Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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...
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:
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...
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Developmental outcomes of young children with an autism diagnosis and its associated clinical correlates.

Frontiers in psychiatry·2026
Same author

Atherosclerotic cardiovascular disease modifies ambulatory blood pressure response to mandibular advancement device vs. CPAP in obstructive sleep apnoea (ASCVD modifies BP response to OSA therapy).

European journal of preventive cardiology·2026
Same author

Dual inhibition of heterocyclic amines and advanced glycation end-products by polymethoxyflavones via trapping reactive intermediates and scavenging free radicals.

Current research in food science·2026
Same author

Treatment of OSA using mandibular advancement versus CPAP in improving cardiovascular health.

The American journal of medicine·2026
Same author

The Influence of the <i>FGF8</i> Gene on the Proliferation and Differentiation of Preadipocytes in Sheep.

Animals : an open access journal from MDPI·2026
Same author

Explainable Contrastive Learning for KL Grading Classification in Knee Osteoarthritis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Related Experiment Video

Updated: Jun 26, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Closing the implementation-evidence gap using data science: a transferable workflow applied to medical cost analysis.

Fengyi Gao1, Siew-Pang Chan1,2

  • 1Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.

Frontiers in Health Services
|June 25, 2026
PubMed
Summary

This study introduces a reproducible data science pipeline to model healthcare costs, adaptable across different health systems. It aims to guide future policy and technology for chronic disease management.

Keywords:
CFIRbehavioural medicinedata sciencemedical costssmart medicine

Related Experiment Videos

Last Updated: Jun 26, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Health Economics
  • Data Science
  • Computational Health

Background:

  • Rising healthcare expenditure presents a global challenge, exacerbated by aging populations and chronic diseases.
  • Existing research often identifies cost determinants but lacks transferable, reproducible analytical workflows for diverse health systems.

Purpose of the Study:

  • To present a data science-based analytic pipeline for modeling medical expenditure.
  • To emphasize methodological transparency, reproducibility, and potential transferability across health systems.
  • To illustrate data-driven approaches for future healthcare policy and chronic disease management.

Main Methods:

  • Development of a data science pipeline incorporating feature engineering.
  • Application of statistical and machine learning models for expenditure analysis.
  • Inclusion of model comparison and interpretability analysis using a large public health expenditure dataset.

Main Results:

  • A reproducible and transferable analytic pipeline for modeling healthcare expenditure was developed.
  • The methodology emphasizes transparency and adaptability for cross-system application.
  • Findings highlight the potential of data-driven approaches for informing policy and chronic disease management strategies.

Conclusions:

  • The presented pipeline offers a methodological advancement for analyzing healthcare expenditure.
  • Data-driven insights can inform future policy design, stakeholder engagement, and technology-enabled care.
  • Local adaptation of the pipeline is crucial for effective implementation in diverse health systems.