Blockchain-enabled tokenization for health insurance claims: trends, challenges, and future directions

Claim servicing in health insurance is an important area for insurers. Third party administrators (TPAs) are a critical link in India that process health insurance claims. Several insurers have started in-house claim teams for health insurance claims instead of outsourcing the work to TPAs. The study aims to explore the role played by external TPAs in terms of value addition done by them for insurers, policyholders and healthcare providers. The study aims to find the benefits of roping in an in-house team to settle health claims and the benefits accruing out of this decision to policyholders and hospitals. The role of TPAs in claim processing, issues faced by customers while making a claim, and improvement areas in the process have also been examined. The author has tried to explore and research this area so that policyholders can choose a health insurance company without bias whether it has an in-house claim settlement process or engages an external TPA. The study brings out the concerns of customers and hospitals regarding the settlement of health insurance claims and suggests improvement areas required for both TPAs and in-house teams in claim settlements by evaluating the same.

Using an alternative data source to examine randomization in the Canadian National Breast Screening Study

Confidentiality Protections for Adolescents and Young Adults in the Health Care Billing and Insurance Claims Process

Background: JKN Health Claim is a process carried out by the Hospital to submit a bill for JKN participant treatment costs. JKN claims can be categorized into two categories, namely successful claims which are claims processes that have been successfully completed and pending claims which are claims processes that are delayed. Files that are declared incomplete by JKN Health are a factor causing delays in claims which have an impact on delays in claim payments. Objective: To find out how the inpatient claim process is, to find out the factors causing delays in national health insurance (JKN) claims for inpatients at Hospital X in Tangerang Regency. Method: This study is a descriptive study with a qualitative approach. In this study, in-depth interviews were conducted with 4 informants. Data collection used interview techniques, observation and document review. Results: Based on the results of the study, it shows that the factors causing delays in National Health Insurance (JKN) claims for inpatients are Man: lack of number of officers, lack of accuracy and skills of officers, length of service of officers less than 1 year. Material: incomplete filling in of patient medical record files. Method: there is no SOP yet, officers do not work according to SOP. Machine: Unstable internet network. Conclusion: Factors causing delays in claims for National Health Insurance (JKN) for inpatients with a fishbone diagram include Human Factors: limited number of human resources in Inpatient registration, lack of discipline of DPJP doctors in filling out medical resumes and signatures, assembling officers must be careful and skilled in recapitulating claim files, the coding doctor's work period of less than 1 year results in data input errors and inaccurate coding. Material Factors: the results showed that pending claims were caused by incomplete or inaccurate inpatient claim files. Method Factors: Hospital X, Tangerang Regency already has a Standard Operating Procedure (SOP) but the current SOP has not been approved by the director. Machine Factors: originating from internet network disruptions and power outages.

Automating the processing of health insurance claims to achieve "Straight-Through Processing" is one of the holy grails that all insurance companies aim to achieve. One of the major impediments to this automation is the difficulty in establishing the relationship between the underwriting exclusions that a policy has and the incoming claim's diagnosis information. Typically, policy underwriting exclusions are captured in free-text such as "Respiratory illnesses are excluded due to a pre-existing asthma condition". A medical claim coming from a hospital would have the diagnosis represented using the International Classification of Disease (ICD) codes from the World Health Organization. The complex and labour-intensive task of establishing the relationship between free-text underwriting exclusions in health insurance policies and medical diagnosis codes from health insurance claims is critical towards determining if a claim should be rejected due to underwriting exclusions. In this work, we present a novel framework that leverages both explicit and implicit domain knowledge present in medical ontologies and pre-trained language models respectively, to effectively establish the relationship between free-text describing medical conditions present in underwriting exclusions and the ICD-10CM diagnosis codes in health insurance claims. Termed KAMEL (Knowledge Aware Medical Entity Linkage), our proposed framework addresses the limitations faced by prior approaches when evaluated on real-world health insurance claims data. Our proposed framework have been deployed in several multi-national health insurance providers to automate their health insurance claims.

Fraud in health insurance claims has become a significant problem whose rampant growth has deeply affected the global delivery of health services. In addition to financial losses incurred, patients who genuinely need medical care suffer because service providers are not paid on time as a result of delays in the manual vetting of their claims and are therefore unwilling to continue offering their services. Health insurance claims fraud is committed through service providers, insurance subscribers, and insurance companies. The need for the development of a decision support system (DSS) for accurate, automated claim processing to offset the attendant challenges faced by the National Health Insurance Scheme cannot be overstated. This paper utilized the National Health Insurance Scheme claims dataset obtained from hospitals in Ghana for detecting health insurance fraud and other anomalies. Genetic support vector machines (GSVMs), a novel hybridized data mining and statistical machine learning tool, which provide a set of sophisticated algorithms for the automatic detection of fraudulent claims in these health insurance databases are used. The experimental results have proven that the GSVM possessed better detection and classification performance when applied using SVM kernel classifiers. Three GSVM classifiers were evaluated and their results compared. Experimental results show a significant reduction in computational time on claims processing while increasing classification accuracy via the various SVM classifiers (linear (80.67%), polynomial (81.22%), and radial basis function (RBF) kernel (87.91%).

BackgroundKorea has single health insurance system and insurance claim information on almost all medical practices in Korean hospitals is collected and processed by the Health Insurance Review and Assessment Service (HIRA). Since information about prescription of almost all hospitals is available in National Health Insurance (NHI) claim data, recently established the Korea National Antimicrobial Use Analysis System (KONAS) has been using NHI claim data as data source. The purpose of this study is to validate the accuracy of NHI claim data.MethodsData on all antimicrobial agents prescribed in four tertiary-care hospitals in Korea between January 2019 and December 2019 were obtained using NHI claim data extracted by HIRA and data extracted by common data model based on electronic health record (EHR) in each hospital. Antibiotics and antifungal agents according to the Anatomical Therapeutic Chemical class J01 and J02 were included while antiviral, antitubercular, antiparasitic, and topical antimicrobial agents were excluded. Antimicrobial consumption was measured as days of therapy (DOT) and standardized to per 1,000 patient-days. The ratio of monthly antimicrobial consumption calculated using the NHI claim data compared to that calculated using the common data model was demonstrated (HIRA/EHR ratio).ResultsThe monthly HIRA/EHR ratio of broad-spectrum antibiotics predominantly used for hospital-onset infections was 1.08-1.12 and that of broad-spectrum antibiotics predominantly used for community-acquired infections was 1.11-1.21. The monthly HIRA/EHR ratio of other antimicrobial classes are as follows: antibacterial agents predominantly used for resistant gram-positive infections 1.15-1.31, narrow-spectrum beta-lactam agents 1.00-1.05, antifungal agents predominantly used for invasive candidiasis 1.00-1.27, and antibacterial agents predominantly used for extensive antibiotic-resistant gram-negative bacteria 0.70-1.09.ConclusionThe monthly antimicrobial consumption calculated using NHI claim data differs from that calculated using EHR data by up to 30%. It would be desirable to establish a system that can analyze and monitor antimicrobial consumption using EHR data in each hospital in Korea in the future.DisclosuresHyunki Woo, BS, Evidnet Inc.: Employee changhui Kim, BS, Evidnet Inc.: Employee.

Routinely collected data from health insurance claims and electronic health records in vascular research - a success story and way to go.

BackgroundLack of health insurance claims (HIC) in the last year of life might indicate suboptimal end-of-life care, but reasons for no HIC are not fully understood because information on causes of death is often missing. We investigated association of no HIC with characteristics of individuals and their place of residence.MethodsWe analysed HIC of persons who died between 2008 and 2010, which were obtained from six providers of mandatory Swiss health insurance. We probabilistically linked these persons to death certificates to get cause of death information and analysed data using sex-stratified, multivariable logistic regression. Supplementary analyses looked at selected subgroups of persons according to the primary cause of death.ResultsThe study population included 113,277 persons (46% males). Among these persons, 1199 (proportion 0.022, 95% CI: 0.021–0.024) males and 803 (0.013, 95% CI: 0.012–0.014) females had no HIC during the last year of life. We found sociodemographic and health differentials in the lack of HIC at the last year of life among these 2002 persons. The likelihood of having no HIC decreased steeply with older age. Those who died of cancer were more likely to have HIC (adjusted odds ratio for males 0.17, 95% CI: 0.13–0.22; females 0.19, 95% CI: 0.12–0.28) whereas those dying of mental and behavioural disorders (AOR males 1.83, 95% CI:1.42–2.37; females 1.65, 95% CI: 1.27–2.14), and males dying of suicide (AOR 2.15, 95% CI: 1.72–2.69) and accidents (AOR 2.41, 95% CI: 1.96–2.97) were more likely to have none. Single, widowed, and divorced persons also were more likely to have no HIC (AORs in range of 1.29–1.80). There was little or no association between the lack of HIC and characteristics of region of residence. Patterns of no HIC differed across main causes of death. Associations with age and civil status differed in particular for persons who died of cancer, suicide, accidents and assaults, and mental and behavioural disorders.ConclusionsParticular groups might be more likely to not seek care or not report health insurance costs to insurers. Researchers should be aware of this aspect of health insurance data and account for persons who lack HIC.

Type of the article: Research ArticleAbstractThis study examines how patients’ health insurance claims were denied by different insurance providers at a Saudi Academic Medical Center (AMC), exploring the reasons for these rejections, their relationship to claim characteristics, and the factors that predict health insurance claim rejections. A descriptive study design was employed, involving a retrospective review of all insurance claims submitted by both inpatients and outpatients between January and December 2023 at a tertiary care AMC in Saudi Arabia. Following data screening using the UCAF 2.0 form, all denied insurance claims cases (n = 1,117) were subjected to qualitative analysis. The majority of rejected health insurance claims were submitted by female patients (56.9%) and outpatients (93.6%). Among the insurance companies studied, “Tawuniya” rejects the most insurance claims (n = 730). Variables such as age, gender, and insurance company were significantly associated with the reasons for denying claims (p < 0.05). Furthermore, variables such as age, cost, department type (inpatient/outpatient), and the month of claims are significant predictors of claim rejections (p < 0.05). However, gender, insurance companies, and clinical diagnosis were not significant (p > 0.05). The primary reasons for insurance claim denials in Saudi Arabia are missing medical data, system errors, and non-coverage of specific conditions. This study will help insurance companies and patients identify trends and reasons for claim rejections, enabling them to implement more effective preventive and corrective measures.AcknowledgmentsThe authors expressed their gratitude to Imam Abdulrahman Bin Faisal University for granting permission [IRB-2024-03-188] to conduct this study.

IntroductionInadequate, inefficient and slow processing of claims are major contributors to the cost of health insurance schemes, and therefore undermining their sustainability. This study uses the Technology, Organisation and Environment (TOE) framework to examine the preparedness of health facilities of the Christian Health Association of Ghana (CHAG) to implement a digital mobile health insurance claims processing software (CLAIM-it), which aims to increase efficiency.MethodsThe study used a cross-sectional mixed method design to collect data (technology and human capital capacity and baseline operational performance of claims management) from a sample of 20 CHAG health facilities across Ghana. While quantitative data was analysed using simple descriptive statistics statistics (frequencies, mean, minimum and maximum values), qualitative interviews were recorded, transcribed and abstracted into two major themes that were reported to re-enforce the quantitative findings.ResultsThe quantitative results revealed challenges including inadequate computers and accessories, adequate numbers and skills for claims processing, poor intranets and internet access, absence of a robust post-implementation support system and inadequate standard operating procedures (SOPs) for seamless automation of claims processing. In addition to the above, the qualitative results emphasised the need to make CLAIM-it more flexible and capable of being integrated into third-party softwares. Notwithstanding the challenges, decision-makers in CHAG health facilities see the CLAIM-it software as having better functionality and superior capabilities compared to existing claims processing systems in Ghana.ConclusionNotwithstanding the challenges, the CLAIM-it software is more likely to be adopted by decision-makers, given the positive perception in terms of superior functionality. It is important that key actors in claims management at the National Health Insurance collaborate with relevant stakeholders to adopt the CLAIM-it software for claims processing and management in Ghana.

Due to the changing payment system of healthcare services in the era of National Health Insurance from fee for service to be INACBGs (Indonesian Case Base Groups), health care financing system has changed from fee for service to INACBGs (Indonesian Case Base Groups) package system. In this case, hospitals especially the private ones, must have a stable financial capacity to be able to survive and operate. One of efforts to overcome this issue is to improve efficiency in all aspects and to make the claim submission on time. A continous improvement effort is needed to identify waste and improve the efficiency of a process, one of which is application of Lean Management. This study aimed to identify waste which can delays the process of claim submission. This study was conducted at a private hospital in Central Java. The research is a qualitative research with descriptive analytic methods. The result showed that the waste in the outpatient claim process was 52,4% and the waste in the inpatient claim process was 52,2 % of all activities. The implementation of lean management for the process of health insurance claims was estimated to reduce the time cycle of outpatient claim process from 97.018 seconds to 181 seconds and the time cycle of inpatient claim process from 109.897 seconds to 406 seconds.

ObjectiveWe developed a machine learning-based algorithm to identify patients with chronic hepatitis C infection in health insurance claims data.IntroductionHepatitis C virus (HCV) infection is a leading cause of liver disease-related morbidity and mortality in the United States. Monitoring the burden of chronic HCV infection requires robust methods to identify patients with infection. Insurance claims data are a potentially rich source of information about disease burden, but often lack the laboratory results necessary to define chronic HCV infection. We developed a machine learning-based algorithm to identify patients with chronic HCV infection using health insurance claims alone and compared it a previously developed ICD-9 code-based algorithm.MethodsWe obtained insurance claims, demographics, enrollment information, and hepatitis C laboratory results from the IBM MarketScan® Commercial Claims and Encounters databases. We defined chronic HCV infection cases as a patient with one or more positive HCV RNA result and required controls to have a negative HCV antibody result and no positive HCV RNA or antibody results. Patients were required to be continuously enrolled in a health insurance plan during the six months before and after the first positive or negative test result (index date). Outpatient and inpatient insurance claims for the six months before and after the index date were included in the analyses. The study period spanned from 2011 to 2014.Subjects were randomly divided into a training sample (80%) and test (20%) sample. We trained a random forest classifier using age, sex, region, Charlson comorbidity index, and variables defining the presence and frequency of 67 ICD-9 diagnosis codes and CPT procedure codes related to HCV and liver disease. We up-weighted cases to account for the low prevalence of infection in our sample. We generated forests of 1,000 trees for all models. The initial model included all variables. Permutation-based variable importance scores from this initial model were used to select variables for the final model. The previously developed algorithm defined chronic HCV infection as either two claims with codes for chronic hepatitis infection > 60 days apart after an HCV RNA test claim or three claims with codes for chronic HCV infection on different dates after an HCV RNA test claim. We compared the predicted classification to HCV laboratory result-defined classification and calculated percent agreement, Kappa, sensitivity, specificity, positive predictive value, and negative predictive value. We then applied the final classifier to all individuals continuously enrolled in commercial and/or Medicare supplemental insurance to estimate the prevalence of chronic HCV infection in this population in 2014. Analyses were performed in SAS version 9.4.ResultsWe identified 5,780 (5.6%) cases with chronic HCV infection and 97,831 controls with negative HCV test results. The training dataset consisted of 82,888 individuals with approximately six million inpatient and outpatient claims. The final model included 23 variables related to hepatitis C (e.g., number of HCV RNA test claims), liver disease (e.g., cirrhosis diagnosis code), and comorbidities. In the training dataset, percent agreement, Kappa, sensitivity, specificity, positive predictive value, and negative predictive value were 99.2%, 0.92, 92.3%, 99.6%, 93.2%, and 99.5%, respectively. The presence of a CPT code for HCV RNA testing had the highest variable importance score. The test dataset included 20,723 individuals with approximately 1.5 million inpatient and outpatient claims. In the test dataset, percent agreement, Kappa, sensitivity, specificity, positive predictive value, and negative predictive value for the final classifier were 98.9%, 0.89, 89.9%, 99.4%, 89.0%, and 99.4%, respectively. Percent agreement, Kappa, sensitivity, specificity, positive predictive value, and negative predictive value for the previously developed algorithm were 96.3%, 0.50, 35.0%, 99.9%, 96.7%, 96.3%, respectively. Among the 35.6 million individuals with continuous commercial and/or Medicare supplemental insurance in 2014, 317,932 (0.9%) were classified as having chronic HCV infection.ConclusionsOur machine learning-based algorithm was able to identify chronic hepatitis C cases in commercial health insurance claims data with relatively high estimates for percent agreement, Kappa, sensitivity, specificity, positive predictive value, and negative predictive. Future analyses and models will explore the ability of the algorithm to estimate the prevalence of HCV infection in different populations covered by different health plan types (e.g., commercial, Medicaid, Medicare, or no insurance) and for populations where laboratory testing data is not available or collected.

Validation of Proportional Distribution Method (Pdm) for Estimating Disease-Specific Costs from Health Insurance Claims: An Empirical Approach

ObjectivesThe objective of this paper is to describe the Health Insurance Review and Assessment Service (HIRA)'s payment request (PARE) system that plays the role of the gateway for all health insurance claims submitted to HIRA, and the claim review support (CRS) system that supports the work of claim review experts in South Korea.MethodsThis study describes the two systems' information technology (IT) infrastructures, their roles, and quantitative analysis of their work performance. It also reports the impact of these systems on claims processing by analyzing the health insurance claim data submitted to HIRA from April 1 to June 30, 2011.ResultsThe PARE system returned to healthcare providers 2.7% of all inpatient claims (97,930) and 0.1% of all outpatient claims (317,007) as un-reviewable claims. The return rate was the highest for the hospital group as 0.49% and the lowest rate was found in clinic group. The CRS system's detection rate of the claims with multiple errors in inpatient and outpatient areas was 23.1% and 2.9%, respectively. The highest rate of error detection occurred at guideline check-up stages in both inpatient and outpatient groups.ConclusionsThe study found that HIRA's two IT systems had a critical role in reducing heavy administrative workloads through automatic data processing. Although the return rate of the problematic claims to providers and the error detection rate by two systems was low, the actual count of the returned claims was large. The role of IT will become increasingly important in reducing the workload of health insurance claims review.