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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:
Integrated Healthcare System01:20

Integrated Healthcare System

An integrated healthcare system (IHS) is a set of organizations that provides for or arranges to provide coordinated and continuous service to a defined population. The IHS takes responsibility for that particular population's health status and outcome, both clinically and fiscally. An integrated healthcare system is a well-organized, well-coordinated, and collaborative network. The integrated delivery system is a network that connects different healthcare providers to deliver organized,...
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:
Introduction to GIS01:28

Introduction to GIS

Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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...

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Related Experiment Video

Updated: Jul 17, 2026

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

Integrating Clinical Classifications Software Refined, Process Indicators, and Geographic Information System Mapping

Joshua Kuan Tan1, Hao Yi Tan1, Gerald Gui Ren Sng2

  • 1Health Services Research Unit, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore, 65 62223322.

JMIR Medical Informatics
|July 15, 2026
PubMed
Summary

A new population health intelligence dashboard integrates diabetes data for better care. Geospatial mapping identifies high-risk areas, enabling targeted interventions and improved patient outcomes.

Keywords:
complicationsdata visualizationdiabetes mellitusgeographic information systemshealth informaticspopulation health analyticspopulation health management

Related Experiment Videos

Last Updated: Jul 17, 2026

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

Area of Science:

  • Population Health Management
  • Health Informatics
  • Geospatial Health Analytics

Background:

  • Effective population health management necessitates tools to translate complex clinical data into actionable insights.
  • Such insights are crucial for guiding care coordination, community outreach, and strategic system-level planning.

Purpose of the Study:

  • To develop and implement a population health intelligence dashboard tailored for diabetes mellitus patients.
  • The dashboard integrates inpatient utilization, process indicators, and health status data.
  • It employs a clinically meaningful classification system and geospatial visualization for enhanced analysis.

Main Methods:

  • Utilized data from the SingHealth Diabetes Registry (2019-2024) to construct an interactive R Shiny dashboard.
  • Developed a semiautomated algorithm to map ICD-10-AM codes to Clinical Classifications Software Refined (CCSR) categories.
  • Incorporated inpatient utilization, diabetes care process indicators, and health status metrics, with Geographic Information System (GIS) mapping for spatial analysis.

Main Results:

  • Diabetes mellitus with complication (END003) was the leading cause of admission, followed by pneumonia and fluid/electrolyte disorders.
  • Analysis revealed distinct patterns in conditions driving admission frequency versus prolonged length of stay.
  • GIS mapping identified residential clusters with high inpatient utilization, care gaps, and poor cardiometabolic control, supporting targeted interventions.

Conclusions:

  • The developed dashboard offers an innovative, interactive method for visualizing inpatient use, care gaps, and health status.
  • It facilitates targeted, place-based interventions for diabetes management.
  • The framework is scalable for population health intelligence across other chronic diseases and healthcare systems.