Dataset Information

Cohort profile: St. Michael's Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processing.

ABSTRACT:

Background

Tuberculosis (TB) is a major cause of death worldwide. TB research draws heavily on clinical cohorts which can be generated using electronic health records (EHR), but granular information extracted from unstructured EHR data is limited. The St. Michael's Hospital TB database (SMH-TB) was established to address gaps in EHR-derived TB clinical cohorts and provide researchers and clinicians with detailed, granular data related to TB management and treatment.

Methods

We collected and validated multiple layers of EHR data from the TB outpatient clinic at St. Michael's Hospital, Toronto, Ontario, Canada to generate the SMH-TB database. SMH-TB contains structured data directly from the EHR, and variables generated using natural language processing (NLP) by extracting relevant information from free-text within clinic, radiology, and other notes. NLP performance was assessed using recall, precision and F1 score averaged across variable labels. We present characteristics of the cohort population using binomial proportions and 95% confidence intervals (CI), with and without adjusting for NLP misclassification errors.

Results

SMH-TB currently contains retrospective patient data spanning 2011 to 2018, for a total of 3298 patients (N = 3237 with at least 1 associated dictation). Performance of TB diagnosis and medication NLP rulesets surpasses 93% in recall, precision and F1 metrics, indicating good generalizability. We estimated 20% (95% CI: 18.4-21.2%) were diagnosed with active TB and 46% (95% CI: 43.8-47.2%) were diagnosed with latent TB. After adjusting for potential misclassification, the proportion of patients diagnosed with active and latent TB was 18% (95% CI: 16.8-19.7%) and 40% (95% CI: 37.8-41.6%) respectively.

Conclusion

SMH-TB is a unique database that includes a breadth of structured data derived from structured and unstructured EHR data by using NLP rulesets. The data are available for a variety of research applications, such as clinical epidemiology, quality improvement and mathematical modeling studies.

SUBMITTER: Landsman D

PROVIDER: S-EPMC7928444 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Cohort profile: St. Michael's Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processing.

Landsman David D Abdelbasit Ahmed A Wang Christine C Guerzhoy Michael M Joshi Ujash U Mathew Shaun S Pou-Prom Chloe C Dai David D Pequegnat Victoria V Murray Joshua J Chokar Kamalprit K Banning Michaelia M Mamdani Muhammad M Mishra Sharmistha S Batt Jane J

PloS one 20210303 3

<h4>Background</h4>Tuberculosis (TB) is a major cause of death worldwide. TB research draws heavily on clinical cohorts which can be generated using electronic health records (EHR), but granular information extracted from unstructured EHR data is limited. The St. Michael's Hospital TB database (SMH-TB) was established to address gaps in EHR-derived TB clinical cohorts and provide researchers and clinicians with detailed, granular data related to TB management and treatment.<h4>Methods</h4>We col ...[more]

PMID: 33657184

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Project description:BackgroundAbstracting cerebrovascular disease (CeVD) from inpatient electronic medical records (EMRs) through natural language processing (NLP) is pivotal for automated disease surveillance and improving patient outcomes. Existing methods rely on coders' abstraction, which has time delays and under-coding issues. This study sought to develop an NLP-based method to detect CeVD using EMR clinical notes.MethodsCeVD status was confirmed through a chart review on randomly selected hospitalized patients who were 18 years or older and discharged from 3 hospitals in Calgary, Alberta, Canada, between January 1 and June 30, 2015. These patients' chart data were linked to administrative discharge abstract database (DAD) and Sunrise™ Clinical Manager (SCM) EMR database records by Personal Health Number (a unique lifetime identifier) and admission date. We trained multiple natural language processing (NLP) predictive models by combining two clinical concept extraction methods and two supervised machine learning (ML) methods: random forest and XGBoost. Using chart review as the reference standard, we compared the model performances with those of the commonly applied International Classification of Diseases (ICD-10-CA) codes, on the metrics of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).ResultOf the study sample (n = 3036), the prevalence of CeVD was 11.8% (n = 360); the median patient age was 63; and females accounted for 50.3% (n = 1528) based on chart data. Among 49 extracted clinical documents from the EMR, four document types were identified as the most influential text sources for identifying CeVD disease ("nursing transfer report," "discharge summary," "nursing notes," and "inpatient consultation."). The best performing NLP model was XGBoost, combining the Unified Medical Language System concepts extracted by cTAKES (e.g., top-ranked concepts, "Cerebrovascular accident" and "Transient ischemic attack"), and the term frequency-inverse document frequency vectorizer. Compared with ICD codes, the model achieved higher validity overall, such as sensitivity (25.0% vs 70.0%), specificity (99.3% vs 99.1%), PPV (82.6 vs. 87.8%), and NPV (90.8% vs 97.1%).ConclusionThe NLP algorithm developed in this study performed better than the ICD code algorithm in detecting CeVD. The NLP models could result in an automated EMR tool for identifying CeVD cases and be applied for future studies such as surveillance, and longitudinal studies.

Project description:OBJECTIVES:To demonstrate the utility of a natural language processing (NLP) algorithm for mining kidney stone composition in a large-scale electronic health records (EHR) repository. METHODS:We developed StoneX, a pattern-matching method for extracting kidney stone composition information from clinical notes. We trained the extraction algorithm on manually annotated text mentions of calcium oxalate monohydrate, calcium oxalate dihydrate, hydroxyapatite, brushite, uric acid, and struvite stones. We employed StoneX to identify patients with kidney stone composition data and mine >125 million notes from our institutional EHR. Analyses performed on the extracted patients included stone type conversions over time, survival analysis from a second stone surgery, and disease associations by stone composition to validate the phenotyping method against known associations. RESULTS:The NLP algorithm identified 45,235 text mentions corresponding to 11,585 patients. Overall, the system achieved positive predictive value >90% for calcium oxalate monohydrate, calcium oxalate dihydrate, hydroxyapatite, brushite, and struvite; except for uric acid (positive predictive value?=?87.5%). Survival analysis from a second stone surgery showed statistically significant differences among stone types (P?=?.03). Several phenotype associations were found: uric acid-type 2 diabetes (odds ratio, OR?=?2.69, 95% confidence intervals, CI?=?1.91-3.79), struvite-neurogenic bladder (OR?=?12.27, 95% CI?=?4.33-34.79), struvite-urinary tract infection (OR?=?7.36, 95% CI?=?3.01-17.99), hydroxyapatite-pulmonary collapse (OR?=?3.67, 95% CI?=?2.10-6.42), hydroxyapatite-neurogenic bladder (OR?=?5.23, 95% CI?=?2.05-13.36), brushite-calcium metabolism disorder (OR?=?4.59, 95% CI?=?2.14-9.81), and brushite-hypercalcemia (OR?=?4.09, 95% CI?=?1.90-8.80). CONCLUSION:NLP extraction of kidney stone composition from large-scale EHRs is feasible with high precision, enabling high-throughput epidemiological studies of kidney stone disease. These tools will enable high fidelity kidney stone research from the EHR.

Project description:ImportanceAccurate prediction of outcomes among patients in intensive care units (ICUs) is important for clinical research and monitoring care quality. Most existing prediction models do not take full advantage of the electronic health record, using only the single worst value of laboratory tests and vital signs and largely ignoring information present in free-text notes. Whether capturing more of the available data and applying machine learning and natural language processing (NLP) can improve and automate the prediction of outcomes among patients in the ICU remains unknown.ObjectivesTo evaluate the change in power for a mortality prediction model among patients in the ICU achieved by incorporating measures of clinical trajectory together with NLP of clinical text and to assess the generalizability of this approach.Design, setting, and participantsThis retrospective cohort study included 101 196 patients with a first-time admission to the ICU and a length of stay of at least 4 hours. Twenty ICUs at 2 academic medical centers (University of California, San Francisco [UCSF], and Beth Israel Deaconess Medical Center [BIDMC], Boston, Massachusetts) and 1 community hospital (Mills-Peninsula Medical Center [MPMC], Burlingame, California) contributed data from January 1, 2001, through June 1, 2017. Data were analyzed from July 1, 2017, through August 1, 2018.Main outcomes and measuresIn-hospital mortality and model discrimination as assessed by the area under the receiver operating characteristic curve (AUC) and model calibration as assessed by the modified Hosmer-Lemeshow statistic.ResultsAmong 101 196 patients included in the analysis, 51.3% (n = 51 899) were male, with a mean (SD) age of 61.3 (17.1) years; their in-hospital mortality rate was 10.4% (n = 10 505). A baseline model using only the highest and lowest observed values for each laboratory test result or vital sign achieved a cross-validated AUC of 0.831 (95% CI, 0.830-0.832). In contrast, that model augmented with measures of clinical trajectory achieved an AUC of 0.899 (95% CI, 0.896-0.902; P < .001 for AUC difference). Further augmenting this model with NLP-derived terms associated with mortality further increased the AUC to 0.922 (95% CI, 0.916-0.924; P < .001). These NLP-derived terms were associated with improved model performance even when applied across sites (AUC difference for UCSF: 0.077 to 0.021; AUC difference for MPMC: 0.071 to 0.051; AUC difference for BIDMC: 0.035 to 0.043; P < .001) when augmenting with NLP at each site.Conclusions and relevanceIntensive care unit mortality prediction models incorporating measures of clinical trajectory and NLP-derived terms yielded excellent predictive performance and generalized well in this sample of hospitals. The role of these automated algorithms, particularly those using unstructured data from notes and other sources, in clinical research and quality improvement seems to merit additional investigation.

Project description:ImportanceStigmatizing language in the electronic health record (EHR) may alter treatment plans, transmit biases between clinicians, and alienate patients. However, neither the frequency of stigmatizing language in hospital notes, nor whether clinicians disproportionately use it in describing patients in particular demographic subgroups are known.ObjectiveTo examine the prevalence of stigmatizing language in hospital admission notes and the patient and clinician characteristics associated with the use of such language.Design, setting, and participantsThis cross-sectional study of admission notes used natural language processing on 48 651 admission notes written about 29 783 unique patients by 1932 clinicians at a large, urban academic medical center between January to December 2018. The admission notes included 8738 notes about 4309 patients with diabetes written by 1204 clinicians; 6197 notes about 3058 patients with substance use disorder by 1132 clinicians; and 5176 notes about 2331 patients with chronic pain by 1056 clinicians. Statistical analyses were performed between May and September 2021.ExposuresPatients' demographic characteristics (age, race and ethnicity, gender, and preferred language); clinicians' characteristics (gender, postgraduate year [PGY], and credential [physician vs advanced practice clinician]).Main outcome and measuresBinary indicator for any vs no stigmatizing language; frequencies of specific stigmatizing words. Linear probability models were the main measure, and logistic regression and odds ratios were used for sensitivity analyses and further exploration.ResultsThe sample included notes on 29 783 patients with a mean (SD) age of 46.9 (27.6) years. Of these patients, 1033 (3.5%) were non-Hispanic Asian, 2498 (8.4%) were non-Hispanic Black, 18 956 (63.6%) were non-Hispanic White, 17 334 (58.2%) were female, and 2939 (9.9%) preferred a language other than English. Of all admission notes, 1197 (2.5%) contained stigmatizing language. The diagnosis-specific stigmatizing language was present in 599 notes (6.9%) for patients with diabetes, 209 (3.4%) for patients with substance use disorders, and 37 (0.7%) for patients with chronic pain. In the whole sample, notes about non-Hispanic Black patients vs non-Hispanic White patients had a 0.67 (95% CI, 0.15 to 1.18) percentage points greater probability of containing stigmatizing language, with similar disparities in all 3 diagnosis-specific subgroups. Greater diabetes severity and the physician-author being less advanced in their training was associated with more stigmatizing language. A 1 point increase in the diabetes severity index was associated with a 1.23 (95% CI, .23 to 2.23) percentage point greater probability of a note containing stigmatizing language. In the sample restricted to physicians, a higher PGY was associated with less use of stigmatizing language overall (-0.05 percentage points/PGY [95% CI, -0.09 to -0.01]).Conclusions and relevanceIn this cross-sectional study, stigmatizing language in hospital notes varied by medical condition and was more often used to describe non-Hispanic Black patients. Training clinicians to minimize stigmatizing language in the EHR might improve patient-clinician relationships and reduce the transmission of bias between clinicians.

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Dataset Information

Cohort profile: St. Michael's Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processing.

Background

Methods

Results

Conclusion

Publications

Cohort profile: St. Michael's Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processing.

Similar Datasets

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