The Economic Impact of AI-Driven Remote Patient Monitoring: A Business Intelligence Perspective on Healthcare Cost Optimization.

Healthcare cost optimization has improved through the implementation of Artificial Intelligence in remote patient monitoring because it increases operational efficiency and predictive analytics and allows immediate patient care management. This paper examines AI-powered RPM's economic value by showing its capacity to decrease hospital admissions together with reducing healthcare expenses while directing resources effectively. Business intelligence tools with AI help RPM systems create predictive intervention strategies for better patient results without affecting operational costs. This research uses quantitative methods alongside real-world healthcare institution data, financial calculations along with cost-benefit assessment for examining AI-driven RPM sustainability. The economic advantages of Artificial Intelligence are measured through analytical methods which combine cost-effectiveness analysis and machine learning predictive models together with regression analyses. Ai-powered RPM implementation cuts hospital stay expenses by a minimum of thirty percent while diminishing emergency room usage by 25 percent and enables better healthcare workforce allocation that enhances employee productivity and decreases operational expenses. This investigation brings originality through its combination of business intelligence methodology and predictive analytics with AI-driven RPM for better organizational choices. The paper fills knowledge gaps in the literature by using empirical data to prove the extended financial advantages of AI-based healthcare solutions. AI-based RPM systems demonstrate both technological advancement and financial practicability which enables healthcare policymakers together with hospital administrators and insurers to make well-informed investment choices. Researchers must study ways to scale AI-driven RPM systems which will enable their successful implementation throughout healthcare systems with various characteristics.

Authors

Publication Details

Journal
International Interdisciplinary Business Economics Advancement Journal
Published
2025-05-06
DOI
https://doi.org/10.55640/business/volume06issue05-03
Citations
1
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
2.28
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Economic Impact of AI-Driven Remote Patient Monitoring: A Business Intelligence Perspective on Healthcare Cost Optimization.

Mir Abrar Hossain, K. A. Desai, Muhammad Yeasir Arafat, Sharmin Akter et al.
1 citations
International Interdisciplinary Business Economics Advancement Journal
Big Data and Business Intelligence
2.28
article

The Economic Impact of AI-Driven Remote Patient Monitoring: A Business Intelligence Perspective on Healthcare Cost Optimization.

Mir Abrar Hossain, K. A. Desai, Muhammad Yeasir Arafat, Sharmin Akter, Ayesha Islam Asha
article en
1 citations

Abstract

Healthcare cost optimization has improved through the implementation of Artificial Intelligence in remote patient monitoring because it increases operational efficiency and predictive analytics and allows immediate patient care management. This paper examines AI-powered RPM's economic value by showing its capacity to decrease hospital admissions together with reducing healthcare expenses while directing resources effectively. Business intelligence tools with AI help RPM systems create predictive intervention strategies for better patient results without affecting operational costs. This research uses quantitative methods alongside real-world healthcare institution data, financial calculations along with cost-benefit assessment for examining AI-driven RPM sustainability. The economic advantages of Artificial Intelligence are measured through analytical methods which combine cost-effectiveness analysis and machine learning predictive models together with regression analyses. Ai-powered RPM implementation cuts hospital stay expenses by a minimum of thirty percent while diminishing emergency room usage by 25 percent and enables better healthcare workforce allocation that enhances employee productivity and decreases operational expenses. This investigation brings originality through its combination of business intelligence methodology and predictive analytics with AI-driven RPM for better organizational choices. The paper fills knowledge gaps in the literature by using empirical data to prove the extended financial advantages of AI-based healthcare solutions. AI-based RPM systems demonstrate both technological advancement and financial practicability which enables healthcare policymakers together with hospital administrators and insurers to make well-informed investment choices. Researchers must study ways to scale AI-driven RPM systems which will enable their successful implementation throughout healthcare systems with various characteristics.

International Interdisciplinary Business Economics Advancement JournalVol. 06(05)
Openalex Percentile: Top 12%
Big Data and Business Intelligence
2.28
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.