Clinical concept extraction: A methodology review

An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP‐assisted observational studies exist. The absence of detailed reporting guidelines may create ambiguity in the use of NLP‐derived content, knowledge gaps in the current research reporting practices, and reproducibility challenges. To address these issues, we conducted a scoping review of NLP‐assisted observational clinical studies and examined their reporting practices, focusing on NLP methodology and evaluation. Through our investigation, we discovered a high variation regarding the reporting practices, such as inconsistent use of references for measurement studies, variation in the reporting location (reference, appendix, and manuscript), and different granularity of NLP methodology and evaluation details. To promote the wide adoption and utilization of NLP solutions in clinical research, we outline several perspectives that align with the six principles released by the World Health Organization (WHO) that guide the ethical use of artificial intelligence for health.

Recommended practices and ethical considerations for natural language processing‐assisted observational research: A scoping review

Background Scabies and its complications are a public health problem in the low- and middle-income countries of the Western Pacific region. However, no data are available for the relatively wealthy French territory of New Caledonia. This study aimed to determine the incidence of scabies and its association with skin and soft tissue infection (SSTI) in Loyalty Islands Province (LIP) (20,000 inhabitants), New Caledonia. Methodology/Principal findings This retrospective observational study reviewed cases of scabies and SSTI extracted from the electronic health record databases of LIP clinics for the period 2004–2018. Data were validated through double sampling. The overall scabies incidence rate (IR) and scabies IRs by sex and age group were calculated. Scabies seasonality was evaluated. For children <5 years, the presence of SSTI was compared between the 3-month period preceding scabies diagnosis/treatment and the 3-month period preceding the 1-year anniversary of scabies diagnosis/treatment (self-matching). A total of 16,843 scabies cases were extracted using a detection algorithm with a sensitivity of 96.7% and a specificity of 99.9%. From 2004 to 2018, the average overall scabies IR was 5.9% and the average scabies IR in children <1 year was 18.4%. Almost two-thirds of children aged 14 years had a history of scabies. Females were more affected, especially in the 20–39 age group (sex ratio>2). A strong seasonality was observed, with a 30% increase in winter. In children <5 years, SSTIs were 4.3 times more frequent in the 3 months preceding the scabies diagnosis than in the 3 months preceding the 1-year anniversary of scabies treatment (p<0.001). Conclusions Although health care is much better in New Caledonia than in neighboring countries, scabies is highly endemic in LIP. The disease is especially common in children <2 years and is associated with many SSTIs in children <5 years. Mass drug administration should be considered.

/pdf/scabies-incidence-and-association-with-skin-and-soft-tissue-1ko85qc5.pdf

Scabies incidence and association with skin and soft tissue infection in Loyalty Islands Province, New Caledonia: A 15-year retrospective observational study using electronic health records

The secondary use of electronic health records (EHRs) faces challenges in the form of varying data quality-related issues. To address that, we retrospectively assessed the quality of functional status documentation in EHRs of persons participating in Mayo Clinic Study of Aging (MCSA). We used a convergent parallel design to collect quantitative and qualitative data and independently analyzed the findings. We discovered a heterogeneous documentation process, where the care practice teams, institutions, and EHR systems all play an important role in how text data is documented and organized. Four prevalent instrument-assisted documentation (iDoc) expressions were identified based on three distinct instruments: Epic smart form, questionnaire, and occupational therapy and physical therapy templates. We found strong differences in the usage, information quality (intrinsic and contextual), and naturality of language among different type of iDoc expressions. These variations can be caused by different source instruments, information providers, practice settings, care events and institutions. In addition, iDoc expressions are context specific and thus shall not be viewed and processed uniformly. We recommend conducting data quality assessment of unstructured EHR text prior to using the information.

/pdf/quality-assessment-of-functional-status-documentation-in-3dv8yr3d.pdf

Quality assessment of functional status documentation in EHRs across different healthcare institutions

The effective use of EHR data for clinical research is challenged by the lack of methodologic standards, transparency, and reproducibility. For example, our empirical analysis on clinical research ontologies and reporting standards found little-to-no informatics-related standards. To address these issues, our study aims to leverage natural language processing techniques to discover the reporting patterns and data abstraction methodologies for EHR-based clinical research. We conducted a case study using a collection of full articles of EHR-based population studies published using the Rochester Epidemiology Project infrastructure. Our investigation discovered an upward trend of reporting EHR-related research methodologies, good practice, and the use of informatics related methods. For example, among 1279 articles, 24.0% reported training for data abstraction, 6% reported the abstractors were blinded, 4.5% tested the inter-observer agreement, 5% reported the use of a screening/data collection protocol, 1.5% reported that team meetings were organized for consensus building, and 0.8% mentioned supervision activities by senior researchers. Despite that, the overall ratio of reporting/adoption of methodologic standards was still low. There was also a high variation regarding clinical research reporting. Thus, continuously developing process frameworks, ontologies, and reporting guidelines for promoting good data practice in EHR-based clinical research are recommended.

Natural Language Processing for the Evaluation of Methodological Standards and Best Practices of EHR-based Clinical Research.

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Natural Language Processing for the Evaluation of Methodological Standards and Best Practices of EHR-based Clinical Research.