Inaccurate recording of routinely collected data items influences identification of COVID-19 patients

Klappe, Eva S.; Cornet, Ronald; Dongelmans, Dave A.; Keizer, Nicolette F. de

doi:10.1016/j.ijmedinf.2022.104808

Cited by 11 publications

(10 citation statements)

References 56 publications

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“…In addition, it took months to establish a gold standard by reviewing charts from Epic. Similarly, from our literature review, other COVID-19 data repository validations targeted only one derived concept (e.g., severity phenotype 40 and COVID-19 label 30 ) and did not use NLP-based analytic tools for chart reviews.…”

Section: Discussionmentioning

confidence: 99%

“…The lack of data to mine raised concerns about the reliability of our preexisting phenotyping algorithms [16][17][18][19][20][21][22][23][24][25][26][27][28] . Therefore, validating the COVID-19 data sets became an urgent goal 29,30 .…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…In the international Consortium National COVID-19 Cohort Collaborative (4CE), 96 member hospitals from seven countries have built a harmonized data repository for COVID-19 research 39 . However, only a few evaluated the data quality 30,33,40 .…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…[16][17][18][19][20][21][22][23][24][25][26][27][28] Therefore, validating the COVID-19 data sets became an urgent goal. 29,30 There are many organizations that have built COVID-19 data resources to use EHR data for research. [31][32][33][34][35] On a larger scale, National COVID-19 Cohort Collaborative offers harmonized EHR data across clinical organizations in the United States.…”

Section: Introductionmentioning

confidence: 99%

“…39 However, only a few evaluated the data quality. 30,33,40 Unlike our previous validations, [41][42][43][44][45][46] COVID-19 Data Mart validation needed to be completed in a short time, targeted broad disease domains, and was expected to require more note reviews. Moreover, we needed to rely on volunteers with diverse clinical backgrounds and different chart review skills.…”

Section: Introductionmentioning

confidence: 99%

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The Digital Analytic Patient Reviewer (DAPR) for COVID-19 Data Mart Validation

et al. 2022

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Objective: To provide high-quality data for COVID-19 research, we validated derived COVID-19 clinical indicators and 22 associated machine learning phenotypes, in the Mass General Brigham (MGB) COVID-19 Data Mart. Materials and Methods: Fifteen reviewers performed a retrospective manual chart review for 150 COVID-19 positive patients in the data mart. To support rapid chart review for a wide range of target data, we offered a Natural Language Processing (NLP)-based chart review tool, the Digital Analytic Patient Reviewer (DAPR). For this work, we designed a dedicated patient summary view and developed new 127 NLP logics to extract COVID-19 relevant medical concepts and target phenotypes. Moreover, we transformed DAPR for research purposes, so that patient information is used for an approved research purpose only and enabled fast access to the integrated patient information. Lastly, we performed a survey to evaluate the validation difficulty and usefulness of the DAPR. Results: The concepts for COVID-19 positive cohort, COVID-19 index date, COVID-19 related admission, and the admission date were shown to have high values in all evaluation metrics. However, three phenotypes showed notable performance degradation than the Positive Predictive Value (PPV) in the pre-pandemic population. Based on these results, we removed the three phenotypes from our data mart. In the survey about using the tool, participants expressed positive attitudes towards using DAPR for chart review. They assessed the validation was easy and DAPR helped find relevant information. Some validation difficulties were also discussed. Discussion and Conclusion: Use of NLP technology in the chart review helped to cope with the challenges of the COVID-19 data validation task and accelerated the process. As a result, we could provide more reliable research data promptly and respond to the COVID-19 crisis. DAPR’s benefit can be expanded to other domains. We plan to operationalize it for wider research groups.

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Section: Discussionmentioning

confidence: 99%