Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing

Cheng, Lionel Tim-Ee; Zheng, Jiaping; Savova, Guergana; Erickson, Bradley J.

doi:10.1007/s10278-009-9215-7

Cited by 82 publications

(56 citation statements)

References 29 publications

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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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“…Rather than employ statistical or machine-learning methods to classify data, which is used in many modern NLP systems [12][13][14], the methods used in this study are more similar to traditional rule-based approaches to text classification [15,16]. Some authors in the information retrieval field believe that rulebased or knowledge-engineering approaches are the most accurate and reliable methods for text classification [17].…”

Section: Discussionmentioning

confidence: 99%

Automated Detection of Critical Results in Radiology Reports

2011

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The goal of this study was to develop and validate text-mining algorithms to automatically identify radiology reports containing critical results including tension or increasing/new large pneumothorax, acute pulmonary embolism, acute cholecystitis, acute appendicitis, ectopic pregnancy, scrotal torsion, unexplained free intraperitoneal air, new or increasing intracranial hemorrhage, and malpositioned tubes and lines. The algorithms were developed using rule-based approaches and designed to search for common words and phrases in radiology reports that indicate critical results. Certain text-mining features were utilized such as wildcards, stemming, negation detection, proximity matching, and expanded searches with applicable synonyms. To further improve accuracy, the algorithms utilized modality and examspecific queries, searched under the "Impression" field of the radiology report, and excluded reports with a low level of diagnostic certainty. Algorithm accuracy was determined using precision, recall, and F-measure using human review as the reference standard. The overall accuracy (F-measure) of the algorithms ranged from 81% to 100%, with a mean precision and recall of 96% and 91%, respectively. These algorithms can be applied to radiology report databases for quality assurance and accreditation, integrated with existing dashboards for display and monitoring, and ported to other institutions for their own use.

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“…In this manner, reports can be standardized by (for example) replacing each word with its stem. The aggregate of stems in a report can then be more readily searched for the stems related to the concept of interest (9)(10)(11).…”

Section: Figurementioning

confidence: 99%

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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Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing

Cited by 82 publications

References 29 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

Automated Detection of Critical Results in Radiology Reports

Natural Language Processing Technologies in Radiology Research and Clinical Applications

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