Dataset Information

Improving the Efficacy of the Data Entry Process for Clinical Research With a Natural Language Processing-Driven Medical Information Extraction System: Quantitative Field Research.

ABSTRACT: BACKGROUND:The growing interest in observational trials using patient data from electronic medical records poses challenges to both efficiency and quality of clinical data collection and management. Even with the help of electronic data capture systems and electronic case report forms (eCRFs), the manual data entry process followed by chart review is still time consuming. OBJECTIVE:To facilitate the data entry process, we developed a natural language processing-driven medical information extraction system (NLP-MIES) based on the i2b2 reference standard. We aimed to evaluate whether the NLP-MIES-based eCRF application could improve the accuracy and efficiency of the data entry process. METHODS:We conducted a randomized and controlled field experiment, and 24 eligible participants were recruited (12 for the manual group and 12 for NLP-MIES-supported group). We simulated the real-world eCRF completion process using our system and compared the performance of data entry on two research topics, pediatric congenital heart disease and pneumonia. RESULTS:For the congenital heart disease condition, the NLP-MIES-supported group increased accuracy by 15% (95% CI 4%-120%, P=.03) and reduced elapsed time by 33% (95% CI 22%-42%, P<.001) compared with the manual group. For the pneumonia condition, the NLP-MIES-supported group increased accuracy by 18% (95% CI 6%-32%, P=.008) and reduced elapsed time by 31% (95% CI 19%-41%, P<.001). CONCLUSIONS:Our system could improve both the accuracy and efficiency of the data entry process.

SUBMITTER: Han J

PROVIDER: S-EPMC6672807 | biostudies-other | 2019 Jul

REPOSITORIES: biostudies-other

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Improving the Efficacy of the Data Entry Process for Clinical Research With a Natural Language Processing-Driven Medical Information Extraction System: Quantitative Field Research.

Han Jiang J Chen Ken K Fang Lei L Zhang Shaodian S Wang Fei F Ma Handong H Zhao Liebin L Liu Shijian S

JMIR medical informatics 20190716 3

<h4>Background</h4>The growing interest in observational trials using patient data from electronic medical records poses challenges to both efficiency and quality of clinical data collection and management. Even with the help of electronic data capture systems and electronic case report forms (eCRFs), the manual data entry process followed by chart review is still time consuming.<h4>Objective</h4>To facilitate the data entry process, we developed a natural language processing-driven medical info ...[more]

PMID: 31313661

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Project description:BackgroundThe intent of plain-language resources (PLRs) reporting medical research information is to advance health literacy among the general public and enable them to participate in shared decision-making (SDM). Regulatory mandates coupled with academic and industry initiatives have given rise to an increasing volume of PLRs summarizing medical research information. However, there is significant variability in the quality, format, readability, and dissemination channels for PLRs. In this scoping review, we identify current practices, guidance, and barriers in developing and disseminating PLRs reporting medical research information to the general public including patients and caregivers. We also report on the PLR preferences of these intended audiences.MethodsA literature search of three bibliographic databases (PubMed, EMBASE, Web of Science) and three clinical trial registries (NIH, EMA, ISRCTN registry) was performed. Snowball searches within reference lists of primary articles were added. Articles with PLRs or reporting topics related to PLRs use and development available between January 2017 and June 2023 were identified. Evidence mapping and synthesis were used to make qualitative observations. Identified PLRs were quantitatively assessed, including temporal annual trends, availability by field of medicine, language, and publisher types.ResultsA total of 9116 PLRs were identified, 9041 from the databases and 75 from clinical trial registries. The final analysis included 6590 PLRs from databases and 72 from registries. Reported barriers to PLR development included ambiguity in guidance, lack of incentives, and concerns of researchers writing for the general public. Available guidance recommendations called for greater dissemination, increased readability, and varied content formats. Patients preferred visual PLRs formats (e.g., videos, comics), which were easy to access on the internet and used short jargon-free text. In some instances, older audiences and more educated readers preferred text-only PLRs. Preferences among the general public were mostly similar to those of patients. Psychology, followed by oncology, showed the highest number of PLRs, predominantly from academia-sponsored research. Text-only PLRs were most commonly available, while graphical, digital, or online formats were less available. Preferred dissemination channels included paywall-free journal websites, indexing on PubMed, third-party websites, via email to research participants, and social media.ConclusionsThis scoping review maps current practices, recommendations, and patients' and the general public's preferences for PLR development and dissemination. The results suggest that making PLRs available to a wider audience by improving nomenclature, accessibility, and providing translations may contribute to empowerment and SDM. Minimizing variability among available guidance for PLR development may play an important role in amplifying the value and impact of these resources.

Dataset Information

Improving the Efficacy of the Data Entry Process for Clinical Research With a Natural Language Processing-Driven Medical Information Extraction System: Quantitative Field Research.

Publications

Improving the Efficacy of the Data Entry Process for Clinical Research With a Natural Language Processing-Driven Medical Information Extraction System: Quantitative Field Research.

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