A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

A Hybrid Mining Approach to Facilitate Health Insurance Decision: Case Study of Non-Traditional Data Mining Applications in Taiwan NHI Databases

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance databases. In order to obtain the best payment management, a hybrid mining approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytical processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will facilitate the health insurance decision-making process, is built. Drawing from lessons learned in case study, results showed that not only is hybrid mining approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Researchers should develop hybrid mining approach combined with their own application systems in the future

Emergence, convergence, and differentiation of organizational forms of health data governance: The U.S. All-payer Claims Databases (APCD) movement

In this research we are investigating how different organizational forms of data governance develop in response to the opportunities and challenges to aggregate, curate, and utilize digital health data for health systems improvement and market regulation. We are examining (i) how/when do governance arrangements coalesce around specific domains of health data resources as identifiable organizational forms; (ii) what influences how (or whether) these forms develop in a health care market, and (iii) what factors contribute to convergence or divergence in organizational forms across markets? To address these questions, we are conducting an in-depth, multi-level field study of the movement to establish all payer claims database (APCD) organizations in the U.S. healthcare sector. Among states with an APCD there is substantial variety in the data domains, stakeholders, governance goals and structures of the organization, indicating local variation and divergence, as well interstate and national initiatives to encourage convergence along some dimensions. This provides a rich opportunity to study institutional and market factors that contribute to (or inhibit) emergence, convergence, or divergence of health data governance forms and the implications for health care sector management and improvement that may result. In this paper we report preliminary findings and analysis of this study

Translating data analytics into improved spine surgery outcomes: A roadmap for biomedical informatics research in 2021

STUDY DESIGN: Narrative review. OBJECTIVES: There is growing interest in the use of biomedical informatics and data analytics tools in spine surgery. Yet despite the rapid growth in research on these topics, few analytic tools have been implemented in routine spine practice. The purpose of this review is to provide a health information technology (HIT) roadmap to help translate data assets and analytics tools into measurable advances in spine surgical care. METHODS: We conducted a narrative review of PubMed and Google Scholar to identify publications discussing data assets, analytical approaches, and implementation strategies relevant to spine surgery practice. RESULTS: A variety of data assets are available for spine research, ranging from commonly used datasets, such as administrative billing data, to emerging resources, such as mobile health and biobanks. Both regression and machine learning techniques are valuable for analyzing these assets, and researchers should recognize the particular strengths and weaknesses of each approach. Few studies have focused on the implementation of HIT, and a variety of methods exist to help translate analytic tools into clinically useful interventions. Finally, a number of HIT-related challenges must be recognized and addressed, including stakeholder acceptance, regulatory oversight, and ethical considerations. CONCLUSIONS: Biomedical informatics has the potential to support the development of new HIT that can improve spine surgery quality and outcomes. By understanding the development life-cycle that includes identifying an appropriate data asset, selecting an analytic approach, and leveraging an effective implementation strategy, spine researchers can translate this potential into measurable advances in patient care

Unsupervised learning for anomaly detection in Australian medical payment data

Fraudulent or wasteful medical insurance claims made by health care providers are costly for insurers. Typically, OECD healthcare organisations lose 3-8% of total expenditure due to fraud. As Australia’s universal public health insurer, Medicare Australia, spends approximately $A 34 billion per annum on the Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme, wasted spending of$A1–2.7 billion could be expected.However, fewer than 1% of claims to Medicare Australia are detected as fraudulent, below international benchmarks. Variation is common in medicine, and health conditions, along with their presentation and treatment, are heterogenous by nature. Increasing volumes of data and rapidly changing patterns bring challenges which require novel solutions. Machine learning and data mining are becoming commonplace in this field, but no gold standard is yet available. In this project, requirements are developed for real-world application to compliance analytics at the Australian Government Department of Health and Aged Care (DoH), covering: unsupervised learning; problem generalisation; human interpretability; context discovery; and cost prediction. Three novel methods are presented which rank providers by potentially recoverable costs. These methods used association analysis, topic modelling, and sequential pattern mining to provide interpretable, expert-editable models of typical provider claims. Anomalous providers are identified through comparison to the typical models, using metrics based on costs of excess or upgraded services. Domain knowledge is incorporated in a machine-friendly way in two of the methods through the use of the MBS as an ontology. Validation by subject-matter experts and comparison to existing techniques shows that the methods perform well. The methods are implemented in a software framework which enables rapid prototyping and quality assurance. The code is implemented at the DoH, and further applications as decision-support systems are in progress. The developed requirements will apply to future work in this fiel

A Comprehensive Survey on Data Utility and Privacy: Taking Indian Healthcare System as a Potential Case Study

The authors would like to thank the anonymous reviewers and editors who have been involved in examining this manuscript.Background: According to the renowned and Oscar award-winning American actor and film director Marlon Brando, “privacy is not something that I am merely entitled to, it is an absolute prerequisite.” Privacy threats and data breaches occur daily, and countries are mitigating the consequences caused by privacy and data breaches. The Indian healthcare industry is one of the largest and rapidly developing industry. Overall, healthcare management is changing from disease-centric into patient-centric systems. Healthcare data analysis also plays a crucial role in healthcare management, and the privacy of patient records must receive equal attention. Purpose: This paper mainly presents the utility and privacy factors of the Indian healthcare data and discusses the utility aspect and privacy problems concerning Indian healthcare systems. It defines policies that reform Indian healthcare systems. The case study of the NITI Aayog report is presented to explain how reformation occurs in Indian healthcare systems. Findings: It is found that there have been numerous research studies conducted on Indian healthcare data across all dimensions; however, privacy problems in healthcare, specifically in India, are caused by prevalent complacency, culture, politics, budget limitations, large population, and existing infrastructures. This paper reviews the Indian healthcare system and the applications that drive it. Additionally, the paper also maps that how privacy issues are happening in every healthcare sector in India. Originality/Value: To understand these factors and gain insights, understanding Indian healthcare systems first is crucial. To the best of our knowledge, we found no recent papers that thoroughly reviewed the Indian healthcare system and its privacy issues. The paper is original in terms of its overview of the healthcare system and privacy issues. Social Implications: Privacy has been the most ignored part of the Indian healthcare system. With India being a country with a population of 130 billion, much healthcare data are generated every day. The chances of data breaches and other privacy violations on such sensitive data cannot be avoided as they cause severe concerns for individuals. This paper segregates the healthcare system’s advances and lists the privacy that needs to be addressed first

Evidence and Extrapolation: Mechanisms for Regulating Off-Label Uses of Drugs and Devices

A recurring, foundational issue for evidence-based regulation is deciding whether to extend governmental approval from an existing use with sufficient current evidence of safety and efficacy to a novel use for which such evidence is currently lacking. This extrapolation issue arises in the medicines context when an approved drug or device that is already being marketed is being considered (1) for new conditions (such as off-label diagnostic categories), (2) for new patients (such as new subpopulations), (3) for new dosages or durations, or (4) as the basis for approving a related drug or device (such as a generic or biosimilar drug). Although the logic of preapproval testing and the precautionary principle—first, do no harm—would counsel in favor of prohibiting extrapolation approvals until after traditional safety and efficacy evidence exists, such delays would unreasonably sacrifice beneficial uses. The harm of accessing unsafe products must be balanced against the harm of restricting access to effective products. In fact, the Food and Drug Administration\u27s (FDA\u27s) current regulations in many ways reject the precautionary principle because they largely permit individual physicians to prescribe medications for off-label uses before any testing tailored to those uses has been done. The FDA\u27s approach empowers physicians, but overshoots the mark by allowing enduring use of drugs and devices with insubstantial support of safety and efficacy. This Article instead proposes a more dynamic and evolving evidence-based regime that charts a course between the Scylla and Charybdis of the overly conservative precautionary principle on one hand, and the overly liberal FDA regime on the other. Our approach calls for improvements in reporting, testing, and enforcement regulations to provide a more layered and nuanced system of regulatory incentives. First, we propose a more thoroughgoing reporting of off-label use (via the disclosure of diagnostic codes and detailing data) in manufacturers\u27 annual reports to the FDA, in the adverse event reports to the FDA, in Medicare/Medicaid reimbursement requests, and, for a subset of FDA-designated drugs, in prescriptions themselves. Second, we would substantially expand the agency\u27s utilization of postmarket testing, and we provide a novel framework for evaluating the need for postmarket testing. Finally, our approach calls for a tiered labeling system that would allow regulators and courts to draw finer reimbursement and liability distinctions among various drug uses, and would provide the agency both the regulatory teeth and the flexibility it presently lacks. Together, these reforms would improve the role of the FDA in the informational marketplace underlying physicians\u27 prescribing decisions. This evolutionary extrapolation framework could also be applied to other contexts

Managing health insurance using blockchain technology

Health insurance plays a significant role in ensuring quality healthcare. In response to the escalating costs of the medical industry, the demand for health insurance is soaring. Additionally, those with health insurance are more likely to receive preventative care than those without health insurance. However, from granting health insurance to delivering services to insured individuals, the health insurance industry faces numerous obstacles. Fraudulent actions, false claims, a lack of transparency and data privacy, reliance on human effort and dishonesty from consumers, healthcare professionals, or even the insurer party itself, are the most common and important hurdles towards success. Given these constraints, this chapter briefly covers the most immediate concerns in the health insurance industry and provides insight into how blockchain technology integration can contribute to resolving these issues. This chapter finishes by highlighting existing limitations as well as potential future directions.Comment: 37 pages, 2 figures, 12 table