment/Test system benefits from both world knowledge in evaluating the similarity of two mentions and contextual semantic extractors in identifying semantic clues. The Pronoun system can automatically detect whether a Pronoun mention is coreferent to that of the other four types. This study demonstrates that it is feasible to accomplish the coreference task in discharge summaries.

SUBMITTER: Xu Y

PROVIDER: S-EPMC3422828 | biostudies-other | 2012 Sep-Oct

REPOSITORIES: biostudies-other

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A classification approach to coreference in discharge summaries: 2011 i2b2 challenge.

Xu Yan Y Liu Jiahua J Wu Jiajun J Wang Yue Y Tu Zhuowen Z Sun Jian-Tao JT Tsujii Junichi J Chang Eric I-Chao EI

Journal of the American Medical Informatics Association : JAMIA 20120413 5

<h4>Objective</h4>To create a highly accurate coreference system in discharge summaries for the 2011 i2b2 challenge. The coreference categories include Person, Problem, Treatment, and Test.<h4>Design</h4>An integrated coreference resolution system was developed by exploiting Person attributes, contextual semantic clues, and world knowledge. It includes three subsystems: Person coreference system based on three Person attributes, Problem/Treatment/Test system based on numerous contextual semantic ...[more]

PMID: 22505762

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Project description:BackgroundMedical notes are a rich source of patient data; however, the nature of unstructured text has largely precluded the use of these data for large retrospective analyses. Transforming clinical text into structured data can enable large-scale research studies with electronic health records (EHR) data. Natural language processing (NLP) can be used for text information retrieval, reducing the need for labor-intensive chart review. Here we present an application of NLP to large-scale analysis of medical records at 2 large hospitals for patients hospitalized with COVID-19.ObjectiveOur study goal was to develop an NLP pipeline to classify the discharge disposition (home, inpatient rehabilitation, skilled nursing inpatient facility [SNIF], and death) of patients hospitalized with COVID-19 based on hospital discharge summary notes.MethodsText mining and feature engineering were applied to unstructured text from hospital discharge summaries. The study included patients with COVID-19 discharged from 2 hospitals in the Boston, Massachusetts area (Massachusetts General Hospital and Brigham and Women's Hospital) between March 10, 2020, and June 30, 2020. The data were divided into a training set (70%) and hold-out test set (30%). Discharge summaries were represented as bags-of-words consisting of single words (unigrams), bigrams, and trigrams. The number of features was reduced during training by excluding n-grams that occurred in fewer than 10% of discharge summaries, and further reduced using least absolute shrinkage and selection operator (LASSO) regularization while training a multiclass logistic regression model. Model performance was evaluated using the hold-out test set.ResultsThe study cohort included 1737 adult patients (median age 61 [SD 18] years; 55% men; 45% White and 16% Black; 14% nonsurvivors and 61% discharged home). The model selected 179 from a vocabulary of 1056 engineered features, consisting of combinations of unigrams, bigrams, and trigrams. The top features contributing most to the classification by the model (for each outcome) were the following: "appointments specialty," "home health," and "home care" (home); "intubate" and "ARDS" (inpatient rehabilitation); "service" (SNIF); "brief assessment" and "covid" (death). The model achieved a micro-average area under the receiver operating characteristic curve value of 0.98 (95% CI 0.97-0.98) and average precision of 0.81 (95% CI 0.75-0.84) in the testing set for prediction of discharge disposition.ConclusionsA supervised learning-based NLP approach is able to classify the discharge disposition of patients hospitalized with COVID-19. This approach has the potential to accelerate and increase the scale of research on patients' discharge disposition that is possible with EHR data.

Project description:Discharge summaries are a vital tool to communicate information from Hospital to Primary Care teams; updating GPs about what happened during an admission, and handing over care detailing any follow up care required. Historically, Discharge Summaries have been posted to hospitals, increasing costs for hospitals, creating administrative work for GP practices receiving the letters, and resulting in some letters being lost or delayed in reaching the GP, with implications for patient safety if follow up requests are not received and acted upon. In an effort to improve patient care, the Clinical Commissioning Group in Surrey drew up a contract with Ashford and St Peter's Foundation Trust, aiming to increase the percentage of discharge summaries sent electronically from the rate of 9% sent within 24 hours, to over 75%. This contract set targets of 50% in May, 65% in June, and 80% in July. Financial penalties would be imposed if targets were not achieved, starting in June 2013. The Trust set up a working group comprising of doctors, IT personnel and ward PAs to devise a multi-pronged solution to achieve this target. The electronic discharge summary system was reviewed and improvements were designed and developed to make the process of signing off letters easier, and transmission of signed off letters became automated rather than requiring manual transmission by ward PAs. Presentations and leaflets to explain the importance of prompt completion and transmission of discharge summaries were given to Doctors to improve compliance using the revised IT system. Figures on transmission rates were automatically emailed to key stakeholders every day (Ward PAs, Divisional Leads) showing performance on each ward. This helped identify areas requiring more intervention. Areas (e.g. Day Surgery) that had not used electronic discharge summaries were engaged with, and persuaded to take part. As a result, transmission rates of Discharge Summaries within 24 hours of patient discharge increased from 9% on May 11th 2013, to 76% by June 29th 2013. This has improved communication with GPs, led to more reliable handover of care, and reduced costs for the Trust (both in processing and postage costs, and by avoiding fines).

Project description:BackgroundAcute kidney injury (AKI) increases the risk of hospital readmission, chronic kidney disease, and death. Therefore, effective communication in discharge summaries is essential for safe transitions of care.ObjectiveThe objectives of this study were to determine the quality of discharge summaries in AKI survivors and identify predictors of higher quality discharge summaries.DesignRetrospective chart review.SettingTertiary care academic center in Ontario, Canada.PatientsWe examined the discharge summary quality of 300 randomly selected adult patients who survived a hospitalization with AKI at our tertiary care hospital, stratified by AKI severity. We included 150 patients each from 2015 to 2016 and 2018 to 2019, before and after introduction of a post-AKI clinic in 2017.MeasurementsWe reviewed charts for 9 elements of AKI care to create a composite score summarizing discharge summary quality.MethodsWe used multivariable logistic regression to identify predictors of discharge summary quality.ResultsThe median discharge summary composite score was 4/9 (interquartile range, 2-6). The least frequently mentioned elements were baseline creatinine (n = 55, 18%), AKI-specific follow-up labs (n = 66, 22%), and medication recommendations (n = 80, 27%). The odds of having a higher quality discharge summary (composite score ≥4/9) was greater for every increase in baseline creatinine of 25 μmol/L (adjusted odds ratio [aOR]: 1.27; 95% confidence interval [CI]: 1.03, 1.56), intrarenal etiology (aOR: 2.32; 95% CI: 1.26, 4.27), and increased AKI severity (stage 2 aOR: 2.57; 95% CI: 1.35, 4.91 and stage 3 aOR: 3.36; 95% CI: 1.56, 7.22). There was no association between discharge summary quality and the years before and after introduction of a post-AKI clinic (aOR: 0.77; 95% CI: 0.46, 1.29).LimitationsThe single-center study design limits generalizability.ConclusionsMost discharge summaries are missing key AKI elements, even in patients with severe AKI. These gaps suggest several opportunities exist to improve discharge summary communication following AKI.

Project description:BackgroundThe National Institute for Health and Care Excellence recommends documenting all delirium episodes in the discharge summary using the term "delirium". Previous studies demonstrate poor delirium documentation rates in discharge summaries and no studies have assessed delirium documentation quality. The aim of this study was to determine the frequency and quality of delirium documentation in discharge summaries and explore differences between medical and surgical services.MethodsThis was a multi-center retrospective chart review. We included 110 patients aged ≥ 65 years identified to have delirium during their hospitalization using the Chart-based Delirium Identification Instrument (CHART-DEL). We assessed the frequency of any delirium documentation in discharge summaries, and more specifically, for the term "delirium". We evaluated the quality of delirium discharge documentation using the Joint Commission on Accreditation of Healthcare Organization's framework for quality discharge summaries. Comparisons were made between medical and surgical services. Secondary outcomes included assessing factors influencing the frequency of "delirium" being documented in the discharge summary.ResultsWe identified 110 patients with sufficient chart documentation to identify delirium and 80.9 % of patients had delirium documented in their discharge summary ("delirium" or other acceptable term). The specific term "delirium" was reported in 63.6 % of all delirious patients and more often by surgical than medical specialties (76.5 % vs. 52.5 %, p = 0.02). Documentation quality was significantly lower by surgical specialties in reporting delirium as a diagnosis (23.5 % vs. 57.6 %, p < 0.001), documenting delirium workup (23.4 % vs. 57.6 %, p = 0.001), etiology (43.3 % vs. 70.4 %, p = 0.03), treatment (36.7 % vs. 66.7 %, p = 0.02), medication changes (44.4 % vs. 100 %, p = 0.002) and follow-up (36.4 % vs. 88.2 %, p = 0.01).ConclusionsThe frequency of delirium documentation is higher than previously reported but remains subpar. Medical services document delirium with higher quality, but surgical specialties document the term "delirium" more frequently. The documentation of delirium in discharge summaries must improve to meet quality standards.

Project description:BackgroundObtaining accurate clinical information about recent acute care visits is extremely important for outpatient providers. However, documents used to communicate this information are often difficult to use. This puts patients at risk of adverse events. Elderly patients who are seen by more providers and have more care transitions are especially vulnerable.ObjectiveThis study aimed to (1) identify the information about elderly patients' recent acute care visits needed to coordinate their care, (2) use this information to assess discharge summaries, and (3) provide recommendations to help improve the quality of electronic health record (EHR)-generated discharge summaries, thereby increasing patient safety.MethodsA literature review, clinician interviews, and a survey of outpatient providers were used to identify and categorize data needed to coordinate care for recently discharged elderly patients. Based upon those data, 2 guidelines for creating useful discharge summaries were created. The new guidelines, along with 17 previously developed medical documentation usability heuristics, were applied to assess 4 simulated elderly patient discharge summaries.ResultsThe initial research effort yielded a list of 29 items that should always be included in elderly patient discharge summaries and a list of 7 "helpful, but not always necessary" items. Evaluation of 4 deidentified elderly patient discharge summaries revealed that none of the documents contained all 36 necessary items; between 14 and 18 were missing. The documents each had several other issues, and they differed significantly in organization, layout, and formatting.ConclusionsVariations in content and structure of discharge summaries in the United States make them unnecessarily difficult to use. Standardization would benefit both patients, by lowering the risk of care transition-related adverse events, and outpatient providers, by helping reduce frustration that can contribute to burnout. In the short term, acute care providers can help improve the quality of their discharge summaries by working with EHR vendors to follow recommendations based upon this study. Meanwhile, additional human factors work should determine the most effective way to organize and present information in discharge summaries, to facilitate effective standardization.

Dataset Information

A classification approach to coreference in discharge summaries: 2011 i2b2 challenge.

Publications

A classification approach to coreference in discharge summaries: 2011 i2b2 challenge.

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