Using Natural Language Processing to Improve Efficiency of Manual Chart Abstraction in Research: The Case of Breast Cancer Recurrence

Carrell, David; Halgrim, Scott; Tran, Diem Thy; Buist, Diana S.M.; Chubak, Jessica; Chapman, Wendy W.; Savova, Guergana

doi:10.1093/aje/kwt441

Cited by 133 publications

(110 citation statements)

References 34 publications

(38 reference statements)

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“…Recent studies have shown that NLP approaches can effectively extract meaningful information, such as adverse drug reactions, cancer staging, and disease progression from clinical notes. [55][56][57][58][59][60] Preliminary results suggest that NLP can identify documentation of advance care planning. 61 Several groups are currently working on using NLP or machine learning to identify and evaluate these discussions.…”

Section: Challenges and Lessons Learned From Ehr-based Metricsmentioning

confidence: 99%

Using Electronic Health Records for Quality Measurement and Accountability in Care of the Seriously Ill: Opportunities and Challenges

Curtis

Sathitratanacheewin

Starks

et al. 2018

Journal of Palliative Medicine

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Background: As our population ages and the burden of chronic illness rises, there is increasing need to implement quality metrics that measure and benchmark care of the seriously ill, including the delivery of both primary care and specialty palliative care. Such metrics can be used to drive quality improvement, value-based payment, and accountability for population-based outcomes. Methods: In this article, we examine use of the electronic health record (EHR) as a tool to assess quality of serious illness care through narrative review and description of a palliative care quality metrics program in a large healthcare system. Results: In the search for feasible, reliable, and valid palliative care quality metrics, the EHR is an attractive option for collecting quality data on large numbers of seriously ill patients. However, important challenges to using EHR data for quality improvement and accountability exist, including understanding the validity, reliability, and completeness of the data, as well as acknowledging the difference between care documented and care delivered. Challenges also include developing achievable metrics that are clearly linked to patient and family outcomes and addressing data interoperability across sites as well as EHR platforms and vendors. This article summarizes the strengths and weakness of the EHR as a data source for accountability of communityand population-based programs for serious illness, describes the implementation of EHR data in the palliative care quality metrics program at the University of Washington, and, based on that experience, discusses opportunities and challenges. Our palliative care metrics program was designed to serve as a resource for other healthcare systems. Discussion: Although the EHR offers great promise for enhancing quality of care provided for the seriously ill, significant challenges remain to operationalizing this promise on a national scale and using EHR data for population-based quality and accountability.

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Section: Challenges and Lessons Learned From Ehr-based Metricsmentioning

confidence: 99%

Using Electronic Health Records for Quality Measurement and Accountability in Care of the Seriously Ill: Opportunities and Challenges

Curtis

Sathitratanacheewin

Starks

et al. 2018

Journal of Palliative Medicine

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“…For example, if ambiguous terms such as "suggestive of" are mentioned and are accepted as favoring the diagnosis of a finding, an automated system's balance of sensitivity and specificity may be altered with a bias, whereas an expert may be able to consistently infer disposition from context (43). Ambiguity of abbreviations is another example.…”

Section: Resultsmentioning

confidence: 99%

“…This cascaded approach yielded a sensitivity and specificity of 80.6% and 91.6%, respectively, for overall status classification; 79.3% and 89.4%, respectively, for magnitude classification; and 68.6% and 85.9%, respectively, for certainty classification. Similarly, Carrell et al (43) used cTAKES to consolidate pathology and radiology reports plus clinical notes to detect cancer recurrence in women with early-stage invasive breast cancer. A custom-built dictionary with 1360 entries was created for pathologic findings.…”

Section: Cancermentioning

confidence: 99%

“…The dictionary for radiology reports and clinical notes included 4891 findings and more complex logic query rules necessary to integrate indirect evidence, such as a change in imaging findings over time. The system was able to reduce the number of patient charts that had to be manually reviewed to identify confirmed cases of breast cancer recurrence by 90%, while missing 8% of recurrent cases, similar to manual review (43).…”

Section: Cancermentioning

confidence: 99%

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Natural Language Processing Technologies in Radiology Research and Clinical Applications

et al. 2016

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The migration of imaging reports to electronic medical record systems holds great potential in terms of advancing radiology research and practice by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the heterogeneity of how these data are formatted. Indeed, although there is movement toward structured reporting in radiology (ie, hierarchically itemized reporting with use of standardized terminology), the majority of radiology reports remain unstructured and use free-form language. To effectively "mine" these large datasets for hypothesis testing, a robust strategy for extracting the necessary information is needed. Manual extraction of information is a time-consuming and often unmanageable task. "Intelligent" search engines that instead rely on natural language processing (NLP), a computer-based approach to analyzing free-form text or speech, can be used to automate this data mining task. The overall goal of NLP is to translate natural human language into a structured format (ie, a fixed collection of elements), each with a standardized set of choices for its value, that is easily manipulated by computer programs to (among other things) order into subcategories or query for the presence or absence of a finding. The authors review the fundamentals of NLP and describe various techniques that constitute NLP in radiology, along with some key applications. After completing this journal-based SA-CME activity, participants will be able to:■ Describe the set of technologies that compose present-day natural language processing in radiology.■ List examples of how these technologies have been combined to achieve specific objectives in radiology research and, potentially, clinical practice.■ Discuss current capabilities and possible future applications of use of natural language processing in radiology.

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“…Reuse of this unstructured data requires either manual abstraction, or automated information extraction approaches based on NLP [124]. Most information extraction efforts focused on phenotyping and chart abstraction improvement [125], research subjects recruitment and cohort identification for retrospective studies, and patient identification for improved treatment and follow-up. The extraction of phenotypes and other types of information include diseases and problems, investigations, treatments, combined in the 4th i2b2 NLP challenge [126], or medication details for example [127].…”

Section: F Extraction Of Information From Unstructured Clinical Datamentioning

confidence: 99%

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

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

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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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Using Natural Language Processing to Improve Efficiency of Manual Chart Abstraction in Research: The Case of Breast Cancer Recurrence

Cited by 133 publications

References 34 publications

Using Electronic Health Records for Quality Measurement and Accountability in Care of the Seriously Ill: Opportunities and Challenges

Using Electronic Health Records for Quality Measurement and Accountability in Care of the Seriously Ill: Opportunities and Challenges

Natural Language Processing Technologies in Radiology Research and Clinical Applications

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

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