Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure

Garvin, Jennifer H.; DuVall, Scott L.; South, Brett R.; Bray, Bruce E.; Bolton, Dan; Heavirland, Julia; Pickard, Steve; Heidenreich, Paul A.; Shen, Shuying; Weir, Charlene R.; Samore, Matthew H.; Goldstein, Mary K.

doi:10.1136/amiajnl-2011-000535

Cited by 83 publications

(77 citation statements)

References 14 publications

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“…If an outpatient HF diagnosis was followed by hospitalization within 48 hours or if the diagnosis was made as an inpatient, the discharge date was used as the index date. As validated before,17, 18, 19 a natural language processing tool was used to extract EF values from text integration utilities documents, including history and physical examination notes, progress notes, discharge summary notes, echocardiography reports, nuclear medicine reports, cardiac catheterization reports, and other cardiology notes. All recorded LVEF values were used for classifying cases as HFpEF.…”

Section: Methodsmentioning

confidence: 99%

Prognostic Significance of Baseline Serum Sodium in Heart Failure With Preserved Ejection Fraction

Patel

Kurgansky

Imran

et al. 2018

JAHA

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BackgroundThe purpose of this study was to evaluate the relationship between serum sodium at the time of diagnosis and long term clinical outcomes in a large national cohort of patients with heart failure with preserved ejection fraction.Methods and ResultsWe studied 25 440 patients with heart failure with preserved ejection fraction treated at Veterans Affairs medical centers across the United States between 2002 and 2012. Serum sodium at the time of heart failure diagnosis was analyzed as a continuous variable and in categories as follows: low (115.00–134.99 mmol/L), low‐normal (135.00–137.99 mmol/L), referent group (138.00–140.99 mmol/L), high normal (141.00–143.99 mmol/L), and high (144.00–160.00 mmol/L). Multivariable Cox regression and negative binomial regression were performed to estimate hazard ratios (95% confidence interval [CI]) and incidence density ratios (95% CI) for the associations of serum sodium with mortality and hospitalizations (heart failure and all‐cause), respectively. The average age of patients was 70.8 years, 96.2% were male, and 14% were black. Compared with the referent group, low, low‐normal, and high sodium values were associated with 36% (95% CI, 28%–44%), 6% (95% CI, 1%–12%), and 9% (95% CI, 1%–17%) higher risk of all‐cause mortality, respectively. Low and low‐normal serum sodium were associated with 48% (95% CI, 10%–100%) and 38% (95% CI, 8%–77%) higher risk of number of days of heart failure hospitalizations per year, and with 44% (95% CI, 32%–56%) and 18% (95% CI, 10%–27%) higher risk of number of days of all‐cause hospitalizations per year, respectively.ConclusionsBoth elevated and reduced serum sodium, including values currently considered within normal range, are associated with adverse outcomes in patients with heart failure with preserved ejection fraction.

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Section: Methodsmentioning

confidence: 99%

Prognostic Significance of Baseline Serum Sodium in Heart Failure With Preserved Ejection Fraction

Patel

Kurgansky

Imran

et al. 2018

JAHA

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“…For example, several groups have used natural language processing and text mining algorithms to increase the accuracy of eCQM data and improve quality. 33–35 Like natural language processing, machine learning algorithms relying on the text of clinical notes also provide new opportunities to capture data not found in standard structured data.…”

Section: Introductionmentioning

confidence: 99%

A new era of quality measurement in rheumatology: electronic clinical quality measures and national registries

Tonner¹,

Schmajuk

Yazdany³

2017

Current Opinion in Rheumatology

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Purpose of Review We review the evolution of quality measurement in rheumatology, highlighting new health-information technology infrastructure and standards that are enabling unprecedented innovation in this field. Recent Findings Spurred by landmark legislation that ties physician payment to value, the widespread use of electronic health records (EHRs), and standards such as the Quality Data Model, quality measurement in rheumatology is rapidly evolving. Rather than relying on retrospective assessments of care gathered through administrative claims or manual chart abstraction, new electronic clinical quality measures (eCQMs) allow automated data capture from EHRs. At the same time, Qualified Clinical Data Registries, like the American College of Rheumatology’s RISE registry, are enabling large-scale implementation of eCQMs across national EHR networks with real-time performance feedback to clinicians. While successful examples of eCQM development and implementation in rheumatology and other fields exist, there also remain challenges, such as lack of health system data interoperability and problems with measure accuracy. Summary Quality measurement and improvement is increasingly an essential component of rheumatology practice. Advances in health information technology are likely to continue to make implementation of eCQMs easier and measurement more clinically meaningful and accurate in coming years.

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“…These notes, which are intended to document a patient's clinical status or achievement during a hospitalization or a course of outpatient treatment, are a rich source of clinical detail and have received a lot of attention from medical informatics specialists [Pakhomov et al 2008;Garvin et al 2012;Matheny et al 2012;McCart et al 2013]. Medicine is also one of the areas that has been able to sustain long-term efforts in natural language processing and text analytics through the National Institutes of Health, the National Library of Medicine, and peer international organizations.…”

Section: Sources Of Noise In Textmentioning

confidence: 99%

A Case Study of Data Quality in Text Mining Clinical Progress Notes

Berndt

McCart

Finch

et al. 2015

ACM Trans. Manage. Inf. Syst.

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Text analytic methods are often aimed at extracting useful information from the vast array of unstructured, free format text documents that are created by almost all organizational processes. The success of any text mining application rests on the quality of the underlying data being analyzed, including both predictive features and outcome labels. In this case study, some focused experiments regarding data quality are used to assess the robustness of Statistical Text Mining (STM) algorithms when applied to clinical progress notes. In particular, the experiments consider the impacts of task complexity (by removing signals), training set size, and target outcome quality. While this research is conducted using a dataset drawn from the medical domain, the data quality issues explored are of more general interest. . 2015. A case study of data quality in text mining clinical progress notes.

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Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure

Abstract: An automated information extraction system can be used to accurately extract EF for quality measurement.

Cited by 83 publications

References 14 publications

Prognostic Significance of Baseline Serum Sodium in Heart Failure With Preserved Ejection Fraction

Prognostic Significance of Baseline Serum Sodium in Heart Failure With Preserved Ejection Fraction

A new era of quality measurement in rheumatology: electronic clinical quality measures and national registries

A Case Study of Data Quality in Text Mining Clinical Progress Notes

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