Anatomic and Advanced Adenoma Detection Rates as Quality Metrics Determined via Natural Language Processing

Gawron, Andrew J.; Thompson, William K.; Keswani, Rajesh N.; Rasmussen, Luke V.; Kho, Abel N.

doi:10.1038/ajg.2014.147

Cited by 41 publications

(18 citation statements)

References 31 publications

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“…Examples focused on colonoscopy reports included assessing the reports' quality [212], and detecting patients with polyps or adenomas. Gawron and colleagues developed a NLP application reaching 94% recall and precision when detecting the location and histology of adenomas, and 69% when counting their number [213]. Raju and colleagues compared a manual abstraction with an NLP-based process to extract screening information, correctly identifying 91.3% of them with NLP, and 87.8% manually [214].…”

Section: H Clinical Practice and Research Integrationmentioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

158

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SummaryObjective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

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Section: H Clinical Practice and Research Integrationmentioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

158

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“… 66 Custom approaches can generically circumnavigate this limitation. For example, the colon polyp phenotype in the eMERGE network 67 used colonoscopy surgical and pathology reports, which are not yet labeled in a standard manner or mapped to CDMs in most of the IDR systems in the network. This algorithm separates the implementation into transportable tasks (e.g., concept extraction through NLP, grouping, extraction of covariates) implemented as a fully executable Konstanz Information Miner (KNIME) package with institutional adaptation tasks (i.e., database querying for the proper document types).…”

Section: Desideratamentioning

confidence: 99%

Desiderata for computable representations of electronic health records-driven phenotype algorithms

Thompson

Rasmussen

et al. 2015

Journal of the American Medical Informatics Association

Self Cite

110

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Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms.Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility.Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

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“…This is particularly meaningful when using these subgroups in comparing quality metrics for different endoscopists, practices, and regions of the country because patient demographics and evolving standards of practice have bearing on the ADR. 15-17 Also, the ability of NLP to search pathology databases, transcribed medical documents, and endoscopy databases for prior examinations allows for separating screening examinations from surveillance examinations to report quality metrics. Once the NLP is set up, it can review tens of thousands of records quickly and provide accurate reports.…”

Section: Discussionmentioning

confidence: 99%

Natural language processing as an alternative to manual reporting of colonoscopy quality metrics

Raju

Lum

Slack

et al. 2015

Gastrointestinal Endoscopy

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BACKGROUND & AIMS The adenoma detection rate (ADR) is a quality metric tied to interval colon cancer occurrence. However, manual extraction of data to calculate and track the ADR in clinical practice is labor-intensive. To overcome this difficulty, we developed a natural language processing (NLP) method to identify patients, who underwent their first screening colonoscopy, identify adenomas and sessile serrated adenomas (SSA). We compared the NLP generated results with that of manual data extraction to test the accuracy of NLP, and report on colonoscopy quality metrics using NLP. METHODS Identification of screening colonoscopies using NLP was compared with that using the manual method for 12,748 patients who underwent colonoscopies from July 2010 to February 2013. Also, identification of adenomas and SSAs using NLP was compared with that using the manual method with 2259 matched patient records. Colonoscopy ADRs using these methods were generated for each physician. RESULTS NLP correctly identified 91.3% of the screening examinations, whereas the manual method identified 87.8% of them. Both the manual method and NLP correctly identified examinations of patients with adenomas and SSAs in the matched records almost perfectly. Both NLP and manual method produce comparable values for ADR for each endoscopist as well as the group as a whole. CONCLUSIONS NLP can correctly identify screening colonoscopies, accurately identify adenomas and SSAs in a pathology database, and provide real-time quality metrics for colonoscopy.

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Anatomic and Advanced Adenoma Detection Rates as Quality Metrics Determined via Natural Language Processing

Cited by 41 publications

References 31 publications

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Desiderata for computable representations of electronic health records-driven phenotype algorithms

Natural language processing as an alternative to manual reporting of colonoscopy quality metrics

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