โก Quick Summary
This study presents a data-driven healthcare insurance system that utilizes machine learning and blockchain technologies to combat healthcare fraud and enhance patient-doctor recommendations. The system achieved a root mean square error (RMSE) of 0.478 and a mean absolute error (MAE) of 0.0422, demonstrating its effectiveness in real-world applications.
๐ Key Details
- ๐ Dataset: Real data from employees of a local hospital
- ๐งฉ Features used: Patient and doctor profiles, service quality ratings
- โ๏ธ Technology: Machine learning algorithms including SVD, KNN-based CF, Item-based CF, TF-IDF, and K-means clustering
- ๐ Blockchain: Smart contract technology on the Ethereum blockchain for claims reimbursement
๐ Key Takeaways
- ๐ก Intelligent system developed to identify and monitor healthcare fraud.
- ๐ค Machine learning techniques used for recommending doctors and insurance plans.
- ๐ Significant metrics: RMSE of 0.478 and MAE of 0.0422.
- ๐ Blockchain technology enhances security and transparency in insurance claims.
- ๐ First of its kind in Pakistan for recommending doctors based on professional conduct.
- ๐ฅ Reduced overall expenditure for the local hospital through optimized insurance plans.
- ๐ Improved patient experience by providing quality-based recommendations.
๐ Background
The healthcare industry is increasingly facing challenges related to fraud in health insurance, which not only affects the financial stability of healthcare providers but also compromises patient care. As healthcare recommendations and insurance become more intertwined, there is a pressing need for innovative solutions that leverage technology to enhance service quality and reduce fraudulent activities.
๐๏ธ Study
This study aimed to develop a comprehensive healthcare insurance system that integrates machine learning and blockchain technologies. By creating separate profiles for patients and doctors based on service quality ratings, the researchers employed various algorithms to recommend healthcare providers and insurance plans effectively. The system was validated using real data from a local hospital, ensuring its practical applicability.
๐ Results
The proposed system demonstrated impressive performance metrics, achieving a root mean square error (RMSE) of 0.478 and a mean absolute error (MAE) of 0.0422. These results indicate a high level of accuracy in the recommendations provided by the system. Additionally, the integration of blockchain technology significantly enhanced the security and transparency of the insurance claims process, effectively reducing instances of fraud.
๐ Impact and Implications
The implications of this study are profound, particularly in regions like Pakistan where such systems are not yet established. By utilizing machine learning and blockchain, this innovative approach not only addresses the issue of healthcare fraud but also improves the overall quality of patient care. The potential for broader applications in healthcare systems worldwide could lead to more efficient, transparent, and patient-centered services.
๐ฎ Conclusion
This study highlights the transformative potential of combining machine learning and blockchain technologies in the healthcare insurance sector. By providing accurate recommendations and enhancing the claims process, the proposed system paves the way for a more secure and efficient healthcare environment. Continued research and development in this area could lead to significant advancements in healthcare delivery and fraud prevention.
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Data driven healthcare insurance system using machine learning and blockchain technologies.
Abstract
Healthcare recommendations and insurance have recently been one of the most emerging research areas in health informatics. The fraud in health insurance is becoming increasingly common day by day. To handle healthcare insurance fraud, there is an urgent need for an intelligent system that cannot only identify and monitor doctors’ and hospitals’ behavior regarding the health services they provide to patients but can also recommend doctors and hospitals to insured employees based on the quality of services they provided previously. This system creates patient and doctor profiles separately, based on their rating. The proposed system combines singular value decomposition (SVD), K-nearest neighbors based collaborative filtering (KNN-based CF), item-based collaborative filtering (Item-based CF), content-based filtering using term frequency-inverse document frequency (TF-IDF), and K-means clustering and probability distributions to recommend doctors and insurance plans. The system measures similarity scores between patients and doctors using cosine similarity, which helps to determine similarity scores and refine the recommendations. This study also uses blockchain technology to automate insurance claims reimbursement. The results are validated using real data from the employees of a local hospital. The system provides recommendations with a root mean square error (RMSE) value of 0.478 and a mean absolute error (MAE) value of 0.0422. The insurance plans developed using the proposed system have reduced the overall expenditure of the local hospital, with a reduction in total expenses. Blockchain technology further helps prevent healthcare fraud. In the proposed system, a healthcare insurance claims reimbursement system is built using smart contract technology on the Ethereum blockchain, ensuring security & transparency and lowering the number of healthcare frauds. The system includes roles for the insurance company, healthcare provider, and patients. It also provides a platform for claim submission, approval, or refusal. In Pakistan, no such system existed before recommending doctors from different hospitals based on their professional conduct or the good health services they provide.
Author: [‘Matloob I’, ‘Khan S’, ‘Bashir B’, ‘Rukaiya R’, ‘Khan JA’, ‘Alfraihi H’]
Journal: PeerJ Comput Sci
Citation: Matloob I, et al. Data driven healthcare insurance system using machine learning and blockchain technologies. Data driven healthcare insurance system using machine learning and blockchain technologies. 2025; 11:e2980. doi: 10.7717/peerj-cs.2980