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Issues And Trends In Healthcare Delivery System

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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.
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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

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Health in All Policy Making Utilizing Big Data.

Alice G Vassiliou^1,2, Christina Georgakopoulou¹, Alexandra Papageorgiou¹

¹Hellenic National Organization for the Provision of Health Services, Athens, Greece.

Acta Informatica Medica : AIM : Journal of the Society for Medical Informatics of Bosnia & Herzegovina : Casopis Drustva Za Medicinsku Informatiku Bih

|March 27, 2020

Summary

The Policy Development Toolkit (PDT) leverages big data analytics to support Health in All Policies (HiAP), offering causal analysis, forecasting, and risk stratification for better healthcare policy development.

Keywords:

Big Data Health Policy Policy-Making

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Area of Science:

Healthcare Policy
Big Data Analytics
Public Health

Background:

Health in All Policies (HiAP) is a recognized strategy for effective healthcare policy development.
Big data analysis offers significant potential benefits for individuals and clinicians in healthcare.

Purpose of the Study:

To explore the application of Big Data in supporting the development of Health in All Policies (HiAP).
To introduce the Policy Development Toolkit (PDT) as a tool for healthcare policy innovation.

Main Methods:

Utilizing heterogeneous data sources within the CrowdHEALTH project.
Employing the Policy Development Toolkit (PDT) model for data analysis.
Analyzing inpatient and outpatient healthcare data, including reimbursement and medical costs.

Main Results:

The PDT model was applied to the NOHS Story Board case study.
Analysis included data from disease-related groups (DRGs) and outpatient medical costs.
The PDT demonstrated capabilities in analyzing complex healthcare datasets.

Conclusions:

The PDT shows promise as a decision support system for policymakers implementing HiAP.
PDT facilitates Causal Analysis, Forecasting Information, and Risk Stratification in healthcare management.
PDT can aid policymakers in reducing costs, improving clinical effectiveness, and preventing fraud.