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Methods of Documentation II: POMR01:26

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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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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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Automated problem list generation and physicians perspective from a pilot study.

Murthy V Devarakonda1, Neil Mehta2, Ching-Huei Tsou1

  • 1IBM Research, USA.

International Journal of Medical Informatics
|July 29, 2017
PubMed
Summary
This summary is machine-generated.

Automated problem list generation using AI shows promise. A pilot study found AI-generated lists were rated higher than existing EHR lists and identified missed patient problems.

Keywords:
Electronic health recordsIBM WatsonLongitudinal patient recordsMachine learningNatural language processingProblem list

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • Electronic Health Record (EHR) problem lists are often inaccurate, duplicative, and outdated.
  • Accurate problem lists are crucial for patient-centered care.
  • Advances in machine learning and natural language processing (NLP) offer potential for automated problem list generation.

Purpose of the Study:

  • To describe an automated problem list generation method using NLP and machine learning.
  • To assess physicians' evaluation of AI-generated problem lists compared to existing EHR problem lists and physician-curated lists.

Main Methods:

  • A pilot study evaluated the Watson method for generating problem lists from clinical notes and structured EHR data.
  • 15 de-identified patient records were reviewed by internal medicine physicians.
  • Physicians compared the usefulness of their own curated lists (P), Watson-generated lists (W), and existing EHR lists (E) on a 10-point scale.

Main Results:

  • Physicians rated their own curated lists (P) highest.
  • Watson-generated lists (W) were rated higher than existing EHR lists (E).
  • In 89% of assessments, Watson identified important problems missed by physicians.

Conclusions:

  • AI-powered cognitive computing systems can potentially create accurate, up-to-date problem lists.
  • Automated problem list generation may improve efficiency, clinical decision support, and patient care quality.