A Synoptic Reporting System for Bone Marrow Aspiration and Core Biopsy Specimens

Abstract Context.—Advances in information technology have made electronic systems productive tools for pathology report generation. Structured data formats are recommended for better understanding of pathology reports by clinicians and for retrieval of pathology reports. Suitable formats need to be developed to include structured data elements for report generation in electronic systems. Objective.—To conform to the requirement of protocol-based reporting and to provide uniform and standardized data entry and retrieval, we developed a synoptic reporting system for generation of bone marrow cytology and histology reports for incorporation into our hospital information system. Design.—A combination of macro text, short preformatted templates of tabular data entry sheets, and canned files was developed using a text editor enabling protocol-based input. The system is flexible and has facility for appending free text entry. It also incorporates SNOMED coding and codes for teaching, research, and internal auditing. Results.—This synoptic reporting system is easy to use and adaptable. Features and advantages include pick-up text with defined choices, flexibility for appending free text, facility for data entry for protocol-based reports for research use, standardized and uniform format of reporting, comparable follow-up reports, minimized typographical and transcription errors, and saving on reporting time, thus helping shorten the turnaround time. Conclusions.—Simple structured pathology report templates are a powerful means for supporting uniformity in reporting as well as subsequent data viewing and extraction, particularly suitable to computerized reporting.

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Automating the Capture of Structured Pathology Data for Prostate Cancer Clinical Care and Research

JCO Clinical Cancer Informatics ◽

10.1200/cci.18.00084 ◽

2019 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Anobel Y. Odisho ◽

Mark Bridge ◽

Mitchell Webb ◽

Niloufar Ameli ◽

Renu S. Eapen ◽

...

Keyword(s):

Prostate Cancer ◽

Gold Standard ◽

Clinical Care ◽

Data Entry ◽

Structured Data ◽

Lymph Node Status ◽

Free Text ◽

Routine Clinical Care ◽

Pathology Reports ◽

Pathology Data

Purpose Cancer pathology findings are critical for many aspects of care but are often locked away as unstructured free text. Our objective was to develop a natural language processing (NLP) system to extract prostate pathology details from postoperative pathology reports and a parallel structured data entry process for use by urologists during routine documentation care and compare accuracy when compared with manual abstraction and concordance between NLP and clinician-entered approaches. Materials and Methods From February 2016, clinicians used note templates with custom structured data elements (SDEs) during routine clinical care for men with prostate cancer. We also developed an NLP algorithm to parse radical prostatectomy pathology reports and extract structured data. We compared accuracy of clinician-entered SDEs and NLP-parsed data to manual abstraction as a gold standard and compared concordance (Cohen’s κ) between approaches assuming no gold standard. Results There were 523 patients with NLP-extracted data, 319 with SDE data, and 555 with manually abstracted data. For Gleason scores, NLP and clinician SDE accuracy was 95.6% and 95.8%, respectively, compared with manual abstraction, with concordance of 0.93 (95% CI, 0.89 to 0.98). For margin status, extracapsular extension, and seminal vesicle invasion, stage, and lymph node status, NLP accuracy was 94.8% to 100%, SDE accuracy was 87.7% to 100%, and concordance between NLP and SDE ranged from 0.92 to 1.0. Conclusion We show that a real-world deployment of an NLP algorithm to extract pathology data and structured data entry by clinicians during routine clinical care in a busy clinical practice can generate accurate data when compared with manual abstraction for some, but not all, components of a prostate pathology report.

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Implementing Structured Clinical Templates at a Single Tertiary Hospital: Survey Study

JMIR Medical Informatics ◽

10.2196/13836 ◽

2020 ◽

Vol 8 (4) ◽

pp. e13836

Author(s):

Ji Eun Hwang ◽

Byung Ook Seoung ◽

Sang-Oh Lee ◽

Soo-Yong Shin

Keyword(s):

Data Extraction ◽

Vital Signs ◽

Data Entry ◽

Tertiary Hospital ◽

Survey Study ◽

Pathology Report ◽

Free Text ◽

Log Data ◽

Pathology Reports ◽

Time Required

Background Electronic health record (EHR) systems have been widely adopted in hospitals. However, since current EHRs mainly focus on lowering the number of paper documents used, they have suffered from poor search function and reusability capabilities. To overcome these drawbacks, structured clinical templates have been proposed; however, they are not widely used owing to the inconvenience of data entry. Objective This study aims to verify the usability of structured templates by comparing data entry times. Methods A Korean tertiary hospital has implemented structured clinical templates with the modeling of clinical contents for the last 6 years. As a result, 1238 clinical content models (ie, body measurements, vital signs, and allergies) have been developed and 492 models for 13 clinical templates, including pathology reports, were applied to EHRs for clinical practice. Then, to verify the usability of the structured templates, data entry times from free-texts and four structured pathology report templates were compared using 4391 entries from structured data entry (SDE) log data and 4265 entries from free-text log data. In addition, a paper-based survey and a focus group interview were conducted with 23 participants from three different groups, including EHR developers, pathology transcriptionists, and clinical data extraction team members. Results Based on the analysis of time required for data entry, in most cases, beginner users of the structured clinical templates required at most 70.18% more time for data entry. However, as users became accustomed to the templates, they were able to enter data more quickly than via free-text entry: at least 1 minute and 23 seconds (16.8%) up to 5 minutes and 42 seconds (27.6%). Interestingly, well-designed thyroid cancer pathology reports required 14.54% less data entry time from the beginning of the SDE implementation. In the interviews and survey, we confirmed that most of the interviewees agreed on the need for structured templates. However, they were skeptical about structuring all the items included in the templates. Conclusions The increase in initial elapsed time led users to hold a negative opinion of SDE, despite its benefits. To overcome these obstacles, it is necessary to structure the clinical templates for optimum use. In addition, user experience in terms of ease of data entry must be considered as an essential aspect in the development of structured clinical templates.

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Implementing Structured Clinical Templates at a Single Tertiary Hospital: Survey Study (Preprint)

10.2196/preprints.13836 ◽

2019 ◽

Author(s):

Ji Eun Hwang ◽

Byung Ook Seoung ◽

Sang-Oh Lee ◽

Soo-Yong Shin

Keyword(s):

Data Extraction ◽

Vital Signs ◽

Data Entry ◽

Tertiary Hospital ◽

Survey Study ◽

Pathology Report ◽

Free Text ◽

Log Data ◽

Pathology Reports ◽

Time Required

BACKGROUND Electronic health record (EHR) systems have been widely adopted in hospitals. However, since current EHRs mainly focus on lowering the number of paper documents used, they have suffered from poor search function and reusability capabilities. To overcome these drawbacks, structured clinical templates have been proposed; however, they are not widely used owing to the inconvenience of data entry. OBJECTIVE This study aims to verify the usability of structured templates by comparing data entry times. METHODS A Korean tertiary hospital has implemented structured clinical templates with the modeling of clinical contents for the last 6 years. As a result, 1238 clinical content models (ie, body measurements, vital signs, and allergies) have been developed and 492 models for 13 clinical templates, including pathology reports, were applied to EHRs for clinical practice. Then, to verify the usability of the structured templates, data entry times from free-texts and four structured pathology report templates were compared using 4391 entries from structured data entry (SDE) log data and 4265 entries from free-text log data. In addition, a paper-based survey and a focus group interview were conducted with 23 participants from three different groups, including EHR developers, pathology transcriptionists, and clinical data extraction team members. RESULTS Based on the analysis of time required for data entry, in most cases, beginner users of the structured clinical templates required at most 70.18% more time for data entry. However, as users became accustomed to the templates, they were able to enter data more quickly than via free-text entry: at least 1 minute and 23 seconds (16.8%) up to 5 minutes and 42 seconds (27.6%). Interestingly, well-designed thyroid cancer pathology reports required 14.54% less data entry time from the beginning of the SDE implementation. In the interviews and survey, we confirmed that most of the interviewees agreed on the need for structured templates. However, they were skeptical about structuring all the items included in the templates. CONCLUSIONS The increase in initial elapsed time led users to hold a negative opinion of SDE, despite its benefits. To overcome these obstacles, it is necessary to structure the clinical templates for optimum use. In addition, user experience in terms of ease of data entry must be considered as an essential aspect in the development of structured clinical templates.

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A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

Methods of Information in Medicine ◽

10.1055/s-0038-1635050 ◽

1994 ◽

Vol 33 (05) ◽

pp. 454-463 ◽

Cited By ~ 22

Author(s):

A. M. van Ginneken ◽

J. van der Lei ◽

J. H. van Bemmel ◽

P. W. Moorman

Keyword(s):

Domain Knowledge ◽

Data Entry ◽

Knowledge Bases ◽

Structured Data ◽

Research Quality ◽

Free Text ◽

Specific Knowledge ◽

User Input ◽

The One ◽

Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

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Are Structured Data Structured Identically?

Methods of Information in Medicine ◽

10.1055/s-0038-1634019 ◽

2005 ◽

Vol 44 (05) ◽

pp. 631-638 ◽

Cited By ~ 6

Author(s):

J. Roukema ◽

A. M. van Ginneken ◽

M. de Wilde ◽

J. van der Lei ◽

R. K. Los

Keyword(s):

Clinical Practice ◽

Data Use ◽

Data Entry ◽

Patient Data ◽

Structured Data ◽

Free Text ◽

Patient Records ◽

Paper Record ◽

Recorded Data ◽

Insight Into

Summary Objective: OpenSDE is an application that supports structured recording of narrative patient data to enable use of the data in both clinical practice and clinical research. Reliability and accuracy of collected data are essential for subsequent data use. In this study we analyze the uniformity of data entered with OpenSDE. Our objective is to obtain insight into the consensus and differences of recorded data. Methods: Three pediatricians transcribed 20 paper patient records using OpenSDE. The transcribed records were compared and all recorded findings were classified into one of six categories of difference. Results: Of all findings 22% were recorded identically; 17% of the findings were recorded differently (predominantly as free text); 61% was omitted, inferred, or in conflict with the paper record. Conclusion: The results of this study show that recording patient data using structured data entry does not necessarily lead to uniformly structured data.

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Automated Analysis and Codification of Free Text Pathology Reports

Blood ◽

10.1182/blood.v112.11.2394.2394 ◽

2008 ◽

Vol 112 (11) ◽

pp. 2394-2394

Author(s):

Bill Long ◽

Aliyah Rahemtullah ◽

Christiana E. Toomey ◽

Adam Ackerman ◽

Jeremy S. Abramson ◽

...

Keyword(s):

B Cell ◽

Cell Lymphoma ◽

B Cell Lymphoma ◽

Final Diagnosis ◽

Hematologic Malignancies ◽

Cancer Center ◽

Pathology Report ◽

Free Text ◽

Large B Cell Lymphoma ◽

Pathology Reports

Abstract Introduction: Epidemiologic research in hematologic malignancies has been dependent on three sources of data; SEER data, patients accrued to large clinical trials, and databases generated by individual providers or departments. Each of these sets of data have major limitations. In theory the adoption of computerized databases by pathology departments and the adoption of electronic medical records should have greatly expanded the ability to identify patients with particular types of malignancies. In practice the need to manually classify tens of thousands of free text pathology reports has made this resource unavailable. We have developed a computer program to extract and codify the final diagnoses described in free text pathology reports according to the World Health Organization (WHO) classification of hematologic malignancies. This enables us to identify sets of cases of desired pathologies for further study. Methods: A medical records review protocol was approved by the Dana Farber Harvard Cancer Center Institutional Review Board. Using our clinical lymphoma database we collected records of patients at Massachusetts General Hospital (MGH) Cancer Center who carried a diagnosis of follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL).The program is modified from one developed to extract diagnoses from discharge summaries in a different context [Long, AMIA 2007]. The approach is to use punctuation and a few words (conjunctions and some common verbs) to divide the text into phrases and then use a search procedure to find the most specific matching concepts in the UMLS (Unified Medical Language System). The search uses all of the alternate phrases for each concept as included in the UMLS normalized string table, matched against normalized subphrases from the text. We mapped UMLS concepts to the desired WHO concepts. To ensure a complete list of UMLS concepts we used the hierarchical relations in the UMLS and examined both more specific and more general concepts for possible inclusion in the search. Since not all diseases in a report are part of the diagnosis, we developed pattern matching procedures to identify the parts of the pathology report containing the final diagnosis (as opposed to “note” or “clinical data” that might have patient specific clinical information that are not diagnosed in the current pathology report). The program also uses a strategy for identifying modifiers that change the sense of the diagnosis (“suggestive of”, “rule out”, “no”, etc.) to exclude diseases that are absent or only possible. Results: We used the system to identify cases of FL and DLBCL and compared the results to lists of cases generated manually. The current program was 90% accurate in automatically classifying pathology reports as describing follicular lymphoma. Of 150 cases of FL, the program found 133 (eg, MALIGNANT LYMPHOMA, FOLLICLE CENTER) and 3 were in reports not available to the program (133/147 90%). Of 100 DLBCL cases, 76 were available and 63 were found (83%) (eg, HISTIOCYTE-RICH LARGE B-CELL LYMPHOMA). There were several reasons for the missed cases. Most commonly (13) the diagnosis was not in the identified diagnosis section, either because the section was divided by a note or the diagnosis was in an addendum. Only 3 cases had phrases not found in the UMLS. For 2 others, the program missed because the phrase was not contiguous (eg, B-CELL LYMPHOMA, CONSISTENT WITH FOLLICLE CENTER CELL TYPE). In 5 of the DLBCL cases the stated final diagnosis was more general than the desired diagnoses and 2 of the FL cases were listed as “strongly suggestive” which the program concluded was not a definite diagnosis. Discussion: This program is useful for identifying desired cohorts of cases and can be improved with better identification of the sections containing the diagnosis, the addition of a few missing phrases as they are discovered, and the addition of techniques for handling common discontinuities in disease descriptions (eg, allowing “consistent with” in the phrase). We have shown that very simple natural language techniques are sufficient to extract most of the desired disease descriptions from free text reports, enabling automatic selection of cases and greatly enhancing the usefulness of large repositories of pathology reports.

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Automated Classification of Free-text Pathology Reports for Registration of Incident Cases of Cancer

Methods of Information in Medicine ◽

10.3414/me11-01-0005 ◽

2012 ◽

Vol 51 (03) ◽

pp. 242-251 ◽

Cited By ~ 23

Author(s):

G. Defossez ◽

A. Burgun ◽

P. le Beux ◽

P. Levillain ◽

P. Ingrand ◽

...

Keyword(s):

Supervised Machine Learning ◽

Cancer Registration ◽

International Agency ◽

Support Vector ◽

Svm Classifier ◽

Pathology Report ◽

Free Text ◽

Multiple Primary Cancer ◽

Pathology Reports ◽

Incident Cases

SummaryObjective: Our study aimed to construct and evaluate functions called “classifiers”, produced by supervised machine learning techniques, in order to categorize automatically pathology reports using solely their content.Methods: Patients from the Poitou-Charentes Cancer Registry having at least one pathology report and a single non-metastatic invasive neoplasm were included. A descriptor weighting function accounting for the distribution of terms among targeted classes was developed and compared to classic methods based on inverse document frequencies. The classification was performed with support vector machine (SVM) and Naive Bayes classifiers. Two levels of granularity were tested for both the topographical and the morphological axes of the ICD-O3 code. The ability to correctly attribute a precise ICD-O3 code and the ability to attribute the broad category defined by the International Agency for Research on Cancer (IARC) for the multiple primary cancer registration rules were evaluated using F1-measures.Results: 5121 pathology reports produced by 35 pathologists were selected. The best performance was achieved by our class-weighted descriptor, associated with a SVM classifier. Using this method, the pathology reports were properly classified in the IARC categories with F1-measures of 0.967 for both topography and morphology. The ICD-O3 code attribution had lower performance with a 0.715 F1-measure for topography and 0.854 for morphology.Conclusion: These results suggest that free-text pathology reports could be useful as a data source for automated systems in order to identify and notify new cases of cancer. Future work is needed to evaluate the improvement in performance obtained from the use of natural language processing, including the case of multiple tumor description and possible incorporation of other medical documents such as surgical reports.

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A Novel Method Enables General Pathologists to Create Web Dynamic Forms for Synoptic Tumor Reporting

Archives of Pathology & Laboratory Medicine ◽

10.5858/arpa.2020-0030-oa ◽

2020 ◽

Author(s):

Zhenhong Qu ◽

Keran Zhao ◽

Jason Guo Jin ◽

Elaine Qu; ◽

Zongshan Lai

Keyword(s):

Reporting System ◽

Pathology Report ◽

Dynamic Form ◽

Daily Task ◽

Reporting Templates ◽

Novel Method ◽

Pathology Reports ◽

Reporting Method ◽

Web Platform ◽

The Web

Context.— Tumor reporting constitutes a significant daily task of pathologists. An efficient tumor-reporting methodology is thus vitally important. The Web dynamic form (WbDF) method offers a multitude of advantages over the prevailing transcription-mediated reporting method based on static-text checklists. However, its adaptation has been severely hampered for 2 decades by its costly needs to maintain a complex back-end system and to change the system for frequent updates of reporting content. Objective.— To overcome these 2 obstacles with a serverless Web platform that enables users to create, customize, use and download WbDFs as synoptic templates for structured tumor reporting. Design.— Deploy ReactJS as a Web platform. Create form components in JavaScript Object Notation files. Use JavaScript Object Notation files to make WbDFs on the Web platform. Use the WbDFs to generate final pathology reports. Results.— Ordinary users (pathologists) can create/customize reporting templates as WbDFs on the Web platform. The WbDF can be used to make a pathology report and stored/shared like ordinary document files. There is no back-end system to change, nor a requirement for computer programming skills. Conclusions.— This strategy eliminates the need for a complex back-end system and the associated cost when updating tumor-reporting standards, making it possible to adopt the WbDF method without the technological drawbacks associated with content updates. It also opens a new field of how the tumor-reporting system should be organized, updated, and implemented.

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The Registry Case Finding Engine (CaFE): An automated approach for cancer patient identification from unstructured, free-text pathology reports

Journal of Clinical Oncology ◽

10.1200/jco.2006.24.18_suppl.6080 ◽

2006 ◽

Vol 24 (18_suppl) ◽

pp. 6080-6080

Author(s):

D. A. Hanauer ◽

A. M. Chinnaiyan ◽

G. Miela ◽

D. W. Blayney

Keyword(s):

Cancer Registry ◽

Basal Cell ◽

Pathology Report ◽

Free Text ◽

Coding System ◽

Patient Identification ◽

Time Saving ◽

Custom Made ◽

Basal Cell Carcinomas ◽

Pathology Reports

6080 Background: A vital component to maintaining an accurate cancer registry is the identification of patients with cancer. The University of Michigan Cancer Registry identifies more than 90% of all registry patients by manually reading free-text pathology reports and their associated SNOMED codes. This method is labor and time intensive and is subject to errors of omission. Methods: We created an application to scan free-text pathology reports to identify cases of interest to the registry. It uses a custom-made list of approximately 3,300 words, phrases, and SNOMED codes to positively identify relevant cases and to eliminate non-relevant cases, including those which may mention cancer-related terms. Experienced registrars reviewed 2,451 pathology reports and marked cases of interest to the registry; this served as the gold standard. These reports were also analyzed by the Registry CaFE. The time required for case identification was recorded for both processes. Results: Experienced registrars marked 795 (32.4%) cases as being of interest compared to the CaFE which marked 1,009 (41.1%). The sensitivity of the CaFE was 100% whereas the specificity was 87.1%. An analysis of the 214 errors made by the CaFE revelead that 30 cases (14%) were due to incorrect SNOMED codes assigned by our auto-coding system (Cerner Corporation, Kansas City, MO) and 89 (41.6%) were either skin squamous or basal cell carcinomas (most non-melanomatous skin cancers are not tracked in the registry). Registrars required an average of 21 seconds per pathology report whereas the Registry CaFE processed each report in less than a second. Conclusions: The Registry CaFE identified all relevant cases and correctly eliminated most cases that were not important; it is both effective and time-saving. Future efforts directed at improving the CaFE for squamous and basal cell carcinomas would yield the largest improvement in accuracy. No significant financial relationships to disclose.

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