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Related Concept Videos

Methods of Documentation II: POMR01:26

Methods of Documentation II: POMR

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The Problem-Oriented Medical Record (POMR) revolutionized medical record-keeping by introducing a systematic approach focusing on the patient's problems rather than merely listing symptoms. Dr. Lawrence Weed's introduction of this method in the 1960s marked a significant advancement in medical documentation. The POMR framework consists of four key components: the database, problem list, plan of care, and progress notes.
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Assessment of the Gastrointestinal System I: Subjective Data01:17

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Assessing the gastrointestinal (GI) system is a complex process that begins with collecting subjective data. This data, collected through patient interviews, provides crucial insights into the patient's health history, perception patterns, and lifestyle habits, all contributing significantly to GI health.
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The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study.

Tomohiro Nishiyama1, Ayane Yamaguchi2, Peitao Han1

  • 1Department of Information Science, Nara Institute of Science and Technology, Ikoma, Japan.

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Summary
This summary is machine-generated.

This study developed a natural language processing (NLP) system to detect adverse drug events (ADEs) from multiple electronic health record document types. Leveraging various documents improved ADE detection accuracy compared to single-document analysis.

Keywords:
EHREHRsMLNLPadverseadverse drug reactionadverse eventcancerdetectdetectingdetectiondrugdrugsmachine learningmedicationmedicationsnamed entity recognitionnatural language processingneuropathynotenotesoncologyperipheral neuropathypharmaceuticpharmaceuticalpharmaceuticalspharmaceuticspharmacologypharmacotherapyrecordrecordsreportreportssymptomsymptomstexttextstextual

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Clinical Data Analysis

Background:

  • Electronic health records (EHRs) contain valuable patient data, including adverse drug event (ADE) signals.
  • Analyzing diverse EHR document types (e.g., physician notes, nursing records) is crucial for comprehensive patient status assessment.
  • Current methods often analyze documents in isolation, potentially missing critical information.

Purpose of the Study:

  • To develop and evaluate a natural language processing (NLP) system for detecting ADEs from multiple EHR document types.
  • To assess the system's performance in identifying peripheral neuropathy (PN) as an ADE in breast cancer patients.
  • To compare the effectiveness of analyzing multitype EHR documents versus single-type documents for ADE detection.

Main Methods:

  • Developed an NLP system processing 6 types of Japanese EHR documents: physician notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist notes.
  • The system includes named entity recognition, symptom normalization, and aggregation of data from multiple documents and patients.
  • Evaluated system performance using data from 2289 breast cancer patients, focusing on paclitaxel- or docetaxel-induced PN, with performance assessed via correlation coefficients and regression analysis.

Main Results:

  • The NLP system detected paclitaxel-induced PN in 60.7% of cases, a slight underestimation compared to previous manual extraction (74.0%).
  • A high Pearson correlation coefficient (0.87) was observed between system results and manual extraction.
  • While pharmacist notes showed the highest individual detection rate, aggregating data from all document types significantly improved overall performance compared to single-type analysis.

Conclusions:

  • The developed NLP system effectively leverages multitype EHR documents to improve ADE detection accuracy.
  • This approach offers a significant advantage by enabling rapid estimation of treatment duration (e.g., for PN) without requiring new model fine-tuning.
  • The findings underscore the value of integrating diverse clinical data sources for more robust ADE surveillance and patient care.