Health insurance claims management systems: Potential factors affecting their decisions

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

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.

The paper aims to briefly examine the history and development of the Information Science Undergraduate Program offered by Imam Abdulrahman Bin Faisal University to enrich LIS literature in Saudi Arabia. It also aims to assess this LIS program according to the LIS Foundational Knowledge Areas (FKAs) Model provided by the IFLA Guidelines for Professional Library and Information Science (LIS) Education Programmes. In a qualitative study, a case study design was followed to formulate the research steps and answer the questions. For data collection, document analysis was conducted, followed by a focus group with the head of the school and the key members of the program development team. The results of the document analysis and the focus group demonstrated that the courses taught in the LIS program at Imam Abdulrahman Bin Faisal University were distributed across the IFLA’s eight Foundational Knowledge Areas (FKAs). However, the distribution across the eight FKAs was not even, as some areas were heavily covered while others were only slightly recognized by the current LIS curricula. In addition, several FKAs were combined into one or more LIS courses, and a given FKA was covered within or by one course or distributed over several courses. The present study is one of the very few research efforts focusing on the LIS programs in Saudi Arabia; it is also the only known study that examines the LIS program at Imam Abdulrahman Bin Faisal University. Therefore, it has recommendations and implications not only for the examined program but also for researchers and academics interested in LIS education in Saudi Arabia.

Pharmacology, Toxicology and Biomedical Reports,2019,5,1,44-48.DOI:10.5530/PTB.2019.5.8Published:January 2019Type:Research ArticleAuthors:Yousef Ahmed Alomi, Nesreen Alshabaar, Nadia Lubad, and Fatimah Ali Albusalih Author(s) affiliations:Yousef Ahmed Alomi*, The Past General Manager of General Administration of Pharmaceutical Care Head, National Clinical Pharmacy and Pharmacy Practice Head, Pharmacy R & D Administration, Ministry of Health, Riyadh 11392, SAUDI ARABIA. Nesreen Alshabaar, King Salman Hospital, Ministry of Health, Riyadh 11392, SAUDI ARABIA. Nadia Lubad, King Salman Hospital, Ministry of Health, Riyadh 11392, Riyadh, SAUDI ARABIA. Fatimah Ali Albusalih, College of Clinical Pharmacy, Imam Abdulrahman Bin Faisal University (University of Dammam) Dammam, SAUDI ARABIA. Abstract:Objectives: To explore the inpatient medication errors and pharmacist intervention at Ministry of Health Hospital, Riyadh, Saudi Arabia. Methods: This is a 9-month cross-sectional study conducted at a 300-bed public hospital to evaluate pharmacist response and prevention of inpatient medication errors in adult and pediatric patients. There is a medication safety officer in the hospital along with a medication safety committee. The following information on medication errors were documented in the form available at the hospital: patient’s demographic information, sources of medication errors, time of errors, type of errors, description of errors, causes of errors, recommendation to prevent such errors and the outcome of errors. The form was developed by using the National Coordinating Council (NCC) for Medication Error Reporting and Prevention (MERP) system. Results: According to the results, the pharmacist prevented a total of 3089 medication errors within 805 patients. About 3.8 errors per prescription were prevented. Most of the prevention occurred during prescribing stage (705 (99.2%)). Patient-related errors (1564 (50.63%)) and prescriber-related errors (1435 (46.46%)) were the most type of prevented errors. Allergy was the most prevented subtype of errors (560 (91.4%)) followed by patient’s body weight (543 (88.6%)) and prescriber data missing/unclear (347 (56.6%)). Most of the errors that were prevented were near miss (93.3%) followed by 6.3% of the errors that reached the patient but did not cause any harm. The highest percentage with respect to the causes of medication errors was missing clinical information (649 (83.7%)) and miscommunication of drug order (627 (80.9%)). The top 20 medications involved in medication errors were oral and intravenous injections (Paracetamol and enoxaparin injection, respectively). Conclusion: The pharmacist plays a very crucial role in preventing medication errors. In order to prevent medication errors and improve patient outcome, the pharmacist provides education to the healthcare professional about medication safety and establishes the intravenous medication guidelines. Keywords:Inpatient, Intervention, Medication errors, Ministry of Health, Pharmacist, Riyadh, Saudi ArabiaView:PDF (192.53 KB) PDF PDF Images Table 2A: Pharmacist intervention of stage of medication operation.

International Journal of Pharmacology and Clinical Sciences,2019,8,2,105-109.DOI:10.5530/ijpcs.2019.8.18Published:June 2019Type:Research ArticleAuthors:Yousef Ahmed Alomi, Nisreen Al-Shubaar, Nadia Lubad, and Fatimah Ali Albusalih Author(s) affiliations:Yousef Ahmed Alomi1,* , Nisreen Al-Shubaar2, Nadia Lubad2, Fatimah Ali Albusalih3 1The Former General Manager of General Administration of Pharmaceutical Care, Former Head, National Clinical Pharmacy and Pharmacy Practice, Former Head, Pharmacy R and D Administration, Ministry of Health, Riyadh, SAUDI ARABIA. 2King Salman Hospital, Ministry of Health, Riyadh, SAUDI ARABIA. 3College of Clinical Pharmacy, Imam Abdulrahman Bin Faisal University (University of Dammam) Dammam, SAUDI ARABIA. Abstract:Objective: Medication errors have noteworthy implications in the field of patient safety. Error detection through a dynamic supervision and an efficient error reporting system unveils medication errors and boosts safe practices. The overall goal of this study is to explore the pharmacist’s adherence to medication errors reporting system in adults and pediatrics at the public hospital in Riyadh, Saudi Arabia. Methods: This article describes 9 months retrospective cohort study in year of 2015. A retrospective study was conducted on all inpatients at a 300-bed hospital where all medication procedures in each ward were monitored by a clinical pharmacist. The study was conducted at the Public Hospital in Riyadh, Saudi Arabia. The hospital had medication safety officer with medication safety committee. The program led by trained pharmacist and delivered basic medication safety education programs to all health professional. The medication error report consisted of patient demographic information, qualification of committing mistakes, time of errors occurs, type of medication errors, reasons for medication errors, medications stages involved and errors outcome. Results: The total number of reports were 805. The most common adherence documentation of error involving medications were cost related information (100%), error related information with an average (90.59%), patient related information (50.37%) and drug related information (49.39%). While the action related information (0.57%) was harmless. The most common completed of error related information were causes of medication errors 775 (96.3%) and type of medication errors 770 (95.6%) followed by outcome of medication errors 764 (94.9%) and medication process stages involved 711 (88.3%). While the patient’s demographic information completed only (50%) in reports. Conclusion: Despite the medication error reporting is consider new at the public hospital, the number of reporting was high. The system needs more determination to follow completeness medication errors reporting system and that by development of an electronic reporting system, program awareness and positive documentation system feedback changes in pharmacy practice. Keywords:Adherence, Medication errors, Ministry of Health, Public, Riyadh, Saudi ArabiaView:PDF (323.67 KB) PDFClick here to download the PDF file. Images Medication Error Reports Compliance

According to HIPAA (Health Insurance Portability and Accountability Act), the medical insurance claim process is carried out by healthcare providers, insurance companies, and clearinghouses. The clearinghouse coordinates the medical insurance claims between providers and insurance companies. As centralized communication hubs, clearinghouses may maliciously or unintentionally leak patient information. In this work, we propose a distributed solution to replace the role of clearinghouses during health insurance claim process and mitigate the risk of data leakage among parties in Healthcare sector. Our solution enhances the patients' privacy protection through developing a HIPAA compliance system for the medical insurance claim process in a decentralized manner using blockchain technology. Blockchain ensures transaction integrity and anonymity of cryptocurrencies by using distributed immutable ledgers. We first design data structures for patient information, medical service record, insurance payment, and insurance agreements within the ledger. We then focus on defining smart contracts for privacy assurance, as well as automating the insurance claim process. We implement and evaluate the proposed framework with Hyperledger Fabric, showing promising performance and response time.

Properly analyzing health insurance claims data could lead to significant business insights and benefits for health service providers and insurance companies. Yet, health insurance data is often high dimensional and contains complex interleave sequences of claims. Instead of conducting machine learning tasks directly on the raw data, a better approach is performing the tasks on high-quality embeddings of the raw data. Driven by the real business need of Solution Segic Inc., a Canadian technology company in the group insurance industry, we extract health insurance claims embeddings with neural networks in the context of anomaly detection. We propose and thoroughly examine six embedding components that are customized based on different possible assumptions made on the data. One of our proposed embedding components, EC-ReStepRec, significantly outperforms other candidates on two anomaly detection tasks. This is the first embedding study done on health insurance claims for anomaly detection.

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

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.

The Strengths and Limitations of Claims Based Research in Countries With Fee for Service Reimbursement

Objective: This study aimed to assess the prevalence and risk factors of irritable bowel syndrome (IBS) among medical and non-medical students at Imam Abdulrahman bin Faisal University, Saudi Arabia. Methods: A cross-sectional study was conducted among medical and non-medical students at Imam Abdulrahman bin Faisal University in Al Khobar, Saudi Arabia. Participants completed a self-administered questionnaire that included demographic data, risk factors, Rome IV criteria to diagnose IBS, and Perceived Stress Scale (PSS) to assess stress levels and their association with IBS development. Results: Out of 710 participants, 522 met the inclusion criteria. Among the participants, 224 (42.9%) were medical students, while non-medical students accounted for 298 (57.1%) of the participants. The prevalence of IBS was 15.6% (n = 35) among medical students and 25.8% (n = 77) among non-medical students. Significant associations with IBS were found for female gender (p-value = 0.000), marital status (p-value = 0.001), family history of IBS (p-value = 0.007), and the use of non-steroidal anti-inflammatory drugs (NSAIDs) (p-value = 0.010). Additionally, both mild and severe stress levels as well as sleeping for 8 hours or more, were significantly associated with IBS. Conclusion: This study found a high prevalence of IBS among undergraduate students, particularly among non-medical students. Key risk factors identified include female gender, mild and high stress levels, being married, positive family history, NSAID use, and sleeping for 8 hours or more.

Students’ perception towards using M-Health applications for education in medical colleges of Imam Abdulrahman bin Faisal University

Objectives:To estimate the prevalence of eating disorders among Imam Abdulrahman bin Faisal University (IAU) in Dammam, Kingdom of Saudi Arabia, female students and to investigate the associated factors.Methods:A cross-sectional study was conducted at IAU, Dammam, Kingdom of Saudi Arabia, in 2016 with 670 participants. Data was collected by self-administered questionnaires, including the Eating attitude test 26 (EAT-26) and the sociocultural attitudes toward appearance questionnaire (SATAQ-4). Eating behavior questions were asked to determine whether the respondents should seek evaluation for an eating disorder (ED) from a professional.Results:Eating attitude test 26 results showed that 29.4% of participants were categorized at a high level of concern for an ED, and 45.5% were categorized as at risk of having problematic feeding behaviors. Of the participants who scored above 20 on the EAT-26, 60.7% demonstrated high-risk feeding behavior attitudes, and approximately 11.5% who scored a body mass index (BMI) classified as underweight or very underweight required referral to a psychologist. A significant relationship was found between family stress to lose weight and EAT-26 scores above 20. The media was also found to represent external influence to lose weight.Conclusion:This study concluded that early detection of EDs or problematic feeding behaviors and attitude is vital to minimize the risks to physical health and well-being of the sufferers’ experiences.

Health Insurance represents the largest sectors among all types of insurance in Saudi Arabia with a Gross Written Premiums of 51% of the whole insurance market in Saudi Arabia. The high growth of health insurance sector in Saudi Arabia was driven by the obligation mandated by the Ministry of Health "MOH" and Council of Cooperative Health Insurance "CCHI" for expatriates and their families living in Saudi Arabia to have their health insurance, affected mid of 2014. However, the regulations didn’t specify the types and quality of health insurance used. The regulations also didn’t take into consideration the salaries of the expatriates which might affect their financial positions in case of policy cancelation. No studies have been conducted on the Saudi Arabian health insurance market to explore the efficiency and quality of expatriates’ health insurance policy. This paper attempts to fill the gap. The main aim of this study was to explore the availability and efficiency of health care system to expatriates. The quality of health insurance policy relies on the selections made by the employers. The research used qualitative methodology for collection of primary data. A total of 324 responds were received and considered usable for the research. The results were astonishing that most of the participants have their own health insurance policy, however not all service are available to them. Most of the participants clarified that their health insurance policy, did not cover of most of the risk they encountered, it did not cover the medical treatment expenses, as well as the surgery and operation expenses. Such results will put financial burden on expatriates in case their health insurance claims been canceled.

Healthcare fraud is intentionally submitting false claims or producing misinterpretation of facts to obtain entitlement payments. Thus, it wastes healthcare financial resources and increases healthcare costs. Subsequently, fraud poses a substantial financial challenge. Therefore, supervised machine and deep learning analytics such as random forest, logistic regression, and artificial neural networks are successfully used to detect healthcare insurance fraud. This study aims to develop a health model that automatically detects fraud from health insurance claims in Saudi Arabia. The model indicates the greatest contributing factor to fraud with optimal accuracy. The labeled imbalanced dataset used three supervised deep and machine learning methods. The dataset was obtained from three healthcare providers in Saudi Arabia. The applied models were random forest, logistic regression, and artificial neural networks. The SMOT technique was used to balance the dataset. Boruta object feature selection was applied to exclude insignificant features. Validation metrics were accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC). Random forest classifiers indicated policy type, education, and age as the most significant features with an accuracy of 98.21%, 98.08% precision, 100% recall, an F1 score of 99.03%, specificity of 80%, and an AUC of 90.00%. Logistic regression resulted in an accuracy of 80.36%, 97.62% precision, 80.39% recall, an F1 score of 88.17%, specificity of 80%, and an AUC of 80.20%. ANN revealed an accuracy of 94.64%, 98.00% precision, 96.08% recall, an F1 score of 97.03%, a specificity of 80%, and an AUC of 88.04%. This predictive analytics study applied three successful models, each of which yielded acceptable accuracy and validation metrics; however, further research on a larger dataset is advised.