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Related Concept Videos

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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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Decision Making01:20

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Decision Making: Traditional Method01:14

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Documentation in long-term care facilities and home healthcare settings is crucial for ensuring continuous, coordinated, and comprehensive care for patients. Each setting has its specific documentation processes and tools:
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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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Related Experiment Video

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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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EHR-Independent Predictive Decision Support Architecture Based on OMOP.

Philipp Unberath1, Hans Ulrich Prokosch1, Julian Gründner1

  • 1Department of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

Applied Clinical Informatics
|June 4, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an electronic health record-independent system for deploying cancer relapse prediction models in clinical settings. The developed tool, based on the Observational Medical Outcomes Partnership common data model, achieved high usability scores, paving the way for wider adoption.

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Area of Science:

  • Biomedical Informatics
  • Machine Learning in Healthcare
  • Clinical Decision Support Systems

Background:

  • Increasing availability of cancer patient molecular and clinical data fuels machine learning for clinical decision support.
  • Despite promising prediction models for relapse risk, clinical deployment remains a challenge.

Purpose of the Study:

  • To demonstrate a generic integration method for prediction tools into clinical settings.
  • To provide an exemplary use case for predicting melanoma patient relapse risk.

Main Methods:

  • Developed an electronic health record (EHR)-independent architecture using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM).
  • Implemented an extract-transform-load process to map data to OMOP and adapted the OMOP WebAPI for data retrieval.
  • Evaluated usability via user interviews, thinking-aloud protocol, and the System Usability Scale (SUS) questionnaire.

Main Results:

  • An EHR-independent extract-transform-load process and WebAPI adaptation were successfully developed.
  • The decision support Web application enabled physicians to consult prediction services and monitor data.
  • The application achieved a high System Usability Scale (SUS) score of 86.7.

Conclusions:

  • Proposes an EHR-independent method for integrating prediction models using OMOP CDM for clinical deployment.
  • The usability evaluation indicates the application is suitable for routine clinical use with minor areas for improvement.