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The combination of blockchain technology and smart contracts has become a viable way to expedite claims processing and payouts in the quickly changing insurance industry. Enhancing efficiency, transparency, and reliability for the industry may be achieved by automating certain procedures and initiating them on predetermined triggers, smart contracts that is event-based. Conventional insurance procedures can be laborious, slow, and prone to human mistake, which can cause inefficiencies and delays in the resolution of claims. This research proposes a simplified system that automates the whole claims process from submission to reimbursement by utilizing blockchain technology and smart contracts. The suggested method does away with the requirement for human claim filing by having policyholders' claims automatically triggered by predetermined occurrences. These occurrences might be anything from medical emergencies to natural calamities, enabling prompt and precise claim start. The whole claims process is managed by smart contracts that are programmed with precise triggers and conditions, guaranteeing transaction immutability, security, and transparency. Moreover, reimbursements are carried out automatically after the triggering event has been verified, disregarding conventional bureaucratic processes and drastically cutting down on processing times. This strategy decreases the possibility of fraud and disagreement while also improving operational efficiency by combining self-executing contracts with decentralized ledger technology. Insurance companies and policyholders will both eventually profit from an accelerated, transparent, and reliable claims processing procedure thanks to the use of event-based smart contracts. A Python-implemented system achieving 97.6% accuracy using the proposed method, demonstrates its efficacy and reliability for the given task.

Introduction:Since the inception of the Ghana National Health Insurance Scheme (NHIS), it has been pursuing a number of provider payment mechanisms that could not only control the continuous escalating costs of claims payout, but also facilitate the claims processing time. In lieu of this, electronic processing of claims (E-claims) was introduced in 2013 as part of the World Bank supported Health Insurance project that sought to facilitate the financial and operational management of the NHIS. It was piloted in 29 health facilities up to March 2014. They reported cost savings made by the NHIS using E-claims, creating interest in scaling it up. However, the comparative effectiveness and cost effectiveness of E-claims to the health system compared to manual claims processing is unknown. Therefore, to provide decision makers with the appropriate information to choose between manual and E-claims processing, this study sought to evaluate the cost-benefit of E-claims.Methods:A benefit-cost analysis was used to evaluate the efficiency of E-claims from the perspective of the health system. Health providers and the purchaser (NHIS claims processing center) were the study population. Resource use and costs were obtained from the study population. The volumes and values of claims reimbursed and the claims rejection rate were used as the benefits of claims processing. The incremental benefit-cost ratio (IBCR) was estimated for the provider, purchaser and the entire health system. Analysis was conducted in Microsoft Excel.Results:The total cost per claim for providers were USD 1,177.04 and USD 1,240.65 for E-claims and paper claims respectively. The total cost per E-claims and paper claims for the purchaser were 592.17 and 502.19 respectively. Total benefit per E-claim and paper claim processing for the providers were USD 8,562.90 and USD 8,888.37 respectively while that for the purchaser was USD 11,037.62 and USD 8,737.60 respectively. Processing claims electronically led to incremental gains by both providers and purchasers. Providers gained additional USD 2008.51 while the purchaser gained USD 2,300.02. The IBCR was estimated at −19.75, 25.56 and 5.10 for all providers, purchaser and both providers and purchaser of the health system respectively. Thus the IBCR was less than 1for the providers and more than 1 for purchaser and both purchaser and providers.Conclusions:The electronic processing of claims is more efficient compared to manual processing in the Ghana NHIS. This provides decision makers with evidence for scaling it up to all the facilities in Ghana.

Purpose The Pension Trust Company (PTC) in Ghana is the sole agency responsible for the management of the first-tier pension scheme as well as processing of claims submitted by beneficiaries for this scheme. The claim processing system at PTC was wrought with significant delays resulting in severe customer dissatisfaction and hardship to retirees. Hence, a new system – Age 54+ project – was developed to address the problems related to claims processing. The purpose of this paper is to report on the efficiency gains from the new claim processing system implemented at PTC and to use the philosophies behind the lean operations concept to explain the results. Design/methodology/approach Data for this study were obtained from the benefits system of PTC for the period 2009–2013. The data consist of a series of benefits processing time for two groups of 56,000 claimants – those cleared under the Age 54+ project and those cleared under the old processing system. The processes of the two claim processing systems were analysed and their processing times compared. Findings The new system – Age 54+ – decreased the average processing time for new claims by 20 per cent. The new system is a simple approach which is driven by a “Let’s Start in Time” idea. Originality/value The operations management literature suggests that process redesign approaches and the implementation of continuous improvement techniques represent mechanisms for achieving performance improvements at governmental agencies. This study shows and discusses the redesign of a social security scheme process using a lean operation concept of waste elimination method and application of kanban to deliver performance improvement.

The easy way to start a career in medical billing and coding With healthcare providers moving to electronic record systems, data accuracy and efficient data processing is more important than ever. Medical offices need professionally trained billers and coders, either in the office or via telecommute, to handle records for internal and external accessibility and efficiency. Medical Billing & Coding For Dummies gives you everything you need to know to get started in medical billing and coding. It gives you practical, easy-to-follow coverage and advice on how to find a course, educational topics you should review and pursue in order to stay competitive in the field, and the laws and other regulations you'll encounter in your work. Certification requirements and standard industry practices Tips for dealing with government agencies and insurance companies Ethical and legal issues If you're looking to start a career in the popular field of medical billing and coding, this hands-on, friendly guide has you covered!.

JMDC, Inc. (JMDC) has created a database, using data collected from health insurance societies in Japan, consisting of ledgers of insureds, claims (for hospitalization, outpatient treatment, drug preparation, and dental treatment), and health checkup results. The earliest data are from the claims in January 2005, except dental claims from December 2009 and health checkup results from April 2008. Currently (the end of June 2020), the number of insureds included is approximately 9.8 million. This database is unique for Japan and has the following characteristics: (a) the basic population can be ascertained; (b) standardization is carried out using a dictionary; and (c) anonymized individual IDs can be followed on the basis of a time‐series over various periods, with the earliest starting date being January 2005. However, it has certain limitations, in that the disease status and test results cannot be ascertained, and there is insufficient access to data for elderly people. JMDC, Inc. (JMDC) has created a database, using data collected from health insurance societies in Japan, consisting of ledgers of insureds, claims (for hospitalization, outpatient treatment, drug preparation, and dental treatment), and health checkup results. This database is unique for Japan and has the following characteristics: (a) the basic population can be ascertained; (b) standardization is carried out using a dictionary; and (c) anonymized individual IDs can be followed on the basis of a time‐series over various periods, with the earliest starting date being January 2005

Vehicle exterior inspection is a critical operation for identifying defects and ensuring the overall safety and integrity of vehicles. Visual-based inspection of moving objects, such as vehicles within dynamic environments abounding with reflections, presents significant challenges, especially when time and accuracy are of paramount importance. Conventional exterior inspections of vehicles require substantial labor, which is both costly and prone to errors. Recent advancements in deep learning have reduced labor work by enabling the use of segmentation algorithms for defect detection and description based on simple RGB camera acquisitions. Nonetheless, these processes struggle with issues of image orientation leading to difficulties in accurately differentiating between detected defects. This results in numerous false positives and additional labor effort. Estimating image poses enables precise localization of vehicle damages within a unified 3D reference system, following initial detections in the 2D imagery. A primary challenge in this field is the extraction of distinctive features and the establishment of accurate correspondences between them, a task that typical image matching techniques struggle to address for highly reflective moving objects. In this study, we introduce an innovative end-to-end pipeline tailored for efficient image matching and stitching, specifically addressing the challenges posed by moving objects in static uncalibrated camera setups. Extracting features from moving objects with strong reflections presents significant difficulties, beyond the capabilities of current image matching algorithms. To tackle this, we introduce a novel filtering scheme that can be applied to every image matching process, provided that the input features are sufficient. A critical aspect of this module involves the exclusion of points located in the background, effectively distinguishing them from points that pertain to the vehicle itself. This is essential for accurate feature extraction and subsequent analysis. Finally, we generate a high-quality image mosaic by employing a series of sequential stereo-rectified pairs.

This study evaluates the relationship between obstructive sleep apnea (OSA) and asthma. Literature search was carried out in several electronic databases and random effects meta-analyses were performed to obtain pooled estimates of the prevalence of OSA, OSA risk and sleep disordered breathing (SDB) in asthma patients and pooled odds ratios of the prevalence between asthma and non-asthma patients. In adult asthma patients, the prevalence [95% confidence interval] of OSA, OSA risk, and SDB was 49.50 [36.39, 62.60] %, 27.50 [19.31, 35.69] %, and 19.65 [14.84, 24.46] % respectively. The odds of having OSA, OS risk and SDB by the asthma patients were 2.64 [1.76, 3.52], 3.73 [2.90, 4.57] and 1.73 [1.11, 2.36] times higher (p < 0.00001 for all) in asthma than in non-asthma patients, respectively. Adult asthma patients with OSA had significantly higher BMI in comparison with asthma patients without OSA. This study reveals that the prevalence of OSA in asthma patients is considerably higher; even higher than OSA risk and SDB. Sleep studies should be performed in asthma patients with symptoms suggestive of OSA/OSA risk/SDB.

Traditional health insurance claim processing systems are plagued by inefficiencies and vulnerabilities, often resulting in significant financial losses due to fraudulent activities. Existing fraud detection methods are largely manual, time-consuming, and inadequate for handling the complexity and scale of modern fraudulent schemes. Moreover, the trust-based relationships between insurers and healthcare providers lack mechanisms to ensure data integrity and prevent manipulation. While several blockchain-based systems have been proposed to improve transparency and tamper resistance, they typically focus on structured data and predefined fraud types, offering limited adaptability and analytical insight. This paper proposes a novel solution leveraging blockchain technology and Large Language Models (LLMs) to transform fraud detection. The system uses Ethereum smart contracts (SCs) to securely store medical records and claim details on a decentralized, tamper-proof ledger that ensures data integrity, traceability, and accountability. This immutable data is accessed by an LLM via a Retrieval-Augmented Generation (RAG) system, which enables intelligent retrieval and analysis of relevant clinical information to detect fraud patterns and inconsistencies. To support complex scenarios involving free-text documents, unstructured clinical data, such as lab reports, are stored using decentralized off-chain storage and retrieved during LLM analysis. In addition, an LLM-powered chatbot also allows insurance providers to interact with the system in natural language for claim inquiries, explanations, and summaries. The architecture, sequence diagrams, and implementation algorithms outline the development process, while testing scenarios demonstrate the system’s ability to detect fraud such as inflated costs, unnecessary treatments, and unrendered services. Evaluation using both synthetic and public clinical datasets showed strong performance, with the LLM achieving up to 99% fraud detection accuracy. Cost, security, and scalability analyses confirm the system’s practicality and resilience, with the complete detection process executing in just 13 seconds. By overcoming the limitations of traditional systems, this framework offers a scalable and adaptable approach for healthcare and other domains. The SCs and source code are publicly available on GitHub.

Gaining traction There are already examples of claims concierge services in the global market, handling anything from medical and personal injury claims, through to property damage, vehicle repairs and more. \"Using Claim Central's concierge service, users can upload videos and photos of damaged property to an online claim file, communicate at any time of the day or night, and upload invoices for repairs as they come in.\" \"Itis a unique and innovative example of how the concierge model can be used across industries, by making it even easier for people to insure their home during the housepurchase process and ensuring they have the right insurance for their needs,\" says Jonathan Beale, managing director of partnerships at Tower Insurance.