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01:18Methods of Documentation I: Source-Oriented Records
Source-oriented records, or SOR, are medical record-keeping organized by the data source. The SOR system was first developed in the mid-1900s to organize the growing patient data in hospitals and other healthcare facilities.
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:
In an SOR, each discipline involved in patient care maintains a separate medical record section. This record-keeping method enables easy tracking of patient progress and ensures healthcare staff have access to up-to-date information.
Key Attributes include the following:
01:26Methods of Documentation II: POMR
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.
01:26Methods of Documentation IV: Focus Charting
Focus Charting, also known as the focus charting system or "focus documentation," is a systematic documentation approach used in healthcare to organize patient information in medical records.
It typically involves three columns for recording information:
It typically involves three columns for recording information:
01:15Methods of Documentation VI: Case Management Model
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.
For example, a patient with a chronic illness...
For example, a patient with a chronic illness...
01:30Health Information Technology and Healthcare Information System
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
01:27Nursing Clinical Information System
Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
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A self-supervised framework for cross-modal search in histopathology archives using scale harmonization.
Danial Maleki1, Shahryar Rahnamayan2, H R Tizhoosh3,4
1Kimia Lab, University of Waterloo, Waterloo, ON, Canada.
Scientific Reports
|April 27, 2024
Summary
This study introduces a novel harmonization method to improve cross-modal retrieval for gigapixel whole slide images in digital pathology. The technique refines the DINO paradigm, enhancing tissue imaging analysis and showing potential for other gigapixel applications.
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Area of Science:
- Digital pathology
- Computational biology
- Medical imaging analysis
Background:
- Exponential growth in medical data necessitates advanced multimodal big data analysis techniques.
- Computational pathology faces challenges in analyzing diverse tissue data, particularly with gigapixel whole slide images.
- Cross-modal retrieval struggles with effective representation of tissue features, especially for visual models due to limited labeled data.
Purpose of the Study:
- To address the challenge of tissue feature representation in cross-modal retrieval for digital pathology.
- To enhance the performance of visual models by overcoming data scarcity issues.
- To refine the self-supervised learning scheme, DINO (self-distillation with no labels), for gigapixel image analysis.
Main Methods:
- Introduced a novel concept of harmonization to extend the DINO paradigm.
- Developed a scale harmonization technique using a novel patching approach.
- Applied the method to overcome complexities associated with gigapixel whole slide images.
Main Results:
- The proposed approach significantly enhances cross-modal retrieval in tissue imaging.
- Demonstrated improved performance on diverse datasets.
- Validated the effectiveness in handling gigapixel whole slide images.
Conclusions:
- The scale harmonization method refines the DINO paradigm for digital pathology.
- The approach shows significant potential for improving cross-modal retrieval in tissue imaging.
- The technique holds promise for other fields utilizing gigapixel imaging.