Provider-specific quality measurement for ERCP using natural language processing

Imler, Timothy D.; Sherman, Stuart; Imperiale, Thomas F.; Xu, Huiping; Ouyang, Fangqian; Beesley, C. R.; Hilton, Charity; Coté, Gregory A.

doi:10.1016/j.gie.2017.04.030

Cited by 17 publications

(8 citation statements)

References 21 publications

(21 reference statements)

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“…We used nDepth, a natural language processing tool developed at the Regenstrief Institute to extract tumor-node-metastasis concepts from oncology notes provided by the comprehensive cancer centers in Indiana to the INPC. The work was developed from previous research and validated in earlies studies [ 32 - 35 ]. The cancer-staging variable was classified as I, II, III, and IV based on the tumor-node-metastasis classification [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Diagnosis and Risk of Death Among Adults With Cancer in Indiana: Retrospective Cohort Study

Valvi¹,

Patel²,

Bakoyannis³

et al. 2022

JMIR Cancer

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Background Prior studies, generally conducted at single centers with small sample sizes, found that individuals with cancer experience more severe outcomes due to COVID-19, caused by SARS-CoV-2 infection. Although early examinations revealed greater risk of severe outcomes for patients with cancer, the magnitude of the increased risk remains unclear. Furthermore, prior studies were not typically performed using population-level data, especially those in the United States. Given robust prevention measures (eg, vaccines) are available for populations, examining the increased risk of patients with cancer due to SARS-CoV-2 infection using robust population-level analyses of electronic medical records is warranted. Objective The aim of this paper is to evaluate the association between SARS-CoV-2 infection and all-cause mortality among recently diagnosed adults with cancer. Methods We conducted a retrospective cohort study of newly diagnosed adults with cancer between January 1, 2019, and December 31, 2020, using electronic health records linked to a statewide SARS-CoV-2 testing database. The primary outcome was all-cause mortality. We used the Kaplan-Meier estimator to estimate survival during the COVID-19 period (January 15, 2020, to December 31, 2020). We further modeled SARS-CoV-2 infection as a time-dependent exposure (immortal time bias) in a multivariable Cox proportional hazards model adjusting for clinical and demographic variables to estimate the hazard ratios (HRs) among newly diagnosed adults with cancer. Sensitivity analyses were conducted using the above methods among individuals with cancer-staging information. Results During the study period, 41,924 adults were identified with newly diagnosed cancer, of which 2894 (6.9%) tested positive for SARS-CoV-2. The population consisted of White (n=32,867, 78.4%), Black (n=2671, 6.4%), Hispanic (n=832, 2.0%), and other (n=5554, 13.2%) racial backgrounds, with both male (n=21,354, 50.9%) and female (n=20,570, 49.1%) individuals. In the COVID-19 period analysis, after adjusting for age, sex, race or ethnicity, comorbidities, cancer type, and region, the risk of death increased by 91% (adjusted HR 1.91; 95% CI 1.76-2.09) compared to the pre–COVID-19 period (January 1, 2019, to January 14, 2020) after adjusting for other covariates. In the adjusted time-dependent analysis, SARS-CoV-2 infection was associated with an increase in all-cause mortality (adjusted HR 6.91; 95% CI 6.06-7.89). Mortality increased 2.5 times among adults aged 65 years and older (adjusted HR 2.74; 95% CI 2.26-3.31) compared to adults 18-44 years old, among male (adjusted HR 1.23; 95% CI 1.14-1.32) compared to female individuals, and those with ≥2 chronic conditions (adjusted HR 2.12; 95% CI 1.94-2.31) compared to those with no comorbidities. Risk of mortality was 9% higher in the rural population (adjusted HR 1.09; 95% CI 1.01-1.18) compared to adult urban residents. Conclusions The findings highlight increased risk of death is associated with SARS-CoV-2 infection among patients with a recent diagnosis of cancer. Elevated risk underscores the importance of adhering to social distancing, mask adherence, vaccination, and regular testing among the adult cancer population.

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Section: Methodsmentioning

confidence: 99%

COVID-19 Diagnosis and Risk of Death Among Adults With Cancer in Indiana: Retrospective Cohort Study

Valvi¹,

Patel²,

Bakoyannis³

et al. 2022

JMIR Cancer

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“…The nDepth natural language processing system, developed by the Indiana Clinical and Translational Science Institute (CTSI) and Regenstrief, will be used to allow novel methods to identify essential findings in text reports, such as symptomology or family history, and further augment CoRDaCo (17)(18)(19)(20)(21).…”

Section: Data Elementsmentioning

confidence: 99%

Resource Profile: The Regenstrief Institute COVID-19 Research Data Commons (CoRDaCo)

Allen

Zidan²,

Dey³

et al. 2021

Preprint

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The primary objective of the COVID-19 Research Data Commons (CoRDaCo) is to provide broad and efficient access to a large corpus of clinical data related to COVID-19 in Indiana, facilitating research and discovery. This curated collection of data elements provides information on a significant portion of COVID-19 positive patients in the State from the beginning of the pandemic, as well as two years of health information prior its onset. CoRDaCo combines data from multiple sources, including clinical data from a large, regional health information exchange, clinical data repositories of two health systems, and state laboratory reporting and vital records, as well as geographic-based social variables. Clinical data cover information such as healthcare encounters, vital measurements, laboratory orders and results, medications, diagnoses, the Charlson Comorbidity Index and Pediatric Early Warning Score, COVID-19 vaccinations, mechanical ventilation, restraint use, intensive care unit and ICU and hospital lengths of stay, and mortality. Interested researchers can visit ridata.org or email askrds@regenstrief.org to discuss access to CoRDaCo.

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“…12 While natural language processing has shown promise as a tool for tracking adherence to QIs, variability in physician report documentation poses another obstacle to systematic monitoring of ERCP quality. 13 This study demonstrates that the incorporation of mandatory structured data fields into the routine completion of ERCP procedure reports facilitates accurate extraction of key QIs and obviates the need for manual adjudication of medical records. As compared to voluntary reporting of outcomes, most of these data fields can be autopopulated as the author completes the procedure note using their standard workflow.…”

Section: Discussionmentioning

confidence: 87%

Development of an Automated ERCP Quality Report Card Using Structured Data Fields

Coté

Elmunzer

Förster

et al. 2021

Techniques and Innovations in Gastrointestinal Endoscopy

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Provider-specific quality measurement for ERCP using natural language processing

Abstract: NLP in conjunction with data mining facilitates individualized tracking of ERCP providers for quality metrics without the need for manual medical record review. Incorporation of these tools across multiple centers may permit tracking of ERCP quality measures through national registries.

Cited by 17 publications

References 21 publications

COVID-19 Diagnosis and Risk of Death Among Adults With Cancer in Indiana: Retrospective Cohort Study

COVID-19 Diagnosis and Risk of Death Among Adults With Cancer in Indiana: Retrospective Cohort Study

Resource Profile: The Regenstrief Institute COVID-19 Research Data Commons (CoRDaCo)

Development of an Automated ERCP Quality Report Card Using Structured Data Fields

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