Introduction: This study examines the legal aspects of medical record data misuse by healthcare facilities in Indonesia, emphasizing its ethical, legal, and systemic implications. Medical records are crucial to prevent data privacy violations, unauthorized access, and commercial exploitation. Methodology: Using the normative juridical research method, this qualitative study analyzes the legal framework, including Law Number 17 of 2023 on Health, the Personal Data Protection Law, and other relevant legislation, complemented by scientific literature and case studies. The research findings reveal that medical record misuse often occurs through unauthorized access, false claims, data manipulation, and illegal data sharing with third parties, reflecting gaps in law enforcement and weak institutional governance. This study highlights the strong link between medical record misuse and healthcare fraud, demonstrating how systemic pressures and inadequate oversight encourage unethical practices. This study recommends strengthening regulatory integration, improving cybersecurity infrastructure, implementing stricter sanctions, and increasing ethical awareness among healthcare professionals. This research contributes to the health law discourse by offering actionable legal and policy recommendations while emphasizing the need for a cultural shift toward transparency and data management. Future studies should incorporate empirical fieldwork and explore advanced digital security innovations to address emerging challenges.

BackgroundThe goals of medical insurance fund supervision will influence the selection of supervisory methods and their effectiveness. This study aims to identify the multiple goals of China’s medical insurance fund supervision and to examine the hierarchy and proximity among these goals.Methods133 national-level policies related to the goals of medical insurance fund supervision in China were selected. Frequency ranking and social network analysis were used to explore the hierarchical relationships among these goals. The Ochiai coefficient in the similarity matrix was used to assess the proximity. Clustering analysis was performed via the generation of a dendrogram, while multidimensional scaling was applied to validate the clustering results.ResultsFund security was the most frequently cited goal (11.01%) among the 84 goals, followed by ending insurance fraud (8.55%) and protecting the rights and interests of citizens (6.38%). Efficiency and economy-related goals, such as fund security and intelligence, were prioritized over public-oriented goals like social participation and justice. The correlation coefficients among the goals ranged from 0.000 to 0.602, with the strongest association observed between fairness and justice (r = 0.602).ConclusionsThe supervision of China’s medical insurance fund involves diverse goals that are interrelated and centered on the core objectives of ensuring fund security, combating medical insurance fraud, and safeguarding citizens’ rights and interests. They can be distilled into five categories: fairness, citizenship, efficiency, administration, and social welfare. These findings can provide empirical evidence for the governance of goal conflicts in medical insurance fund supervision and the construction of an assessment framework for supervisory efforts.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12913-025-13775-6.

ABSTRACT Healthcare insurance fraud is a growing global issue that undermines the integrity of healthcare systems and imposes significant financial losses on governments, insurers, and consumers. Fraudulent activities, including false claims, overbilling, and identity theft, inflate healthcare costs and reduce the efficiency of insurance systems. Traditional fraud detection systems rely on centralized databases and intermediaries, which are prone to inefficiencies, security vulnerabilities, and manipulation. In response to these challenges, this article proposed a decentralized blockchain‐based solution to detect and prevent healthcare insurance fraud more effectively. In healthcare insurance, blockchain can create a trusted, real‐time system for recording insurance claims and transactions. By securely sharing data between healthcare providers, insurance companies, and regulatory bodies, blockchain enables greater transparency and auditability. Smart contracts, self‐executing contracts coded on the blockchain, can automate claim processes, reducing the potential for fraudulent claims by ensuring that all predefined conditions are met before payments are processed. Furthermore, blockchain can track every healthcare and insurance process step, from patient records and treatments to billing and reimbursement, allowing for real‐time detection of irregularities. The proposed decentralized blockchain approach offers a secure and efficient solution for detecting and preventing healthcare insurance fraud. It minimizes reliance on centralized authorities, reduces operational costs, and builds a more transparent and trustworthy system (For 10 patients, the total execution time includes 0.007 ms for hashing, 0.065 ms for encryption, and 0.060 ms for decryption). The proposed system's smart contracts obtained results (Ether cost) show that submitting a claim costs 0.01677963 ETH, flagging a claim costs 0.002234 ETH, approving a claim costs 0.00385737 ETH, and the fraud detection constructor incurs the highest cost of 0.11428872 ETH. By leveraging blockchain's strengths, the healthcare industry can proactively combat fraud, protect sensitive data, and improve overall efficiency.

ObjectivesTo design and validate a comprehensive, expert-informed national framework to prevent and control fraud and misconduct in Iran’s health system. Fraud in this context refers to practices such as false claims, overbilling, unnecessary prescriptions, informal payments and beneficiary manipulation, which undermine efficiency and trust.DesignA sequential mixed-methods study, including an exploratory qualitative phase followed by a two-round Delphi consensus process.SettingNational-level study across multiple sectors of Iran’s health system, including insurance, governance and health policy institutions.ParticipantsIn the qualitative phase, 12 experts (senior managers, auditors and policymakers) were interviewed, achieving data saturation. In the Delphi phase, 31 experts participated in Round 1 and 27 completed Round 2. All had ≥5 years of relevant experience in health management, fraud detection or policy-making.ResultsFramework analysis of interviews identified six strategic categories: cultural, educational, legal, technological, insurance-related and structural interventions. These were validated and prioritised through the Delphi process. High consensus (≥75%) was achieved for most items, with unanimous agreement on legal clarity, enforceable sanctions and electronic identity verification. The final framework comprised three overarching domains—capacity building, strengthening oversight systems and modernisation of operational infrastructure—operationalised through six strategic pillars.ConclusionsThis study proposes a validated and context-specific anti-fraud framework tailored to Iran’s health system. By combining qualitative exploration with structured consensus, the framework offers practical strategies for enhancing transparency, accountability and resilience. It may also serve as a model for other low- and middle-income countries facing similar governance challenges.Trial registrationNot applicable.

Healthcare fraud detection has undergone a fundamental transformation through the integration of Artificial Intelligence and Machine Learning technologies within clinical Business Intelligence platforms. Traditional rule-based detection systems show significant limitations in identifying sophisticated fraudulent activities due to their static parameters and inability to adapt to evolving fraud patterns. Modern AI-driven frameworks use advanced algorithms, including gradient boosting machines, random forest algorithms, and deep neural networks, to process vast volumes of healthcare data with superior accuracy and reduced false positive rates. These predictive models incorporate comprehensive training methodologies using extensive historical claims databases. This enables the identification of hidden anomalies and suspicious patterns that conventional systems frequently miss. Technical implementation includes seamless data pipeline infrastructures, real-time processing architectures, and dynamic risk scoring systems that enable immediate decision-making for claim processing workflows. Governance frameworks ensure regulatory compliance with HIPAA, HITECH, and state-specific requirements while maintaining algorithmic transparency and comprehensive audit trails.Future technological developments include natural language processing integration for unstructured data evaluation, graph analytics for network-based fraud identification, and federated learning architectures enabling privacy-preserving collaborative model development across distributed healthcare networks.

This study develops and examines a comprehensive deep learning framework for the detection of multi-class healthcare fraud in National Health Insurance Scheme (NHIS) claims. We examined 20,388 NHIS healthcare claims revealing four specific fraud patterns: Phantom Billing, Wrong Diagnosis, Ghost Enrollee, and legitimate claims. Four different deep neural network architectures were developed and evaluated: Simple NN, Deep Wide NN, Regularized NN, and Residual NN, in addition to ensemble methods. The Simple Neural Network achieved the highest overall performance, with a test accuracy of 79.84% and an F1-macro score of 77.76%. Despite possessing only 100,324 parameters (five times fewer than the Wide Deep Neural Network), it outperformed more complex designs while achieving the fastest training time of 40.61 seconds. Multiclass analysis demonstrated exceptional performance in Ghost Enrollee detection (97.84% F1-score) and moderate performance in Phantom Billing detection (61.15% F1-score).

With the digitization of medical information, illegal activities such as medical crimes and insurance fraud through tampering have increased. Medical images are particularly vulnerable due to their nature as soft copies and their transmission over networks. National research institutions such as NIST provide guidelines that define security control elements for managing medical images, primarily out of concern for system vulnerabilities. However, there is still a lack of established or standardized digital forensic methodologies specifically tailored to the medical imaging domain. This study proposes a digital forensic technique for detecting manipulation in medical images. Two widely adopted PACS (Picture Archiving and Communication System) platforms were selected, and a dataset comprising 82 samples across 40 types of tampering scenarios was constructed. Tampering behaviors such as the editing or deletion of DICOM files were categorized, and forensic analysis of DICOM tags and system artifacts enabled identification of the type and origin of changes. An automated detection module was developed and tested on 110 validation cases. The results demonstrated accurate detection in all instances, depending on whether the changes were reflected in the actual DICOM files. This research marks the first digital forensic approach to medical image tampering detection and is expected to serve as a foundation for future investigative techniques in response to medical‐related crimes.

Predictive analysis for healthcare fraud detection: Integration of probabilistic model and interpretable machine learning

Classification of unbalanced multiclass datasets is still a major challenge in machine learning in many fields of applications, including medical diagnostics, fraud detection, and picture classification, where minority classes are the most crucial, but at the same time under-represented. Classical classification algorithms designed for balanced data tend to overfit the majority classes deeming a large number of minority classes misclassified and, as a result, compromising the model's performance. This review covers the main state-of-the-art techniques for class imbalance problems including under-sampling and over-sampling techniques, ensemble approaches, cost-sensitive learning, and producing synthetic data via SMOTE (synthetic minority oversampling technique). Recently, GANs (Generative Adversarial Networks) have also been employed to generate synthetic data, specifically valuable for complex datasets where realistic data augmentation is needed. Each of these techniques is analyzed in terms of their capability of dealing with imbalanced data through conventional metrics such as accuracy and specific metrics for imbalanced datasets such as F1-score, G-mean, and others. Recent advancements, such as hybrid approaches and learning from deep learning models are also discussed as viable solutions given the complexities associated with big data (high dimensional and large) and their corresponding models. Such comparative analysis should facilitate the construction of more robust models that handle complex data in modern applications.

ABSTRACT In the decades after the Civil War, people in rural communities were happy to be entertained by touring lecturers of varying degrees of notoriety. Humorists, trying to expand their popularity and supplement their income, were among those who visited small towns, their arrival eagerly anticipated by young and old. During this period, characterized by social fluidity and marketplace upheaval, visiting humorists at times played the role of an advertiser whose wit helped quack doctors sell their patent medicines and at others the role of a respectable lecturer on the lessons of life. This article aims to place the figure of the Gilded Age comic lecturer in its larger historical context.

This Viewpoint explores the importance of the False Claims Act in detecting and deterring health care fraud in light of recent limitations to the scope of the Act.

Kidney stones and quack doctors.

Stakeholders in the breast implant industry in Korea have recently experienced a crisis from breast implant-associated anaplastic large cell lymphoma and the first Korean case of a medical device fraud. We compared the short-term safety between the microtextured devices that are commercially available after the occurrence of breast implant crisis in Korea. The current study was conducted in a cohort of Korean women who had received an implant-based augmentation mammaplasty for aesthetic purposes between November 14, 2020 and October 13, 2022. We considered risk factors of complications in analyzing the safety of devices for the current study. A total of 801 Korean women (n = 801) were finally assessed. Incidences of capsular contracture were 1.79% (3/168), 3.64% (21/577), 8.11% (3/37), and 10.53% (2/19) in the patients receiving Motiva Ergonomix, Sebbin Sublimity, Sebbin Integrity, and Eurosilicone Round Collection, respectively. These differences reached statistical significance (P < .05). There were 2 women with rupture after receiving Sebbin Sublimity, although there were no cases of rupture in association with other brands of breast implants. Overall capsular contracture-free survival was estimated at 681.470 ± 8.314 (95% confidence interval [CI] 665.174–697.766) days. By breast implants, it was 708.899 ± 8.595 (95% CI 692.053–725.745), 599.327 ± 6.607 (95% CI 586.378–612.277), 584.941 ± 22.965 (95% CI 539.931–629.952), and 572.492 ± 37.374 (95% CI 499.240–645.745) days in the patients receiving Motiva Ergonomix, Sebbin Sublimity, Sebbin Integrity, and Eurosilicone Round Collection, respectively, in the increasing order. In conclusion, our results indicate that Motiva Ergonomix Round SilkSurface is currently a relatively safer device as compared with others in the context of the first Korean case of a medical device fraud. The breast implant industry in Korea should be aware of the importance of the social sustainability in manufacturing a device.

PurposeTo assess nuclear medicine researchers’ experiences and attitudes toward image fraud, as well as their perspectives on preventive measures.MethodsThis survey targeted corresponding authors who published in three nuclear medicine journals between 2021 and 2024. Participants were asked about their experiences related to medical image fraud, as well as their views on its prevalence, causes, and potential preventive measures.ResultsOf the 2,837 corresponding authors invited, 284 (10.0%) completed the survey. Most of the 284 respondents were mid-career European male MDs with over 10 years of research experience. While 91% reported never feeling pressured to falsify medical images, 13.7% admitted doing so in the past five years, and 38.7% had witnessed colleagues engaging in such practices. Common forms included cherry-picking, unauthorized image reuse, and misleading enhancements. In the past five years, 1.1% admitted using AI to falsify medical images, while 2.8% reported witnessing colleagues do so. No demographic factors were significantly associated with misconduct. Key drivers cited were publication pressure, competition, and aesthetic expectations. Respondents emphasized the need for greater transparency, oversight, and cultural change. Current safeguards were generally considered ineffective. Stricter policies, increased awareness, and AI tools were suggested as potential solutions.ConclusionsImage fraud in nuclear medicine research appears to be relatively prevalent. It is more frequently witnessed among other colleagues than self-reported by individual researchers. The findings highlight the need to fostering a culture of research integrity and for stronger preventive measures, including greater awareness, stricter journal policies, and improved control.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00259-025-07515-5.

Background: Fraud in the National Health Insurance (JKN) program managed by BPJS Kesehatan poses a serious challenge to maintaining the sustainability of healthcare services in Indonesia. This study aims to analyze the forms of fraud, their impact on the healthcare system, and prevention strategies based on ethical and regulatory perspectives, specifically Health Law No. 17 of 2023 and Minister of Health Regulation No. 16 of 2019. Methodology: This study uses a normative legal approach with a descriptive-qualitative analysis method. Data were obtained through a literature review of applicable regulations, academic literature, and document analysis related to fraud in BPJS health. Primary legal sources include Health Law No. 17 of 2023 and various related regulations, while secondary sources consist of journal articles and research reports. The analysis was conducted by identifying fraud patterns, evaluating the effectiveness of supervision and administrative sanctions, and reviewing the role of technology in fraud mitigation. Results: Research shows that BPJS health fraud occurs due to weak supervision, minimal participant literacy regarding rights and obligations, and gaps in the claims and verification system. Implementing an information technology-based anti-fraud system, participant education, and increased oversight are strategic steps to minimize fraud. With a multidisciplinary approach, it is hoped that the JKN system can function optimally, ensure transparency, and increase the accountability of healthcare providers

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.

Bangladesh, a lower-middle-income country in South Asia, has made substantial strides in healthcare since its independence in 1971. However, healthcare expenses remain a significant challenge for households, particularly in rural areas. This study focuses on understanding the direct health costs incurred by households in a community of Comilla District, Bangladesh. The research investigates the burden of out-of-pocket health expenditures, encompassing factors such as doctor consultations, medications, diagnostics, and hospitalizations. Despite improvements in health indicators, the increasing healthcare expenditure is exacerbated by the dual burden of disease and demographic transitions in Bangladesh. Limited research has explored healthcare expenditure at the community level, especially in Comilla District. Therefore, this study examines the healthcare expenditure patterns, demographics, and treatment choices of households in this community. A cross-sectional study was conducted, involving 200 rural household heads in specific areas of Daudkandi, Comilla. The study found that households with lower monthly incomes allocate a higher percentage of their earnings to medical expenses, reflecting a financial burden. Additionally, a significant proportion of households resort to seeking treatment from quack doctors. The findings underscore the need for targeted interventions to mitigate the financial strain on households and inform healthcare policies. By understanding the direct health costs incurred by households in Comilla District, this study contributes to the development of strategies for improving healthcare access, reducing costs, and enhancing overall community health outcomes.

Aim: To conduct a comparative legal study of healthcare fraud in the United States of America and Ukraine. Materials and Methods: This study is based on the analysis of the US federal regulatory legislation (False Claims Act, Anti-Kickback Statute, Stark Law); data from the Fraud Section of the US Department of Justice for the last five years; case law in Ukraine (more than 30 court verdicts were analysed); data from the National Health Service of Ukraine. Dialectical, hermeneutic, comparative, analytical, synthetic, and systems analysis research methods were used. Results: The criminal legislation acts that provide for criminal liability for healthcare fraud under the laws of the United States and Ukraine are analysed; the criteria for identifying types of healthcare fraud and related criminal offences are defined. It is determined that healthcare fraud has a multidimensional nature, which can manifest itself in different ways, but the common purpose in these manifestations of criminal behaviour is deception or intentional distortion of facts to obtain money or property that is under the control of or owned by any healthcare benefit programme (in the US) or medical guarantees programme (in Ukraine). There are various illegal manipulations with the state programme of medical guarantees. Starting from 2021, the judicial practice of Ukraine lacks a single acceptable approach to the criminal legal assessment of such illegal manipulations. Conclusions: The experience of the United States in determining the types of criminal offences that constitute healthcare fraud and establishing criminal liability for their commission is appropriate to borrow.

The objective of this study is to create and assess an extensive machine learning framework for identifying healthcare fraud in the National Health Insurance Scheme (NHIS) claims, targeting the significant financial losses and degradation of patient care resulting from fraudulent practices. This work examined 20,388 NHIS medical claim data exhibiting phantom billing, incorrect diagnoses, and ghost enrollee fraud trends. A systematic feature engineering approach increased 8 initial characteristics to 27 engineered features, encompassing temporal patterns, financial abnormalities, medical classifications, and indicators of patient behavior. Six machine learning algorithms were assessed: Random Forest, Logistic Regression, Gradient Boosting, XGBoost, Support Vector Machine, and Neural Network, utilizing extensive performance criteria such as accuracy, AUC, calibration quality, and demographic fairness analysis. Gradient Boosting attained the highest test AUC of 0.9213 with an accuracy of 80.11%, whilst XGBoost exhibited superior computational efficiency (0.71 seconds training time) alongside competitive performance (AUC: 0.9187, accuracy: 80.48%). Financial variables predominantly influenced fraud detection judgments, with daily billing rates (AMOUNT_PER_DAY: 0.55) and total billed amounts (0.36) contributing to 91% of model predictions. Significant calibration difficulties were detected across models, with minor demographic bias noted. Ensemble tree-based algorithms routinely surpass alternative approaches in the identification of healthcare fraud. Nevertheless, the primary dependence on financial attributes can cause vulnerabilities to sophisticated fraud schemes that keep accurate billing amounts while capitalizing on weaknesses in medical coding. This research offers healthcare administrators actionable insights for the implementation of real-time fraud detection systems, emphasizing the necessity of balancing detection accuracy with computational efficiency and the enhancement of medical coding analysis capabilities.

The increasing number of patient complaints to the Professional Disciplinary Council on doctors is something that needs to be considered. Patient complaints received by the Professional Disciplinary Council are closely related to deviations in medical management standards that are detrimental to patients. One of the important things in carrying out medical service management carried out by doctors is informed consent and informed refusal which are absolutely necessary before carrying out medical actions. In this writing, the author conducts a conceptual analysis and legal comparison of informed consent and informed refusal from several countries that require both consents, with the consideration that the countries being compared have carried out medical practices that are among the best in the world. Then in the writing will be explained the potential for medical actions by doctors that can be categorized as medical fraud along with their handling. The research method in this writing uses the normative juridical method with primary, secondary and tertiary legal materials and comparative law in the Anglo-Saxon and continental European legal traditions. In addition, a comparative study of Indonesian positive law was also conducted in this case general law (jure generali) which overlaps and special health law (jure specialis) along with its implementing regulations.