Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients

Moon, Sungrim; Liu, Sijia; Chen, David; Wang, Yanshan; Wood, Douglas L.; Chaudhry, Rajeev; Liu, Hongfang; Kingsbury, Paul R.

doi:10.1007/s41666-019-00044-5

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…Systematic misclassification toward false negatives could induce biases in research, particularly for patient populations that are highly transient and may change care providers frequently. Strategies to reduce information gaps and to improve the collection of surgical history include leveraging the NLP technology with optical character recognition technology to digitalize paper-based records or acquiring the records digitally via a health information exchange [22][23][24][25]. Lastly, the implementation of systematic questionnaires to gather prior surgical information may significantly reduce information gaps as well.…”

Section: Discussionmentioning

confidence: 99%

Identifying Information Gaps in Electronic Health Records by Using Natural Language Processing: Gynecologic Surgery History Identification

Moon¹,

Carlson²,

Moser³

et al. 2022

J Med Internet Res

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Background Electronic health records (EHRs) are a rich source of longitudinal patient data. However, missing information due to clinical care that predated the implementation of EHR system(s) or care that occurred at different medical institutions impedes complete ascertainment of a patient’s medical history. Objective This study aimed to investigate information discrepancies and to quantify information gaps by comparing the gynecological surgical history extracted from an EHR of a single institution by using natural language processing (NLP) techniques with the manually curated surgical history information through chart review of records from multiple independent regional health care institutions. Methods To facilitate high-throughput evaluation, we developed a rule-based NLP algorithm to detect gynecological surgery history from the unstructured narrative of the Mayo Clinic EHR. These results were compared to a gold standard cohort of 3870 women with gynecological surgery status adjudicated using the Rochester Epidemiology Project medical records–linkage system. We quantified and characterized the information gaps observed that led to misclassification of the surgical status. Results The NLP algorithm achieved precision of 0.85, recall of 0.82, and F1-score of 0.83 in the test set (n=265) relative to outcomes abstracted from the Mayo EHR. This performance attenuated when directly compared to the gold standard (precision 0.79, recall 0.76, and F1-score 0.76), with the majority of misclassifications being false negatives in nature. We then applied the algorithm to the remaining patients (n=3340) and identified 2 types of information gaps through error analysis. First, 6% (199/3340) of women in this study had no recorded surgery information or partial information in the EHR. Second, 4.3% (144/3340) of women had inconsistent or inaccurate information within the clinical narrative owing to misinterpreted information, erroneous “copy and paste,” or incorrect information provided by patients. Additionally, the NLP algorithm misclassified the surgery status of 3.6% (121/3340) of women. Conclusions Although NLP techniques were able to adequately recreate the gynecologic surgical status from the clinical narrative, missing or inaccurately reported and recorded information resulted in much of the misclassification observed. Therefore, alternative approaches to collect or curate surgical history are needed.

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Section: Discussionmentioning

confidence: 99%

Identifying Information Gaps in Electronic Health Records by Using Natural Language Processing: Gynecologic Surgery History Identification

Moon¹,

Carlson²,

Moser³

et al. 2022

J Med Internet Res

Self Cite

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“…Most electronic health records (EHRs) contain scanned documents sent to clinicians via fax or other means. 1 Due to these scanned documents not being digitized, they are not readily retrievable, searchable, nor classifiable/organizable. 2 While in theory EHRs allow for seamless electronic health information exchange (HIE), in practice this is not always the case, and as a result documents are often transferred via digital scanning or fax.…”

Section: Background and Significancementioning

confidence: 99%

Searching the PDF Haystack: Automated Knowledge Discovery in Scanned EHR Documents

2021

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Background Clinicians express concern that they may be unaware of important information contained in voluminous scanned and other outside documents contained in electronic health records (EHRs). An example is “unrecognized EHR risk factor information,” defined as risk factors for heritable cancer that exist within a patient's EHR but are not known by current treating providers. In a related study using manual EHR chart review, we found that half of the women whose EHR contained risk factor information meet criteria for further genetic risk evaluation for heritable forms of breast and ovarian cancer. They were not referred for genetic counseling. Objectives The purpose of this study was to compare the use of automated methods (optical character recognition with natural language processing) versus human review in their ability to identify risk factors for heritable breast and ovarian cancer within EHR scanned documents. Methods We evaluated the accuracy of the chart review by comparing our criterion standard (physician chart review) versus an automated method involving Amazon's Textract service (Amazon.com, Seattle, Washington, United States), a clinical language annotation modeling and processing toolkit (CLAMP) (Center for Computational Biomedicine at The University of Texas Health Science, Houston, Texas, United States), and a custom-written Java application. Results We found that automated methods identified most cancer risk factor information that would otherwise require clinician manual review and therefore is at risk of being missed. Conclusion The use of automated methods for identification of heritable risk factors within EHRs may provide an accurate yet rapid review of patients' past medical histories. These methods could be further strengthened via improved analysis of handwritten notes, tables, and colloquial phrases.

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“…For instance, if the site doing the in-silico phenotyping is a tertiary medical institution, a substantial amount of history will not be available structurally (e.g., only available via scanned images or clinical text). If only a structured data source is used for phenotyping, the results will be biased as rural/underrepresented populations may have a substantial history captured in text or image 29 and thus inaccessible to the phenotyping algorithm.…”

Section: Introductionmentioning

confidence: 99%

The IMPACT framework and implementation for accessible in silico clinical phenotyping in the digital era

Wen

et al. 2023

npj Digit. Med.

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Clinical phenotyping is often a foundational requirement for obtaining datasets necessary for the development of digital health applications. Traditionally done via manual abstraction, this task is often a bottleneck in development due to time and cost requirements, therefore raising significant interest in accomplishing this task via in-silico means. Nevertheless, current in-silico phenotyping development tends to be focused on a single phenotyping task resulting in a dearth of reusable tools supporting cross-task generalizable in-silico phenotyping. In addition, in-silico phenotyping remains largely inaccessible for a substantial portion of potentially interested users. Here, we highlight the barriers to the usage of in-silico phenotyping and potential solutions in the form of a framework of several desiderata as observed during our implementation of such tasks. In addition, we introduce an example implementation of said framework as a software application, with a focus on ease of adoption, cross-task reusability, and facilitating the clinical phenotyping algorithm development process.

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Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients

Cited by 7 publications

References 29 publications

Identifying Information Gaps in Electronic Health Records by Using Natural Language Processing: Gynecologic Surgery History Identification

Identifying Information Gaps in Electronic Health Records by Using Natural Language Processing: Gynecologic Surgery History Identification

Searching the PDF Haystack: Automated Knowledge Discovery in Scanned EHR Documents

The IMPACT framework and implementation for accessible in silico clinical phenotyping in the digital era

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