Searching Full-Text Anatomic Pathology Reports Using Business Intelligence Software

Arvisais-Anhalt, Simone; Lehmann, Christoph U.; Bishop, Justin A.; Balani, Jyoti; Boutte, Laurie; Morales, M. E.; Park, Jason; Araj, Ellen

doi:10.1016/j.jpi.2022.100014

Cited by 5 publications

(4 citation statements)

References 18 publications

(16 reference statements)

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“…While there do exist dashboards to assist in pathology report analysis, they do not operate within our specific niche. 17 In this study, we document the development of a web application that assists with interrogating the results from an NLP prediction pipeline and comment on its potential application for identifying misbilling. We imagine this tool would serve as a research resource for others to employ in similar assessments.…”

Section: Introductionmentioning

confidence: 99%

Development of an interactive web dashboard to facilitate the reexamination of pathology reports for instances of underbilling of CPT codes

Greenburg¹,

Lu²,

Lu³

et al. 2023

Journal of Pathology Informatics

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Section: Introductionmentioning

confidence: 99%

Development of an interactive web dashboard to facilitate the reexamination of pathology reports for instances of underbilling of CPT codes

Greenburg¹,

Lu²,

Lu³

et al. 2023

Journal of Pathology Informatics

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“…While the concept of a kidney biopsy registry is not new, with many such registries described in the literature, 13 , 15 , 26 - 28 we adopted a novel method for data extraction using a natural language processing algorithm, demonstrating the feasibility of the creation of such a registry without an exhaustive chart review of potentially thousands of patients. Although natural language processing software has been used for data extraction from pathology reports in oncology, 29 - 31 to our knowledge this is the first application of such software for data extraction from kidney biopsy pathology reports. Our methods could serve as a model to other centers looking to create a similar registry with high-quality data, without an in-depth manual review of potentially thousands of biopsy reports.…”

Section: Discussionmentioning

confidence: 99%

The Development of a Comprehensive Clinicopathologic Registry for Glomerular Diseases Using Natural Language Processing

Barr

Harasemiw

Gibson

et al. 2023

Can J Kidney Health Dis

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Background: Glomerulonephritis (GN) represents a common cause of chronic kidney disease, and treatment to slow or prevent progression of GN is associated with significant morbidity. Large patient registries have improved the understanding of risk stratification, treatment selection, and definitions of treatment response in GN, but can be resource-intensive, with incomplete patient capture. Objective: To describe the creation of a comprehensive clinicopathologic registry for all patients undergoing kidney biopsy in Manitoba, using natural language processing software for data extraction from pathology reports, as well as to describe cohort characteristics and outcomes. Design: Retrospective population-based cohort study. Setting: Tertiary care center in the province of Manitoba. Patients: All patients undergoing a kidney biopsy in the province of Manitoba from 2002 to 2019. Measurements: Descriptive statistics are presented for the most common glomerular diseases, along with outcomes of kidney failure and mortality for the individual diseases. Methods: Data from native kidney biopsy reports from January 2002 to December 2019 were extracted into a structured database using a natural language processing algorithm employing regular expressions. The pathology database was then linked with population-level clinical, laboratory, and medication data, creating a comprehensive clinicopathologic registry. Kaplan-Meier curves and Cox models were constructed to assess the relationship between type of GN and outcomes of kidney failure and mortality. Results: Of 2421 available biopsies, 2103 individuals were linked to administrative data, of which 1292 had a common glomerular disease. The incidence of yearly biopsies increased almost 3-fold over the study period. Among common glomerular diseases, immunoglobulin A (IgA) nephropathy was the most common (28.6%), whereas infection-related GN had the highest proportions of kidney failure (70.3%) and all-cause mortality (42.3%). Predictors of kidney failure included urine albumin-to-creatinine ratio at the time of biopsy (adjusted hazard ratio [HR] = 1.43, 95% confidence interval [CI] = 1.24-1.65), whereas predictors of mortality included age at the time of biopsy (adjusted HR = 1.05, 95% CI = 1.04-1.06) and infection-related GN (adjusted HR = 1.85, 95% CI = 1.14-2.99, compared with the reference category of IgA nephropathy). Limitations: Retrospective, single-center study with a relatively small number of biopsies. Conclusions: Creation of a comprehensive glomerular diseases registry is feasible and can be facilitated through the use of novel data extraction methods. This registry will facilitate further epidemiological research in GN.

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“…Our pathology department at a mid-sized academic center, is transitioning from an Oracle Cerner laboratory information system (LIS) to EPIC Beaker LIS. We were motivated by previous attempts to create business intelligence systems that could swiftly retrieve structured and unstructured pathology reporting data 8 . As a result, we designed our own in-house solution with enhanced search capabilities, called Dendrite.…”

Section: Introductionmentioning

confidence: 99%

Dendrite: A Structured, Accessible, and Queryable Pathology Search Database for Streamlined Experiment Planning

Lu,

Hamilton,

Greenberg

et al. 2023

Preprint

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Pathology reports contain vital information, yet a significant portion of this data remains underutilized in electronic medical record systems due to the unstructured and varied nature of reporting. Although synoptic reporting has introduced reporting standards, the majority of pathology text remains free-form, necessitating additional processing to enable accessibility for research and clinical applications. This paper presents Dendrite, a web application designed to enhance pathology research by providing intelligent search capabilities and streamlining the creation of study cohorts. Leveraging expert knowledge and natural language processing algorithms, Dendrite converts free-form pathology reports into structured formats, facilitating easier querying and analysis. Using a custom Python script, Dendrite organizes pathology report data, enabling record linkages, text searches, and structured drop-down menus for information filtering and integration. A companion web application enables data exploration and export, showcasing its potential for further analysis and research. Dendrite, derived from existing laboratory information systems, outperforms existing implementations in terms of speed, responsiveness, and flexibility. With its efficient search functionality and support for clinical research and quality improvement efforts in the pathology field, Dendrite proves to be a valuable tool for pathologists. Future enhancements encompass user management integration, integration of natural language processing and machine learning to enhance structured reporting capabilities and seamless integration of Dendrite with the vast repository of genomics and imaging data.

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Searching Full-Text Anatomic Pathology Reports Using Business Intelligence Software

Cited by 5 publications

References 18 publications

Development of an interactive web dashboard to facilitate the reexamination of pathology reports for instances of underbilling of CPT codes

Development of an interactive web dashboard to facilitate the reexamination of pathology reports for instances of underbilling of CPT codes

The Development of a Comprehensive Clinicopathologic Registry for Glomerular Diseases Using Natural Language Processing

Dendrite: A Structured, Accessible, and Queryable Pathology Search Database for Streamlined Experiment Planning

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