The Economic Ripple Effect of AI-Powered Claims Processing in Healthcare: Transforming Costs and Productivity
Keywords:
AI, claims processing, healthcareAbstract
AI altered healthcare claims and more. We study the productivity and cost effects of AI-powered healthcare claims processing systems. AI improves healthcare claims processing speed and accuracy, improving profits. Study: machine learning, natural language processing, and automation handle healthcare claims swiftly and correctly. An extensive literature review and empirical case studies evaluate how AI influences operational costs, claim adjudication timelines, and healthcare practitioner productivity. AI cuts errors and streamlines revenue cycle management. Studies suggest that these advancements reduce administrative duties, helping healthcare professionals manage resources and enhance patient care.
Economic benefits of AI claims processing go beyond cost savings. Operational efficiency may help healthcare companies develop and adapt to complex laws. This study discusses data security, algorithmic bias, and AI workforce retraining. This strategic AI deployment planning research examines these issues. AI-powered claims processing accelerates healthcare economic changes. Innovative ideas save costs and increase revenues. AI claims processing research and development are recommended to address healthcare industry demands and boost economic efficiency.
References
Z. Mo et al., "Innovations in predictive analytics for chronic disease management," Journal of Medical Systems, vol. 44, no. 4, pp. 82, 2020.
V. G. Slade et al., "Funding mechanisms for preventive care: A review of policies and practices," Health Policy, vol. 123, no. 5, pp. 534-542, May 2019.
Reddy Machireddy, Jeshwanth. “Revolutionizing Claims Processing in the Healthcare Industry: The Expanding Role of Automation and AI”. Hong Kong Journal of AI and Medicine, vol. 2, no. 1, Jan. 2022, pp. 10-36
K. C. Weichert, “Enhancing Customer Experience Through Optimized Claims Processing,” Journal of Service Research, vol. 23, no. 3, pp. 284–298, 2021.
M. K. Shouman, “Predictive Analytics for Health Insurance Fraud Detection: A Review,” Journal of Theoretical and Applied Information Technology, vol. 97, no. 14, pp.1896-1908, 2019.
Z. M. Abaza, “A Data Analytics Framework for Detecting Health Insurance Fraud,” International Journal of Information Technology and Management, vol. 19, no. 1, pp. 37-53,2020.
Kasaraneni, Ramana Kumar. "AI-Enhanced Virtual Screening for Drug Repurposing: Accelerating the Identification of New Uses for Existing Drugs." Hong Kong Journal of AI and Medicine 1.2 (2021): 129-161.
Ahmad, Tanzeem, et al. "Hybrid Project Management: Combining Agile and Traditional Approaches." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 122-145.
Amsa Selvaraj, Deepak Venkatachalam, and Priya Ranjan Parida, “Advanced Image Processing Techniques for Document Verification: Emphasis on US Driver’s Licenses and Paychecks”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, pp. 516–555, Jun. 2023
Sharmila Ramasundaram Sudharsanam, Praveen Sivathapandi, and D. Venkatachalam, “Enhancing Reliability and Scalability of Microservices through AI/ML-Driven Automated Testing Methodologies”, J. of Artificial Int. Research and App., vol. 3, no. 1, pp. 480–514, Jan. 2023
Sahu, Mohit Kumar. "AI-Based Supply Chain Optimization in Manufacturing: Enhancing Demand Forecasting and Inventory Management." Journal of Science & Technology 1.1 (2020): 424-464.
Rambabu, Venkatesha Prabhu, Munivel Devan, and Chandan Jnana Murthy. "Real-Time Data Integration in Retail: Improving Supply Chain and Customer Experience." Journal of Computational Intelligence and Robotics 3.1 (2023): 85-122.
Pradeep Manivannan, Sharmila Ramasundaram Sudharsanam, and Jim Todd Sunder Singh, “Trends, Future and Potential of Omnichannel Marketing through Integrated MarTech Stacks”, J. Sci. Tech., vol. 2, no. 2, pp. 269–300, Jun. 2021
Pattyam, Sandeep Pushyamitra. "Data Engineering for Business Intelligence: Techniques for ETL, Data Integration, and Real-Time Reporting." Hong Kong Journal of AI and Medicine 1.2 (2021): 1-54.
Amsa Selvaraj, Priya Ranjan Parida, and Chandan Jnana Murthy, “Enhancing Automotive Safety and Efficiency through AI/ML-Driven Telematics Solutions”, J. Computational Intel. & Robotics, vol. 3, no. 2, pp. 82–122, Oct. 2023.
Pradeep Manivannan, Priya Ranjan Parida, and Chandan Jnana Murthy. “The Influence of Integrated Multi-Channel Marketing Campaigns on Consumer Behavior and Engagement”. Journal of Science & Technology, vol. 3, no. 5, Oct. 2022, pp. 48-87
Bonam, Venkata Sri Manoj, et al. "Secure Multi-Party Computation for Privacy-Preserving Data Analytics in Cybersecurity." Cybersecurity and Network Defense Research 1.1 (2021): 20-38.
Pushadapu, Navajeevan. "Advanced Artificial Intelligence Techniques for Enhancing Healthcare Interoperability Using FHIR: Real-World Applications and Case Studies." Journal of Artificial Intelligence Research 1.1 (2021): 118-156.
Rambabu, Venkatesha Prabhu, Amsa Selvaraj, and Chandan Jnana Murthy. "Integrating IoT Data in Retail: Challenges and Opportunities for Enhancing Customer Engagement." Journal of Artificial Intelligence Research 3.2 (2023): 59-102.
Amsa Selvaraj, Priya Ranjan Parida, and Chandan Jnana Murthy, “AI/ML-Based Entity Recognition from Images for Parsing Information from US Driver’s Licenses and Paychecks”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, pp. 475–515, May 2023
Praveen Sivathapandi, Sharmila Ramasundaram Sudharsanam, and Pradeep Manivannan. “Development of Adaptive Machine Learning-Based Testing Strategies for Dynamic Microservices Performance Optimization”. Journal of Science & Technology, vol. 4, no. 2, Mar. 2023, pp. 102-137
Priya Ranjan Parida, Chandan Jnana Murthy, and Deepak Venkatachalam, “Predictive Maintenance in Automotive Telematics Using Machine Learning Algorithms for Enhanced Reliability and Cost Reduction”, J. Computational Intel. & Robotics, vol. 3, no. 2, pp. 44–82, Oct. 2023
Thota, Shashi, et al. "Federated Learning: Privacy-Preserving Collaborative Machine Learning." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 168-190.
Jahangir, Zeib, et al. "From Data to Decisions: The AI Revolution in Diabetes Care." International Journal 10.5 (2023): 1162-1179.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
