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Related Concept Videos

Health Information Technology and Healthcare Information System01:30

Health 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:
Methods of Documentation VI: Case Management Model01:15

Methods 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...
Methods of Documentation II: POMR01:26

Methods 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.
Nursing Clinical Information System01:27

Nursing 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:
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...
Hospitals-II00:59

Hospitals-II

Hospitals provide inpatient and outpatient services. Inpatient services provide care to patients that stay in the hospital for an extended period, ranging from days to months. Examples of inpatient services include intensive care units, hospital wards, or surgeries. Outpatient services provide care to patients who come to a hospital for a diagnostic or treatment but do not stay overnight —for example, diagnostic tests, surgical procedures, or health education.
Nurses that work in hospitals have...

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Related Experiment Video

Updated: Jun 10, 2026

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients
03:47

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients

Published on: July 12, 2024

Data-driven network diagnostics for optimizing POCUS management: an actionable hospital analytical approach.

Chengchen Xu1, Chenchen Liu2

  • 1Department of Anesthesiology and Operation, The Affiliated Yixing Clinical School of Medical School of Yangzhou University, Yixing, Jiangsu, China.

BMC Health Services Research
|June 9, 2026
PubMed
Summary

Hospitals face challenges managing point-of-care ultrasound (POCUS) resources. A data-driven network analysis identified vulnerabilities and workload imbalances, enabling evidence-based optimization strategies for improved efficiency and resilience.

Keywords:
Data-driven optimizationDemand forecastingNetwork analysisPoint-of-care ultrasoundResource allocation

Related Experiment Videos

Last Updated: Jun 10, 2026

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients
03:47

Workflow and Framework for Collecting and Implementing Point-of-Care Ultrasound Data in the Management of Heart Failure Patients

Published on: July 12, 2024

Area of Science:

  • Critical Care Medicine
  • Health Systems Management
  • Medical Informatics

Background:

  • Point-of-care ultrasound (POCUS) is essential in critical care settings.
  • Hospitals experience inefficient POCUS resource allocation, leading to delays and burnout.
  • Static scheduling fails to address dynamic patient needs and workflow bottlenecks.

Purpose of the Study:

  • To develop and validate a data-driven analytical approach for optimizing POCUS management.
  • To identify bottlenecks, forecast demand surges, and detect collaborative communities within the POCUS system.
  • To transition POCUS management from intuition-based to evidence-based optimization.

Main Methods:

  • A three-tier, data-driven analytical approach was developed and applied to POCUS data from Yixing People's Hospital.
  • A tripartite network model (patients, departments, sonographers) was created using data from 2,281 POCUS examinations.
  • Structural, temporal, and functional analyses were integrated to diagnose system inefficiencies.

Main Results:

  • The POCUS network displayed 'small-world' properties, indicating efficiency but also vulnerability due to over-reliance on critical hubs.
  • Time-series analysis revealed periodic extreme demand surges that coincided with network contraction.
  • Community detection highlighted significant workload imbalances, with one community managing 57.8% of examinations.

Conclusions:

  • An actionable management strategy includes demand-responsive staffing, priority green channels for critical pathways, and functionally differentiated allocation.
  • This approach enables systematic balancing of efficiency and resilience in POCUS management.
  • The proposed method shifts POCUS management towards evidence-based optimization.