The Public Health Insurance Coverage of Novel Targeted Anticancer Medicines in China—In Favor of Whom? A Retrospective Analysis of the Insurance Claim Data

Objectives: This study took Fuzhou city as a case, described how the public health insurance coverage policy in 2016 of novel anti-lung cancer medicines benefited patients, and who benefited the most from the policy in China.Methods: This was a retrospective study based on health insurance claim data with a longitudinal analysis of the level and trend changes of the monthly number of patients to initiate treatment with the novel targeted anti-lung cancer medicines gefitinib and icotinib before and after health insurance coverage. The study also conducted a multivariate linear regression analysis to predict the potential determinants of the share of patient out-of-pocket (OOP) expenditure for lung cancer treatment with the study medicines.Results: The monthly number of the insured patients in Fuzhou who initiated the treatment with the studied novel targeted anti-lung cancer medication abruptly increased by 26 in the month of the health insurance coverage (95% CI: 14–37, p < 0.01) and kept at an increasing level afterward (p < 0.01). By controlling the other factors, the shares of OOP expenditure for lung cancer treatment of the patients who were formal employee program enrollees not entitled to government-funded supplementary health insurance coverage and resident program enrollees were 18.3% (95% CI: 14.1–22.6) and 26.7% (95% CI: 21.0–32.4) higher than that of the patients who were formal employee program enrollees with government-funded supplementary health insurance coverage.Conclusion: The public health insurance coverage of novel anti-lung cancer medicines benefited patients generally. To enable that patients benefit from this policy more equally and thoroughly, in order to achieve the policy goal of not to leave anyone behind, it is necessary to strengthen the benefits package of the resident program and to optimize the current financing mechanism of the public health insurance system.

Fraud Detection in Healthcare System using Symbolic Data Analysis

In the era of digitization the frauds are found in all categories of health insurance. It is finished next to deliberate trickiness or distortion for acquiring some pitiful advantage in the form of health expenditures. Bigdata analysis can be utilized to recognize fraud in large sets of insurance claim data. In light of a couple of cases that are known or suspected to be false, the anomaly detection technique computes the closeness of each record to be fake by investigating the previous insurance claims. The investigators would then be able to have a nearer examination for the cases that have been set apart by data mining programming. One of the issues is the abuse of the medical insurance systems. Manual detection of frauds in the healthcare industry is strenuous work. Fraud and Abuse in the Health care system have become a significant concern and that too inside health insurance organizations, from the most recent couple of years because of the expanding misfortunes in incomes, handling medical claims have become a debilitating manual assignment, which is done by a couple of clinical specialists who have the duty of endorsing, adjusting, or dismissing the appropriations mentioned inside a restricted period from their gathering. Standard data mining techniques at this point do not sufficiently address the intricacy of the world. In this way, utilizing Symbolic Data Analysis is another sort of data analysis that permits us to address the intricacy of the real world and to recognize misrepresentation in the dataset.

Cloudburst catastrophe modelling: Case study – Jönköping municipality, Sweden

<p>In Europe, flash floods are one of the most significant natural hazards, causing serious risk to life and destruction of buildings and infrastructure. The intense rain causing those floods has a few different names, however, with very similar meaning. The term chosen in this study, ‘cloudburst’, was introduced by Woolley (1946) as “…a torrential downpour of rain which by its spottiness and relatively high intensity suggests the bursting and discharge of the whole cloud at once”. While these events play an important role in the ongoing flood risk management discussion, they are under-represented among flood models.</p><p>The main aim of this study is to demonstrate an approach by showing how methods and techniques can be integrated together to construct a catastrophe model for flash flooding of Jönköping municipality in Sweden. The model is developed in the framework of the ‘Oasis Loss Modelling Framework’ platform, jointly with end-users from the public sector and the insurance industry. Calibration and validation of the model were conducted by comparisons against three historical cloudburst events and corresponding insurance-claim data.</p><p>The analysis has shown that it is possible to get acceptable results from a cloudburst catastrophe model using only rainfall data, and not surface-water level as driving variable. The approach presented opens up for such loss modelling in places where complex hydraulic modelling cannot be done because of lacking data or skill of responsible staff. The Swedish case study indicates that the framework presented can be considered as an important decision making tool, by establishing an area for collaboration between academia; insurance businesses; and local authorities, to reduce long-term disaster risk in Sweden.</p><p> </p><p>Woolley, Ralf R., "Cloudburst Floods in Utah 1850-1938" (1946). Elusive Documents. Paper 55.</p>

Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression

Research has shown that current health expenditure in most countries, especially in sub-Saharan Africa, is inadequate and unsustainable. Yet, fraud, abuse, and waste in health insurance claims by service providers and subscribers threaten the delivery of quality healthcare. It is therefore imperative to analyze health insurance claim data to identify potentially suspicious claims. Typically, anomaly detection can be posited as a classification problem that requires the use of statistical methods such as mixture models and machine learning approaches to classify data points as either normal or anomalous. Additionally, health insurance claim data are mostly associated with problems of sparsity, heteroscedasticity, multicollinearity, and the presence of missing values. The analyses of such data are best addressed by adopting more robust statistical techniques. In this paper, we utilized the Bayesian quantile regression model to establish the relations between claim outcome of interest and subject-level features and further classify claims as either normal or anomalous. An estimated model component is assumed to inherently capture the behaviors of the response variable. A Bayesian mixture model, assuming a normal mixture of two components, is used to label claims as either normal or anomalous. The model was applied to health insurance data captured on 115 people suffering from various cardiovascular diseases across different states in the USA. Results show that 25 out of 115 claims (21.7%) were potentially suspicious. The overall accuracy of the fitted model was assessed to be 92%. Through the methodological approach and empirical application, we demonstrated that the Bayesian quantile regression is a viable model for anomaly detection.

Association of the Magnitude of Nurses With the Use of Health Information Exchanges: Analyzing the National Health Insurance Claim Data of Hospitals and Clinics in Korea

Background Many features of health care organizations (HCOs) have been identified to be associated with health information exchange (HIE), but subcategories of organizational factors focusing on nurse workforces still need to be identified. The objective of this study is to investigate the association of number of nurses with HIE use in Korea. Methods This study had a retrospective study design and used health insurance claim data from June 1, 2016 to June 30, 2018. The unit of analysis was the HCO, and any health insurance claims having HIE were counted by HCO. There were a total of 1490 HCOs having any HIE and 24 026 HCOs not having HIE. For statistical analysis, two-part model was used: logistic regression for HIE participation and the generalized linear model for the volume of HIE use. Results HIE was used by 44.6% of general hospitals, and 8.6% and 5.3% of small hospitals and clinics, respectively. Both HIE use and its volume were significantly positively associated with nurse variables. The use of HIE was significantly positively associated with nurse-to-bed ratio in general hospitals (OR 1.028; 1.016 to 1.041) and in small hospitals (OR 1.021; 1.016 to 1.027), and with the number of nurses (OR 1.041; 1.028 to 1.054) in clinics (P<.001). The volume of HIE use was also positively associated with nurse-to-bed ratio in general hospitals (OR 1.010; 1.004 to 1.017) and in small hospitals (OR 1.014; 1.006 to 1.022), and with the number of nurses (OR 1.055; 1.037 to 1.073) in clinics (P<.01). Conclusion This study found that there was a low rate of HIE use in small hospitals and clinics. The number of nurses was critically associated with the use of HIE and the volume of HIE claims. HIE policy makers need to be aware of this factor in seeking to accelerate HIE.

Can the Use of Health Insurance Claim Data Benefit the Risk-Based Supervision of General Practitioner Practices? An Exploratory Study in the Netherlands

Background: The Dutch Health and Youth Care Inspectorate has organized a study investigating whether there are benefits to using claim data in the risk-based supervision of general practitioner (GP) practices. Methods: We identified and selected signals of risks based on interviews with experts. Next, we selected 3 indicators that could be measured in the claim database. These were: the expected and actual costs of the GP practice; the percentage of reserve antibiotics prescribed; and the percentage of patients undergoing an emergency admission during the weekend. We corrected the scores of the GP practices based on their casemix and identified practices with the most unfavorable scores, ‘red flags,’ in 2015, or the trend between 2013-2015. Finally, we analysed the data of GP practices already identified as delivering substandard care by the Health and Youth Care Inspectorate and calculated the sensitivity and specificity of using the indicators to identify poor performing GP practices. Results: By combining the 3 indicators, we identified 1 GP practice with 3 red flags and 24 GP practices with 2 red flags. The a priori chance of identifying a GP practice that shows substandard care is 0.3%. Using the indicators, this improved to 1.0%. The sensitivity was 26.7%, the specificity was 92.8%. Conclusion: The Dutch Health and Youth Care Inspectorate might use claim data to calculate indicators on costs, the prescribing of reserve antibiotics and emergency admissions during the weekend, when setting priorities for its visits to GP practices. Visiting more GP practices by the Health and Youth Care Inspectorate, and identifying substandard care, is necessary to validate the use of these indicators.

Revisiting same day administration of pegfilgrastim in the age of biosimilars: A review of literature

Purpose Since 2018, several pegfilgrastim biosimilars were approved, which may affect insurance reimbursement. Guidelines recommend pegfilgrastim be administered the days following chemotherapy to prevent hematopoietic toxicity. To date, only the reference pegfilgrastim product has an available autoinjector-device. This has contributed to logistical issues in administering biosimilar agents per guideline recommendations. Administration on the same day as chemotherapy may be a potential alternative when logistical issues are present. This review will assess current evidence on this practice to inform clinical decisions. Data sources: A comprehensive literature search was performed in PubMed/Medline for studies examining the administration of pegfilgrastim on the same day as chemotherapy. Data summary: Several studies were identified, including a systematic review, retrospective reviews, and insurance claim data. Studies had significant limitations, and chemotherapy regimens and cancer types varied among studies. Studies showed inconsistent results in terms of incidence, duration, and severity of febrile neutropenia. In studies with patients with head and neck, urothelial, gynecologic, gastrointestinal, and prostate cancer, no difference in outcomes was detected or outcomes supported the feasibility of same-day administration. In patients with breast cancer, outcomes were worse with same-day administration. Outcomes were mixed in studies with non-Hodgkin’s lymphoma, non-small cell lung cancer, and various solid tumors. Conclusion Administration of pegfilgrastim on the same day as chemotherapy may be safe and an acceptable alternative, if logistics prohibit a patient from receiving administration the days after chemotherapy. Clinicians should consider patient risk factors and prescribed chemotherapy regimens, along with available evidence when contemplating administration of same-day pegfilgrastim.